Reviewerly/exHarmony huggingface dataset:
DevSecOps In Embedded Systems: An Empirical Study Of Past Literature

Over the last decade, DevSecOps principles have gained widespread acceptance, replacing many traditional approaches to software development. DevSecOps has helped developers shorten the overall software development life cycle, and as a result, decreased the time to market. Following the broad success of DevSecOps, the next logical progression is to apply DevSecOps principles to other fields to achieve similar results, such as embedded systems. While embedded systems practices may stand to benefit greatly from the inclusion of DevSecOps principles, the field offers many new and unique challenges that have not been faced with traditional software systems. Existing DevSecOps frameworks cannot simply be applied to embedded systems. It is necessary to adapt current DevSecOps frameworks specifically to embedded systems. This piece will first lay out current DevSecOps principles and their application to software systems. Then, an empirical examination of existing work on DevSecOps in embedded systems will be presented. The required components of a DevSecOps framework that have been excluded from previous research will be highlighted, and from this, future areas of research in DevSecOps for embedded systems will be presented. The goal of this work is to summarize and analyze the current state of knowledge on DevSecOps in embedded systems and outline a path for future research.
The larger the fairer?

Along with the progress of AI democratization, neural networks are being deployed more frequently in edge devices for a wide range of applications. Fairness concerns gradually emerge in many applications, such as face recognition and mobile medical. One fundamental question arises: what will be the fairest neural architecture for edge devices? By examining the existing neural networks, we observe that larger networks typically are fairer. But, edge devices call for smaller neural architectures to meet hardware specifications. To address this challenge, this work proposes a novel Fairness- and Hardware-aware Neural architecture search framework, namely FaHaNa. Coupled with a model freezing approach, FaHaNa can efficiently search for neural networks with balanced fairness and accuracy, while guaranteed to meet hardware specifications. Results show that FaHaNa can identify a series of neural networks with higher fairness and accuracy on a dermatology dataset. Target edge devices, FaHaNa finds a neural architecture with slightly higher accuracy, 5.28X smaller size, 15.14% higher fairness score, compared with MobileNetV2; meanwhile, on Raspberry PI and Odroid XU-4, it achieves 5.75X and 5.79X speedup.
Graph Convolutional Networks for Chemical Relation Extraction

Extracting information regarding novel chemicals and chemical reactions from chemical patents plays a vital role in the chemical and pharmaceutical industry. Due to the increasing volume of chemical patents, there is an urgent need for automated solutions to extract relations between chemical compounds. Several studies have used models that apply attention mechanisms such as Bidirectional Encoder Representations from Transformers (BERT) to capture the contextual information within a text. However, these models do not capture the global information about a specific vocabulary. On the other hand, Graph Convolutional Networks (GCNs) capture global dependencies between terms within a corpus but not the local contextual information. In this work, we propose two novel approaches, GCN-Vanilla and GCN-BERT, for chemical relation extraction. GCN-Vanilla approach builds a single graph for the whole corpus based on word co-occurrence and sentence-word relations. Then, we model the graph with GCN to capture the global information and classify the sentence nodes. GCN-BERT approach combines GCN and BERT to capture both global and local information, and build together a final representation for relation extraction. We evaluate our approaches on the CLEF-2020 dataset. Our results show the combined GCN-BERT approach outperforms standalone BERT and GCN models, and achieves a higher F1 than that reported in our previous studies.
Mathematical Methods for IoT-Based Annotating Object Datasets with Bounding Boxes

Object datasets used in the construction of object detectors are typically annotated with horizontal or oriented bounding rectangles for IoT-based. The optimality of an annotation is obtained by fulfilling two conditions: (i) the rectangle covers the whole object and (ii) the area of the rectangle is minimal. Building a large-scale object dataset requires annotators with equal manual dexterity to carry out this tedious work. When an object is horizontal for IoT-based, it is easy for the annotator to reach the optimal bounding box within a reasonable time. However, if the object is oriented, the annotator needs additional time to decide whether the object will be annotated with a horizontal rectangle or an oriented rectangle for IoT-based. Moreover, in both cases, the final decision is not based on any objective argument, and the annotation is generally not optimal. In this study, we propose a new method of annotation by rectangles for IoT-based, called robust semi-automatic annotation, which combines speed and robustness. Our method has two phases. The first phase consists in inviting the annotator to click on the most relevant points located on the contour of the object. The outputs of the first phase are used by an algorithm to determine a rectangle enclosing these points. To carry out the second phase, we develop an algorithm called RANGE-MBR, which determines, from the selected points on the contour of the object, a rectangle enclosing these points in a linear time. The rectangle returned by RANGE-MBR always satisfies optimality condition (i). We prove that the optimality condition (ii) is always satisfied for objects with isotropic shapes. For objects with anisotropic shapes, we study the optimality condition (ii) by simulations. We show that the rectangle returned by RANGE-MBR is quasi-optimal for the condition (ii) and that its performance increases with dilated objects, which is the case for most of the objects appearing on images collected by aerial photography.
Apple vs. EMA

Cryptographic instruction set extensions are commonly used for ciphers which would otherwise face unacceptable side channel risks. A prominent example of such an extension is the ARMv8 Cryptographic Extension, or ARM CE for short, which defines dedicated instructions to securely accelerate AES. However, while these extensions may be resistant to traditional "digital" side channel attacks, they may still be vulnerable to physical side channel attacks.
Successive Trajectory Privacy Protection with Semantics Prediction Differential Privacy

The publication of trajectory data provides critical information for various location-based services, and it is critical to publish trajectory data safely while ensuring its availability. Differential privacy is a promising privacy protection technology for publishing trajectory data securely. Most of the existing trajectory privacy protection schemes do not take into account the user's preference for location and the influence of semantic location. Besides, differential privacy for trajectory protection still has the problem of balance between the privacy budget and service quality. In this paper, a semantics- and prediction-based differential privacy protection scheme for trajectory data is proposed. Firstly, trajectory data are transformed into a prefix tree structure to ensure that they satisfy differential privacy. Secondly, considering the influence of semantic location on trajectory, semantic sensitivity combined with location check-in frequency is used to calculate the sensitivity of each position in the trajectory. The privacy level of the position is classified by setting thresholds. Moreover, the corresponding privacy budget is allocated according to the location privacy level. Finally, a Markov chain is used to predict the attack probability of each position in the trajectory. On this basis, the allocation of the privacy budget is further adjusted and its utilization rate is improved. Thus, the problem of the balance between the privacy budget and service quality is solved. Experimental results show that the proposed scheme is able to ensure data availability while protecting data privacy.
A VR Multiplayer Application for Fire Fighting Training Simulations

The need to guarantee employees good training in the field of safety in workplaces, risk assessment, fire prevention and fire fighting, or management of dangerous situations is a priority in different sectors, from maritime, airport and military to schools, offices. The application of Virtual Reality (VR) technologies, as a substitute or complement to traditional teaching methods, is a relevant topic both for researchers and companies specialized in the organization of training courses. Here, we present a multiplayer immersive VR application for safety and fire prevention training, developed in collaboration with experts adopting a user-centered design approach. A preliminary usability test on 20 subjects showed promising results regarding the usability and the efficacy of the VR intervention but a low level of sense of presence, maybe due to the non-photo realistic graphic rendering or to the fact that participants were unfamiliar with the interaction methods and frequently asked questions disrupting the immersion. Feedback received will be used to modify and improve the application.
A conceptual comparative approach on personal AI assistants and external service robots

Abstract In the past years artificial intelligence (AI) has become an important subject for both companies and consumer due to the growth of personal AI assistants and external service robots. It is clear that many aspects of our life are not like they used to be. The fact that every one of us is sooner or later going to incorporate various AI functions in daily routine activities becomes somehow certain. It is not only our home-life that is transforming, but also the way we, as consumers, are going to interact with different product and service providers. In this paper we provide a comparative literature review on the challenges and research topics regarding personal AI assistants and external service robots. While the personal AI assistants intervene more in the private sphere of the consumer, the relation to the external service robots is more distant. The results of our literature review show that the relation between consumer and external service robot is more characterized by interaction, enjoyment and engagement, it is expected to have a parasocial friendship relation between consumer and personal AI assistants. Taking this difference of perspective the two types of AI will be differently involved in the future business and marketing activities of companies.
Direct relation detection for knowledge-based question answering

This study addresses the problem of relation detection for answering single-relation factoid questions over knowledge bases (KBs). In this kind of questions, the answer is obtained from a single KB fact in the form of subject-predicate-object. Conventional fact extraction methods have two steps: entity linking and relation detection, in which the output of the entity linking is used by the relation detection step to first find candidate relations, and then choose the best relation from candidate relations. Such methods have difficulties with the relation detection if there is an error or ambiguity in the entity linking step. This paper explores the relation detection task without the entity-linking step utilizing the hierarchical structure of relations and an out-of-box POS tagger. As relations are of different levels of abstraction, the proposed solution uses multiple classifiers in pipeline, each of which uses separate BiGRU neural networks fed with questions embedded with one-hot encoding at the character level. Besides, to increase the accuracy of the proposed model and to avoid the need for large amounts of training data, after each word of the question, its POS tag is inserted before feeding the network. The experimental results show that the accuracy of the proposed solution for the direct relation detection is 89.5%. In addition, the proposed solution can be used for the indirect relation detection whose accuracy is 96.3%, which is higher than state-of-the-art relation detection techniques. Finally, the positive effects of using POS tags have been examined.
The impact of attitude toward peer interaction on middle school students' problem-solving self-efficacy during the COVID-19 pandemic

The outbreak of the COVID-19 epidemic has promoted the popularity of online learning, but has also exposed some problems, such as a lack of interaction, resulting in loneliness. Against this background, students' attitudes toward peer interaction may have become even more important. In order to explore the impact of attitude toward peer interaction on students' mindset including online learning motivation and critical thinking practice that could affect their problem-solving self-efficacy during the COVID-19 pandemic, we developed and administered a questionnaire, receiving 1,596 valid responses. The reliability and validity of the questionnaire were re-tested, and structural equation modeling was applied. It was found that attitude toward peer interaction could positively predict middle school students' online learning motivation and critical thinking. Learning motivation and critical thinking also positively supported problem-solving self-efficacy. It is expected that the results of this study can be a reference for teachers to adopt student-centered online learning in problem solving courses.
DOES FINANCIAL GOVERNANCE STEER FINANCIAL DEVELOPMENT TOWARD SUSTAINABLE DEVELOPMENT?

Unbridled financial development (FD) leads to financial crisis and undermines sustainable development (SD), which underscores the need for financial governance (FG). Following this backdrop, we seek to explore the role of FG in shaping the FD–SD nexus for a panel of 122 countries covering the period 2000–2017 using advanced econometric methods. Results reveal that the FD–SD relationship is nonlinear, and FG positively influences SD. The impact of FG–FD confluence on SD depends on specific FG instruments. These findings are valuable for policymakers to set FD and FG policies and to achieve SD. The study, to the best of our knowledge, is the first that explains the role of FG in the FD–SD nexus.
Design of Efficient Intelligent Autonomous Surface Cleaner

In the time of Covid-19, when social distancing is one of the important solution for avoiding virus infection, human cleaner become the one of the major spreaders of the virus. Also day-by-day human cleaner cost and the number of senior citizens are increasing, autonomous surface cleaner is in demand. It is also useful for the industry places, offices, and other public and private places. Commercial surface cleaners are available in the market with having limited functionality of covering the entire surface area. In addition, these surface cleaners demands a good amount of human efforts. In this paper author proposed design of an efficient autonomous surface cleaner using deep learning and embedded technology. It is having complete area coverage planning and dynamic obstacles avoidance strategies. The prototype of the proposed design is developed and tested in a room area of the domestic environment. It covers the significant surface areas and clean the surface efficiently with minimal human efforts.
Detection of Two-Wheeler Traffic Rule Violation Using Deep Learning

Computer vision has become a potential research area due to its diverse applications. Object detection is probably the most challenging and complex well-known problem in computer vision. It has found many applications such as tracking objects, counting the number of objects, self-driving cars, and detection of vehicles. Over the past few years, two-wheeler accidents have gone up exponentially in India due to the negligence of traffic laws by the riders. Therefore, It is obligatory to find out more innovative ways of Detection and Tracking traffic rules violators to ensure the safety of bike riders. This paper proposed a framework to detect two-wheeler traffic rule violators such as helmet and non-helmet bike riders. Three models, YOLOv5, Faster RCNN, and RetinaNet, were compared and analyzed. Experimental result shows that YOLOv5 gives good results. Using pre-trained YOLOv5 model weights, an accuracy of 92.6% was recorded, proving the effectiveness of helmet detection.
Rewrite Rules for Automated Depth Reduction of Encrypted Control Expressions with Somewhat Homomorphic Encryption

This paper presents topological sorting methods to minimize the multiplicative depth of encrypted arithmetic expressions. The research aims to increase compatibility between nonlinear dynamic control schemes and homomorphic encryption methods, which are known to be limited by the quantity of multiplicative operations. The proposed method adapts rewrite rules originally developed for encrypted binary circuits to depth manipulation of arithmetic circuits. The paper further introduces methods to normalize circuit paths that have incompatible depth. Finally, the paper provides benchmarks demonstrating the improved depth in encrypted computed torque control of a dynamic manipulator and discusses how achieved improvements translate to increased cybersecurity.
Linear TDOA-based Measurements for Distributed Estimation and Localized Tracking

We propose a linear time-difference-of-arrival (TDOA) measurement model to improve distributed estimation performance for localized target tracking. We design distributed filters over sparse (possibly large-scale) communication networks using consensus-based data-fusion techniques. The proposed distributed and localized tracking protocols considerably reduce the sensor network’s required connectivity and communication rate. We, further, consider κ-redundant observability and fault-tolerant design in case of losing communication links or sensor nodes. We present the minimal conditions on the remaining sensor network (after link/node removal) such that the distributed observability is still preserved and, thus, the sensor network can track the (single) maneuvering target. The motivation is to reduce the communication load versus the processing load, as the computational units are, in general, less costly than the communication devices. We evaluate the tracking performance via simulations in MATLAB.
Efficient and secure pedestrian detection in intelligent vehicles based on federated learning

Nowadays, various applications are designed for intelligent vehicles, especially for self-driving vehicles. The cloud server usually gathers data from multiple vehicles in order to obtain accurate model, but the data transmission is vulnerable to attack and the vehicles face a risk of privacy leakage. How to achieve secure multi-party computation in vehicular scenarios is an important issue. In this paper, we propose an efficient and secure pedestrian detection scheme based on federated learning, named PFL. After training the deep neural network with local images, each vehicle, as a party, transmits the encrypted gradients rather than the original data to the server. Homomorphic encryption guarantees that the server conducts the federated averaging without decryption. After the updated global model is distributed from server to vehicles, each vehicle decrypts and updates local model. Experiments show that PFL achieves similar pedestrian detection accuracy to the centralized method, while providing privacy protection.
Machine Learning for Credit Card Fraud Detection System

In recent years, for banks has become terribly troublesome for police investigation the fraud in credit-card system. Machine learning plays a significant role for police investigation the credit-card fraud within the transactions. For predicting these transactions banks build use of assorted machine learning methodologies, past knowledge has been collected and new options square measure been used for enhancing the prophetical power. The performance of fraud police investigation in credit-card transactions is greatly full of the sampling approach on data-set, choice of variables and detection techniques used. This paper investigates the performance of supply regression for credit-card fraud detection. Dataset of credit-card transactions is collected from Kaggle and it contains a complete of two,84,808 credit-card transactions of a ecu bank knowledge set. It considers fraud transactions because the “positive class” and real ones because the “negative class”. the info set is very unbalanced, it's concerning zero.172% of fraud transactions and also the rest square measure real transactions.
Boosting cross‐task adversarial attack with random blur

Deep neural networks are highly vulnerable to adversarial examples, and these adversarial examples stay malicious when transferred to other neural networks. Many works exploit this transferability of adversarial examples to execute black-box attacks. However, most existing adversarial attack methods rarely consider cross-task black-box attacks that are more similar to real-world scenarios. In this paper, we propose a class of random blur-based iterative methods (RBMs) to enhance the success rates of cross-task black-box attacks. By integrating the random erasing and Gaussian blur into the iterative gradient-based attacks, the proposed RBM augments the diversity of adversarial perturbation and alleviates the marginal effect caused by iterative gradient-based methods, generating the adversarial examples of stronger transferability. Experimental results on ImageNet and PASCAL VOC data sets show that the proposed RBM generates more transferable adversarial examples on image classification models, thereby successfully attacking cross-task black-box object detection models.
Normal cones corresponding to credal sets of lower probabilities

Credal sets are one of the most important models for describing probabilistic uncertainty. They usually arise as convex sets of probabilistic models compatible with judgments provided in terms of coherent lower previsions or more specific models such as coherent lower probabilities or probability intervals. In finite spaces, credal sets usually take the form of convex polytopes. Many properties of convex polytopes can be derived from their normal cones, which form polyhedral complexes called normal fans. We analyze the properties of normal cones corresponding to credal sets of coherent lower probabilities. For two important classes of coherent lower probabilities, 2-monotone lower probabilities and probability intervals, we provide a detailed description of the normal fan structure. These structures are related to the structure of the extreme points of the credal sets. To arrive at our main results, we provide some general results on triangulated normal fans of convex polyhedra and their adjacency structure.
User Interface-Based Repeated Sequence Detection Method for Authentication

In this paper, we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface (UI) sequences by analyzing mouse and keyboard data.To this end, an Apriori algorithm based on the keystroke-level model (KLM) of the human-computer interface domain was used.The proposed system can detect repeated UI sequences based on KLM for authentication in the software.The effectiveness of the proposed method is verified through access testing using commercial applications that require intensive UI interactions.The results show using our cognitive mouse-and-keystroke dynamics system can complement authentication at the application level.