Reviewerly/exHarmony huggingface dataset:
A workflow and toolchain proposal for analyzing users’ perceptions in cyber threat intelligence sharing platforms

Cyber Threat Intelligence (CTI) sharing platforms are valuable tools in cybersecurity. However, despite the fact that effective CTI exchange highly depends on human aspects, cyber behavior in CTI sharing platforms has been notably less investigated by the security research community.Motivated by this research gap, we ground our work in the concrete challenge of understanding users’ perceptions of information sharing in CTI platforms. To this end, we propose a conceptual workflow and toolchain that would seek to verify whether users have an accurate comprehension of how far information travels when shared in a CTI sharing platform.We contextualize our concept within MISP as a use case, and discuss the benefits of our socio-technical approach as a potential tool for security analysis, simulation, or education/training support. We conclude with a brief outline of future work that would seek to evaluate and validate the proposed model.
Efficient,Flexible,and Constant-Time Gaussian Sampling Hardware for Lattice Cryptography

This paper proposes a discrete Gaussian sampling hardware design that can flexibly support different sampling parameters,that is more efficient (in area-delay product) compared to the majority of earlier proposals,and that has constant execution time. The proposed design implements a Cumulative Distribution Table (CDT) approach,reduces the table size with Gaussian convolutions,and adopts an innovative fusion tree search algorithm to achieve a compact and fast sampling technique---to our best knowledge,this is the first hardware implementation of fusion tree search algorithm. The proposed hardware can support all the discrete Gaussian distributions used in post-quantum algorithms (FALCON,qTESLA,and FrodoKEM),the homomorphic encryption library of SEAL,and other algorithms such BLISS digital signature and LP public-key encryption. Our proposed hardware can be configured at design-time to optimize a single configuration or at run-time to support multiple Gaussian distribution parameters. Our design,furthermore,has constant-time behavior by design,eliminating timing side-channel attacks---this is achieved by reading all table contents at the same time to also reduce the latency. The results on a Xilinx Virtex-7 FPGA show that our solution can outperform all prior proposals in area-delay product by 1.67--235.88X,only falling short of those designed for the LP encryption scheme.
Lab Equipment Demonstration Using Augmented Reality

This paper introduces an Augmented Reality application for teaching practical of engineering students, in order to improve the student self-ability and interest towards study experiments. In recent years, one of the newest developing technology is Augmented Reality (AR) is used to create unique educational settings considering the factors include its uses, features, good potential in making the learning process more interactive, effective and interesting. This advanced technology enables user to interact with virtual and real time applications and brings the natural experiences to the user. Augmented reality with education has recently attracted research attention because of its ability to allow students interest in realistic experiences. The AR content consists of 3D models, images, which are superimposed on real objects, helping students to study specific object. This article focuses on how AR technology can be applied for education, applications of AR and its benefits for students.
Maintenance of Structural Hole Spanners in Dynamic Networks

Structural Hole (SH) spanners are the set of users who bridge different groups of users and are vital in numerous applications. Despite their importance, existing work for identifying SH spanners focuses only on static networks. However, real-world networks are highly dynamic where the underlying structure of the network evolves continuously. Consequently, we study SH spanner problem for dynamic networks. We propose an efficient solution for updating SH spanners in dynamic networks. Our solution reuses the information obtained during the initial runs of the static algorithm and avoids the recomputations for the nodes unaffected by the updates. Experimental results show that the proposed solution achieves a minimum speedup of 3.24 over recomputation. To the best of our knowledge, this is the first attempt to address the problem of maintaining SH spanners in dynamic networks.
Unsupervised Learning for Salient Object Detection via Minimization of Bilinear Factor Matrix Norm

Saliency detection is an important but challenging task in the study of computer vision. In this article, we develop a new unsupervised learning approach for the saliency detection by an intrinsic regularization model, in which the Schatten-2/3 norm is integrated with the nonconvex sparse l2/3 norm. The l2/3 -norm is shown to be capable of detecting consistent values among sparse foreground by using image geometrical structure and feature similarity, while the Schatten-2/3 norm can capture the lower rank of background by matrix factorization. To improve effective performance of separation for Schatten-2/3-norm and l2/3 -norm, a Laplacian regularization is adopted to the foreground for the smoothness. The proposed model essentially converts the required nonconvex optimization problem into the convex one, conducted by splitting the objective function based on singular value decomposition on one much smaller factor matrix and then optimized by using the alternating direction method of the multiplier. The convergence of the proposed algorithm is discussed in detail. Extensive experiments on three benchmark datasets demonstrate that our unsupervised learning approach is very competitive and appears to be more consistent across various salient objects than the current existing approaches.
AIVF Codes Based on Iterative Algorithm and Dynamic Programming

This paper gives a methodology for optimizing AIVF (almost instantaneous variable-to-fixed length) codes based on two algorithms: The first algorithm is the dynamic programming (DP) technique developed by Dubé and Haddad to construct a set of parse trees of AIVF codes. The second algorithm is the iterative optimization algorithm proposed by Fujita, Iwata, and Yamamoto, which can maximize the average parse length of AIVF codes. As a result, the proposed AIVF code achieves a longer average parse length than the known AIVF codes and Tunstall codes.
Canaries in the Data Mine: Understanding the Proprietary Design of Youth Environments

"Canaries in the Data Mine: Understanding the Proprietary Design of Youth Environments." The Information Society, 37(5), pp. 323–324
Research on Object Detection Network Based on Knowledge Distillation

Object detection is an important technology in the field of computer vision, but a large number of model parameters make it difficult to deploy in embedded devices. Knowledge distillation can compress network models and improve network accuracy. However, this method is mainly used in classification networks whose output is category. This paper proposes an end-to-end knowledge distillation model and applies it to the object detection network structure whose output is a heatmap. This method is not affected by the backbone network structure, and can directly extract the feature information of the network, so that the model can learn the dark knowledge in the teacher network more quickly. Aiming at the problem of insufficient dark knowledge in the single-teacher network, a multi-teacher self-adapting method is designed to integrate knowledge to improve the effect of the lightweight network. Finally, we experiment on the PASACL VOC data set to verify the effectiveness of the method in this paper.
“Smart Contracts” vs Legal Technology in Contract Practice

Purpose: The purpose of the article is determining the advantages and disadvantages of using both «smart contracts» and «classic» contracts in contractual practice, with the possibility of agreeing on the conditions and applying the full range of available legal technology tools.Design/Methodology/Approach: We use dialectic and comparative legal research methods.Findings: The article identifies the concept of smart contracts, their scope, advantages and disadvantages of use. We study the means of legal technology, which are used in the text of a traditional contract. The question of the basis for comparing smart contracts and contracts that exist in the form of text is investigated. The prospects for the using of smart contracts and traditional contracts are determined.Originality/Value: It has been established that the process of negotiating the terms of a traditional contract is carried out through negotiations and the search for the most appropriate formulations. The parties may agree on conditions that most closely reflect their interests and at the same time comply with applicable law.There are also used terms that serve to ensure a uniform understanding of the text of the contract. Such an approach, however, may lead to errors and the need for interpretation of the contract.Smart contracts aren’t based on text, but on computer code, through which the terms of the contract are recorded. This deprives such an agreement of the shortcomings that are usually inherent in the text - errors in the formulation of certain conditions, different understanding of the terms used by the parties.However, smart contracts have a limited scope, and shouldn’t be used in cases of regulation of atypical relations of parties requiring an individual approach.Smart contracts can successfully replace mass accession agreements existing in the form of typing and concluded via the Internet (for example, contracts for the sale of goods in online stores), due to their unconditional advantages - self-fulfillment, the inability to make changes to an already concluded contract, the fundamental lack of interpretation of the treaty.
Machine Learning allows Synthesis and Functional Interpolation of Computational and Field-Data for Worldwide Utility-Scale PV Systems

There has been an accelerated pace of installation variety of utility-scale solar farms across the world. Field data are streaming in from existing plants. Also, highly sophisticated physics-based numerical models and software are being developed to estimate the energy yield of a solar PV system over its lifetime. By necessity, the results are specific to a finite number of arbitrarily chosen geographical locations and require substantial computational resources for a worldwide energy estimation, making it difficult to extrapolate the results to new locations. In this paper, we demonstrate a novel machine learning-based tool that reduces the computational time by 4-orders of magnitude with an accuracy of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ~ 0.99. The tool utilizes the simulation-derived utility-scale solar PV energy yield to functionally interpolate the location-specific inputs and energy yields. This compact, efficient, and versatile representation will transform how large-scale modeling is used to predict the energy yield of various farm configurations for any location around the world.
Multi-label Feature Selection Algorithm via Maximizing Label Correlation-Aware Relevance and Minimizing Redundance with Mutation Binary Particle Swarm Optimization

Multi-label classification deals with a special supervised classification problem where any instance could be associated with multiple class labels simultaneously. As various applications emerge continuously in big data field, their feature dimensionality also increases correspondingly, which generally increases computational burdens and even deteriorates classification performance. To this end, feature selection has become a necessary pre-processing step, in which it is still challenging to design an effective feature selection criterion and its corresponding optimization strategy. In this paper, a novel feature selection criterion is constructed via maximizing label correlation-aware relevance between features and labels, and minimizing redundance among features. Then this criterion is optimized using binary particle swarm optimization with mutation operation, to search for a globally optimal feature selection solution. The experiments on four benchmark data sets illustrate that our proposed feature selection algorithm is superior to three state-of-the-art methods according to accuracy and F1 performance evaluation metrics.
LANCET

Cutting-edge machine learning techniques often require millions of labeled data objects to train a robust model. Because relying on humans to supply such a huge number of labels is rarely practical, automated methods for label generation are needed. Unfortunately, critical challenges in auto-labeling remain unsolved, including the following research questions: (1) which objects to ask humans to label, (2) how to automatically propagate labels to other objects, and (3) when to stop labeling. These three questions are not only each challenging in their own right, but they also correspond to tightly interdependent problems. Yet existing techniques provide at best isolated solutions to a subset of these challenges. In this work, we propose the first approach, called LANCET, that successfully addresses all three challenges in an integrated framework. LANCET is based on a theoretical foundation characterizing the properties that the labeled dataset must satisfy to train an effective prediction model, namely the Covariate-shift and the Continuity conditions. First, guided by the Covariate-shift condition, LANCET maps raw input data into a semantic feature space, where an unlabeled object is expected to share the same label with its near-by labeled neighbor. Next, guided by the Continuity condition, LANCET selects objects for labeling, aiming to ensure that unlabeled objects always have some sufficiently close labeled neighbors. These two strategies jointly maximize the accuracy of the automatically produced labels and the prediction accuracy of the machine learning models trained on these labels. Lastly, LANCET uses a distribution matching network to verify whether both the Covariate-shift and Continuity conditions hold, in which case it would be safe to terminate the labeling process. Our experiments on diverse public data sets demonstrate that LANCET consistently outperforms the state-of-the-art methods from Snuba to GOGGLES and other baselines by a large margin - up to 30 percentage points increase in accuracy.
Addressing Class Overlap under Imbalanced Distribution: An Improved Method and Two Metrics

Class imbalance, as a phenomenon of asymmetry, has an adverse effect on the performance of most machine learning and overlap is another important factor that affects the classification performance of machine learning algorithms. This paper deals with the two factors simultaneously, addressing the class overlap under imbalanced distribution. In this paper, a theoretical analysis is firstly conducted on the existing class overlap metrics. Then, an improved method and the corresponding metrics to evaluate the class overlap under imbalance distributions are proposed based on the theoretical analysis. A well-known collection of the imbalanced datasets is used to compare the performance of different metrics and the performance is evaluated based on the Pearson correlation coefficient and the ξ correlation coefficient. The experimental results demonstrate that the proposed class overlap metrics outperform other compared metrics for the imbalanced datasets and the Pearson correlation coefficient with the AUC metric of eight algorithms can be improved by 34.7488% in average.
Measurement of Soiling Loss on Bifacial PV Modules

Bifacial photovoltaic (PV) modules can absorb light from both front and rear sides of the module, resulting in an additional gain of ~ 10-15% in output. The additional gain depends on the amount of reflected light (albedo) reaching the rear side. This is highly depended on the local site layout, ground cover, distance between modules and the ground, distance between modules in a row and the distance between the rows. There is lot of uncertainty in energy forecast due to the variabilities of albedo due to all these factors. These uncertainties are a hindrance in adoption of bifacial modules in the industry and hence have attracted lot of research interest recently. Measurement of soiling loss in bifacial modules is another area that is very important and suffers from uncertainties in the albedo at a site. The problem is compounded due to a slight spectral mismatch between the front and rear side, and anticipated differences in the rate of soiling on two sides. In this paper we report on experiments using two identical half cut cell bifacial PV modules in Logan, UT for about 6 months during June-December 2020. The short circuit current of the two modules has been measured along with the supporting measurements of incident and reflected light intensity underneath each module. A method is proposed to separate the contributions to module output from front and rear sides. The soiling loss on both sides can also be calculated separately, using this method. The soiling loss is dominated by soiling on the front side as expected. The soiling loss on the rear side is minimal and may be affected by the spatial variations in the albedo across the two modules under comparison.
A natural look at scanpath theory: The way we move our head and eyes predicts scene recognition

It has long been thought that visual perception is represented in sensorimotor processes that unfold over time. One prominent theory states that our memory for a scene contains both the scene's content and the motor commands used to explore the scene (i.e., eye movements). Scanpath theory (Noton & Stark, 1971) has long been contested, with studies providing evidence both for and against it. That past work, however, did not include the fact that visual perception is embodied within an active system of effectors; namely, that people normally move their head as well as their eyes when exploring visible space. The present work tested scanpath theory in fully immersive 360 degree scenes leaving individuals free to move their heads and well as their eyes while they explored scenes for a subsequent memory test. During both encoding and recognition, we recorded their head and eye movements using a virtual reality headset equipped with eye and head tracking. Relevant to scanpath theory, as well as to embodied conceptualizations of perception and memory, our results show that repeating certain head and eye movement patterns made during encoding renders those scenes more likely to be recognized.
Master’s Programs in Russia: Four Paths of Future Development

The publication raises the question of the role of master’s degree in the system of modern higher education. Based on a survey of opinions (three-party survey) of key stakeholder groups (employers; students in higher education programs; managers of master’s degree programs; master’s degree teachers), the authors consider possible options for developing master’s degree as one of the key stages of higher education in Russia. The authors conclude that university development programs in the field of master’s degree education should be designed taking into account the high level of uncertainty in the external environment, which means that they should provide for scenario modeling of the future of the entire scientific and educational ecosystem. The study uses the classification of four types of the future labor market (“technological world”, “world of social responsibility”, “world of entrepreneurship” and “world of corporations”) as the main hypothesis about the options for such scenario planning. The study results are visualized in the form of a roadmap for the strategic development of the Russian master’s degree programs, highlighting key trends for each of the development options. The practical result of the study was the identification of the main “gaps” in the positions of stakeholders on the status of the master’s degree program and effective models of its implementation. The conclusions and results of the publication will be interesting for the purposes of the university strategic management, the formation of a development strategy for the master’s degree, as well as the development of special programs for interaction with employers and students.
Digital Teacher for the 21<sup>st</sup>-century School 4.0

The article deals with some pedagogical aspects of the digitalization of education. As it is shown in the article, the problem of digital education has been systematically analyzed and discussed for the last two decades by various analysts, researchers, and experts. Most of them consider the transition of education into its digital stage as the greatest turning point in the history of civilization and its culture. At present, teachers are facing quite new educational and pedagogical functions. The idea of a digital teacher as the leader of digital educational activity is in the focus of the analysis presented in the article. Educational platforms, online courses, digital textbooks, etc. influence on the transformation of the educational environment. Education is becoming more personalized. The content of teaching and learning is being changed radically according to its new values, meanings, orientations, as well as educational and pedagogical discourses. It is stressed in the article that teacher’s digital skills and competencies should be identified as the key professional pedagogical skills and competencies for his or her life-long learning, professional development and professional growth due to the challenges of the 21 st -century knowledge, information and innovations society and education. To fulfill a system of professional-pedagogical and educational functions more effectively and to guarantee the optimal implementation of information and communication technologies (ICTs), it is quite necessary for teachers to have adequate skills and competencies that characterize his or her digital professional culture of teaching.
Foggy distance warning system based on LoRa technology

In order to overcome the problems of poor performance of distance detection based on deep learning, vehicle borne ultrasonic ranging system and vehicle borne radar automatic braking system in foggy and high-speed environment, this paper proposes a vehicle distance warning system based on the fusion of LoRa technology and laser ranging technology. The algorithm is established by visibility, speed, speed limit, distance and other factors, and the threshold value of minimum safe distance is set. Then the LoRa wireless network is used to monitor the distance between the front and rear vehicles. At the same time, the intelligent warning can be realized when the dangerous distance is between vehicles, so as to ensure the safety of high-speed driving in foggy days. The experimental results show that the system has the characteristics of wide protection range, low power consumption, high reliability and low cost.