Reviewerly/exHarmony huggingface dataset:
Paradigm of interaction between the Siberian Scientiﬁc Agricultural Library and agrarian research and educational institutions of the Siberian region

The article covers the main directions of joint work of scientiﬁc-research institutions (SRI) of agricultural proﬁle in the Siberian region and Siberian Scientiﬁc Agricultural Library (SibSAL) functioning within the structure of the Siberian Branch of VASKHNIL (Russian Agricultural Academy) since its establishment in 1969 up to the reorganization period of science reform (2013-2021). The speciﬁcity of library and information activity of departmental library and multilevel model of interaction with institutes to satisfy information needs of scientists and specialists of agrarian research institutes in pre-reform period is presented. The results of monitoring the state of library and information activities in the institutes that have inﬂuenced the development of the new concept of interaction with SRI are presented. Based on the analysis of reorganization processes, a new structure of scientiﬁc organizations of the agricultural proﬁle of Siberia is presented, which is the basis for the development of a new paradigm of interaction between research teams and the Siberian Scientiﬁc Agricultural Library, which received the status of a branch of the State Public Scientiﬁc and Technical Library of the Siberian Branch of the Russian Academy of Sciences as a result of the reorganization. The analysis carried out in the conditions of reform and reorganization changes on the basis of a questionnaire shows new trends in expanding the range of interests of scientists to the possibilities of the information potential of the SibSAL as the largest branch library in the region. In the context of the change of management and the destruction of organizational and functional ties of agricultural research institutes that have become part of the regional centers of the SB RAS, the orientation to the development and strengthening of communications with the central academic libraries of the regions and the restoration of interaction with libraries of agricultural education on a new basis is noted. The deﬁnition of the system structure of agricultural research institutes and educational institutions of the region within the new boundaries of departmental relations of the Ministry of Agriculture, the Ministry of Science and Higher Education will allow establishing the vector of interaction of the SibSAL as a system-forming link. This will enable the development of the main functional areas of library and information activities in the Siberian region.
Group-Based Atrous Convolution Stereo Matching Network

Stereo matching is the key technology in stereo vision. Given a pair of rectified images, stereo matching determines correspondences between the pair images and estimate depth by obtaining disparity between corresponding pixels. The current work has shown that depth estimation from a stereo pair of images can be formulated as a supervised learning task with an end-to-end frame based on convolutional neural networks (CNNs). However, 3D CNN puts a great burden on memory storage and computation, which further leads to the significantly increased computation time. To alleviate this issue, atrous convolution was proposed to reduce the number of convolutional operations via a relatively sparse receptive field. However, this sparse receptive field makes it difficult to find reliable corresponding points in fuzzy areas, e.g., occluded areas and untextured areas, owing to the loss of rich contextual information. To address this problem, we propose the Group-based Atrous Convolution Spatial Pyramid Pooling (GASPP) to robustly segment objects at multiple scales with affordable computing resources. The main feature of the GASPP module is to set convolutional layers with continuous dilation rate in each group, so that it can reduce the impact of holes introduced by atrous convolution on network performance. Moreover, we introduce a tailored cascade cost volume in a pyramid form to reduce memory, so as to meet real-time performance. The group-based atrous convolution stereo matching network is evaluated on the street scene benchmark KITTI 2015 and Scene Flow and achieves state-of-the-art performance.
A Critical Reflection on ODRL

Rights expression languages (RELs) aim to express and govern legally binding behavior within technological environments. The Open Digital Rights Language (ODRL), used to represent statements about the usage of digital assets, is among the most known RELs today and has become a W3C recommendation to enhance the web's functionality and interoperability. This paper reflects on the representational power of ODRL from a practical perspective; utilizing use cases and examples, we discuss the challenges, issues, and limitations we came across while investigating the language as a potential solution for the regulation of data-sharing infrastructures.
A Study On Ant Colony Optimization

Biomimetics is attracting more and more attention as a powerful clue to cutting-edge breakthroughs in a lot of areas. Of course, it is so powerful not only for material science but also for the areas of social infrastructure such as transportation. I would like to review an example of Biomimetics in transportation, which is called as ant colony optimization (ACO), thoroughly studied in the area of mathematical optimization.
CovidAlert - A Wristwatch-Based System to Alert Users from Face Touching

Worldwide 219 million people have been infected and 4.5 million have lost their lives in ongoing Covid-19 pandemic. Until vaccines became widely available, precautions and safety measures like wearing masks, physical distancing, avoiding face touching were some of the primary means to curb the spread of virus. Face touching is a compulsive human behavior that can not be prevented without constantly making a conscious effort, even then it is inevitable. To address this problem, we have designed a smartwatch-based solution, CovidAlert, that leverages Random Forest algorithm trained on accelerometer and gyroscope data from the smartwatch to detect hand transition to face and sends a quick haptic alert to the users. CovidAlert is highly energy efficient as it employs STA/LTA algorithm as a gatekeeper to curtail the usage of Random Forest model on the watch when user is inactive. The overall accuracy of system is $$88.4\%$$ with low false negatives and false positives. We also demonstrated the system viability by implementing it on a commercial Fossil Gen 5 smartwatch.
TEACHING FOREIGN LANGUAGES IN HIGHER EDUCATION: DISTANCE LEARNING IMPACT ON STUDENT’S METACOGNITIVE SELF-REGULATION

I. Karpovich, O. Sheredekina, M. Bernavskaya, O. MikhailovaPeter the Great St.Petersburg Polytechnic University (RUSSIAN FEDERATION)
XnODR and XnIDR: Two Accurate and Fast Fully Connected Layers For Convolutional Neural Networks

Capsule Network is powerful at defining the positional relationship between features in deep neural networks for visual recognition tasks, but it is computationally expensive and not suitable for running on mobile devices. The bottleneck is in the computational complexity of the Dynamic Routing mechanism used between the capsules. On the other hand, XNOR-Net is fast and computationally efficient, though it suffers from low accuracy due to information loss in the binarization process. To address the computational burdens of the Dynamic Routing mechanism, this paper proposes new Fully Connected (FC) layers by xnorizing the linear projection outside or inside the Dynamic Routing within the CapsFC layer. Specifically, our proposed FC layers have two versions, XnODR (Xnorize the Linear Projection Outside Dynamic Routing) and XnIDR (Xnorize the Linear Projection Inside Dynamic Routing). To test the generalization of both XnODR and XnIDR, we insert them into two different networks, MobileNetV2 and ResNet-50. Our experiments on three datasets, MNIST, CIFAR-10, and MultiMNIST validate their effectiveness. The results demonstrate that both XnODR and XnIDR help networks to have high accuracy with lower FLOPs and fewer parameters (e.g., 96.14% correctness with 2.99M parameters and 311.74M FLOPs on CIFAR-10).
Data article: Full disk real-time Himawari-8/9 satellite AHI imagery from JAXA

Satellite products are essential in many research fields. Geostationary satellite imagery is rapidly updating and offers vast multi-spectral data that is useful in applications such as gridded solar forecasting and resource assessment. However, there is a high-barrier to entry for researchers in obtaining and using satellite data. A new database from the Japan Aerospace Exploration Agency (JAXA) offers near-real-time Himawari-8/9 satellite imagery by Advanced Himawari Imagers (AHIs), for free, to researchers. Typically, this type of data has a high price point; hence, it is a good opportunity for scientific researchers to work in the real real-time operational space. We present a data article complete with a Python package that: (i) downloads full disk Himawari-8/9 AHI Himawari standard data from the JAXA Himawari Monitor P-Tree system, (ii) provides example scripts showing how the data can remapped, cropped, and stored appropriately, and (iii) gives examples how to visualize the data. The Python package can be found on the Python package index at https://pypi.org/project/ftp-himawari8-hsd/.
Multipartite spatial entanglement generated by concurrent nonlinear processes

Continuous variables multipartite entanglement is a key resource for quantum technologies. This works considers the multipartite entanglement generated in separated spatial modes of the same light beam by three different parametric sources: a standard $\chi^{(2)}$ medium pumped by two pumps, a single-pump nonlinear photonic crystal, and a doubly pumped nonlinear photonic crystal. These sources have in common the coexistence of several concurrent nonlinear processes in the same medium, which allows the generation of non-standard 3 and 4-mode couplings. We test the genuine nature of the multipartite entangled states thereby generated in a common framework, using both criteria based on proper bounds for the variances of nonlocal observables and on the positive partial transpose criterion. The relative simplicity of these states allows a (hopefully) useful comparison of the different inseparability tests.
Policing harmful content on social media platforms

Social media content moderation is an important area to explore, as the number of users and the amount of content are rapidly increasing every year. As an effect of the COVID19 pandemic, people of all ages around the world spend proportionately more time online. While the internet undeniably brings many benefits, the need for effective online policing is even greater now, as the risk of exposure to harmful content grows. In this paper, the aim is to understand the context of how harmful content - such as posts containing child sexual abuse material, terrorist propaganda or explicit violence - is policed online on social media platforms, and how it could be improved. It is intended in this assessment to outline the difficulties in defining and regulating the growing amount of harmful content online, which includes looking at relevant current legal frameworks at development. It is noted that the subjectivity and complexity in moderating content online will remain by the very nature of the subject. It is discussed and critically analysed whose responsibility managing toxic online content should be. It is argued that an environment in which all stakeholders (including supranational organisations, states, law enforcement agencies, companies and users) maximise their participation, and cooperation should be created in order to effectively ensure online safety. Acknowledging the critical role human content moderators play in keeping social media platforms safe online spaces, consideration about their working conditions are raised. They are essential stakeholders in policing (legal and illegal) harmful content; therefore, they have to be treated better for humanistic and practical reasons. Recommendations are outlined such as trying to prevent harmful content from entering social media platforms in the first place, providing moderators better access to mental health support, and using more available technological tools.
Drug crime transformation under the effect of online platforms

Many types of drug crimes are already known, but it is common to offender groups to implement them in an organised, coordinated, multi-level framework. Affected by online platforms, drug crimes are undergoing a transformation that presents new challenges for the authorities. Criminal groups also like to (re)take the opportunities offered by the Internet. Platforms that connect to the Dark Web, the Dark Internet (secret network form), have become increasingly popular over the past few years, thanks to the fact that there are also services available which are not available by traditional browsers. Coordinated cooperation between several countries, over several years, is needed to ensure successful detection in cyberspace. This is illustrated by a current EUROPOL report, published on 12 January 2021, which shows that a successful liquidation has occurred in the case of DarkMarket, one of the largest illegal marketplaces, requiring the cooperation of several European Union Member States and several non-European Union countries. In the face of the coronavirus epidemic, organised crime groups are still trying to remain active and resilient, which is not a challenge in the online space at this time, although drug delivery models needed to be changed, but they are trying to maintain the constant cycle of the market through their hiding methods. In my study, I am also looking for answers how the detection and recovery of criminal property is carried out at national and international level, in cases where drug crime takes place in the online space. What is the path of illegally acquired criminal property, how can financial profiling be carried out in these cases? In case of drug offences, we are talking about a special asset recovery where is no victim demanded, because there are very few known victims existing. With my study, I intend to capture the current topicality of drug crime, by detecting methods of committing crimes in the online space, internationally and domestically, and by exploring the current processes of asset discovery and recovery. In this way, I wish to provide results that can be used by the profession.
Wide-Sense Stationarity in Generalized Graph Signal Processing

We consider statistical graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space. This general model encompasses various signals such as the traditional scalar-valued graph signal, multichannel graph signal, and discrete- and continuous-time graph signals, allowing us to build a unified theory of graph random processes. We introduce the notion of joint wide-sense stationarity in this generalized GSP framework, which allows us to characterize a graph random process as a combination of uncorrelated oscillation modes across both the vertex and Hilbert space domains. We elucidate the relationship between the notions of wide-sense stationarity in different domains, and derive the Wiener filters for denoising and signal completion under this framework. Numerical experiments on both real and synthetic datasets demonstrate the utility of our generalized approach in achieving better estimation performance compared to traditional GSP or the time-vertex framework.
Dimension fusion: Dimension-level dynamically composable accelerator for convolutional neural networks

Convolutional neural networks (CNNs) have proven to be promising in various applications such as audio recognition, image classification, and video understanding. Winograd algorithm helps to reduce the complexity of computation in a convolution but suffers from poor compatibility for different convolution shapes. This work introduces a dynamic dimension-level fusion architecture based on Winograd for accelerating different dimensions of CNNs. We explore this Winograd architecture by designing Dimension Fusion, a dimension-level processing engine that dynamically fuses to match the convolution shape of individual CNN layers. The proposed architecture is the first work based on Winograd algorithm to be compatible with all convolution shapes (dimension, stride, and filter-size) and achieves highest PE efficiency up to 1.55x and energy efficiency up to 3.3x compared with the state-of-art accelerators.
$$\textsf {DEVA}$$: Decentralized, Verifiable Secure Aggregation for Privacy-Preserving Learning

Aggregating data from multiple sources is often required in multiple applications. In this paper, we introduce \(\textsf {DEVA}\), a protocol that allows a distributed set of servers to perform secure and verifiable aggregation of multiple users’ secret data, while no communication between the users occurs. \(\textsf {DEVA}\) computes the sum of the users’ input and provides public verifiability, i.e., anyone can be convinced about the correctness of the aggregated sum computed from a threshold amount of servers. A direct application of the \(\textsf {DEVA}\) protocol is its employment in the machine learning setting, where the aggregation of multiple users’ parameters (used in the learning model), can be orchestrated by multiple servers, contrary to centralized solutions that rely on a single server. We prove the security and verifiability of the proposed protocol and evaluate its performance for the execution time and bandwidth, the verification execution, the communication cost, and the total bandwidth usage of the protocol. We compare our findings to the prior work, concluding that \(\textsf {DEVA}\) requires less communication cost for a big amount of users.
Outdoor terrain recognition based on transfer learning

Abstract Terrain recognition exerts an extremely important role in outdoor mobile robot gait planning, speed control, environment perception, etc. Compared with the traditional terrain recognition process that uses color, texture, and other underlying features to describe terrain images, the present study starts from the perspective of transfer learning. MobileNet and DenseNet are employed for high-level feature extraction, and the voting integrated learning algorithm is used to classify high-level feature data sets. In the meanwhile, we have established an outdoor terrain data set that conforms to the traveling process of outdoor mobile robots, and processed the collected video data with key frames and sliding windows. The accuracy of the classification results reached 97%, basically satisfying the needs of actual terrain recognition.
LTL Model Checking for Register Pushdown Systems

A pushdown system (PDS) is known as an abstract model of recursive programs. For PDS, model checking methods have been studied and applied to various software verification such as interprocedural data flow analysis and malware detection. However, PDS cannot manipulate data values from an infinite domain. A register PDS (RPDS) is an extension of PDS by adding registers to deal with data values in a restricted way. This paper proposes algorithms for LTL model checking problems for RPDS with simple and regular valuations, which are labelings of atomic propositions to configurations with reasonable restriction. First, we introduce RPDS and related models, and then define the LTL model checking problems for RPDS. Second, we give algorithms for solving these problems and also show that the problems are EXPTIME-complete. As practical examples, we show solutions of a malware detection and an XML schema checking in the proposed framework.
The Development of Canva as an Economic Learning Media at Tholabie Islamic Boarding School Malang

This study aims to develop Canva as a learning medium with the main subject of economics, namely consumer households for class VIII SMP. The approach used is research and development (R%D). The research method taken is Mixed methods design. The subjects in this study were students of SMP Pondok Pesantren Tholabie Malang. Data collection techniques used by researchers are observations and questionnaires. Canva is developed using the ADDIE development model that has been modified by researchers. The results showed that the Canva learning video media was feasible to be used and applied in economic learning with the subject matter of consumer households for class VIII SMP Pondok Pesantren Tholabie. The researcher uses descriptive quantitative data analysis techniques and shows the assessment of media experts (4.75) with proper and valid categories. This assessment is based on the presentation of material, language, and graphics in writing. In the assessment of material experts (5.0) with a very valid and very feasible category. This assessment is based on the presentation of the material, language, and graphics in writing the content of the material. The researcher also received a response from students of 77% which could be categorized as worthy. The results of these student responses are distributed to obtain results and measure the validity and achievement of students. In this study, researchers were only able to retrieve data online online using the google form as a data collection tool, this was due to the COVID-19 pandemic and the PPKM. The result of data analysis that has been owned by the researcher shows that the percentage level is in a feasible and valid category, this means that Canva's learning video media is effective for use in Tholabie Islamic Boarding School Junior High School.
Blockchain in Oil and Gas Industry: Applications, Challenges, and Future Trends

Today's systems, approaches, and technologies leveraged for managing oil and gas supply chain operations fall short in providing operational transparency, traceability, audit, security, and trusted data provenance features. Also, a large portion of the existing systems is centralized, manual, and highly disintegrated which make them vulnerable to manipulation and the single point of failure problem. In this survey, we explore the potential opportunities and applications of blockchain technology in managing the exploration, production, and supply chain and logistics operations in the oil and gas industry as it can offer traceability, immutability, transparency, and audit features in a decentralized, trusted, and secure manner. We discuss state-of-the-art blockchain-based schemes, research projects, business initiatives, and case studies to highlight the practicability of blockchain in the oil and gas industry. We present the potential opportunities brought about by blockchain technology in various use cases and application scenarios. We introduce several systems that leverage blockchain-based smart contracts to automate the important services in terms of tracking and tracing of petroleum products, protection of international trade documents, and coordination of purchasing and bidding activities for granting oil exploration rights to petroleum exploration and development companies. Finally, we present open challenges acting as future research directions.