Reviewerly/exHarmony huggingface dataset:
Concept of robotic investigational study

Abstract The approach as a concept for automated experimental research conducting under management of a controller and a research program control system is stated in the article. Method of robotic tools using to perform experiments with the replacement of the investigated objects without researcher participation is considered in a concept. Variant for automatic revision of the experiment schedule based on preliminary measurements and their evaluation, including analysis of preliminary results using criteria for accuracy and validity of expected results has been considered. The essence of the concept is shown and implementation examples of the concept have been resented upon automation of experimental research. Advantages and prospects of the proposed approach are shown, in particular the authors believe that experimental studies robotization will enable to evaluate and improve the accuracy and validity of the research results; as well as concept is useful for many types and directions of experimental investigations as aimed at human and economic costs reducing, research tasks solution acceleration.
Red Hat Smart Management and Red Hat Insights

This chapter aims to highlight the management of Red Hat Infrastructure across multiple environments and also describe predictive analytics and how it improves the security of any infrastructure.
Artwork Information Embedding Framework for Multi-source Ukiyo-e Record Retrieval

Ukiyo-e culture has endured throughout Japanese art history to this day. With its high artistic value, ukiyo-e remains an important part of art history. Possibly more than one million ukiyo-e prints have been collected by institutions and individuals worldwide. Many public ukiyo-e databases of various scales have been created in different languages. The sharing of ukiyo-e culture could advance to a new stage if the information from all the databases could be shared without differences in information. However, understanding different languages in different databases, redundant data, missing data, uncertain data, and inconsistent data are all barriers to knowledge discovery in each database. Therefore, this paper uses Ukiyo-e Portal Database [1] prints that were released from the Art Research Center (ARC) of Ritsumeikan University as examples, explains the challenges that are currently solvable, and proposes a multi-source artwork information embedding framework for multimodal and multilingual retrieval.
Designing with Static Capabilities and Effects: Use, Mention, and Invariants (Pearl).

Capabilities (whether object or reference capabilities) are fundamentally tools to restrict effects. Thus static capabilities (object or reference) and effect systems take different technical machinery to the same core problem of statically restricting or reasoning about effects in programs. Any time two approaches can in principle address the same sets of problems, it becomes important to understand the trade-offs between the approaches, how these trade-offs might interact with the problem at hand.Experts who have worked in these areas tend to find the trade-offs somewhat obvious, having considered them in context before. However, this kind of design discussion is often written down only implicitly as comparison between two approaches for a specific program reasoning problem, rather than as a discussion of general trade-offs between general classes of techniques. As a result, it is not uncommon to set out to solve a problem with one technique, only to find the other better-suited.We discuss the trade-offs between static capabilities (specifically reference capabilities) and effect systems, articulating the challenges each approach tends to have in isolation, and how these are sometimes mitigated. We also put our discussion in context, by appealing to examples of how these trade-offs were considered in the course of developing prior systems in the area. Along the way, we highlight how seemingly-minor aspects of type systems - weakening/framing and the mere existence of type contexts - play a subtle role in the efficacy of these systems.
Attitude of regional university students toward life and study in the conditions of self-isolation

This study discusses the attitude of students considering the restrictive measures implemented during the period of quarantine amid the coronavirus pandemic. Based on a survey including 65 students, their attitudes toward the changes in lifestyle and educational forms were analyzed. Overall, the findings of this study show that the students are compliant with the restrictive measures. However, the quality of distance learning is considered to be lower.
PSVDAG: Compact Voxelized Representation of 3D Scenes Using Pointerless Sparse Voxel Directed Acyclic Graphs

This paper deals with the issue of geometry representation of voxelized three-dimensional scenes using hierarchical data structures. These include pointerless Sparse Voxel Octrees that have no pointers on child nodes and allow a compact binary representation. However, if necessary, there is a possibility to reconstruct these pointers for rapid traversing. Sparse Voxel Directed Acyclic Graphs added 32-bit pointers to child nodes and merging of common subtrees, which can be considered lossless compression. By merging common subtrees, no decompression overhead occurs at the time of traversing. The hierarchical data structure proposed herein - the Pointerless Sparse Voxel Directed Acyclic Graph - incorporates the benefits of both - pointerless Sparse Voxel Octrees (by avoiding storing pointers on child nodes) and Sparse Voxel Directed Acyclic Graphs (by allowing the merging of common subtrees due the introduction of labels and callers). The proposed data structure supports the quick and easy reconstruction of pointers by introducing the Active Child Node Count. It also potentially allows Child Node Mask compression of its nodes. This paper presents the proposed data structure and its binary-level encoding in detail. It compares the effectiveness of the representation of voxelized three-dimensional scenes (originally represented in OBJ format) in the proposed data structure with the data structures mentioned above. It also summarizes statistical data providing a more detailed description of the various parameters of the data structure for different scenes stored in multiple resolutions.
Enhancing Tokenization by Embedding Romanian Language Specific Morphology

this paper addresses the significance, complexity and approaches of Romanian tokenization, the first step in NLP. A strategy is proposed for tokenizing the Romanian text with various specificity: standard (literal language), archaic and medical. The strategy is extending existing tokenization models for English, Spanish and Portuguese with Romanian features. Moreover, we attempt to address the Romanian ambiguities caused by the complexity of the grammar and provide support for further Romanian NLP processing. The approach proved that there is no general solution for tokenization and that diverging tokenization strategies for all languages can lead to considerably better results. With the current approach we obtained a high-quality tokenization on all three Romanian formats as follows: for standard sources, for archaic sources and for medical sources.
Research on Spam Filtering Technology Based on New Mutual Information Feature Selection Algorithm

Abstract Aiming at the deficiency of traditional mutual information algorithm in feature selection, this paper proposes a weighted naive Bayesian algorithm based on improved mutual information, called imi-wnb algorithm. In the feature selection stage, the word frequency factor and the difference factor between classes are introduced to improve the traditional mutual information algorithm to achieve feature dimension reduction. In the process of classification, the value of IMI is introduced to weight the attributes of naive Bayes algorithm, which partly eliminates the influence of conditional independence assumption of naive Bayes algorithm on classification, and improves the efficiency and stability of spam classification.
Classification of Customer Reviews on E-commerce Platforms Based on Naive Bayesian Algorithm and Support Vector Machine

Abstract Customer reviews on e-commerce platforms contain valuable information, while sifting through them manually tends to dismay people because of the huge amount of data. Ideally, the identification classifier would analyze the emotional disposition of product reviews (positive, negative) and aggregate opinions about each of them. Previous literature has demonstrated extensive research conclusions on opinion extraction and semantic classification of product reviews on websites. However, analysis on the universality of machine learning algorithms in identifying the emotional tendency of different Chinese e-commerce reviews is not yet studied. This study uses a method, which is based on general machine learning algorithms, to classify feedbacks. Our classifier extracts Chinese word segmentation and text frequency for feature extraction and scoring, and implements the classification with methods of Naive Bayesian and Support Vector Machines. Experimental results on the Alibaba product review sentiment datasets show that our model based on two machine learning algorithms, though results in different performances, can provide suggestions on the selection of the identification classifier using a trade-off strategy and helps obtain fast and accurate classification on reviews of different categories.
A Novel Researcher Search System Based on Research Content Similarity and Geographic Information

Collaborative research is becoming increasingly important because it yields effective results and helps difficult research projects run smoothly. Previous studies have proposed many kinds of collaborator recommendation methods based on research features, such as specialty fields. However, few studies have constructed systems in which users can discover experts who have similar research interests using recommendation techniques. This paper proposes a novel researcher search system where users can efficiently discover potential candidates whose work locations are near theirs. Researchers are visualized on a map by our proposed system and users can use researcher’s names and research keywords to narrow down the search. Specifically, given a researcher’s name as a query, the system displays its relevant individuals based on either one of the following measures among researchers: research content similarity or collaborative relationship similarity. Our experiments demonstrated that recommendation results of these two similarity measures are minimally overlapped one another, indicating that our system could potentially help researchers discover collaborator candidates.
QuantumSkynet: A High Dimensional Quantum Computing Simulator

Recent advances in high dimensional quantum computing systems demonstrate the feasibility of this paradigm. Here, we introduce QuantumSkynet, a cloud-based high dimensional quantum computing simulator. This platform allows simulations of qudit-based quantum gates and circuits.
Blockchain and Geographical Indications: A Natural Fit?

Blockchain technology has garnered an enormous amount of excitement, particularly surrounding more recently developed blockchain platforms, smart contracts, and applications outside of cryptocurrency. One of the most successful applications to date is in food supply chains, which benefit from the features of blockchain that allow greater confidence in a good’s provenance, authenticity, and quality. Blockchain technology is a natural fit for Geographical Indications (GIs), because GIs are grounded in the values of provenance, authenticity, and quality. As a result, it is worthwhile to take a critical look at how blockchain could be used in connection with GI foodstuffs, as well as the practical implications and hurdles to such an application. A broad look at the common legal requirements for protecting GIs in the United States and European Union demonstrates that blockchain would add little value to a GI seeking to establish protection initially, but has substantial potential to be beneficial in maintaining and enforcing GI rights. Combining blockchain technology with smart contracts and Internet of Things solutions will enhance those benefits. A blockchain application for GIs could also bring value for GIs by improving communication with consumers and facilitating the support of sustainability goals. There are criticisms of this type of application and challenges a successful application would face (both those stemming from blockchain generally and the nature of GI producers more specifically). To overcome these challenges, a blockchain application for GIs would need to incorporate a private, permissioned blockchain with a trusted intermediary. The trusted intermediary would serve as a central coordinating force for producers; the role could be filled by the producer groups, Monitoring Parties, or even WIPO for a broader solution.
Designing a maturity model for analyzing information and knowledge management in the public sector

Purpose This study aims to report the design and testing of a maturity model for information and knowledge management in the public sector, intended for use in frequent monitoring, trend analysis and in-depth analysis of the contemporary information and knowledge management practices of an organization. Design/methodology/approach A design science approach was used to develop the proposed model. Creation of the model was based on an extensive literature review. Testing of the model was implemented as a survey receiving 37 responses from nine organizations organizing and purchasing public services. Findings The study presents four alternative profiles for an organization’s status, novice, experimenter, facilitator and advanced exploiter, and investigates the differences between these profiles on the basis of the empirical data gathered. The model was found to be both a valid and practical way to determine the state of an organization’s information and knowledge management and identify development needs. Research limitations/implications Testing was conducted in the Finnish public sector and further studies applying the model could be implemented in other countries. The model presented was designed specifically for the public sector and more research is needed to test its applicability in the private sector. Originality/value Maturity models are useful when evaluating information and knowledge management status in an organization, and beneficial for improving organizational performance. The proposed maturity model combines the fields of knowledge management and information management and contributes to the literature with an overarching maturity model that includes a dimension of satisfaction with the organizational maturity level. While many earlier models originate from the consultancy business, the model presented here was also designed for research purposes and tested in practice.
A Novel Approach for Face Recognition Using Modular PCA and MAP–MRF Classifier

Automated Face Recognition involves the extraction of features of the facial images of an unknown person and the identification by comparing the features with the available feature set of a known person. Each facial image has been divided into 16 independent sub-blocks which have been used in this work for designing and implementation of the Markov Random Field- (MRF) based classifier which helps to generate the Maximum A Posteriori (MAP) probability. Using the relationship of MRF and Gibbs Distribution, the MAP has been converted into an energy function. A novel approach for defining the energy function has also been proposed in this work.
A Preventive Intrusion Detection Architecture Using Adaptive Blockchain Method

This paper presents a network intrusion behavior detection utilizing an adaptive blockchain mechanism. A Layered Voting Rule System (LVRS) is introduced, which contains a positive layer, negative layer and propagation layer which trains the blockchain using the power consumption of the users to transfer and receive the data. The performance of the network is analyzed using Quality of Service (QoS) parameters, including throughput and power consumption. The observed throughput and power consumption of the proposed model is improved by more than 30% as compared to the model without blockchain.
Towards Robust Fine-Grained Recognition by Maximal Separation of Discriminative Features

Adversarial attacks have been widely studied for general classification tasks, but remain unexplored in the context of fine-grained recognition, where the inter-class similarities facilitate the attacker’s task. In this paper, we identify the proximity of the latent representations of local regions of different classes in fine-grained recognition networks as a key factor to the success of adversarial attacks. We therefore introduce an attention-based regularization mechanism that maximally separates the latent features of discriminative regions of different classes while minimizing the contribution of the non-discriminative regions to the final class prediction. As evidenced by our experiments, this allows us to significantly improve robustness to adversarial attacks, to the point of matching or even surpassing that of adversarial training, but without requiring access to adversarial samples. Further, our formulation also improves detection AUROC of adversarial samples over baselines on adversarially trained models.
Cyber security awareness among students and faculty members in a Sudanese college

In the last few years, cyber security has been an essential prerequisite for almost every organization to handle the massive number of emerging cyber attacks worldwide. A critical factor in reducing the possibility of being exploited is cyber security awareness. Not only having the adequate knowledge but how to utilize this knowledge to prevent cyber attacks. In this paper we conducted a survey that focuses on three vital security parameters, which are trust, passwords and defensive attitude respectively. The survey mainly aimed at assessing cyber security knowledge of 200 students and 100 faculty members in a Sudanese college and how secure these participants think they are according to their current cyber behaviour. 56% of the participants are males and 44% are females. The results revealed that all participants were having fairly-low level of security awareness and their defensive attitude is considerably weak and doesn’t protect them either individually or at institutional-level. Nevertheless, faculty member showed better cyber security knowledge and skills by 8% higher than students. This study can be used to develop training approaches that bridge the security gaps depicted by the respondents of the survey questions manipulated in this study.
SYSTEM OF DIGITAL SERVICES FOR SUPPORTING THE INDIVIDUALIZED LEARNING PROCESS

K. Brown1, A. Khalfin2, V. Larionova2, D. Sandler2, E. Sinitsyn2, A. Tolmachev2 1Letterkenny Institute of Technology (IRELAND) 2Ural Federal University (RUSSIAN FEDERATION)
Impediment Management of Agile Software Development Teams

Context: Many agile software development teams deal with blockers that prevent them from working efficiently and achieving their (sprint) goals. There is few guidance concerning impediment management, only that you have to collect impediments and specify them in the impediments backlog. Therefore, many teams developed individual solutions to approach impediments. Objective: This paper should help to understand how agile teams identify, document and solve impediments in practice. Method: Seven semi-structured qualitative interviews were conducted with agile practitioners from German companies. Results: An overview of different ways teams might handle their impediments is provided. Based on the results, an initial model of the impediment management process is proposed. Conclusion: The results of this study support practitioners when defining or improving the way they deal with impediments.
Research on Modulation Recognition of OFDM Signal Based on Hierarchical Iterative Support Vector Machine

Aiming at the problem that the modulation category of Orthogonal Frequency Division Multiplexing (OFDM) signal can't be identified from the single-carrier signals and the Wavelet Packet Modulation (WPM) signals under the condition of low signal to noise ratio (SNR), a method of identifying OFDM signal based on hierarchical iterative support vector machine (SVM) is proposed. Firstly, based on the analysis of the high-order cumulant and double spectral envelope peak characteristics of wireless communication signals, three sets of two-dimensional feature vectors are constructed as input sample data of three-layer classifier. Secondly, a three-layer support vector machine classifier based on radial basis kernel function is designed. Thirdly, the hierarchical iterative method is used to train the classifier parameters, in which each layer is firstly trained and then the trained three-layer classifier is further trained by the overall iterative training to further optimize the parameters of the classifier. Finally, the classifier training process and OFDM signal recognition process are simulated and analyzed. The simulation results show that the proposed method can effectively identify OFDM signals and the recognition accuracy is significantly improved at low SNR compared with the traditional methods.
Accelerating Intra-Party Communication in Vertical Federated Learning with RDMA

Federated learning (FL) has emerged as an elegant privacy-preserving distributed machine learning (ML) paradigm. Particularly, vertical FL (VFL) has a promising application prospect for collaborating organizations owning data of the same set of users but with disjoint features to jointly train models without leaking their private data to each other. As the volume of training data and the model size increase rapidly, each organization may deploy a cluster of many servers to participant in the federation. As such, the intra-party communication cost (i.e., network transfers within each organization's cluster) can significantly impact the entire VFL job's performance. Despite this, existing FL frameworks use the inefficient gRPC for intra-party communication, leading to high latency and high CPU cost. In this paper, we propose a design to transmit data with RDMA for intra-party communication, with no modifications to applications. To improve the network efficiency, we further propose an RDMA usage arbiter to adjust the RDMA bandwidth used for a non-straggler party dynamically, and a query data size optimizer to automatically find out the optimal query data size that each response carries. Our preliminary results show that RDMA based intra-party communication is 10x faster than gRPC based one, leading to a reduction of 9% on the completion time of a VFL job. Moreover, the RDMA usage arbiter can save over 90% bandwidth, and the query data size optimizer can improve the transmission speed by 18%.
Designing Learner Corpora

This chapter aims to discuss variables of importance for designing, collecting, and transcribing corpora by drawing on Learner Corpus Research and Second Language Acquisition expertise in order to facilitate the sharing and use of large sets of learner language. Additionally, the term second language is likely erroneous for a majority of individuals contributing to learner corpora as many are learners/users of multiple languages. Therefore, researchers should also gather data concerning knowledge of all other languages used or studied by the learner, namely, mother tongue(s), home language(s), instructed additional language(s), extensive living-abroad experiences. In designing a corpus, careful selection, documentation, and justification of all criteria will increase the likelihood that the resulting corpus is methodologically-sound. To ensure a balanced representation of learner interlanguage as genre dictates usage, learner corpora should also include texts across genres to represent writing/speaking requirements across the fields of study.