Reviewerly/exHarmony huggingface dataset:
Ransomware as a security threat: Social and criminal legislation aspects

This article focuses on the analysis of social and criminal aspects of the phenomenon of ransomware-malware abuse. The authors' basic hypothesis is that the optimal mechanisms in terms of security and Criminal Code protection against this specific form of attacks on computer systems and data stored therein are still underdeveloped. The review of scientific and professional literature and the use of legal-dogmatic and normative methods by various authors have shown that crime related to the use of ransomware-malware has a potential to seriously endanger certain segments of modern society - economy, sensitive personal data, national and supranational critical infrastructure. The authors have also noted the shortcomings of currently available legal solutions. The authors conclude that it is necessary to conduct tailored educations of computer system users and also to undertake appropriate activities for the purpose of improving security culture. The authors additionally present concrete proposals for improving the legal framework for criminal legislation. Keywords: ransomware-malware, s
Smart Safety Management Systems: Leveraging Blockchain Technology for an Effective Safety System in Oil and Gas Industry

Abstract In last decade the oil and gas industry gradually adopted new innovative technologies and began shifting towards intelligence and digitalization. Emerging Technologies such as the autonomous car, Blockchain and the Internet of Things are playing a more vital role in improving efficiency and effectiveness in many businesses around the globe. Presently blockchain technology applications are more focused around financial and data security sectors. There exists a tremendous opportunity to leverage blockchain technology and explore possible applications in oil and gas. This paper focuses on blockchain technology and answers three basic questions (why, what and how) related to application of blockchain technology and use of distributed ledgers to leverage safety management systems. The novel methodology mentioned in this paper could be used extensively to develop consistent company best practices thereby improving health and safety performance in oil and gas and other related sectors. In addition to the increased efficiency, the Safety Management System section provides a company with an effective tool for designing, developing and implementing a safety management program that helps to be in compliance with regulation. Additionally, it will provide a basis for safe process operation, production and facility equipment reliabality.
Weaknesses in ENT Battery Design

Randomness testing is a key tool to analyse the quality of true (physical) random and pseudo-random number generators. There is a wide variety of tests that are designed for this purpose, i.e., to analyse the goodness of the sequences used. These tests are grouped in different sets called suites or batteries. The batteries must be designed in such a way that the tests that form them are independent, that they have a wide coverage, and that they are computationally efficient. One such battery is the well-known ENT battery, which provides four measures and the value of a statistic (corresponding to the chi-square goodness-of-fit test). In this paper, we will show that this battery presents some vulnerabilities and, therefore, must be redefined to solve the detected problems.
Multihydrophone Fusion Network for Modulation Recognition

Deep learning (DL)-based modulation recognition methods of underwater acoustic communication signals are mostly applied to a single hydrophone reception scenario. In this paper, we propose a novel end-to-end multihydrophone fusion network (MHFNet) for multisensory reception scenarios. MHFNet consists of a feature extraction module and a fusion module. The feature extraction module extracts the features of the signals received by the multiple hydrophones. Then, through the neural network, the fusion module fuses and classifies the features of the multiple signals. MHFNet takes full advantage of neural networks and multihydrophone reception to effectively fuse signal features for realizing improved modulation recognition performance. Experimental results on simulation and practical data show that MHFNet is superior to other fusion methods. The classification accuracy is improved by about 16%.
Geological, fluid inclusion microthermometry and geochemical constraints on the ankeritic-sideritic iron deposits in the Delijan mining district, Malayer-Esfahan metallogenic belt, Iran

The Kahak, Raveh, and Khoreh stratabound sideritic-ankeritic iron deposits are located in the Malayer-Esfahan metallogenic belt (MEMB), Iran. Geological, geochemical, fluid inclusion and S isotope studies have been carried out on the Kahak, Raveh, and Khoreh deposits which are hosted by different types of Lower Cretaceous dolomite, sandstone, and tuff in the Delijan mining district (DMD). Ankerite and siderite ores display a strong lithological control and form stratabound stacked ore lenses hosted in different horizons by Kc2, Kcv, Kc3, and Km units. Economic orebodies are restricted to the hanging wall of the syn-sedimentary normal faults. Three ore types can be distinguished based on the petrographic studies: stringer zone ore facies, bedded ore facies, and massive-replacement ore facies. Ore minerals are predominantly ankerite, siderite, barite, chalcopyrite, sphalerite, galena, and pyrite. Ore mineralization in the Kahak, Raveh, and Khoreh deposits has emplaced in two stages. The fine-grained ore bands (stage 1) which are intricately interlayered with host rock beds, exhibit sedimentary textures such as lamina and bedding and indicate a syn-sedimentary origin. The coarser-grained stage 2 ores show massive and vein-veinlet textures that are considered to form by replacement during sub-seafloor fluid flow. Hydrothermal alteration minerals developed in the wall rock include dolomite, quartz, and calcite. A microthermometric study of fluid inclusions from the barite samples indicates homogenization temperatures between 162 and 210 °C. Salinities of ore-forming fluids range from 7.8 to 16.8 wt% NaCl eq. Fluid inclusion studies suggest that the ore-forming fluids were derived from basinal or formational brines. Fluid mixing (basinal brine and seawater) may have played an important role during Fe mineralization. Calculated δ34S values for the ore fluid vary between + 10.69 and + 31.98‰. Sulfur isotopic compositions suggest that the source of sulfur for barite was derived from seawater. These data show that the Kahak, Raveh, and Khoreh ankeritic-sideritic iron deposits are comparable with those of sub-seafloor carbonate-replacement SEDEX-type deposits.
Learning Local Distribution for Extremely Efficient Single-Image Super-Resolution

Achieving balance between efficiency and performance is a key problem for convolution neural network (CNN)-based single-image super-resolution (SISR) algorithms. Existing methods tend to directly output high-resolution (HR) pixels or residuals to reconstruct the HR image and focus a lot of attention on designing powerful CNN backbones. However, this reconstruction way requires the CNN backbone to have good ability to fit the mapping function from LR pixels to HR pixels, which certainly held these methods back from achieving extreme efficiency and from working in embedded environments. In this work, we propose a novel distribution learning architecture to estimate the local distribution and reconstruct HR pixels by sampling the local distribution with the corresponding 2D coordinates. We also improve the backbone structure to better support the proposed distribution learning architecture. The experimental results demonstrate that the proposed method achieves state-of-the-art performance for extremely efficient SISR and exhibits a good balance between efficiency and performance.
Prohibited Item Detection via Risk Graph Structure Learning

Prohibited item detection is an important problem in e-commerce, where the goal is to detect illegal items online for evading risks and stemming crimes. Traditional solutions usually mine evidence from individual instances, while current efforts try employing advanced Graph Neural Networks (GNN) to utilize multiple risk-relevant structures of items. However, it still remains two essential challenges, including weak structure and weak supervision. This work proposes the Risk Graph Structure Learning model (RGSL) for prohibited item detection. RGSL first introduces structure learning into large-scale risk graphs, to reduce noisy connections and add similar pairs. It then designs the pairwise training mechanism, which transforms the detection process as a metric learning from candidates to their similar prohibited items. Furthermore, RGSL generates risk-aware item representations and searches risk-relevant pairs for structure learning iteratively. We test RGSL on three real-world scenarios, and the improvements to baselines are up to 21.91% in AP and 18.28% in MAX-F1. Meanwhile, RGSL has been deployed on an e-commerce platform, and the improvements to traditional solutions are up to 23.59% in [email protected] and 6.52% in [email protected]
Methods, Models and Tools for Improving the Quality of Textual Annotations

In multilingual textual archives, the availability of textual annotation, that is keywords either manually or automatically associated with texts, is something worth exploiting to improve user experience and successful navigation, search and visualization. It is therefore necessary to study and develop tools for this exploitation. The paper aims to define models and tools for handling textual annotations, in our case keywords of a scientific library. With the background of NLP, machine learning and deep learning approaches are presented. They allow us, in supervised and unsupervised ways, to increase the quality of keywords. The different steps of the pipeline are addressed, and different solutions are analyzed, implemented, evaluated and compared, using statistical methods, machine learning and artificial neural networks as appropriate. If possible, off-the-shelf solutions will also be compared. The models are trained on different datasets already available or created ad hoc with common characteristics with the starting dataset. The results obtained are presented, commented and compared with each other.
Enhancing Knowledge Bases with Quantity Facts

Machine knowledge about the world’s entities should include quantity properties, such as heights of buildings, running times of athletes, energy efficiency of car models, energy production of power plants, and more. State-of-the-art knowledge bases (KBs), such as Wikidata, cover many relevant entities but often miss the corresponding quantities. Prior work on extracting quantity facts from web contents focused on high precision for top-ranked outputs, but did not tackle the KB coverage issue. This paper presents a recall-oriented approach which aims to close this gap in knowledge-base coverage. Our method is based on iterative learning for extracting quantity facts, with two novel contributions to boost recall for KB augmentation without sacrificing the quality standards of the knowledge base. The first contribution is a query expansion technique to capture a larger pool of fact candidates. The second contribution is a novel technique for harnessing observations on value distributions for self-consistency. Experiments with extractions from more than 13 million web documents demonstrate the benefits of our method.
Spatial Alignment for Unsupervised Domain Adaptive Single-Stage Object Detection

Domain adaptation methods are proposed to improve the performance of object detection in new domains without additional annotation costs. Recently, domain adaptation methods based on adversarial learning to align source and target domain image distributions are effective. However, for object detection tasks, image-level alignment enforces the alignment of non-transferable background regions, which affects the performance of important target regions. Therefore, how to balance the alignment of background and target remains a challenge. In addition, the current research with good effect is based on two-stage detectors, and there are relatively few studies on single-stage detectors. To address these issues, in this paper, we propose a selective domain adaptation framework for the spatial alignment of a single-stage detector. The framework can identify the background and target and pay different attention to them. On the premise that the single-stage detector does not generate region suggestions, it can achieve domain feature alignment and reduce the influence of the background, enabling transfer between different domains. We validate the effectiveness of our method for weather discrepancy, camera angles, synthetic to real-world, and real images to artistic images. Extensive experiments on four representative adaptation tasks show that the method effectively improves the performance of single-stage object detectors in different domains while maintaining good scalability.
A Study on Vehicle Monitoring Service Using Attribute-Based Security Scheme in Cyber–Physical Systems

Cyber–physical systems (CPSs) is a manufacturing infrastructure that requires high reliability real-time automatic control characteristics. It is widely used in industrial fields such as production and transportation. An example is a future transportation network system that controls information such as sensors and actuators based on a network. However, CPSs are susceptible to cyberattacks as network-based interconnections increase. Data handled between a car and a fog server can put personal privacy at risk, so it is essential to introduce security technologies such as encryption. A login/password method is required to satisfy the demand for secure sharing and utilization by service users. Based on the attributes of these target service users, the authority to decrypt data is delegated to legitimate users, and a pull-in encryption method is required. In this paper, we propose a method to safely protect the system from attacks through the method of managing attribute-based delegation of authority. Using a semantic security model, the proposed system is proven safe against public attacks by malicious users, and it can be used to protect against attacks on a smart car.
A Scalable Digit-Parallel Polynomial Multiplier Architecture for NIST-Standardized Binary Elliptic Curves

This work presents a scalable digit-parallel finite field polynomial multiplier architecture with a digit size of 32 bits for NIST-standardized binary elliptic fields. First, a dedicated digit-parallel architecture is proposed for each binary field recommended by NIST, i.e., 163, 233, 283, 409 and 571. Then, a scalable architecture having support for all variants of binary fields of elliptic curves is proposed. For performance investigation, we have compared dedicated multiplier architectures with scalable design. After this, the dedicated and scalable architectures are compared with the most relevant state-of-the-art multipliers. All multiplier architectures are implemented in Verilog HDL using the Vivado IDE tool. The implementation results are reported on a 28 nm Virtex-7 FPGA technology. The dedicated multipliers utilize slices of 1182 (for m=163), 1451 (for m=233), 1589 (for m=283), 2093 (for m=409) and 3451 (for m=571). Moreover, our dedicated designs can operate at a maximum frequency of 500, 476, 465, 451 and 443 MHz. Similarly, for all supported binary fields, our scalable architecture (i) utilizes 3753 slices, (ii) achieves 305 MHz clock frequency, (iii) takes 0.013 μs for one finite field multiplication and (iv) consumes 3.905 W power. The proposed scalable digit-parallel architecture is more area-efficient than most recent state-of-the-art multipliers. Consequently, the reported results and comparison to the state of the art reveal that the proposed architectures are well suited for cryptographic applications.
Accountable Monero System with Privacy Protection

Monero is one of the prominent cryptocurrencies bringing robust privacy safeguard levels. However, for Monero lacking accountability, it is easy to use anonymity and privacy for committing the crime without the leakage of identity. And some exchanges are not very receptive to Monero. The purpose of this study is to balance privacy and accountability in Monero. Specifically, we studied the way to provide accountability while keeping privacy. We develop an accountable Monero model. Our model isolates three kinds of roles, users, the trusted registration authority, and a trusted regulator. Accountability enables the trusted regulator to reveal the signer’s identity. Only the trusted regulator can trace users’ public keys as needed. We give a construction for the accountable Monero system by combining CryptoNote protocol with ElGamal encryption. Our instantiation is with a marginal influence on efficiency. The security of our scheme is based on the discrete logarithm and decisional Diffie–Hellman assumption.
Analysis and Evaluation on the Harmfulness of Sports Dance Based on Intelligent Computing

With the rapid development of motion analysis and editing technology, more and more animation technologies based on real human motion analysis are widely used in games, medicine, film and television, sports, and other fields. The role of this technology in the teaching system of sports dance arrangement cannot be ignored. This paper studies the application of intelligent algorithm in injury analysis of sports dance choreography and puts forward a smooth and natural motion editing method which is more in line with the characteristics of human motion. The square spherical surface intelligent algorithm is used to naturally transition the rotation posture of each frame of human joint, and the action modification of computer-aided dance choreography system is more in line with the real action of human body. Simulation results show that interframe data can be used for the same type of dance. Different types of dance arrangement can adopt the method of inserting a new framework between the two dance movements, so as to finally meet the needs of sports dance coaches and students to observe their own dance movements in an all-round way, so as to facilitate the arrangement and modification of sports dance movement design. It provides a certain reference for the application and development of virtual technology in the field of sports and greatly improves the current situation of sports dance teaching equipment. Through animation simulation technology, we can effectively avoid the injury caused by dangerous movements, provide a way of observation and improvement for coaches’ dance teaching, and effectively improve the training effect. Compared with traditional dance choreography methods, the intelligent algorithm in this study can make the animation display of sports dance more coherent and realistic and effectively solve the needs of current events observation and modification in sports dance choreography. It can produce complex movements with simple movements and help teachers arrange and teach sports dance. Avoid the accidental injury caused by the limitation of time and space in traditional teaching and realize the modernization and intelligence of physical education teaching.
OffDQ: An Offline Deep Learning Framework for QoS Prediction

With the increasing trend of web services over the Internet, developing a robust Quality of Service (QoS) prediction algorithm for recommending services in real-time is becoming a challenge today. Designing an efficient QoS prediction algorithm achieving high accuracy, while supporting faster prediction to enable the algorithm to be integrated into a real-time system, is one of the primary focuses in the domain of Services Computing. The major state-of-the-art QoS prediction methods are yet to efficiently meet both criteria simultaneously, possibly due to the lack of analysis of challenges involved in designing the prediction algorithm. In this paper, we systematically analyze the various challenges associated with the QoS prediction algorithm and propose solution strategies to overcome the challenges, and thereby propose a novel offline framework using deep neural architectures for QoS prediction to achieve our goals. Our framework, on the one hand, handles the sparsity of the dataset, captures the non-linear relationship among data, figures out the correlation between users and services to achieve desirable prediction accuracy. On the other hand, our framework being an offline prediction strategy enables faster responsiveness. We performed extensive experiments on the publicly available WS-DREAM dataset to show the trade-off between prediction performance and prediction time. Furthermore, we observed our framework significantly improved one of the parameters (prediction accuracy or responsiveness) without considerably compromising the other as compared to the state-of-the-art methods.
COVID-19 and Distance Learning: International Doctoral Students' Satisfaction With the General Quality of Learning and Aspects of University Support in Russia

Since this catastrophic crisis began, learning at universities has changed to distance mode to reduce person-to-person transmission of the virus. Using a survey, the article compares the experiences of international doctoral students in this COVID crisis with the general quality of learning and their experiences with various aspects of university support. The descriptive results suggest that the overall satisfaction with the general quality of learning was quite high among both Russian-speaking and English-speaking students. There were no statistically significant differences between English-speaking and Russian-speaking doctoral students. However, there was a statistically significant difference in fields of study, with international doctoral students in medical sciences being the most dissatisfied with the general quality of learning. After comparing international doctoral students' experiences with various aspects of university support, the results further reveal that English-speaking international doctoral students were dissatisfied with access to laboratory equipment, software for working on a dissertation, and library services the most. The article advocates that universities provide and improve their support systems, especially by making laboratory equipment, software for working on dissertations, and library services more accessible to English-speaking international doctoral students during this pandemic, to give them a positive learning experience.
BSAL

Given the ubiquitous existence of graph-structured data, learning the representations of nodes for the downstream tasks ranging from node classification, link prediction to graph classification is of crucial importance. Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information. However, the available techniques either heavily count on the network topology which is spurious in practice or cannot integrate graph topology and features properly. To bridge the gap, we propose a bicomponent structural and attribute learning framework (BSAL) that is designed to adaptively leverage information from topology and feature spaces. Specifically, BSAL constructs a semantic topology via the node attributes and then gets the embeddings regarding the semantic view, which provides a flexible and easy-to-implement solution to adaptively incorporate the information carried by the node attributes. Then the semantic embedding together with topology embedding is fused together using an attention mechanism for the final prediction. Extensive experiments show the superior performance of our proposal and it significantly outperforms baselines on diverse research benchmarks.