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The automotive industry is increasingly deploying software solutions to provide value-added features for vehicles, especially in the era of vehicle electrification and automation. However, the ever-increasing cyber components of vehicles (i.e., computation, communication, and control) incur new risks of anomalies, as evident by the millions of vehicle recalls by different automakers. To mitigate these risks, we design the B-Diag, a battery-based diagnostic system detects engine anomalies with a cyber-physical approach. The core idea of B-Diag is to diagnose engines using the physically-induced correlations between battery voltage and engine variables, which is captured as a customized 3-layer correlation graph and a set of data-driven norm models describing the edges thereof. The design of B-Diag is steered by a dataset collected with a prototype system when driving a 2018 Subaru Crosstrek in real-life for over three months. Our evaluation shows B-Diag to detect anomalies of 17 engine variables with a >86% (up to 100%) accuracy.

This paper explores how a deeply embedded norm can be used to exert power and control in the governance of distributed infrastructures, such as the Internet. Through the lens of norm conflict, I analyse a case of resistance against the introduction of norms in the transnational governance of Internet routing. In a mixed-methods case study, I examine through an experiment how a community of network operators resists the introduction of data protection and human rights norms in the Internet routing infrastructure. To provide a possible explanation as to how a prevalent and deeply embedded norm enables the resistance to the introduction of new norms, I develop the notion of ‘infrastructural norms’. This concept could help explain why Internet infrastructure governance thus far has largely defied national and international democratic norms. The understanding of how norms are used in infrastructure governance contributes to the study of power in large technical systems.

Most knowledge representation learning (KRL) methods only use structured knowledge graphs (KGs); however, there is still much multi-modal (textual, visual) knowledge that has not been used. To address this challenge, we propose a novel solution called multi-modal knowledge representation learning (MMKRL) to take advantage of multi-source (structured, textual, and visual) knowledge. Instead of simply integrating multi-modal knowledge with structured knowledge in a unified space, we introduce a component alignment scheme and combine it with translation methods to accomplish multi-modal KRL. Specifically, MMKRL firstly reconstructs multi-source knowledge by summing different plausibility functions and then aligns multi-source knowledge using specific norm constraints to reduce reconstruction errors. We also select an adversarial training strategy to enhance the robustness of MMKRL, which is rarely considered in existing multi-modal KRL methods. Experimental results show that MMKRL can effectively utilize multi-modal knowledge to achieve better link prediction and triple classification than other baselines in two widely used datasets. Further, when relying on structured knowledge or limited multi-source knowledge, MMKRL still achieves competitive results in link prediction, demonstrating our model’s superiority.

This article analyzes platform governance and platform power through the lens of deplatforming sex, and deplatforming of sex through the lens of concentration of norm and infrastructure power. Base...

Person detection and Re-identification are two well-defined support tasks for practically relevant tasks such as Person Search and Multiple Person Tracking. Person Search aims to find and locate all instances with the same identity as the query person in a set of panoramic gallery images. Similarly, Multiple Person Tracking, especially when using the tracking-by-detection pipeline, requires to detect and associate all appeared persons in consecutive video frames. One major challenge shared by the two tasks comes from the contradictory goals of detection and re-identification, i.e, person detection focuses on finding the commonness of all persons while person re-ID handles the differences among multiple identities. Therefore, it is crucial to reconcile the relationship between the two support tasks in a joint model. To this end, we present a novel approach called Norm-Aware Embedding to disentangle the person embedding into norm and angle for detection and re-ID respectively, allowing for both effective and efficient multi-task training. We further extend the proposal-level person embedding to pixel-level, whose discrimination ability is less affected by misalignment. Our Norm-Aware Embedding achieves remarkable performance on both person search and multiple person tracking benchmarks, with the merit of being easy to train and resource-friendly.

E-commerce is one of the innovations that has started to become a new norm in this millennium. Due to the time-consuming physical shopping activities, the increase in living expenses, and the increase of responsibilities, people tend to find new ways to adapt to this new norm. Thus, buying groceries online has also been the preferred way for many people nowadays as it could save time and reduce daily hassles. This paper discusses the design and development of a web-based Groceries Ordering System with QR Code that was built using the web-based applications such as PHP, HTML and MySQL. The design and development of the prototype system involves three main phases, which are the system design, system development and system testing and took into consideration five criteria of the end-user satisfaction of an online shopping system, which are the system’s content, accuracy of information, format of the information displayed, ease of use and timeliness. The prototype system will also incorporate the QR code technology where it provides functions for information of products’ purchases and transactions. Upon completion of the prototype system, testing for usability among potential end-users was conducted, where it focusses on system’s usefulness, information quality and interface quality. Forty participants have completed the usability testing, and the results have showed that the Groceries Ordering System with QR Code has fulfilled all the five criteria of end user’s satisfaction

Through the dialectical method the objective of the article was to analyze the process of the elaboration of modern laws, considering their structure and main problems. There is a major structure and problems inherent in the modern law-making process described in the article. The structure of law-making comprises four parts: 1. Cognitive-analytical part; 2. Theoretical foundations of the legal norms and acts they dictate; 3. Validation of legal act or norm; 4. Monitoring of relevant rules and legal acts. The main legislative task is to draft legal norms that stimulate the active development of all parts of the State and society through a deep perception of all related processes, including those involving standard-setting. It is concluded that the main factor affecting the quality of legislation is the lack of a uniform legislative basis for the issuance of legal acts. There seems to be a real need to develop a uniform legislative act on the law-making process. The Code containing general and specific parts of each law must be developed.

Key words: copyright, work, artificial intelligence, computer program In the article, the problemsof legislation of Ukraine regarding the issues of copyright on works created due to artificialintelligence were analyzed. Particularly, who is the owner of copyright ofworks created due to artificial intelligence. On the one hand, it could be a developer ofa computer program, from the other hand, it could be a client or an employer. Because,it could happen that there is a situation when robots created something newand original, e.g., how it happened with the project “New Rembrandt”. In this case,computers created a unique portrait of Rembrandt. And here is a question, where isin this portrait original and intellectual works of developers of these computers andprograms. In the contrast, this portrait could be created without people who developedspecial machines, programs, and computers. The article’s author proposes to addinto Ukrainian legislation with following norm: the owner of the copyright createddue to artificial intelligence should be a natural person who uses artificial intelligencefor these purposes within the official relationship or on the basis of a contract. In caseof automatic generation of such work by artificial intelligence, the owner of copyrightshould be the developer.Also, another question arises, particularly, who will be responsible for the damagecaused by the artificial intelligence. As an example, of the solution for this issue Resolution2015/2103 (INL) was given, where is mentioned that human agent could be responsiblefor the caused damage. Because, it is not always a developer is responsiblefor the damage.Also, the legislation and justice practice of foreign countries was explored. Theways of overcoming mentioned problems in legislation of Ukraine were proposed.Such as changing our legislation and giving the exact explanation in who is the ownerof copyright on works created due to artificial intelligence and in which cases this personcould become an owner of the copyright. However, probably, these issues shouldbe resolved at international level regarding globalization.

In many applications, one works with neural network models trained by someone else. For such pretrained models, one may not have access to training data or test data. Moreover, one may not know details about the model, e.g., the specifics of the training data, the loss function, the hyperparameter values, etc. Given one or many pretrained models, it is a challenge to say anything about the expected performance or quality of the models. Here, we address this challenge by providing a detailed meta-analysis of hundreds of publicly-available pretrained models. We examine norm based capacity control metrics as well as power law based metrics from the recently-developed Theory of Heavy-Tailed Self Regularization. We find that norm based metrics correlate well with reported test accuracies for well-trained models, but that they often cannot distinguish well-trained versus poorly-trained models. We also find that power law based metrics can do much better -- quantitatively better at discriminating among series of well-trained models with a given architecture; and qualitatively better at discriminating well-trained versus poorly-trained models. These methods can be used to identify when a pretrained neural network has problems that cannot be detected simply by examining training/test accuracies., 35 pages, 8 tables, 17 figures. To appear in Nature Communications

Local governments and transnational city networks (‘TCNs’) have been increasingly engaging with norm-generation in the traditionally state-centric international law and migration governance. We ide...

PurposeThis paper aims to discuss ways in which working in the virtual space can give rise to negative norm violating behaviours of employees in organizations. Further, it describes some of the measures that can be taken by organizations to manage such behaviour such that organizational and individual goals are met.Design/methodology/approachThis paper discusses the impact and importance of managing deviant behaviour of employees. Using relevant examples procured from secondary sources, this paper further provides a glance at how organizations can minimize such behaviour and maintain a productive and supportive work environment.FindingsIn today’s scenario when remote working has become a norm, organizations can prevent employees from engaging in deviant behaviour by providing supportive work environment, recalibrating their policies as per the situation and by adopting a top-down approach of communication.Originality/valueThis paper aims to provide a glance at the people-related challenges that the shift to virtual working may have given rise to. It provides measures that organizations can adopt to keep their employees focussed and prevent them from engaging in deviant behaviours.

This empirical research contributes to the current knowledge of sustainable destination management by applying nudge and value belief norm theories. The objective of this study is to investigate the effects of sustainable intelligence, destination social responsibility (DSR), biospheric value, and visit experience on pro-environmental behaviour in the eco-tourism site of Upo Wetland, South Korea. This study also compares pro-environmental behaviour across two DSR segments (high and low DSR clusters). Results reveal that sustainable intelligence, biospheric value, DSR, and visit experience at ecotourism sites significantly influence pro-environmental behaviour. Sustainable intelligence exerts the highest effect on pro-environmental behaviour among the variables. The impact of the high DSR group on pro-environmental behaviour is stronger than that on the low DSR group. Thus, managers of ecotourism sites should engage in the high DSR group that does care about sustainable intelligence and biospheric value in environmentally friendly activities.

This study examines how the team norm of information exchange and team information processing affects team performance. Data were collected from 354 project teams from software development companies in Thailand. Structural Equation Modelling (SEM) was conducted to test the proposed hypotheses. The results of the study revealed that team norms of information exchange (TNM) has a significant relationship with team information processing (PRO). Team information processing positively influences team performance (TPM). Software development organisations could adopt such norms and this team process to improve software development projects performance and recognise team processes, which is essential for long-term sustainability and competitiveness.

Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To accurately predict the QoS values of candidate Web services, Web service recommendation systems usually need to collect historical QoS data from users, which will potentially pose a threat to the user’s privacy. However, how to simultaneously protect user’s privacy and make an accurate prediction has not been well studied. By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. Specifically, we first design a similarity-maintaining privacy preservation (SPP) strategy, which aims to protect the user’s privacy and maintain the utility of user data in the meanwhile. Then, we propose a location-aware low-rank matrix factorization (LLMF) algorithm, which employs the $L_1$ L 1 -norm low-rank matrix factorization to improve the model’s robustness, and combines the matrix factorization model with two kinds of location information (continent, longitude and latitude) in the prediction process. Experimental results on two publicly available real-world Web service QoS datasets demonstrate the effectiveness of our privacy-preserving QoS prediction approach.

The COVID-19 pandemic has engulfed numerous parts of the world, crippling economies. Many countries’ government has issued lockdown orders to curb the spread of the COVID-19 virus. This lockdown has created a new norm for society. Unfortunately, businesses have been badly affected by the lockdown. Thus, business leaders needed to dig deep and think of ways to sustain their organisation and move forward. This study selected a successful manufacturing company and examined the mediating effects of emotional intelligence against two other variables, namely organisational citizenship behaviour and job satisfaction of the company’s executives during the challenging situation. The questionnaires were adapted from Palmer and Stough (to determine emotional intelligence) and Podsakoff et al. (to examine organisational citizenship behaviour) and were emailed to the executives via the human resource manager. The survey was participated by 110 executives. Results revealed that emotional intelligence played a mediating role in enhancing organisational performance.

Technology advancement has revolutionized the education environment, resulting in Online Distance Learning (ODL) being introduced at the tertiary level to meet the demands of various groups of individuals. While the academic environment embraces technology advancement and ODL, the world was hit with the devastating Covid-19 pandemic, putting the whole world at freeze and greater challenges. This created further challenges towards the ODL implementation, especially in the fields requiring hands-on practical lab and studio practices such as art and design. Despite the technology advancement and flexible platform, the Teaching and Learning process faces unprecedented challenges which required investigation towards its implementation and effectiveness. This article discusses the implementation of Online Distance Learning (ODL) in the teaching and learning (T&L) process of the Graphic Design Field in Malaysian Tertiary Institution. The result from the finding identified what were the challenges and can be used to further improve the implementation of ODL in the related field of study in the future. Keywords: Graphic Design Education, Online Distance Learning, New Norm, Tertiary Education

This study explores how a high-reliability team (HRT) employed an informal communication norm to facilitate access to members’ distributed expertise during forecast decisions, despite time pressure...

Blended Learning has existed for many years, and while it has gradually been gaining a measure of impetus in third-level programmes, it has traditionally been used as a cost-effective way to delive...

Leveraging omnichannel has become a new norm of strategic marketing in the retail industry, with many vendors foregrounding the value of customers who wish to maximize their shopping experiences ac...

The usage of smartphones and apps to communicate, retrieve information, locate places, and entertain is a norm today. However, to this day, many users are unaware of, or indifferent to, how smartph...

This paper is about the pragmatic inferences in play as conversational implicatures (Grice, P. 1989. Studies in the way of words. Cambridge, MA: Harvard University Press) occur. First, it constructs the deductivism versus abductivism debate that transpires from the extant literature but is rarely elaborated. Against deductivism, the paper argues that implicating inferences in conversational implicatures can instantiate an abductive logical form, as abductivism suggests. Against abductivism, however, it grants to deductivism that implicating inferences can have a deductive form provided the latter is of a defeasible type. In sum, it thus argues for pluralist defeasibilism. Second, it turns to the issue of the ontological nature of implicating inferences and advocates normative inferentialism, on which these inferences are not primarily real psychological processes but rules of the practice of implicating. While this allows for the possibility of their psychological instantiation, to be sure, the paper also insists that psychological inferring processes in implicatures are neither necessarily isomorphic to the aforementioned rules nor even necessarily occur as an implicature occurs.

When are you in a position to rely on p in practical reasoning? Existing accounts say that you must know that p, or be in a position to know that p, or be justified in believing that p, or be in a position to justifiably believe it, and so on. This paper argues that all of these proposals face important problems, which I call the Problems of Negative Bootstrapping and of Level Confusions. I offer a diagnosis of these problems, and I argue that an adequate epistemic norm must be transparent in the following sense: According to any transparent epistemic norm, a consideration counts in favor of (or against) relying on p in practical reasoning for action iff, and to the extent that, this consideration also counts in favor of (or against) p being true. I introduce a candidate epistemic norm that satisfies this condition. According to this norm, one should rely on p in practical reasoning only if it must be that p. I show that if we adopt a non-factualist account of “must”, this amounts to a novel and attractive proposal, a proposal that satisfies the transparency condition.

Norm Meyrowitz is currently an Adjunct Professor of the Practice of Computer Science at Brown University. He received an Sc.B. in Computer Science from Brown in 1981, and is recognized for his work on linking and multimedia technology for the Internet and for the evolution of Web development software. In the 1980s, Norm served as a Co-Director of Brown University's Institute for Research in Information and Scholarship, where he led the development of Intermedia, a hypermedia system that influenced both the creator of the Web and the creator of the Mosaic Web browser. In mid-1980s, he helped start two ACM conferences - OOPSLA (Object-Oriented Programming, Systems, and Languages) and Hypertext '87, which continue to this day. Following his work in academia, Norm worked for several years as the Director of System/User Software for pen/tablet pioneer GO Corporation before transitioning to his role as President of Product Development at Macromedia (later acquired by Adobe). At Macromedia, Norm oversaw a variety of Web development and multimedia products at Macromedia, including Shockwave, Dreamweaver, Flash, the latter of which had more than 4 billion downloads in its heyday in the 1990s and early 2000s.

The complexity of 21st-century socio-ecological-technical challenges increasingly strains the capacity of 20th-century policy design approaches. This new context opens an opportunity to evolve norm...

In this paper, we present a smart connected parking lots solution to automatically count and notify drivers about empty parking spots in major cities. As its name implies, the proposed smart IoT system has two operating phases: (i) continuous counting of empty spots in the monitored far-apart parking lots, and (ii) instantaneous driver notification through a lightweight MQTT mechanism. This notification system relies only on information collected from the pre-installed multimedia devices (no other apparatus installation or maintenance such as ground sensors is required). To validate the proper operation of our solution, we have implemented a small-scale version of it and assessed its performance while considering different classical and lightweight deep learning techniques (MobileNetV2, ResNet-50, YOLOv3, SSD-MobileNetV2, Tiny-YOLO, SqueezeDet, and SqueezeDet pruned with $$\ell _1$$ -norm). The experiments have confirmed the proper operation, efficiency, ease of deployment, and ease of extension of our system. They also confirmed that lightweight deep learning solutions are more adequate for small-sized resource-constrained embedded systems. They are more efficient in terms of inference time, size, resource consumption, and yield an accuracy that is close to that of classical solutions.

Mobile messaging has been associated with guilt. Guilt about too much messaging may result from self-control failures during goal conflicts. Conversely, guilt about too little messaging may result from violating the salient norm to be available. This research considers both boundary conditions of guilt about mobile communication—goal conflicts and availability norm salience—simultaneously for the first time. We conducted two preregistered experiments to investigate their interplay. Results from a vignette experiment, but not from a laboratory experiment, support the hypotheses that goal conflicts trigger guilt about using messengers and that guilt about not using messengers arises if the availability norm is salient. In both studies, using messengers elicited more guilt than not using messengers. The boundary conditions did not interact in influencing guilt. Overall, this research emphasizes the importance of self-control, norms, and usage contexts when studying effects of mobile media use on emotional well-being.

Currently, the rigid restrictions on agents with only constrained motivations (corresponding constrained cooperations) cause the high violation rate, low profits and even harm to their reputations without incent motivations in agent mental models, and as a result extremely limit the agent cooperations. To cope with the problems (To incent agents to cooperate with low violation rates and high profits, and to make them more active to cooperate), incent motivations (corresponding incent cooperations) are introduced, 3 kinds of motivations are set forth: the constrained motivations including norm (N), policy (P), and contract (C), the incent motivations including bargain (A), promotion (M), and sticker (S) and the internal motivation desire (D). The NPCAMSD logic of the model is formally depicted, and the semantic, syntax and axiom are analysed. The overcoming of motivation logical omniscience problem is analysed. The two layer motivation conflict resolving method is introduced. The implementation of the model and experiment prove that the mental model has low contract violation rate, high profits in cooperations and works well for the diverse contemporary business cooperations.

The article is devoted to the problem of creating a complete and consistent rule about writing o/e after hushing sounds, which not only corresponds to codified language material, but also is the basis for a systematic assessment of both normal deviations in spellings and various dictionary fixations of new words. Using the example of this rule, the general theoretical issues of combining in the spelling rules the reflective side, corresponding to the language essence of the phenomena, and the conditional one, which makes it possible to adequately assimilate and use the writing norms, are considered. The article provides an analysis of different approaches to the description of the spelling, defines modern language factors that affect the choice of spelling. The new version of the rule proposed as a result takes into account all the material included in the academic Russian Spelling Dictionary. The rule also takes into account the factors that are already formed and are still emerging, which are active in this area of writing. All this provides a reasonable approach to codification of new words and control over the points of interaction of multidirectional factors. Moreover, the rule also contains a purely formal clause that does not require any language categories, which is effective for the automatic implementation of the rule in writing.

Materials having radioactive nuclei and exist naturally on earth are called Naturally Occurring Radioactive Materials (NORM).235U,238U and 232Th series in addition to 40K are few examples of radioactive elements and their daughter nuclei. NORM can be found either in soil, in water, in plants, in animals or in human being. In oil and gas industries NORM accumulates at different locations (the wellheads, pumps and separation vessels), That may take the form of scales, sludge, scrapings and some other wastes. Unless adequate control procedures are employed, it may be hazardous to workers, ordinary people and environment. The most critical concern to public health from exposure to NORM is the possibility of development of cancer. Having no threshold dose, the induction of cancer due to radiation exposure is thus a stochastic process. Nevertheless, no acute or severe effects comparable to those due to high doses of radiation are expected in case of exposure to Technologically Enhanced NORM (TE-Norm). Uncontrolled release of NORM waste will probably contaminate the environment and threaten human health. Such risks can be avoided through the use of a program for NORM control by oil and gas firms. The program will monitor NORM, control contaminated waste, contaminated equipment, train workers and increase their awareness. By the implementation of appropriate protective procedures at work places one can ensure that the basic principles of radiation protection are functionalized. Therefore, the control of exposure and application of sample dosimetry are the main components of any program for health and safety.

The earliest approach by international lawyers to the question of cybersecurity was focused on collective security. Regarding this issue, there is consensus on the evolutionary and dynamic application, difficult in any case, of the basic rules concerning the prohibition of the threat and the use of force that already exists in the international order. However, cybersecurity does not only concern peace and security, as it is considered a problematic issue that poses various challenges from other international legal perspectives. This paper aims to analyse cybersecurity from this more holistic approach, pointing out the shortcomings of this evolutionary application as well as the efforts that have been carried out so far in order to promote international cooperation in this field so as to achieve norm-development.

For modern manufacturing firms, automation has already become a norm but constantly needs to be improved as firms still face strong demand to increase their productivity. This can be achieved by reducing dependability on manpower, reaching lean and even unmanned production and this is where some of the standards of Industry 4.0 come in useful, not to mention: Machine Vision, Image Recognition or Machine Learning. In our paper, we present SODA – our approach to build a flexible ML and AI enabled framework for object detection, analysis, and simulation. The framework is designed to support a development process of solutions requiring real-time analysis of images of different types of moving objects on a conveyor belt. In our work we discuss architectural challenges of the developed framework as well as the basic components of the system. We do also provide information on how to use the framework and present a sample implementation of an actual system employing some of the machine learning methods.

Having learners (K7–10) acquire system thinking skills is challenging. Together with teachers we deploy qualitative representations of complex systems to enable this learning process. Teachers select their own topics for their leaners to work on which makes that lessons vary in content depending on the teacher’s preference. Within this setting we face the challenge of adequately coaching learners while they create their knowledge models. For this, we use norm-model based feedback, ignoring learner specific information. Here we report the working and effectiveness of this approach.

Participating in mobile services by synthesizing trajectories with consistent lifestyle and meaningful mobility as actual traces are the most popular way to protect location privacy. However, recent trajectory synthesizing techniques are still threatened by the information that the attacker inevitably obtains, such as the locations of the accepted tasks in the crowdsourcing application. With this information and the spatiotemporal correlation hidden in the user’s mobility, the attacker can infer the user’s actual location and even future behaviors. It remains open to defend against such inferential attacks in the continual crowdsourcing scenarios. In this paper, we propose a mobility-aware differentially private solution, ConCrowd-DP, for achieving the privacy-preserving continual crowdsourcing application. Specifically, before starting the application, we first construct a spatiotemporal mobile model, STMarkov, to model the spatiotemporal correlation in users’ mobility. Then, a perturbed location is generated for the user to participate in the crowdsourcing application, according to STMarkov and $K$ -norm DP. Finally, we eliminate the privacy threat brought by the accepted task based on $K$ -norm DP and Bayesian posterior theorem. With ConCrowd-DP in place, a mobility-aware differentially private trace is generated for the user to participate in the application continually. Extensive experiments with real-world datasets demonstrate that ConCrowd-DP guarantees the usability of the synthesized trajectory effectively, while providing the DP protection for defending against the inferential attacks which stem from the multiple accepted tasks.

Object detection is significant for event analysis in various intelligent multimedia processing systems. Although there have been many studies conducting research in this area, effective and efficient object detection methods for video sequences are still much desired. In this article, we investigate salient object detection in real-time multimedia processing systems. Considering the intrinsic relationship between top-down and bottom-up saliency features, we present a new effective method for video salient object detection based on deep semantic and spatiotemporal cues. After extracting top-down semantic features for object perception by a 2-D convolutional network, we concatenate them with bottom-up spatiotemporal cues for motion perception extracted by a 3-D convolutional network. In order to combine these features effectively, we feed them into a 3-D deconvolutional network for feature-sharing learning between semantic features and spatiotemporal cues for the final saliency prediction. Additionally, we propose a novel Gaussian-like loss function with an $L_{2}$ -norm regularization term for parameter learning. Experimental results show that the proposed salient object detection approach performs better in terms of both effectiveness and efficiency for video sequences compared with the state-of-the-art models.

Purpose Today, online shopping and online business has become a new norm especially in the current pandemic scenario. With more businesses running online, cyber criminals are coming up with different tactics to steal identity and sensitive information such as credit card and banking credentials either for personal monetary gain or to sell in the dark Web. One form of such attack that is seen in the recent times is formjacking attack. This paper aims to review the current scenario of formjacking attack and its modus operandi. The paper also provides certain counter measures that can be adopted by the users and website owners. Design/methodology/approach The paper mainly focuses on the modus operandi of formjacking attack to understand the severity of the problem. Based on the way the attack is carried out, some guidelines to be followed are provided. Later, a brief review of machine learning techniques is furnished to understand how it may help as secure defense mechanism. Findings Formjacking attacks are on a rise in the past two years, especially during the holiday season. Cyber criminals have been using smart tactics to carry out these attacks which are very difficult to detect. Machine learning techniques may prove to be effective in combating these attacks. Originality/value Formjacking attack is not just a concern of the customers who may lose their sensitive data, but the onus also lies on the companies itself to ensure they protect their customer’s data from theft. Not much research is found regarding formjacking attack, as it is relatively a new form of attack. The paper reviews this attack and provides some measure that can be followed. It also provides few guidelines which can be used for further research in devising a security tool to mitigate this problem.

This paper addresses the figure of conventional control and examines the possibilities of applying this concept within the framework established by the general international law and within the parameters determined by the control of constitutionality. We will address the figure of the diffuse conventional control as it has been used by the Inter-American Court of Human Rights, analizing it from the perspective of the theory of the sources of general international law; in the way in which different States of the region incorporate the international norm within their internal legal systems; and the hierarchy that they assign to said rules once they are incorporated. El presente trabajo aborda la figura del control convencional y examina cuáles son las posibilidades de aplicar este concepto dentro del marco que establece el derecho internacional general y dentro de los parámetros que determina el control de constitucionalidad. Se abordará la figura del control convencional difuso tal como ha sido postulado por la Corte Interamericana de Derechos Humanos, analizándola desde la perspectiva de la teoría de las fuentes del derecho internacional general, de la manera como los diferentes Estados de la región incorporan la norma internacional dentro de sus sistemas jurídicos internos y de lajerarquía que estos les asignan a dichas normas una vez incorporadas.

Enkratic reasoning—reasoning from believing that you ought to do something to an intention to do that thing—seems good. But there is a puzzle about how it could be. Good reasoning preserves correctness, other things equal. But enkratic reasoning does not preserve correctness. This is because what you ought to do depends on your epistemic position, but what it is correct to intend does not. In this paper, I motivate these claims and thus show that there is a puzzle. I then argue that the best solution is to deny that correctness is always independent of your epistemic position. As I explain, a notable upshot is that a central epistemic norm directs us to believe, not simply what is true, but what we are in a position to know.

Although Amedeo Giovanni Conte investigated questions relevant to legal sociology through several decades of studies and discussions, his works specifically addressed to a philosophical sociology of law were mainly written between 2000 and 2011. Through these works Conte has noticeably contributed to shape some basic categories for legal sociology in different areas: the relationship between norm and action, the problem of inference of rules from observed behaviour, the principle of effectiveness, the relationship between law and language. This arti-cle discusses Conte's theory of nomotropism (acting in function of rules) also in the light of the Friedmanian theory of the impact of law. Conte's research, since it is addressed to the elaboration of basic categories which can be used by other scholars to undertake either theoret-ical or empirical investigations, could be conceived of as a contribution towards a pure (reine) sociology of law.

Matching resources to demand is a daily challenge for hospital leadership. In interdisciplinary collaboration, nurse leaders and data scientists collaborated to develop advanced machine learning to support early proactive decisions to improve ability to accommodate demand. When hundreds or even thousands of forecasts are made, it becomes important to let machines do the hard work of mathematical pattern recognition, while efficiently using human feedback to address performance and accuracy problems. Nurse leaders and data scientists collaborated to create a usable, low-error predictive model to let machines do the hard work of pattern recognition and model evaluation, while efficiently using nurse leader domain expert feedback to address performance and accuracy problems. During the evaluation period, the overall census mean absolute percentage error was 3.7%. ALEx's predictions have become part of the team's operational norm, helping them anticipate and prepare for census fluctuations. This experience suggests that operational leaders empowered with effective predictive analytics can take decisive proactive staffing and capacity management choices. Predictive analytic information can also result in team learning and ensure safety and operational excellence is supported in all aspects of the organization.

The Covid-19 pandemic has resulted to virtual learning been implemented as a new norm of learning. Teachers must be able to interact with students using different sources in order to create new learning experience using virtual learning. The main discussion aims to recognise the impact of teaching and learning in introducing virtual learning to students. The problems some teachers and students face include too much workload or online assignment, technology dependent, vague instructions and de-professionalism. The impact of teaching and learning were measured using a framework created by Danielson for teaching and learning which are planning and preparation, classroom environment, instructions and professional responsibilities. The framework will guide teachers to identify their responsibilities and help students improve learning. An online survey was conducted among 41 lecturers using convenience sampling technique. The suggestions given were based on T.E.A.M. concept which consists of transmissions of knowledge and effective framework, establish teachers and students' improvements, assess students in learning and maintain professional development. The key finding of this paper is the impact of implementing T.E.A.M. framework towards the effectiveness of virtual learning.

In the era of modernization, law is no longer understood as a norm system that binds every citizen. In practice, the law is controlled and supervised by the state through a number of regulations created and compiled by certain communities that have direct access to the state. This paper will explore more about the concept of legal pluralism that once applied in the social history of Islamic law, as well as describing cases of legal pluralism that were practiced during the time of the Prophet and also friends.

The subject of the article is the testing process on multi-touch devices for the implementation of psychoneurological studies. The goal of the work is development specialized dynamic tests with the introduction of Multi-touch technology to detect carpal tunnel syndrome and hand dysfunction in the early stages of the disease. An objective of the work is to conduct a theoretical analysis to determine the optimal parameters of touchscreen devices for the implementation of psycho neurological testing; based on experimental studies to determine the main physical indicators of the test results, indicating the presence of functional disorders of the hand. The constant use of computers, smartphones, gadgets provide multiple identical load on the wrist; constant visual and neuro-emotional stress; sitting position of the body; static tension of the musculoskeletal system caused by the need to maintain a working posture; decreased overall motor activity. The methods of computer modeling and visualization of dynamic tests using the multi-platform programming language Python were used. Results. The scientific novelty and practical significance of the obtained results is the development of specialized software and algorithmic software for dynamic testing on sensory devices with the ability to video record the movements of the upper extremities in the mode "Rapid". The advantage of this method is its non-invasiveness, ease of implementation, as well as the absence of sensors that can restrict movement. Using the rapid method, one can easily distinguish the pathological state of motor activity of the hand from the established norm. Conclusions. A specialized software tool was developed, which was adapted for multi-touch screens that allow identifying functional disorders of the hand by registering the main physical parameters during testing. Dynamic tests are adapted for any age category of participants (patients), have additional modes for individual color perception and different levels of complexity.

Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models a...

International audience; Jupyter notebooks have gained popularity in educational settings. In France, it is one of the tools used by teachers in post-secondary classes to teach programming. When students complete their assignments, they send their notebooks to the teacher for feedback or grading. However, the teacher may not be able to reproduce the results contained in the notebooks. Indeed, students rely on the non-linearity of notebooks to write and execute code cells in an arbitrary order. Conversely, teachers are not aware of this implicit execution order and expect to reproduce the results by running the cells linearly from top to bottom. These two modes of usage conflict, making it difficult for teachers to evaluate their students' work. This article investigates the use of immediate visual feedback to alleviate the issue of non-reproducibility of students' notebooks. We implemented a Jupyter plug-in called Notebook Reproducibility Monitor (NoRM) that pinpoints the non-reproducible cells of a notebook under modifications. To evaluate the benefits of this approach, we perform a controlled study with 37 students on a programming assignment, followed by a focus group. Our results show that the plug-in significantly improves the reproducibility of notebooks without sacrificing the productivity of students.

If reality is being augmented as a simulation; then from the modern norm of physics, it is possible that everywhere around us, including us and what we perceive is a simulation simulated by a super powerful computer from a farfetched future times, taken the Einstein’an notion, that, past, present and future occur simultaneously. It is quite probable between two consecutive amplitudes that, the simulation can either be done by the super advanced civilization in greater than or equal to Kardashev 3.0 scale or by some higher order entities existing in a dimensional domain beyond our perceiveness and notion of our understanding. To agree to the fact of the simulation hypothesis, there exists a mathematical foundation of the desired logic behind this simulation, which will be investigated throughout this paper whose another consequence might be the déjà vu or the Mandela effect. The errors arise in this simulation is a form of glitch in the matrix that should happen because of the commutable lagging of the super-intelligent computers of either future ones or higher-order ones. Preciseness about the calculations of dimensions opened a way for t + s = 2 + 10 where the non-locality of the time being perceived as a 2-dimensional entity opens up the door for further investigations. The more will be discussed in detail in this paper.

Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework that combines on-device local training with server-based model synchronization to train a centralized ML model over distributed nodes. In this paper, we propose an asynchronous FL framework with periodic aggregation to eliminate the straggler issue in FL systems. For the proposed model, we investigate several device scheduling and update aggregation policies and compare their performances when the devices have heterogeneous computation capabilities and training data distributions. From the simulation results, we conclude that the scheduling and aggregation design for asynchronous FL can be rather different from the synchronous case. For example, a norm-based significance-aware scheduling policy might not be efficient in an asynchronous FL setting, and an appropriate "age-aware" weighting design for the model aggregation can greatly improve the learning performance of such systems., 5 pages, 4 figures, accepted in 22nd IEEE international workshop on signal processing advances in wireless communications (SPAWC 2021)

The COVID-19 pandemic provoked many changes in our everyday life. For instance, wearing protective face masks has become a new norm and is an essential measure, having been imposed by countries worldwide. As such, during these times, people must wear masks to enter buildings. In view of this compelling need, the objective of this paper is to create a real-time face mask detector that uses image recognition technology to identify: (i) if it can detect a human face in a video stream and (ii) if the human face, which was detected, was wearing an object that it looked like a face mask and if it was properly worn. Our face mask detection model is using OpenCV Deep Neural Network (DNN), TensorFlow and MobileNetV2 architecture as an image classifier and after training, achieved 99.64% of accuracy. © 2021 The authors and IOS Press.