Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Cryptographic Schemes for Blockchain Security of Smart Grids

Blockchain technology can be applied to smart grids to support business and management operations, and its distributed nature is advantageous when considering user-managed renewable systems. However, these new fully distributed systems raise a number of security and privacy issues. For this reason, numerous solutions integrating blockchain with privacy and security enhancing technologies, such as homomorphic encryption, secret sharing and zero-knowledge proof, have been proposed in the literature. The complexity of such systems from an algorithmic and protocol point of view is obvious, while the computational cost is less obvious and often overlooked by the authors. In this paper, we want to experimentally evaluate the computational weight of cryptographic techniques proposed to guarantee security and privacy on blockchains. We will take as reference commercial devices with computational capabilities similar to those we expect to find in smart grid devices. The results show that some techniques are not suitable for these scenarios and that architectural solutions must therefore be carefully designed.
Handwritten Text Recognition Using CRNN

Text recognition is one of the significant and demanding jobs that needed to keep diving into finding the stability result because of the wide range of real-world applications' use. Individual characters are often detected and recognized independently using traditional methods. Character detection is commonly achieved through the use of sliding windows or related components. The performance of these kinds of algorithms does not work well when the quality of the source image is poor or the image is complex. Convolutional Recurrent Neural Network (CRNN) is a deep learning-based end-to-end text recognition system applied in this study to recognize indefinite-length text sequences. The study proposes CRNN for text recognition where the study will detect the line of text with curvature words by detecting the word area and then will use it to recognize the words one by one by a model trained using CRNN with the IAM dataset words. Accordingly, the Character Error Rate (CER) reached 4.57.
Behavioral Features of Users as a Security Solution in Cloud Computing

A large number of establishments and organizations implement clouds to store their databases. More active attacks are used on clouds to get unauthorized access or to do harmful actions that may affect on user’s privacy. Therefore, many studies have proposed to increase the level of security in the clouds depended on several strategies. The behavior is one of the promising strategies that might prevent unauthorized or processes. In this paper, a set of features, are from several previous studies, and these features are based on the user activity and events in a special purpose cloud by which unauthorized process can be prevented and alert user about bad actions during his/her work in the cloud environment. The results of comparison show that the event-based features require less resources and time. Thus, they need to be enhanced by adding more informative features, or some available features form other strategies.
Exploring Attitudes Concerning The Applying Of Mobile Learning In Technical Education With Responsibility And Generativity

Mobile learning is a real issue that has attracted the interest of a students and academics. Despite the fact that m-learning is quickly growing around the world, research into students’ and instructors’ views regarding its use is still limited. One of the concerns identified in this study is that some students have a poor comprehension of the use of smartphone apps. Many problems highlight the need for a stronger emphasis on training to provide technical students with adequate e-learning and mobile learning skills so that they can take use of smartphone technology’s capabilities. To show the state of the art of M-learning, a sample of two hundred and one participants is purposely chosen from technical university students. It focuses at how learners and educators feel about using and adopting M-learning. M-learning has been defined in terms of its integration and deployment with other technology resources. Students’ views on the usage of M-learning have been proven as a result of many factors. After using the application with the same group of students, they stated their satisfaction and satisfaction with it, as well as the influence of its usage in improving the educational students’ performance in this topic. The results of this analysis revealed that in deploying smartphone applications for educational purposes, participants value rapid and simple access to information. Participants suggested potential applications of smartphones in using smartphone applications for educational reasons within Blackboard learning environment, in addition to highlighting the convenience of accessing information.
FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learning

Vanilla Federated learning (FL) relies on the centralized global aggregation mechanism and assumes that all clients are honest. This makes it a challenge for FL to alleviate the single point of failure and dishonest clients. These impending challenges in the design philosophy of FL call for blockchain-based federated learning (BFL) due to the benefits of coupling FL and blockchain (e.g., democracy, incentive, and immutability). However, one problem in vanilla BFL is that its capabilities do not follow adopters’ needs in a dynamic fashion. Besides, vanilla BFL relies on unverifiable clients’ self-reported contributions like data size because checking clients’ raw data is not allowed in FL for privacy concerns. We design and evaluate a novel BFL framework, and resolve the identified challenges in vanilla BFL with greater flexibility and incentive mechanism called FAIR-BFL. In contrast to existing works, FAIR-BFL offers unprecedented flexibility via the modular design, allowing adopters to adjust its capabilities following business demands in a dynamic fashion. Our design accounts for BFL’s ability to quantify each client’s contribution to the global learning process. Such quantification provides a rational metric for distributing the rewards among federated clients and helps discover malicious participants that may poison the global model.
Multi Resource Scheduling with Task Cloning in Heterogeneous Clusters

To mitigate the straggler effect, today’s systems and computing frameworks have adopted redundancy to launch extra copies for stragglers. Two limitations of the existing straggler-mitigation techniques, however, are that resource demand of tasks is only considered in the context of slots and, moreover, redundancy is seldom coordinated with job scheduling. To tackle these issues, in this paper, we present DollyMP, a job scheduler that addresses multi-resource scheduling with task cloning in heterogeneous clusters. DollyMP carefully combines SRPT (Shortest Remaining Processing Time) and SVF (Smallest Volume First) via knapsack optimization to schedule tasks with multi-resource demands and, in the meanwhile, dynamically launches task clones to yield a small job completion time. DollyMP is built on a strong mathematical foundation to guarantee near-optimal performance. The deployment of our Hadoop YARN prototype on a 30-node cluster demonstrates that DollyMP can reduce job response time by 50% under different cluster loads.
An Online Learning Approach for Client Selection in Federated Edge Learning under Budget Constraint

Federated learning (FL) has emerged as a new paradigm that enables distributed mobile devices to learn a global model collaboratively. Since mobile devices (a.k.a, clients) exhibit diversity in model training quality, client selection (CS) becomes critical for efficient FL. CS faces the following challenges: First, the client’s availability, the training data volumes, and the network connection status are time-varying and cannot be easily predicted. Second, clients for training and the number of local iterations would seriously affect the model accuracy. Thus, selecting a subset of available clients and controlling local iterations should guarantee model quality. Third, renting clients for model training needs cost. It is necessary to dynamically administrate the use of the long-term budget without knowledge of future inputs. To this end, we propose a federated edge learning (FedL) framework, which can select appropriate clients and control the number of training iterations in real-time. FedL aims to reduce the completion time while reaching the desired model convergence and satisfying the long-term budget for renting clients. FedL consists of two algorithms: i) the online learning algorithm makes CS and iteration decisions according to historic learning results; ii) the online rounding algorithm translates fractional decisions derived by the online learning algorithm into integers to satisfy feasibility constraints. Rigorous mathematical proof reveals that dynamic regret and dynamic fit have sub-linear upper-bounds with time for a given budget. Extensive experiments based on realistic datasets suggest that FedL outperforms multiple state-of-the-art algorithms. In particular, FedL reduces at least 38% completion time compared with others.
Deep Reinforcement Learning in a Dynamic Environment: A Case Study in the Telecommunication Industry

Reinforcement learning, particularly deep reinforcement learning, has made remarkable progress in recent years and is now used not only in simulators and games but is also making its way into embedded systems as another software-intensive domain. However, when implemented in a real-world context, reinforcement learning is typically shown to be fragile and incapable of adapting to dynamic environments. In this paper, we provide a novel dynamic reinforcement learning algorithm for adapting to complex industrial situations. We apply and validate our approach using a telecommunications use case. The proposed algorithm can dynamically adjust the position and antenna tilt of a drone-based base station to maintain reliable wireless connectivity for mission-critical users. When compared to traditional reinforcement learning approaches, the dynamic reinforcement learning algorithm improves the overall service performance of a drone-based base station by roughly 20%. Our results demonstrate that the algorithm can quickly evolve and continuously adapt to the complex dynamic industrial environment.
An Investigation of Roles, Backgrounds, Knowledge, and Skills of U.S. Government Security Awareness Professionals

Security awareness professionals are tasked with implementing security awareness programs within their organizations to assist employees in recognizing and responding to security issues. Prior industry-focused surveys and research studies identified desired skills for these professionals, finding that many are ill-prepared due to gaps in professional skills (e.g., communication, interpersonal) and a lack of recognition of the unique awareness role. However, it is unclear if these findings are similar for security awareness professionals in the United States (U.S.) federal government sector in which awareness plays an important part in teaching employees how to protect sensitive national and citizen data. To identify the current roles, professional backgrounds, and desired knowledge and skills for government security awareness professionals, we conducted a two-phase research study that leveraged focus group and survey methodologies. Insights gained from these results can inform guidance and other initiatives to aid organizations in building security awareness teams with the appropriate competencies. While focused on the U.S. government, findings may also have implications for other sectors and countries.
HARIN: HoloLens Augmented Reality Indoor Navigation

This paper describes a proposed augmented reality software called HARIN for indoor navigation. Navigating buildings poses a significant problem to a variety of populations, including disabled individuals and maintenance workers. The general population also faces difficulties navigating larger, or unfamiliar, buildings; the potential benefits of a solution are significant in scope. Initial solution attempts have been produced successfully in other research, however there is much room for improvement, particularly in user interface design and localization accuracy. To pursue these improvements, this paper proposes an AR indoor navigation using the HoloLens as a platform for new types of navigation markers to improve user experiences and decrease location estimate error. A proof-of-concept software prototype for a part of the overall HARIN system is shown, and future work is discussed.
Automatic identification of informative code in stack overflow posts

Despite Stack Overflow's popularity as a resource for solving coding problems, identifying relevant information from an individual post remains a challenge. The overload of information in a post can make it difficult for developers to identify specific and targeted code fixes. In this paper, we aim to help users identify informative code segments, once they have narrowed down their search to a post relevant to their task. Specifically, we explore natural language-based approaches to extract problematic and suggested code pairs from a post. The goal of the study is to investigate the potential of designing a browser extension to draw the readers' attention to relevant code segments, and thus improve the experience of software engineers seeking help on Stack Overflow.
High-performance optimization of SM4-GCM based on FPGA

SM4-GCM is an encryption algorithm with authentication function. The algorithm achieves the purpose of data security and information integrity. The SM4-GCM algorithm, implemented using traditional software methods, has low throughput and high resource consumption. In order to further improve the algorithm performance, this paper uses FPGA to optimize the SM4-GCM algorithm to achieve full-pipeline parallel acceleration. Firstly, the SM4 module is optimized using pipelining techniques. Then, the GHASH module is optimized using the Karatsuba algorithm and fast reduction. Finally, a loosely coupled architecture is used to connect various modules with asynchronous FIFOs, which improves the resource utilization and throughput of the FPGA circuit. The experimental results show that the throughput of the optimized SM4-GCM algorithm reaches 28.8 Gbps. It is better than other schemes, has a higher throughput, and meets the actual application requirements.
Deep Reinforcement Learning for Multi-UAV Exploration Under Energy Constraints

Autonomous exploration is the essential task for various applications of unmanned aerial vehicles (UAVs), but there is currently a lack of available energy-constrained multi-UAV exploration methods. In this paper, we propose the RTN-Explorer, an environment exploration strategy that satisfies the energy constraints. The goal of environment exploration is to expand the scope of exploration as much as possible, while the goal of energy constraints is to make the UAV return to the landing zone before the energy is exhausted, so they are a pair of contradictory goals. To better balance these two goals, we use map centering, and local-global map processing methods to improve the system performance and use the minimum distance penalty function to make the multi-UAV system satisfy the energy constraints. We also use the map generator to generate different environment maps to improve generalization performance. A large number of simulation experiments verify the effectiveness and robustness of our method and show superior performance in benchmark comparison.
Study of the value orientations and soft skills in modern senior high school students (in the context of the doctrine of sustainable development)

The concept of sustainable development involves maintaining the stability of social systems through the development of human capital. The formation of values and universal skills of the youth population is an important component of human capital. The article studies the problem of the universal readiness of school leavers for academic activity and professional labor that is expressed by the degree of formation of value orientations, which are the basis for non-specialized soft skills. The level of development of soft skills of senior high school students in modern comprehensive schools, that ensures the behavioral characteristics of self-organization and work ethics, depends on the values they share, which were studied using the survey method. The randomized survey of senior high school students in Rostov region (N = 4037, 59.3% of girls), carried out in 2019, was aimed at identification of the elements of social collectivism - social egoism in the minds of young people and normative - deviant components of behavior motivation. The studied value orientations of young people were re-duced to social-cognitive elements and attitudes. The authors proceeded from the fact that evident social collectivism - social egoism is directly associated with manifestations of normative / deviant behavior. As a result of processing of the data obtained, a quantitative analysis and explication of the development of value orientations and soft skills in the field of work and organizational ethics of the modern contingent of secondary school students was represented, what made it possible to formulate the preferential directions of pedagogic work.
The technology of augmented reality as a motivational factor for EFL future soil scientists in learning foreign language terminology of chemical elements

Currently augmented reality technology, as one of innovative tools, is rapidly penetrating the process of mastering a foreign language, helping to diversify and optimize the learning process for a new generation of students. The use of augmented reality technology in the foreign language educational process helps to increase motivation of students to study a foreign language at a university. The purpose of the study is to examine and analyze the impact of augmented reality technology on future soil specialists’ motivation in the process of learning English as a foreign language. To achieve the goal of the study, a comprehensive research methodology was chosen: theoretical analysis of scientific, pedagogical and methodological literature on the topic of research, pedagogical observation, survey, interview, analysis of the research results. Along with traditional methods, the elements of augmented reality were introduced in the process of teaching a foreign language to first-year students of non-linguistic specialties in order to identify the effectiveness of the influence of the technology on students’ motivation in learning English as a foreign language. The authors conclude that the technology of augmented reality has a didactic potential in the study of a foreign language in general and in the study of foreign language terminology of chemical elements in particular.
The problem of preparing a person for life in the conditions of sustainable development of digital technologies and products

The article gives a brief description of the new conditions of human life in the world, characterized by the search for optimal and effective resources for sustainable development. The relevance of the topic is due to new require-ments for society and for a person. New conditions create new problems in solving the problems of training specialists who are able to work productively in a digital society. The purpose of the study was to identify the degree of readiness of young people of the "digital generation" to adapt to the new educational environment, the perception of new formats for the digital transformation of the education system. The analysis of the results of this study can contribute to the development of new didactic principles in the field of the use of digital technologies. One of the ways to overcome the conflict in the implementation of new ideas for the sustainable development of the economy, society and the professional training system is the development of a new scientific direction - digital didactics. This direction provides tools that explain the specifics of such components as: construc-tivism, operationalism, technicalism, pragmatism, reductionism, utilitarianism. Using the method of large-scale monitoring and analysis of respondents' answers, we found out that today the "digital generation" is more ready for digital transformation than the carriers of professional and academic knowledge. Over-coming intergenerational contradictions is becoming one of the main problems of modern society and the education system.
BTextCAN: Consumer fraud detection via group perception

Traditional consumer fraud detection usually relies on the relevant regulatory authorities to conduct inspections through sampling. This would be labor-intensive and inefficient. To address this issue, we conducted a statistical analysis to explore the relationship between frauds and consumer perceptions. Based on the statistical results, we propose a novel deep mixture model-based consumer fraud detection method BTextCAN to detect consumer frauds via the perception of consumer group. By designing a text convolutional attention network (TextCAN) to extract local features with contextual semantic relations from consumer reviews, our approach can mine the opinions of consumers and use their group perception to detect consumer fraud behaviors. Experimental results show that our method outperforms the baseline models. In particular, BTextCAN achieves an accuracy of 79.8% in the binary detection task and 76.5% in the multiclassification detection task. This work is the first research effort to detect fraudulent merchant behavior from consumer reviews. In addition, we have collated and made publicly available the first dataset in this area.
Supervised Prototypical Variational Autoencoder for Shilling Attack Detection in Recommender Systems

Collaborative filtering-based recommender systems are vulnerable to shilling attacks. How to detect shilling attacks has become a popular research direction. Some recent works have applied deep learning to the field of shilling attack detection. However, most of the existing deep learning-based shilling attack detection models are based on user-item scoring matrices, which do not apply manual scoring features well and cannot be used to detect cold-start shilling attackers. Thus, we propose a shilling attack detection algorithm based on Supervised Prototypical Variational Auto-Encoder (SP-VAE). Specially, SP-VAE can obtain a unified user-profile representation that can be easily used to down-stream applications of shilling attack detection classifiers. Then, the algorithm constructs the prototype representation of various shilling attacker, and a classifier is used to classify various shilling attack users and normal users. The experimental results show that our method consistently outperforms the traditional method in the case of cold-start profile of the shilling attack.
Data Encryption Approach Using Hybrid Cryptography and Steganography with Combination of Block Ciphers

Information security has been one of the prominent fields due to the concerns regarding it, for the past few decades. Any loopholes in these may lead to dire consequences depending upon the importance of the message being transmitted. Thus there have emerged many techniques for this purpose, one such is crypto steganography which has been implemented in this paper, our algorithm uses a combination of Advanced Encryption Standard (AES) and columnar block ciphers to encrypt message and embedding it into the image using 1-Least Significant Bit (1-LSB) method and the reverse is done to get the data back at the other end. Hence this algorithm adds another layer of security with strong encryption techniques while maintaining the structure of the image. There is low distortion in the image with Mean Squared Error (MSE) as 0.028, Peak Signal-to-Noise Ratio (PSNR) as 63.719 and Structural Similarity Index (SSIM) values as 0.999 for png image and 0.044, 61.728, 0.999 respectively for bmp image.