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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.

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

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

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.

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.

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.

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.

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.

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.

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.

The pandemic caused by the COVID 19 virus had a tremendous impact on the retail and tourism industry resulting in a loss of around two trillion dollars per year. Recovering completely from the pandemic may take several years and safety guidelines like social distancing will remain a norm for many years to come. Many governments have levied heavy fines for businesses that fail to implement social distancing guidelines. Implementing manual social distancing policies can be expensive and error prone. This paper proposes a smart system that uses cameras along with Artificial Intelligence Internet of Things (AIoT) edge devices like the Nvidia Nano GPU to automatically detect social distance violations on the edge. The system achieved an accuracy of 92.2% and an F1-Score of 0.851 using a single camera only. On an Nvidia Nano, the system has a very acceptable rate of 1.7 frames per second on the edge obviating the need for any expensive back-end transmission and processing. The system uses the MQTT protocol to instantly notify the manager or owner of the business via a mobile App and a Web application for necessary action.

There is currently the widest breadth of video codecs available for the massive $200B video services industry, comprising broadcast, streaming, and other services, in history: MPEG-2, MPEG-4, AVC, HEVC, VVC, VP8, VP9, AV1, EVC, and LC-EVC. While these codecs compete in the marketplace for share of streams, the consumer surely benefits from having advanced services at lower rates. Is 4K HDR HEVC going to become the new norm for broadcast/streaming? But this is a challenging environment for developers and service provides. In this panel, we explore the breadth of consumer services that are enabled by these technologies, including high resolution: 4K, 8K, and beyond, as well as HDR, and AR/VR – will these finally take off and fulfill their promise? And is 8K the end of the line for consumer devices such as TVs, and even computers, tablets, and smartphones?

Information and communication technology has evolved rapidly over the past decades, with a substantial development being the emergence of social media. It is the new norm that people share their information instantly and massively through social media platforms. The downside of this is that fake news also spread more rapidly and diffuse deeper than before. This has caused a devastating impact on people who are misled by fake news. In the interest of mitigating this problem, fake news detection is crucial to help people differentiate the authenticity of the news. In this research, an enhanced convolutional neural network (CNN) model, referred to as Fake News Net (FN-Net) is devised for fake news detection. The FN-Net consists of more pairs of convolution and max pooling layers to better encode the high-level features at different granularities. Besides that, two regularization techniques are incorporated into the FN-Net to address the overfitting problem. The gradient descent process of FN-Net is also accelerated by the Adam optimizer. The empirical studies on four datasets demonstrate that FN-Net outshines the original CNN model.

The COVID‐19 pandemic introduced the new norm that changed the way we work and live. During these unprecedented times, most of the organizations expected their employees to work from home. Remote working created new opportunities for hackers since more users were making use of digital platforms for online shopping, accessing Virtual Private Network (VPN), videoconferencing platforms, and software alike. Consequently, cybercrime increased due to the increase in the attack surface, and software vulnerabilities were exploited for launching cyberattacks. There is existing research that explores vulnerability disclosure on Twitter. However, there is a lack of study on opportunistic targeted attacks where specific vulnerabilities are exploited in a way that benefit adversaries the most in times such as COVID‐19. The primary aim of this work is to study the effectiveness of vulnerability disclosure pattern on Twitter in COVID‐19, and discuss how Twitter can be leveraged as Open‐Source Intelligence (OSINT) during a pandemic where the global users can follow a coordinated approach to share security‐related information and conduct awareness campaigns. The study identifies Twitter as an apt source for conducting cybersecurity awareness campaigns as 99.83% of the security vulnerabilities are found to be accurate. The information can help global cybersecurity agencies to proactively identify vulnerabilities, coordinate activities, and plan for mitigation strategies since releasing patches from the vendor might take time.

This research presents a recommender system designed on the basis of a bottom-up knowledge base from textbooks. While other ontologies that are usually applied to such tasks are hand-crafted, our automated approach is a possible answer to the knowledge acquisition bottleneck. We extract concept hierarchies from section titles and use co-occurrences in book sections as evidence for possible contextual relationships between the therein mentioned entities. Motivated by a legal use case of recommending upcoming changes in law, the design is targeting three major challenges: different abstraction levels between entities of legal documents and the parliament protocols announcing norm changes, as well as engineering an explainable retrieval mechanism using the knowledge base which can additionally offer decent usability despite a high-recall requirement. Although the system is developed for a specific legal use case, there are many aspects of general applicability in the fields of recommender systems, information retrieval and information extraction, entity resolution, explainable artificial intelligence and usability. We validate selected parts of the system design also on other applications, such as educational media research.

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples. Adversarial examples are malicious images with visually imperceptible perturbations. While these carefully crafted perturbations restricted with tight Lp norm bounds are small, they are still easily perceivable by humans. These perturbations also have limited success rates when attacking black-box models or models with defenses like noise reduction filters. To solve these problems, we propose Demiguise Attack, crafting "unrestricted" perturbations with Perceptual Similarity. Specifically, we can create powerful and photorealistic adversarial examples by manipulating semantic information based on Perceptual Similarity. Adversarial examples we generate are friendly to the human visual system (HVS), although the perturbations are of large magnitudes. We extend widely-used attacks with our approach, enhancing adversarial effectiveness impressively while contributing to imperceptibility. Extensive experiments show that the proposed method not only outperforms various state-of-the-art attacks in terms of fooling rate, transferability, and robustness against defenses but can also improve attacks effectively. In addition, we also notice that our implementation can simulate illumination and contrast changes that occur in real-world scenarios, which will contribute to exposing the blind spots of DNNs.

Cloud computing is perhaps the most pre-predominant ideal models in ongoing patterns for registering and putting away purposes. Information security and protection of information is one of the significant worry in the distributed computing. To conquer the present circumstance of data divulgence, the specialist co-ops are upholding a specific norm of scrambling the touchy information prior to transferring on to the cloud server’s. It becomes evident that, the plain content catchphrase search is inconceivable. As the aggregate sum of information put away openly mists increments in a dramatic way, it turns out to be too difficult to even think about supporting proficient catchphrase based questions and positioning the coordinating outcomes on scrambled information. In the proposed framework, utilizing the security spillage compel segment savvy encryption has been accomplished for decode particle information’s in the middle datasets. What’s more, an idea encoding the information subsequently discovering reference property between information tables are accomplished. Notwithstanding the above framework, we are giving a programmed booking calculation to keep a log based following for continuous and un-frequently utilization of information under the time models.

Current blockchain approaches for business contracts are based on smart contracts, namely, software programs placed on a blockchain that are automatically executed to realize a contract. However, smart contracts lack flexibility and interfere with the autonomy of the parties concerned. We propose Hercule, an approach for declaratively specifying blockchain applications in a manner that reflects business contracts. Hercule represents a contract via regulatory norms that capture the involved parties' expectations of one another. It computes the states of norms (hence, of contracts) from events in the blockchain. Hercule's novelty and significance lie in that it operationalizes declarative contracts over semistructured databases, the underlying representation for practical blockchain such as Hyperledger Fabric and Ethereum. Specifically, it exploits the map-reduce capabilities of such stores to compute norm states. We demonstrate that our implementation over Hyperledger Fabric can process thousands of events per second, sufficient for many applications. IEEE

Internet technology has brought about significant improvement in economical drive thereby making automated processes the new norm. With this new technological drive comes the upsurge in criminal activities as technology has proved to be a densely crime-perpetrated territory. Operating Systems (OS) have had their fair share of this debacle with significant updates being pushed out regularly to mitigate threats. Particularly, the windows OS has the firewall feature which has been a huge success in Intrusion Prevention and Detection systems. The Windows 10 version of the OS will always have significant patches and updates regularly to mitigate security threats. However, there have been several techniques and experiments that proves that firewalls are not sufficient enough for system protection. Advanced techniques in firewall evasions are new generation firewall mechanisms with a combination of techniques usually used to bypass standard security tools, such as intrusion detection and prevention systems, which might detect a protection mechanism. This singular fact that the use of multiple combinations of simpler components is possible, hundreds of thousands of potential Advanced Evasion Techniques exists. This paper therefore takes an overview two of the most significant techniques when it comes to bypassing firewalls - HTTP Tunneling and Nmap Evasion. A comparative study of both techniques helps us look at their similarities and differences and future works.

Virtual reality is a fascinating technology in which the user is completely immersed in a simulated 3D environment. The three distinct features of VR that set it apart from other types of media are user dynamic control of viewpoint, 3D Stereovision, and the surrounding experience. In this paper virtual reality and animation is used to teach digital electronics lab. Earlier VR was expensive and not easily accessible to consumers. As processors became small, power-efficient and fast, we saw the emergence of consumer VR systems in 2013. The different applications of VR can be seen in the fields of the travel industry, healthcare, shopping and e-commerce, entertainment, education and training, etc. Especially, VR has gathered traction in education and training. To understand the circuitry subjects well, practical implementation of the circuit is very essential. In the current pandemic situation, online teaching has become a norm, be it a theory subject or a lab. Students are getting bored and exhausted because of online teaching which is delivered monotonously throughout the lecture on platforms as Google meet, Zoom, etc. The level of immersion VR or a 360 video provides is unmatched by a 2D video. VR is a transition from this monotonous learning as it provides an immersive fun learning experience. The environment and the equipment are built and the animations are done using blender, an open source software for 3d modeling and creating animations. The animated video is exported as a 360 video and uploaded to YouTube. Integrating animation in teaching laboratory experiments increases student's interest in learning. This animated can be used to access digital electronics lab remotely.

COVID-19 has hit hard on the global community, and organizations are working diligently to cope with the new norm of "work from home". However, the volume of remote work is unprecedented and creates opportunities for cyber attackers to penetrate home computers. Attackers have been leveraging websites with COVID-19 related names, dubbed COVID-19 themed malicious websites. These websites mostly contain false information, fake forms, fraudulent payments, scams, or malicious payloads to steal sensitive information or infect victims' computers. In this paper, we present a data-driven study on characterizing and detecting COVID-19 themed malicious websites. Our characterization study shows that attackers are agile and are deceptively crafty in designing geolocation targeted websites, often leveraging popular domain registrars and top-level domains. Our detection study shows that the Random Forest classifier can detect COVID-19 themed malicious websites based on the lexical and WHOIS features defined in this paper, achieving a 98% accuracy and 2.7% false-positive rate., Comment: 6 pages, 5 figures, manuscript published in 2020 IEEE International Conference on Intelligence and Security Informatics (ISI 2020)

This chapter briefly introduces NORM (No-ORM) - a methodology of application development that avoids many of pitfals of widely accepted development patterns. Within NORM, the interactions between application code and the database are restricted by a contract that limits the number of interactions and ensures high performance. To address the consequences of impedance mismatch, an additional model, called transfer model, is mapped to both the database model and the application model, providing means for interactions in terms of high-level complex data objects.

Grading is the process of interpreting learning competence to inform learners and instructors of the current learning ability levels and necessary improvement. For norm-referenced grading, the instructors use a conventionally statistical method, z score. It is difficult for such a method to achieve explainable grade discrimination to resolve dispute between learners and instructors. To solve such difficulty, this paper proposes a simple and efficient algorithm for explainable norm-referenced grading. Moreover, the rise of artificial intelligence nowadays makes machine learning techniques attractive to the norm-referenced grading in general. This paper also investigates two popular clustering methods, K-means and partitioning around medoids. The experiment relied on the data sets of various score distributions and a metric, namely, Davies–Bouldin index. The comparative evaluation reveals that our algorithm overall outperforms the other three methods and is appropriate for all kinds of data sets in almost all cases. Our findings however lead to a practically useful guideline for the selection of appropriate grading methods including both clustering methods and z score.

The emergence of connected and autonomous vehicles is at a rapid and unprecedented pace. A recent state-sponsored initiative calls for building a transportation information network that utilizes intelligent sensors on sections of state highways. Indeed, vehicle to infrastructure communication systems are slowly evolving. Peripheral sensors that are used to assist the human operator in lane changing, obstacle avoidance, and parking is becoming a norm in modern automotive vehicles. Although this newly found convenience is a boon to society, both socially and economically, it presents security challenges that are endemic to connected technologies. These challenges underscore the need to look closely at the state of automotive vehicle security. In conjunction with this effort, it is imperative that we investigate the metrics with which security can be measured. As a major component of continuous improvement, quantitative and qualitative measures must be devised to be able to make a full appreciation of the process. This chapter describes automotive vehicle security metrics and derives sample attack calculations to illustrate their applicability.

The Automotive SPICE for Cybersecurity Assessment Model has been published as a first version in February 2021. It will be used for cybersecurity homologation assessments of projects. A new UNECE norm requires from summer 2022 onwards the presence of a CSMS (Cybersecurity Management System) for organisations and the VDA AK13 published in Feb 2021 a new assessment model for the cybersecurity of Automotive development projects. The SOQRATES group as a working party of Automotive suppliers and research and training bodies has developed a cybersecurity engineer job role and training and best practice examples of how to cover the new security engineering practices. Also, first trials of using the new security related questions have been done and first experiences are shared in this paper.

With the advent of evolution of cloud computing, large organizations have been scaling the on-premise IT infrastructure to the cloud. Although this being a popular practice, it lacks comprehensive efforts to study the aspects of automated negotiation of resources among cloud customers and providers. This paper proposes a full-fledged framework for the multi-party, multi-issue negotiation system for cloud resources. It introduces a robust cloud marketplace system to buy and sell cloud resources. The belief–desire–intention (BDI) model-based cloud customer and provider agents concurrently negotiate on multiple issues, pursuing a hybrid tactic of time- and resource-based dynamic deadline algorithms to generate offers and counter-offers. The cloud marketplace-based system is further augmented with the assignment of behavior norm score and Reputation Index to the agents to establish trust among them.

Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, we demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts. We let six experienced radiologists rate the interpretability of saliency maps in datasets of X-rays, computed tomography, and magnetic resonance imaging scans. Significant improvements are found for our adversarial models, which are further improved by the application of dual-batch normalization. Contrary to previous research on adversarially trained models, we find that accuracy of such models is equal to standard models, when sufficiently large datasets and dual batch norm training are used. To ensure transferability, we additionally validate our results on an external test set of 22,433 X-rays. These findings elucidate that different paths for adversarial and real images are needed during training to achieve state of the art results with superior clinical interpretability., Unmasking the decision making process of machine learning models is essential for implementing diagnostic support systems in clinical practice. Here, the authors demonstrate that adversarially trained models can significantly enhance the usability of pathology detection as compared to their standard counterparts.

This chapter considers three enduring nuclear challenges that will shape our near-term nuclear future. First, how we can safely and securely manage the hundreds of civilian nuclear power and research facilities across the globe in order to ensure against the threat of weapons proliferation. Second, whether the nuclear taboo and norm of non-nuclear use that appears to have characterised our nuclear past can continue to hold into the future. Third, it considers to what extent new nuclear and non-nuclear weapon technologies and a technology-driven shift in the global nuclear environment will have a disruptive and destabilising impact on the global nuclear order.

Following the CENELEC railway standards for development of safe railway control and protection systems, their generic safety case and its technical safety report will be analyzed and applied in detail for a satellite-based train localization system. A norm compliant qualification procedure and a collection for both the GNSS localization's and other sensor component's performance values as well as for the localization system build is defined.

The IR 4.0 and automated construction progress detection are greenfield areas among researchers in current times. However, the implementation of the IR 4.0 theme for progress detection technologies needs special considerations as an emerging concept. This study aims to understand and develop a theoretical framework for IR 4.0 operational through the machine learning (ML) integrated towards automated construction progress detection and data acquisition technologies. Therefore, the detailed literature reviews were conducted in reference to construction progress detection technologies, with machine learning (ML) integrated techniques within IR 4.0 norm. Based on the literature outcomes, the theoretical framework was designed for the ML integrated project progress detection technologies. The designed IR 4.0 framework emphasises the overall effectiveness and efficiency of the monitoring operations. Moreover, it also highlights the challenges to overcome, such as financial impacts of technological adoption, interoperability issues between technologies etc. It has been concluded that there is a need for the development of field-based experimented IR 4.0 automated progress detection for the effective implementation of technologies.

Transfer learning based on pretraining language models on a large amount of raw data has become a new norm to reach state-of-the-art performance in NLP. Still, it remains unclear how this approach should be applied for unseen languages that are not covered by any available large-scale multilingual language model and for which only a small amount of raw data is generally available. In this work, by comparing multilingual and monolingual models, we show that such models behave in multiple ways on unseen languages. Some languages greatly benefit from transfer learning and behave similarly to closely related high resource languages whereas others apparently do not. Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages. Transliterating those languages improves very significantly the ability of large-scale multilingual language models on downstream tasks., Accepted at NAACL-HLT 2021

Talking to the electronic device and getting the required information at a minimal time has become today's norm. Although AI-powered conversational agents have percolated the commercial market, their use in a communal setting is still evolving. We postulate that the deployments of chatbots in disaster-prone areas can be beneficial to watch, monitor, and warn people during the crisis. Furthermore, the successful implementation of such technology can be life-saving. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques.

Adversarial examples are theorized to exist for every type of neural network application. Adversarial examples have been proven to exist in neural networks for visual-spectrum applications and that they are highly transferable between such neural network applications. In this paper, we study the existence of adversarial examples for Infrared neural networks that are applicable to military and surveillance applications. This paper specifically studies the effectiveness of adversarial attacks against neural networks trained on simulated Infrared imagery and the effectiveness of adversarial training. Our research demonstrates the effectiveness of adversarial attacks on neural networks trained on Infrared imagery, something that hasn&rsquo, t been shown in prior works. Our research shows that an increase in accuracy was shown in both adversarial and unperturbed Infrared images after adversarial training. Adversarial training optimized for the L&infin, norm leads to an increase in performance against both adversarial and non-adversarial targets.

This paper introduces the MBSSM (Mechanism-Based Social Systems Modelling) software architecture that is designed for expressing mechanisms of social theories with individual behaviour components in a unified way and implementing these mechanisms in an agent-based simulation model. The MBSSM architecture is based on a middle-range theory approach most recently expounded by analytical sociology and is designed in the object-oriented programming paradigm with Unified Modelling Language diagrams. This paper presents two worked examples of using the architecture for modelling individual behaviour mechanisms that give rise to the dynamics of population-level alcohol use: a single-theory model of norm theory and a multi-theory model that combines norm theory with role theory. The MBSSM architecture provides a computational environment within which theories based on social mechanisms can be represented, compared, and integrated. The architecture plays a fundamental enabling role within a wider simulation model-based framework of abductive reasoning in which families of theories are tested for their ability to explain concrete social phenomena.

This this paper positions cybersecurity capacity building in the realm of the debate on international norms. It is argued that cybersecurity capacity building on the one side is a necessity to make...

In the context of granular computing, sequential three-way decision is a useful tool to triadic thinking, triadic computing and triadic processing from coarser to finer under multilevel and multiview granularity space. In this paper, we mainly explore a novel framework of sequential three-way decision for the fusion of mixed data from the subjective and objective dynamic perspectives. The former focuses on the decision maker’s dynamic behavior without considering the time-evolving data, and the latter emphasizes on dealing with dynamic mixed data over time by multi-stage decision-making. We firstly utilize four T -norm operators and kernel-based similarity relations to integrate different types of dynamic data. Then the subjective and objective models of sequential three-way decision are investigated based on decision thresholds, attribute importance and cost reduction. Finally, the comparative experiments are reported to verify that our proposed models can achieve the lower decision cost and the acceptable accuracy.

Given the advances made in conventional weapon capabilities, precision should by now be the accepted and expected norm in cyberspace as well. In this article I argue that developing precision cyber...

Cloud computing is powerful computing which is transformed the traditional computing and industries. Cloud is not a new concept. Many organizations like Google, Microsoft, and Amazon accelerate in developing this computing and provide the services for lots of users and storing the data through cloud now become a norm. But there are many issues that arrive to store the data in cloud. In this paper we review some securities issues and give a survey solution that have been done to minimize the security risk and describe future research work about all these risk that occur when data is stored in cloud computing.

Autonomous software agents operating in dynamic environments need toconstantly reason about actions in pursuit of their goals, while taking intoconsideration norms which might be imposed on those actions. Normativepractical reasoning supports agents making decisions about what is best forthem to (not) do in a given situation. What makes practical reasoning chal-lenging is the interplay between goals that agents are pursuing and the normsthat the agents are trying to uphold. We offer a formalisation to allow agentsto plan for multiple goals and norms in the presence of durative actions thatcan be executed concurrently. We compare plans based on decision-theoreticnotions (i.e. utility) such that the utility gain of goals and utility loss of normviolations are the basis for this comparison. The set of optimal plans consistsof plans that maximise the overall utility, each of which can be chosen by theagent to execute. We provide an implementation of our proposal in AnswerSet Programming, thus allowing us to state the original problem in terms ofa logic program that can be queried for solutions with specific properties.

Since the last edition in 2009 the transformation of information processing has accelerated at a rapid speed. The phrase “Big Data” is now more a norm, and the web-based databases that host various information pertaining to toxicology have become the central depositories of the original datasets, technical reports, and documents. Coupled with “Big Data” is the idea of data-mining, which is based on machine learning algorithms to understand the trends, make appropriate interpretations, and to make reliable predictions. Along with “Big Data,” data analytics and data visualization have also become the mainstream tools to understand the toxicological data beyond the traditional analyses and reporting. This chapter provides examples on the latest trends and serves as the resources for toxicologists to learn and to gain knowledge in the new frontier.

Cloud computing is increasingly becoming a norm in the global industrial landscape with web services using it as a critical backbone service provider including significant video streaming services. The purpose of this paper is to present an overview of some of the Amazon Web Service (AWS) tools and features. The paper also discusses how AWS contributes to a range of industries making them dependent on AWS’s agility, scalability and deliverability of services. Implementation using information available on AWS leading portals as well as the practical experiments involving the discussed features are given. As more and more industries become dependent on Big data and AI, one can state that AWS will play a key role in the future developments across myriads of industries.

Software systems that share potentially sensitive data are subjected to laws, regulations, policies and/or contracts. The monitoring, control and enforcement processes applied to these systems are currently to a large extent manual, which we rather automate by embedding the processes as dedicated and adaptable software services in order to improve efficiency and effectiveness. This approach requires such regulatory services to be closely aligned with a formal description of the relevant norms. This paper presents eflint, a domain-specific language developed for formalizing norms. The theoretical foundations of the language are found in transition systems and in Hohfeld’s framework of legal fundamental conceptions. The language can be used to formalize norms from a large variety of sources. The resulting specifications are executable and support several forms of reasoning such as automatic case assessment, manual exploration and simulation. Moreover, the specifications can be used to develop regulatory services for several types of monitoring, control and enforcement. The language is evaluated through a case study formalizing articles 6(1)(a) and 16 of the General Data Protection Regulation (GDPR). A prototype implementation of eflint is discussed and is available online.

Internet of things (IoT) is an emerging field which is increasingly becoming influential instrument in daily life. It endeavors to provide comfort and easiness in doing day to day activities in life. Moreover, it has increased the effectiveness of tasks by reducing the time span of the activity and augmenting its accuracy. It is being done with the help of internet service. IoT connects the activities with internet where it becomes easier to watch and monitor the process and the progress of the activity. Internet, the most important phenomenon of the 21st century, has become a common norm in the society. Thus, IoT is making use of this dynamic feature of human innovation to add more innovative techniques to human lives. Besides all these innovation, human race has been subject to daunting and detrimental challenges which are putting its survival at risk. The problem of accumulating garbage is the most important one. The gigantic amount of waste material is also a big threat to climate of the earth. It is sine qua non to have a special mechanism to deal with the hectic task of collecting and transporting garbage in cities, however, recycling is another dimension of this huge problem. The focus of this research is to come up with a remedy for the problem of ever-increasing garbage. Here, an IoT based costeffective system has been developed. It has the capability to monitor the garbage in real time by using smart technology with the help of WeMos and Ultrasonic sensors. This system makes use of electronic devices and ThingSpeak platform to minimize the expenditures and time duration in the collection and transportation of the waste. This concept is unique in itself as it makes garbage bins able to communicate their status, periodically, to the authorities. This data can be seen on internet and it is not bounded by any specific location as internet is a global phenomenon and so do the proposed system.

The paper is devoted to the problem of network interaction of users in telemedicine systems that are actively created and implemented in healthcare. For this, the analysis of network resources and services of telemedicine systems for various purposes and their capabilities are presented. A classification of such systems is proposed and the types of user interaction are described. The features of systems that implement monitoring of the health status of patients are considered. In such telemedicine systems, the decision on the patient's state can be made in different ways: under the supervision of the attending physician, on the basis of the analysis of controlled parameters (norm - pathology) or automated by a mathematical model describing the functioning of the human body. An example of the created telemedicine health monitoring system is given and the implementation of its basic services is considered. For this purpose, a diagram of system use cases is presented and the main functional processes occurring in the system are analyzed. Particular attention is paid to the remote interaction of users and the differentiation of their access rights, since the information collected in the system must be reliably protected. It is noted that the delimitation of user rights is carried out both by categories and by passwords. The scheme of the algorithm for distinguishing access rights is given.

The assessment of psychological health is an important part of the construction of smart city. In order to establish psychological crisis discrimination criterion and analyze the contribution of each factor, this study uses machine learning algorithms based on big scale data collected during the construction of smart cities. Firstly, the psychological crisis level is discriminated by using ISOMAP algorithm and K-means algorithm. Then the logistic regression is modeled to estimate the influence coefficient of each factor. Finally, the above models were used to assess each individual's psychological states. The results show that the classification performance of machine learning algorithm is better than that of the traditional norm. Moreover, the coefficients obtained by logistic regression can be used to represent the contribution of various factors to individual's psychological state.

Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

This paper proposes a cooperative mechanism for detecting potentially deceptive cyber attacks that attempt to disregard average voltage regulation and current sharing in cyber-physical dc microgrids. Considering a set of conventional cyber attacks, the detection becomes fairly easy for distributed observer based techniques. However, a well-planned set of balanced attacks, termed as the stealth attack, can bypass the conventional observer based detection theory as the control objectives are met without any physical error involved. In this paper, we discuss the formulation and associated scope of instability from stealth attacks to deceive distributed observers realizing the necessary and sufficient conditions to model such attacks. To address this issue, a novel cooperative vulnerability factor (CVF) framework for each agent is introduced, which accurately identifies the attacked agent(s) under various scenarios. To facilitate detection under worst cases, the CVFs from the secondary voltage control sublayer is strategically cross coupled to the current sublayer, which ultimately disorients the control objectives in the presence of stealth attacks and provides a clear norm for triggering defense mechanisms. Finally, the performance of the proposed detection strategy is simulated in MATLAB/SIMULINK environment and experimentally validated for false data injection and stealth attacks on sensors and communication links.

Modern cloud datacenters indicate the frequent ex-istence of complex failure manifestation. Failures have becomethe norm, and not the exception. This is a key challengesince assumptions that underpin designing reliable systems aremonitoring systems status and detecting anomaly at runtime. Performance Monitoring Unit on CPU (PMU) can obtainfine-grained monitoring data by adopting interrupt samplingmethod based on hardware events. However, profilers in virtualmachines fail to receive PMU relevant information directlydue to the limited capacity of PMU virtualization. In thispaper, we present a fine-grained monitoring system SysOpticbased on PMU virtualization. First, we propose a method toexpose PMU information PMU and ensure the visibility ofsuch information at virtual machine level. Second, to maximizethe PMU reusability, SysOptic supports the PMU sharing andsimultaneous monitoring among multiple virtual machines. Furthermore, we also describe how to synchronize interruptson physical machines to virtual machines by using injectinginterrupts. Experimental results show that with the aid ofSysOptic, profiler tools in virtual machines manage to perceivethe existence of PMU and collect the monitoring data. Theadditional overhead incurred by SysOptic is at most 9.8%.

Information Management Systems that outsource part of the functionality to other (likely unknown) services need an effective way to communicate with these services, so that a mutually beneficial solution can be generated. This includes bargaining for their optimal customizations and the discovery of overlooked potential solutions. In this article, we present an automated negotiation framework for information systems (denoted as WebNeg ) that can be used by both the parties for conducting negotiations. WebNeg uses a Genetic Algorithm (GA)-based approach for finding acceptable solutions in multiparty and multiobjective scenarios. The GA is enhanced using a new operator called Norm , which represents the cumulative knowledge of all the parties involved in the negotiation process. Norm incorporates the dependencies of different quality attributes of independently developed component services for the system composition. This enables WebNeg to find a better solution in the context of the current requirements. Experiment results indicate the applicability and improved performance of WebNeg (in comparison with existing similar works) in facilitating the negotiation management involved in a web service-based information composition process.