The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in the (...) network. Through the use of a multi-agent reinforcement learning algorithm, this paper proposes a novel algorithm for malware detection in IoT devices. The proposed algorithm is not strongly dependent on the size of the IoT network due to the that its training is adapted using time differences if the IoT network size is small or Monte Carlo otherwise. To validate the proposed algorithm in an environment as close to reality as possible, we proposed a scenario based on a real IoT network, where we tested different malware propagation models. Different simulations varying the number of agents and nodes in the IoT network have been developed. The result of these simulations proves the efficiency and adaptability of the proposed algorithm in detecting malware, regardless of the malware propagation model. (shrink)

Automatic public malware analysis services provide controlled, isolated and virtual environments to analyse malicious software samples. Unfortunately, malware is currently incorporating techniques to recognize execution onto a virtual or sandbox environment; when an analysis environment is detected, malware behaves as a benign application or even shows no activity. In this work, we present an empirical study and characterization of automatic PMAS, considering 26 different services. We also show a set of features that allow to easily fingerprint these (...) services as analysis environments; the lower the unlikeability of these features, the easier for us to fingerprint the analysis service they belong to. Finally, we propose a method for these analysis services to counter or at least mitigate our proposal. (shrink)

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron (...) neural network, decision tree, random forest, and K-nearest neighbors. The principal component analysis and logistic regression methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F-measure, accuracy, and area under the curve were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC-ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC-ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications. (shrink)

In cloud security, intrusion detection system (IDS) is one of the challenging research areas. In a cloud environment, security incidents such as denial of service, scanning, malware code injection, virus, worm, and password cracking are getting usual. These attacks surely affect the company and may develop a financial loss if not distinguished in time. Therefore, securing the cloud from these types of attack is very much needed. To discover the problem, this paper suggests a novel IDS established on (...) a combination of a leader-based k-means clustering (LKM), optimal fuzzy logic system. Here, at first, the input dataset is grouped into clusters with the use of LKM. Then, cluster data are afforded to the fuzzy logic system (FLS). Here, normal and abnormal data are inquired by the FLS, while FLS training is done by the grey wolf optimization algorithm through maximizing the rules. The clouds simulator and NSL-Knowledge Discovery and DataBase (KDD) Cup 99 dataset are applied to inquire about the suggested method. Precision, recall, and F-measure are conceived as evaluation criteria. The obtained results have denoted the superiority of the suggested method in comparison with other methods. (shrink)

Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach (...) is for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest. (shrink)

Cybersecurity technology has the ability to detect malware through a variety of methods, such as signature recognition, logical rules or the identification of known malware stored in a database or public source. However, threat actors continuously try to create new variants of existing malware by obfuscating or altering parts of the code to evade detection by antivirus engines. Infostealers are one of the most common malicious programs aimed at obtaining personal or banking information from an infected (...) system and exfiltrating it. In addition, they are the precursors of potentially high-security incidents because attackers gain a entry into companies’ internal systems and may even access them with administrator permissions. This article demonstrates how a feature vector can be obtained from the assembly code of a Windows binary and how a a Graph Neural Network can be used to determine, with ninety percent accuracy, whether it is an infostealer. (shrink)

The suggestion has been made that future advanced artificial intelligence (AI) that passes some consciousness-related criteria should be treated as having moral status, and therefore, humans would have an ethical obligation to consider its well-being. In this paper, the author discusses the extent to which software and robots already pass proposed criteria for consciousness; and argues against the moral status for AI on the grounds that human malware authors may design malware to fake consciousness. In fact, the article (...) warns that malware authors have stronger incentives than do authors of legitimate software to create code that passes some of the criteria. Thus, code that appears to be benign, but is in fact malware, might become the most common form of software to be treated as having moral status. (shrink)

Malware has been around since the 1980s and is a large and expensive security concern today, constantly growing over the past years. As our social, professional and financial lives become more digitalised, they present larger and more profitable targets for malware. The problem of classifying and preventing malware is therefore urgent, and it is complicated by the existence of several specific approaches. In this paper, we use an existing malware taxonomy to formulate a general, language independent (...) functional description of malware as transformers between states of the host system and described by a trust relation with its components. This description is then further generalised in terms of mechanisms, thereby contributing to a general understanding of malware. The aim is to use the latter in order to present an improved classification method for malware. (shrink)

We present a taxonomy of errors in the scientific literature and an account of how the errors are distributed over the categories. We have developed the taxonomy by studying substantial errors in the scientific literature as described in retraction notices published in the journal Science over the past 35 years. We then examine how the sorts of errors that lead to retracted papers can be prevented and detected, considering the perspective of collaborating scientists, journal editors and referees, and readers of (...) the published articles. (shrink)

The gradual increase in the density of highway vehicles and traffic flow makes the abnormal driving state of vehicles an indispensable tool for assisting traffic dispatch. Intelligent transportation systems can detect and track vehicles in real time, acquire characteristics such as vehicle traffic, vehicle speed, vehicle flow density, and vehicle trajectory, and further perform advanced tasks such as vehicle trajectory. The detection of abnormal vehicle trajectory is an important content of vehicle trajectory understanding. And the development of the Internet (...) of Things and 5G technology has led to a continuous increase in the rate of data information circulation. The “Internet of Vehicles” generated based on the practice of 5G communication technology constitutes a vehicle abnormal trajectory detection system, which has very high feasibility and safety and stability. Therefore, this research is aimed at the needs of preventing major accidents and forensic analysis during highway vehicles. Based on the integration of the Internet of Things 5G communication technology, a trajectorial anomaly detection of highway vehicle trajectory based on the integration of the Internet of Things 5G is proposed. By accurately sensing unsafe events at the perception layer, network layer, and application layer, the vehicle driving trajectory state is divided into several simple semantic representations. The semantic representation is analyzed, and then the moving target detection and moving target tracking algorithms needed to extract the vehicle trajectory are introduced. Through video detection and tracking of moving vehicle targets, the driving trajectory of the vehicle is obtained, and the movement characteristics of the vehicle in each frame of image are extracted. According to the relationship between the trajectory of the vehicle and the lane line, the vehicle trajectory analysis is realized, and then it is judged whether the vehicle has abnormal trajectory. Compared with the traditional method of manually detecting the driving condition of the vehicle, the abnormal trajectory detection of the vehicle based on the integration of the Internet of Things and 5G can quickly detect the abnormal trajectory of the vehicle in the traffic monitoring video. (shrink)

There are three main problems in track fastener defect detection based on image: The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. The potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. The detection of the track fastener clip (...) will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network is proposed. Firstly, the track fastener image segmentation method is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder and Restricted Boltzmann Machine, and the features are fused. Finally, the similarity measurement method Mahalanobis Distance is used to detect defects in track fasteners. The effectiveness of the proposed method is verified by real Pandrol track fastener images. (shrink)

To investigate whether implicit detection occurs uniformly during change blindness with single or combination feature stimuli, and whether implicit detection is affected by exposure duration and delay, two one-shot change detection experiments are designed. The implicit detection effect is measured by comparing the reaction times of baseline trials, in which stimulus exhibits no change and participants report “same,” and change blindness trials, in which the stimulus exhibits a change but participants report “same.” If the RTs of (...) blindness trials are longer than those of baseline trials, implicit detection has occurred. The strength of the implicit detection effect was measured by the difference in RTs between the baseline and change blindness trials, where the larger the difference, the stronger the implicit detection effect. In both Experiments 1 and 2, the results showed that the RTs of change blindness trials were significantly longer than those of baseline trials. Whether under set size 4, 6, or 8, the RTs of the change blindness trials were significantly longer than those in the baseline trials. In Experiment 1, the difference between the baseline trials’ RTs and change blindness trials’ RTs of the single features was significantly larger than that of the combination features. However, in Experiment 2, the difference between the baseline trials’ RTs and the change blindness trials’ RTs of single features was significantly smaller than that of the combination features. In Experiment 1a, when the exposure duration was shorter, the difference between the baseline and change blindness trials’ RTs was smaller. In Experiment 2, when the delay was longer, the difference between the two trials’ RTs was larger. These results suggest that regardless of whether the change occurs in a single or a combination of features and whether there is a long exposure duration or delay, implicit detection occurs uniformly during the change blindness period. Moreover, longer exposure durations and delays strengthen the implicit detection effect. Set sizes had no significant impact on implicit detection. (shrink)

Five aspects of visual change detection are reviewed. The first concerns the concept of change itself, in particular the ways it differs from the related notions of motion and difference. The second involves the various methodological approaches that have been developed to study change detection; it is shown that under a variety of conditions observers are often unable to see large changes directly in their field of view. Next, it is argued that this “change blindness” indicates that focused (...) attention is needed to detect change, and that this can help map out the nature of visual attention. The fourth aspect concerns how these results affect our understanding of visual perception—for example, the implication that a sparse, dynamic representation underlies much of our visual experience. Finally, a brief discussion is presented concerning the limits to our current understanding of change detection. (shrink)

Explores the figure of the detective as a pursuer of knowledge in four noir films.

Precise and timely diagnosis of Parkinson’s disease is important to control its progression among subjects. Currently, a neuroimaging technique called dopaminergic imaging that uses single photon emission computed tomography (SPECT) with 123I-Ioflupane is popular among clinicians for detecting Parkinson’s disease in early stages. Unlike other studies, which consider only low-level features like gray matter, white matter, or cerebrospinal fluid, this study explores the non-linear relation between different biomarkers (SPECT + biological) using deep learning and multivariate logistic regression. Striatal binding ratios (...) are obtained using 123I-Ioflupane SPECT scans from four brain regions which are further integrated with five biological biomarkers to increase the diagnostic accuracy. Experimental results indicate that this investigated approach can differentiate subjects with 100% accuracy. The obtained results outperform the ones reported in the literature. Furthermore, logistic regression model has been developed for estimating the Parkinson’s disease onset probability. Such models may aid clinicians in diagnosing this disease. (shrink)

Can managers detect honest people’s lies in a handwritten message? In this article, I will briefly discuss graphology and a basic model of interpersonal communication. I will then develop a fundamental theoretical framework of eight principles for detecting lies based on the basic communication model, handwriting analyses, and the following assumptions: For most people, it is easier to tell the truth than to tell lies. This applies to handwritings also. When most honest people lie, they try to hide their stressful (...) emotions in the encoding process. As a consequence, they deviate from their own normal writing and violate their own personal moral standards. Interestingly enough, the art or science of detecting a lie in a handwritten sample is to focus not on what they write, but on how they write it. These 24 exhibits (cases) written in 11 languages—used in different parts of the world—help managers apply this important theoretical framework of interpersonal communication, understand the encoding process, pinpoint these sudden emotional changes, decode handwritten messages, unlock the secrets, reveal the message’s true meanings, and detect people’s lies. (shrink)

Over the last few decades, the Internet has brought about a myriad of benefits to almost every aspect of our daily lives. However, malware attacks have also widely proliferated, mainly aiming at legitimate network users, resulting in millions of dollars in damages if proper protection and response measures are not settled and enforced. In this context, the paper at hand proposes MalSEIRS, a novel dynamic model, to predict malware distribution in a network based on the SEIRS epidemiological model. (...) As a result, the time-dependent rates of infection, recovery, and loss of immunity enable us to capture the complex dynamism of malware spreading behavior, which is influenced by a variety of external circumstances. In addition, we describe both offensive and defensive techniques, based on the proposed MalSEIRS model, through extensive experimentation, as well as disclosing real-life malware campaigns that can be better understood by using the suggested model. (shrink)

This book explores the thought of - and is dedicated to - David Frisby, as one of the leading sociologists of the late twentieth and early twenty-first centuries. Presenting original examinations of his unique social theory and underlining his interdisciplinary approach to the critical interpretation of modern metropolitan society and culture, it emphasises Frisby's legacy in highlighting the role of the social researcher as a collector, reader, observer, detective and archivist of the phenomena and ideas that exemplify the modern metropolis (...) as society. With contributions from sociologists, historians of the city, urban geographers, cultural theorists, architectural historians, architectural theorists and urban designers, The Detective of Modernity constitutes a wide-ranging engagement with Frisby's profound legacy in social and cultural theory. (shrink)

Viruses are essentially composed of a nucleic acid (segmented or not, DNA, or RNA) and a protein coat. Despite their simplicity, these small pathogens are responsible for significant economic and humanitarian losses that have had dramatic consequences in the course of human history. Since their discovery, scientists have developed different strategies to efficiently detect viruses, using all possible viral features. Viruses shape, proteins, and nucleic acid are used in viral detection. In this review, the development of these techniques, especially (...) for plant and mammalian viruses, their strengths and weaknesses as well as the latest cutting-edge technologies that may be playing important roles in the years to come are described. (shrink)

This paper argues that A-determinations (past, present, and future) and B-relations (simultaneity and succession) have the same empirical status in that they are all neither historically discoverable nor sensible, but are detectable and are detectable in the same way. This constitutes a reason for thinking they are in the same class with respect to objectivity, contrary to the Russellian view that “in a world in which there was no experience there would be no past, present, or future, but there might (...) well be earlier and later.” The argument is developed to furnish an explanation of how in fact (and contra McTaggart) we are “immediately certain of the reality of time,” the explanation being that we detect time. (shrink)

The principles of classical and quantum holography are used to develop the theoretical basis for a non-phonemic method of detecting membership in secret social groups, such as cults, criminal gangs, drug cartels, and terrorist cells. Grounded in the basic sociological premise that every group develops a distinctive sociocultural order, the theory postulates that the primary features of a group's collective identity will be encoded, via a multilevel socio-psycho-physiological process, into the field of bio-emotional relations connecting group members. The principles of (...) classical holography (Gabor 1948) are used to describe how this information is recorded in the members' brains and communicated in interaction by a holographic-like process as a tacit signature of group affiliation —a constancy in group-related interactions. It is hypothesized that the identity signature is enfolded as a hologram in the energy spectra of vocal interactions among group members. The principles of quantum holography (Gabor 1946) are used to provide a description of the group's ongoing interactions as a dynamic process—a continuous, quantized series of snapshots (quantum holograms). Linked together, two forms of holography provide a model of how the group's collective identity evolves and is communicated over time to its members, as the group adapts to the ever-changing reality of its endogenous and exogenous circumstances. Using spectral analysis techniques, it should be possible to detect the group's identity signature in the recordings of relatively small samples of vocal interactions. In clandestine groups, the effort to remain hidden actually enhances the signal strength of the identity signature. It is predicted, therefore, that detection of unknown members can be achieved by using the spectral commonality identified in the analysis of recordings of vocal interactions among known members as a reference signature for comparison with those of the vocal spectra of suspected or potential members. (shrink)

Detecting contract cheating in written submissions can be difficult beyond direct plagiarism detectable via technology. Successfully identifying potential cases of contract cheating in written work such as essays and reports is largely dependent on the experience of assessors and knowledge of student. It is further dependent on their familiarity with the patterns and clues evident in sections of body text and reference materials to identify irregularities. Consequently, some knowledge of what the patterns and clues look like is required. This paper (...) documents how to identify some of the patterns and clues observed in essay and report submissions. Effective assessment design with specific contextual requirements make irregularities easier to detect and interpret. The irregularities identified were confirmed as instances of contract cheating through conversations held with postgraduate students. An essential element of the conversations was the evidence presented for discussion. Irregularities were noted on a pro-forma specifically developed for this purpose. Patterns identified include misrepresented bibliographic data, inappropriate references, irrelevant material and generalised text that did not address the assessment question or grading criteria. The validated patterns formed the basis of identifying potential instances of contract cheating in later submissions. Timely conversations with students before the end of semester are essential to determining whether the patterns and clues link to poor knowledge of academic writing conventions or classified as contract cheating necessitating the application of appropriate penalties under institutional policies and procedures. (shrink)

Detecting introspective errors about consciousness presents challenges that are widely supposed to be difficult, if not impossible, to overcome. This is a problem for consciousness science because many central questions turn on when and to what extent we should trust subjects’ introspective reports. This has led some authors to suggest that we should abandon introspection as a source of evidence when constructing a science of consciousness. Others have concluded that central questions in consciousness science cannot be answered via empirical investigation. (...) I argue that on closer inspection, the challenges associated with detecting introspective errors can be overcome. I demonstrate how natural kind reasoning—the iterative application of inference to the best explanation to home in on and leverage regularities in nature—can allow us to detect introspective errors even in difficult cases such as judgments about mental imagery, and I conclude that worries about intractable methodological challenges in consciousness science are misguided. (shrink)

The aim of this paper is to raise two questions. The first question is: How is pessimism related to Buddhism (and vice versa)? The second question is: What relation does an immanent philosophy have to pessimism and Buddhism, if any? Using True Detective, an American television crime drama, as my point of departure, first I will outline some of the likenesses between Buddhism and pessimism. At the same time, I will show how the conduct of one of the main characters (...) in True Detective resembles the paths of Buddhism and pessimism, even though he is ethical in a strictly non-pessimistic and non-Buddhist fashion. Last, I will try to place these findings in perspective through the French philosopher Gilles Deleuze’s thoughts. Hereby, I hope to illustrate that joy, not suffering, is basic to human existence, and how human beings may overcome a spiritual pessimism. (shrink)

Present research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning, is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android (...) class='Hi'>malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware. (shrink)

Recent fMRI studies have shown that functional networks can be extracted even from resting state data, the so called “resting state networks” (RSNs) by applying independent component analysis (ICA). However, compared to fMRI, EEG and MEG have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RSNs and their interactions in all frequency bands. We applied (...) low resolution brain electromagnetic tomography-ICA (LORETA-ICA) to resting-state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RSNs in alpha, beta and gamma frequency bands. Next, taking into account these frequency properties, five RSNs were identified; 1) the visual network, 2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) and left posterior dorsal visual pathway (DVP), 3) self-referential processing network, characterized by a positive correlation between the medial PFC and right VVP, 4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus and 5) sensorimotor network. To detect aging-related changes of these five RSNs, the subjects were divided into three age groups: younger, middle aged, and elderly group, and Student's t test with Bonferroni correction on each coefficient of five independent components were performed. We found a significant attenuation in dual-process of visual perception network in elderly relative to middle aged subjects. Overall findings indicate that LORETA-ICA with EEG data can precisely identify five RSNs in their intrinsic frequency bands, and correct correlations and aging-related changes between and within RSNs. (shrink)

With the rapid growth of online product reviews, many users refer to others’ opinions before deciding to purchase any product. However, unfortunately, this fact has promoted the constant use of fake reviews, resulting in many wrong purchase decisions. The effective identification of deceptive reviews becomes a crucial yet challenging task in this research field. The existing supervised learning methods require a large number of labeled examples of deceptive and truthful opinions by domain experts, while the available unsupervised learning methods are (...) inefficient because they depend on the features of reviewers to detect each fake review. Therefore, by focusing on the detection efficiency problem and the limitation of large amount of labeled examples dependence, in this paper, we proposed an effective semisupervised learning approach for detecting spam reviews. Firstly, a time series model of all the reviews of a product is constructed, and then the suspected time intervals are captured based on the burst review increases in these intervals. Secondly, a co-training two-view semisupervised learning algorithm was performed in each captured interval, in which linguistic cues, metadata, and user purchase behaviors were synthetically employed to classify the reviews and check whether they are spam ones or not. A series of numerical experiments on a real dataset acquired from Taobao.com have confirmed the effectiveness of the proposed model, not only reaping benefits in terms of time efficiency and high accuracy but also overcoming the shortcomings of supervised learning methods, which depend on large amounts of labeled examples. And a trade-off balance was obtained between accuracy and efficiency. (shrink)

This article describes a method of automatically detecting, counting and classifying logs on a timber truck using a photograph. An image-processing algorithm is developed to process the photograph to calculate an estimate of the number of logs present and their respective diameters. The algorithm uses color information in multiple color spaces as well as geometrical operators to segment the image and extract the relevant information. This information enables the sawmill to better plan internal logistics and production in advance of the (...) truck’s arrival time. The algorithm is robust with respect to external factors such as varying lighting conditions and camera angle, but some inaccuracies remain, mainly caused by logs being occluded or covered in mud or snow. (shrink)

The aim of this paper is to raise two questions. The first question is: How is pessimism related to Buddhism (and vice versa)? The second question is: What relation does an immanent philosophy have to pessimism and Buddhism, if any? Using True Detective, an American television crime drama, as my point of departure, first I will outline some of the likenesses between Buddhism and pessimism. At the same time, I will show how the conduct of one of the main characters (...) in True Detective resembles the paths of Buddhism and pessimism, even though he is ethical in a strictly non-pessimistic and non-Buddhist fashion. Last, I will try to place these findings in perspective through the French philosopher Gilles Deleuze’s thoughts. Hereby, I hope to illustrate that joy, not suffering, is basic to human existence, and how human beings may overcome a spiritual pessimism. (shrink)

Research on implicit conflict detection suggests that people are sensitive to violations of logical principles. When they make reasoning errors, their epistemic radar presumably detects an anomaly....

Purpose The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of unambiguous instructions, bias is a byproduct of these computations, bringing harm to historically disadvantaged populations. This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Relying upon sociological and technical research, the paper offers commentary on the need for more workplace diversity within high-tech industries and (...) public policies that can detect or reduce the likelihood of racial bias in algorithmic design and execution. Design/methodology/approach The paper shares examples in the US where algorithmic biases have been reported and the strategies for explaining and addressing them. Findings The findings of the paper suggest that explicit racial bias in algorithms can be mitigated by existing laws, including those governing housing, employment, and the extension of credit. Implicit, or unconscious, biases are harder to redress without more diverse workplaces and public policies that have an approach to bias detection and mitigation. Research limitations/implications The major implication of this research is that further research needs to be done. Increasing the scholarly research in this area will be a major contribution in understanding how emerging technologies are creating disparate and unfair treatment for certain populations. Practical implications The practical implications of the work point to areas within industries and the government that can tackle the question of algorithmic bias, fairness and accountability, especially African-Americans. Social implications The social implications are that emerging technologies are not devoid of societal influences that constantly define positions of power, values, and norms. Originality/value The paper joins a scarcity of existing research, especially in the area that intersects race and algorithmic development. (shrink)

Aquaculture plays a significant role in both economic development and food production. Maintaining an ecological environment with good water quality is essential to ensure the production efficiency and quality of aquaculture. Effective management of water quality can prevent abnormal conditions and contribute significantly to food security. Detecting anomalies in the aquaculture environment is crucial to ensure that the environment is maintained correctly to meet healthy and proper requirements for fish farming. This article focuses on the use of deep learning techniques (...) to detect anomalies in water quality data in the aquaculture environment. Four deep learning anomaly detection techniques, including Autoencoder, Variational Autoencoder, Long-Short Term Memory Autoencoder, and Spectral-Residual Convolutional Neural Network, were analysed using multiple real-world sensor datasets collected from IoT aquaculture systems. Extensive experiments were conducted for temperature, dissolved oxygen, and pH parameters, and the evaluation analysis revealed that the Long-Short Term Memory Autoencoder anomaly detection method showed promising results in detecting anomalies for the temperature and oxygen datasets, while the Spectral-Residual Convolutional Neural Network demonstrated the best performance on the pH datasets. (shrink)

BackgroundInternational guidelines recommend ethical and scientific quality standards for managing and reporting adverse events occurring during clinical trials to competent research ethics committees and regulatory authorities. The purpose of this study was to determine whether clinical trial protocols in Cameroon are developed in line with national requirements and international guidelines as far as detecting, reporting and investigating of adverse events is concerned.MethodsIt was a documentary review of all approved clinical trial protocols that were submitted at the Cameroon National Ethics Committee (...) for evaluation from 1997 through 2012. Data were extracted using a preconceived and validated grid. Protocol review process targeted the title, abstract, objectives, methodology, resources, and the chapter on safety.ResultsIn total, 106 clinical trial protocols were identified from 2173 protocols seen in the archive and 104 included for review. Seventy six trials did not include the surveillance of adverse events as part of their objective. A total of 91 protocols did not budget for adverse event surveillance, 76 did not have a data safety management board, 11 included insurance for participants, 47 did not include a case definition for serious adverse events, 33 described procedures to detect adverse events, 33 described procedure for reporting and 22 described procedure for investigating adverse events.DiscussionsMost clinical trial protocols in Cameroon are developed to focus on benefits and pay little attention to harms. The development of national guidelines can improve the surveillance of adverse events in clinical trial research conducted in Cameroon. Adverse events surveillance tools and a budget are critical for an adequate planning for adverse event surveillance when developing trial protocols.ConclusionClinical trial protocols submitted in the Cameroon National Ethics Committee do not adequately plan to assess adverse events in clinical trial protocols. In order to improve on the safety of participants and marketed drug, there is a need to develop national guidelines for clinical trials by the government, and to improve evaluation procedures and monitoring of ongoing trials by the ethics committee. (shrink)

Threshold- and signal-detection-based models have dominated theorizing about recognition memory. Building upon these theoretical frameworks, we have argued for a dual-process model in which conscious recollection and familiarity contribute to memory performance. In the current paper we assessed several memory models by examining the effects of levels of processing and the number of presentations on recognition memory receiver operating characteristics . In general, when the ROCs were plotted in probability space they exhibited an inverted U shape; however, when they (...) were plotted inzspace they exhibited a U shape. An examination of the ROCs showed that the dual-process model could account for the observed ROCs, but that models based solely on either threshold or signal-detection processes failed to provide a sufficient account of the data. Furthermore, an examination of subjects' introspective reports using the remember/know procedure showed that subjects were aware of recollection and familiarity and were able to consistently report on their occurrence. The remember/know data were used to accurately predict the shapes of the ROCs, and estimates of recollection and familiarity derived from the ROC data mirrored the subjective reports of these processes. (shrink)

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose (...) an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload. (shrink)

In this paper my concern is with the collective moral responsibility of criminal investigators for the outcomes of their investigations, bearing in mind that it is important to distinguish collective moral responsibility from, and relate it to, individual moral responsibility. In what sense, if any, are police detectives individually and collectively morally responsible for their success (or, for that matter, their failure) in gathering sufficient evidence to identify, arrest, and charge an offender who has committed a serious crime? Alternatively, in (...) what sense are they morally responsible in cases where they identify, arrest, and charge an innocent person? And in what sense, if any, are police detectives individually and collectively morally responsible for the ultimate outcome of the trial, the finding by the courts of someone they have investigated and charged with a serious crime to be guilty or innocent? (shrink)

Human nature being what it is, individuals engaging in unlawful activity will often seek to avoid having their misconduct detected by law enforcement. This article provides the first legal analysis of what are termed detection avoidance measures, and evaluates whether, and how, they should be subject to criminalization.

Fall detection is a challenging task that has received the attention of the research community in the recent years. This study focuses on FD using data gathered from wearable devices with tri-axial accelerometers, developing a solution centered in elderly people living autonomously. This research includes three different ways to improve a FD method: an analysis of the event detection stage, comparing several alternatives, an evaluation of features to extract for each detected event and an appraisal of up to (...) 6 different clustering scenarios to split the samples in subsets that might enhance the classification. For each clustering scenario, a specific classification stage is defined. The experimentation includes publicly available simulated fall data sets. Results show the guidelines for defining a more robust and efficient FD method for on-wrist 3DACC wearable devices. (shrink)

This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI’s ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors identifying AI-generated content. These submissions were marked by 15 academic staff members alongside genuine student submissions. Although the AI detection tool identified 91% of the experimental submissions as containing AI-generated (...) content, only 54.8% of the content was identified as AI-generated, underscoring the challenges of detecting AI content when advanced prompting techniques are used. When academic staff members marked the experimental submissions, only 54.5% were reported to the academic misconduct process, emphasising the need for greater awareness of how the results of AI detectors may be interpreted. Similar performance in grades was obtained between student submissions and AI-generated content (AI mean grade: 52.3, Student mean grade: 54.4), showing the capabilities of AI tools in producing human-like responses in real-life assessment situations. Recommendations include adjusting the overall strategies for assessing university students in light of the availability of new Generative AI tools. This may include reducing the overall reliance on assessments where AI tools may be used to mimic human writing, or by using AI-inclusive assessments. Comprehensive training must be provided for both academic staff and students so that academic integrity may be preserved. (shrink)

The detection of extrasolar planets presents a good case in which to clarify the distinction between observation and inference from evidence. By asking whether these planets have been observed or inferred from evidence, and by using the scientific details to answer the question, we will get a clearer understanding of the epistemic difference between these two forms of information. The issue of scientific realism pivots on this distinction, and the results of this case will help to articulate the epistemically (...) important factors in assessing theoretical claims.Keywords: Planetary systems; Scientific realism; Observation; Scientific method. (shrink)

Introspection is a fundamental part of our mental lives. Nevertheless, its reliability and its underlying cognitive architecture have been widely disputed. Here, I propose a principled way to model introspection. By using time-tested principles from signal detection theory (SDT) and extrapolating them from perception to introspection, I offer a new framework for an introspective signal detection theory (iSDT). In SDT, the reliability of perceptual judgments is a function of the strength of an internal perceptual response (signal- to-noise ratio) (...) which is, to a large extent, driven by the intensity of the stimulus. In parallel to perception, iSDT models the reliability of introspective judgments as a function of the strength of an internal introspective response (signal-to-noise ratio) which is, to a large extent, driven by the intensity of conscious experiences. Thus, by modelling introspection after perception, iSDT can calibrate introspection’s reliability across a whole range of contexts. iSDT offers a novel, illuminating way of thinking about introspection and the cognitive processes that support it. (shrink)

Plagiarism detection is the process of finding similarities on electronic based documents. Recently, this process is highly required because of the large number of available documents on the internet and the ability to copy and paste the text of relevant documents with simply Control+C and Control+V commands. The proposed solution is to investigate and develop an easy, fast, and multi-language support plagiarism detector with the easy of one click to detect the document plagiarism. This process will be done with (...) the support of intelligent system that can learn, change and adapt to the input document and make a cross-fast search for the content on the local repository and the online repository and link the content of the file with the matching content everywhere found. Furthermore, the supported document type that we will use is word, text and in some cases, the pdf files –where is the text can be extracting from them- and this made possible by using the DLL file from Word application that Microsoft provided on OS. The using of DLL will let us to not constrain on how to get the text from files; and will help us to apply the file on our Delphi project and walk throw our methodology and read the file word by word to grantee the best working scenarios for the calculation. In the result, this process will help in the uprising the documents quality and enhance the writer experience related to his work and will save the copyrights for the official writer of the documents by providing a new alternative tool for plagiarism detection problem for easy and fast use to the concerned Institutions for free. (shrink)

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model’s (...) prediction are more accurate than either alone, but inaccurate model predictions often decrease participants’ accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants’ performance while mostly not affecting the model’s performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance. -/- . (shrink)

Eyewitnesses to crimes sometimes search for a culprit on social media before viewing a police lineup, but it is not known whether this affects subsequent lineup identification accuracy. The present online study was conducted to address this. Two hundred and eighty-five participants viewed a mock crime video, and after a 15–20 min delay either viewed a mock social media site including the culprit, viewed a mock social media site including a lookalike, or completed a filler task. A week later, participants (...) made an identification from a photo lineup. It was predicted that searching for a culprit on social media containing the lookalike would reduce lineup identification accuracy. There was a significant association between social media exposure and lineup accuracy for the Target Present lineup, but for the Target Absent lineup there was no significant association with lineup identification accuracy. The results suggest that if an eyewitness sees a lookalike when conducting a self-directed search on social media, they are less likely to subsequently identify the culprit in the formal ID procedure. (shrink)