Reviewerly/exHarmony huggingface dataset:
Predicting Propositional Satisfiability Based on Graph Attention Networks

Abstract Boolean satisfiability problems (SAT) have very rich generic and domain-specific structures. How to capture these structural features in the embedding space and feed them to deep learning models is an important factor influencing the use of neural networks to solve SAT problems. Graph neural networks have achieved good results, especially for message-passing models. These capture the displacement-invariant architecture well, whether building end-to-end models or improving heuristic algorithms for traditional solvers. We present the first framework for predicting the satisfiability of domain-specific SAT problems using graph attention networks, GAT-SAT. Our model can learn satisfiability features in a weakly supervised setting, i.e., in the absence of problem-specific feature engineering. We test the model to predict the satisfiability of randomly generated SAT instances SR(N) and random 3-SAT problems. Experiments demonstrate that our model improves the prediction accuracy of random 3-SAT problems by 1–4% and significantly outperforms other graph neural network approaches on random SR(N). Compared to NeuroSAT, our model can almost always achieve the same or even higher accuracy with half the amount of iterations. At the end of the paper, we also try to explain the role played by the graph attention mechanism in the model.
Leveraging Organizational Performance through Employee Commitment and Organizational Citizenship Behaviour: A Conceptual Review

This research was aimed to create a conceptual model that investigates the role of organizational citizenship behavior (OCB) in the relationship between employee commitment and organizational performance.The research expands on previous research on organizational behavior and how it affects performance.Six hypotheses are proposed as a result of the literature review.A framework is built by integrating employee commitment with organizational performance, using OCB as a moderating variable.Researchers and practitioners can use the framework as a starting point to investigate organizational performance further.The framework clarifies the determinants of employee commitment components, OCB, and organizational performance, as well as their potential linkages, for researchers.The paradigm can be utilized by practitioners to better understand the function of OCB in employee commitment and organizational performance.
Activity-Free User Identification Using Wearables Based on Vision Techniques

In order to achieve the promise of smart spaces where the environment acts to fulfill the needs of users in an unobtrusive and personalized manner, it is necessary to provide means for a seamless and continuous identification of users to know who indeed is interacting with the system and to whom the smart services are to be provided. In this paper, we propose a new approach capable of performing activity-free identification of users based on hand and arm motion patterns obtained from an wrist-worn inertial measurement unit (IMU). Our approach is not constrained to particular types of movements, gestures, or activities, thus, allowing users to perform freely and unconstrained their daily routine while the user identification takes place. We evaluate our approach based on IMU data collected from 23 people performing their daily routines unconstrained. Our results indicate that our approach is able to perform activity-free user identification with an accuracy of 0.9485 for 23 users without requiring any direct input or specific action from users. Furthermore, our evaluation provides evidence regarding the robustness of our approach in various different configurations.
Sequence-aware news recommendations by combining intra- with inter-session user information

Abstract There exist many research works that strive to answer the question “what news article is a user going to click next given his profile”. These works take into account the time dimension to reveal users’ preferences over time. However, few works exploit adequately the information that is hidden inside user sessions. User sessions include a list of user interactions with items within a short period of time such as 30 min, and can reveal her very last intentions. In this paper, we combine intra- with inter-session item transition probabilities to reveal the short- and long-term intentions of individuals. Thus, we are able to better capture the similarities among items that are co-selected inside a user session but also within any two consecutive sessions. We have evaluated experimentally our method and compare it against state-of-the-art algorithms on three real-life datasets. We demonstrate the superiority of our method over its competitors.
Developing General and Subject Competences of Primary School Pupils in the Context of Integrated Education: the Case of one Lithuanian School

Aim. The aim of the research is to provide a scientific justification for the integrated development of general and subject-specific competences of primary school pupils. Methods. The exploratory qualitative case study was conducted in a private school in Lithuania. The integrated activities covered the content of Lithuanian language and science education, as well as general competences such as communication and digital competences. The activity was implemented in grade 4 with 12 pupils (8 boys and 4 girls). A semi-structured interview with the class teacher was also conducted. Results and conclusion. The data from the empirical study were analysed according to the following thematic clusters: interest in the activity, group work, searching for information in the encyclopaedia and in electronic sources, working with a robot when integrating the digital skills and the content of science education, descriptions of animals as a summarising and consolidating activity. The study found that if the educational process is well thought out, if the pupils are interested, they can work independently and support each other in explaining the content, while the teacher becomes an observer, a facilitator, and can concentrate on the pupils’ individual activities during this process. Well-designed tasks with the robot develop not only digital literacy skills, but also the reinforcement of subject content related to the use of concepts. Originality. The case study provides valuable insights into the implementation of an educational process that integrates general and subject-specific competences, and the detailed description of the activities shows which tasks support students’ independent learning.
Multi-Scale Feature Fusion for Interior Style Detection

Text-based search engines can extract various types of information when a user enters an appropriate search query. However, a text-based search often fails in image retrieval when image understanding is needed. Deep learning (DL) is often used for image task problems, and various DL methods have successfully extracted visual features. However, as human perception differs for each individual, a dataset with an abundant number of images evaluated by human subjects is not available in many cases, although DL requires a considerable amount of data to estimate space ambiance, and the DL models that have been created are difficult to understand. In addition, it has been reported that texture is deeply related to space ambiance. Therefore, in this study, bag of visual words (BoVW) is used. By applying a hierarchical representation to BoVW, we propose a new interior style detection method using multi-scale features and boosting. The multi-scale features are created by combining global features from BoVW and local features that use object detection. Experiments on an image understanding task were conducted on a dataset consisting of room images with multiple styles. The results show that the proposed method improves the accuracy by 0.128 compared with the conventional method and by 0.021 compared with a residual network. Therefore, the proposed method can better detect interior style using multi-scale features.
Quadruple Differential Distinguishers and an Automatic Searching Tool

&lt;p&gt;Paper on cryptography.&lt;/p&gt;
Temperature Trends at Madhira, Khammam District of Telangana State

Climate change is one of the most important global environmental challenges at global, national and regional level. The resultant global warming due to increase in temperature by 1.5°C in the near-term (2021-2040) can cause unavoidable increase in multiple climate hazards and present unknown challenges facing humanity with implications for food production, natural ecosystems, freshwater supply, health, etc. In this context, an analysis was carried out to identify trends in temperature over time series at Madhira, Khammam district in Telangana. In this trend analysis study, the annual average maximum temperature value of skewness was asymmetric and left skewed. The annual mean and maximum temperatures were significant with long-term increasing trend. In the pre monsoon season, maximum and mean temperature showed significant increase in trends in by all methods i.e., M-K (Mann – Kendall), Spearman's Rho and Linear regression tests. But minimum temperature showed non-significant increasing trend. Here interestingly, monsoon season showed non-significance increase in temperature trends in all three mentioned tests. The mean of monthly maximum temperature increased at a faster rate than the average and minimum temperature. The linear regression equation indicated positive slope and R2 was 27.0% of variability for mean annual temperature.
A Novel Image Super-Resolution Reconstruction Framework Using the AI Technique of Dual Generator Generative Adversarial Network (GAN)

Image superresolution (SR) is the process of enlarging and enhancing a low-resolution image. Image superresolution helps in industrial image enhancement, classification, detection, pattern recognition, surveillance, satellite imaging, medical diagnosis, image analytics, etc. It is of utmost importance to keep the features of the low-resolution image intact while enlarging and enhancing it. In this research paper, a framework is proposed that works in three phases and generates superresolution images while keeping low-resolution image features intact and reducing image blurring and artifacts. In the first phase, image enlargement is done, which enlarges the low-resolution image to the 2x/4x scale using two standard algorithms. The second phase enhances the image using an AI-empowered Generative adversarial network (GAN). We have used a GAN with dual generators and named it EffN-GAN (EfficientNet-GAN). Fusion is done in the last phase, wherein the final improved image is generated by fusing the enlarged image and GAN output image. The fusion phase helps in reducing the artifacts. We have used the DIV2K dataset to train the GAN and further tested the results on the images of Set5, Set14, B100, Urban100, Manga109 datasets with ground truth of size 224x224x3. The obtained results were compared with the state-of-the-art superresolution approach based on important image quality parameters, namely, Peak signal-to--to-noise ratio (PSNR), Structural similarity index (SSIM), Visual information fidelity (VIF) image quality parameters. The results show that the proposed framework for generating super-resolution images from 2x/4x resolution downgraded images improves the aforementioned mentioned image quality parameters significantly.
Job offers recommender system based on virtual organizations

Abstract Human interaction has changed considerably with the emergence of the Internet. Today, a large percentage of daily communication takes place on instant messaging applications and social networks. In fact, there has been a considerable increase in the use of social networks because new social networks are being created for specific purposes, such as the search for employment or tourism. When the amount of content on a social network is large, it is necessary to help users find content of their interest. In this regard, artificial intelligence techniques can greatly facilitate the task of searching for relevant content. This paper presents a recommender system for a business and employment oriented social network, on which users are recommended job offers and other user profiles to follow. The presented system is based on virtual organizations of agents, and uses artificial neural networks to determine whether job offers and users should be recommended or not. The system has been evaluated on a real social network; its recommendations regarding job offers and user profiles have had a high acceptance rate.
Speculative bubble tendencies in time series of Bitcoin market prices

This article explores the concepts of cryptocurrencies and speculative bubbles, as Bitcoin’s price behaviour shares characteristics with speculative bubbles that have occurred in recent years. Using a quantitative research design, the study examines daily market prices for the period between 2013 and 2019. Statistical moments, return stationarity, TARCH-type model estimations and Supremum Augmented Dickey-Fuller and Generalised Supremum Augmented Dickey-Fuller tests are analysed. We find evidence for multiple speculative bubble tendencies in Bitcoin prices caused by speculation, which reached their maximum at the end of 2017. Our results are in line with recent studies, which characterise Bitcoin as both highly speculative and vulnerable to financial bubbles.
The Broken Windows Theory Applies to Technical Debt

Context: The term technical debt (TD) describes the aggregation of sub-optimal solutions that serve to impede the evolution and maintenance of a system. Some claim that the broken windows theory (BWT), a concept borrowed from criminology, also applies to software development projects. The theory states that the presence of indications of previous crime (such as a broken window) will increase the likelihood of further criminal activity; TD could be considered the broken windows of software systems. Objective: To empirically investigate the causal relationship between the TD density of a system and the propensity of developers to introduce new TD during the extension of that system. Method: The study used a mixed-methods research strategy consisting of a controlled experiment with an accompanying survey and follow-up interviews. The experiment had a total of 29 developers of varying experience levels completing system extension tasks in already existing systems with high or low TD density. Results: The analysis revealed significant effects of TD level on the subjects' tendency to re-implement (rather than reuse) functionality, choose non-descriptive variable names, and introduce other code smells identified by the software tool SonarQube, all with at least 95% credible intervals. Conclusions: Three separate significant results along with a validating qualitative result combine to form substantial evidence of the BWT's existence in software engineering contexts. This study finds that existing TD can have a major impact on developers propensity to introduce new TD of various types during development.
Hierarchical Transformer with Spatio-Temporal Context Aggregation for Next Point-of-Interest Recommendation

Next point-of-interest (POI) recommendation is a critical task in location-based social networks, yet remains challenging due to a high degree of variation and personalization exhibited in user movements. In this work, we explore the latent hierarchical structure composed of multi-granularity short-term structural patterns in user check-in sequences. We propose a Spatio-Temporal context AggRegated Hierarchical Transformer (STAR-HiT) for next POI recommendation, which employs stacked hierarchical encoders to recursively encode the spatio-temporal context and explicitly locate subsequences of different granularities. More specifically, in each encoder, the global attention layer captures the spatio-temporal context of the sequence, while the local attention layer performed within each subsequence enhances subsequence modeling using the local context. The sequence partition layer infers positions and lengths of subsequences from the global context adaptively, such that semantics in subsequences can be well preserved. Finally, the subsequence aggregation layer fuses representations within each subsequence to form the corresponding subsequence representation, thereby generating a new sequence of higher-level granularity. The stacking of encoders captures the latent hierarchical structure of the check-in sequence, which is used to predict the next visiting POI. Extensive experiments on three public datasets demonstrate that the proposed model achieves superior performance whilst providing explanations for recommendations. Codes are available at https://github.com/JennyXieJiayi/STAR-HiT.
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining

A challenging task when generating summaries of legal documents is the ability to address their argumentative nature. We introduce a simple technique to capture the argumentative structure of legal documents by integrating argument role labeling into the summarization process. Experiments with pretrained language models show that our proposed approach improves performance over strong baselines
Orthogonal layers of parallelism in large-scale eigenvalue computations

We address the communication overhead of distributed sparse matrix-(multiple)-vector multiplication in the context of large-scale eigensolvers, using filter diagonalization as an example. The basis of our study is a performance model which includes a communication metric that is computed directly from the matrix sparsity pattern without running any code. The performance model quantifies to which extent scalability and parallel efficiency are lost due to communication overhead. To restore scalability, we identify two orthogonal layers of parallelism in the filter diagonalization technique. In the horizontal layer the rows of the sparse matrix are distributed across individual processes. In the vertical layer bundles of multiple vectors are distributed across separate process groups. An analysis in terms of the communication metric predicts that scalability can be restored if, and only if, one implements the two orthogonal layers of parallelism via different distributed vector layouts. Our theoretical analysis is corroborated by benchmarks for application matrices from quantum and solid state physics, road networks, and nonlinear programming. We finally demonstrate the benefits of using orthogonal layers of parallelism with two exemplary application cases -- an exciton and a strongly correlated electron system -- which incur either small or large communication overhead.
KRAKEN: A Secure, Trusted, Regulatory-Compliant, and Privacy-Preserving Data Sharing Platform

Abstract The KRAKEN project aims to enable the sharing, brokerage, and trading of personal data including sensitive data (e.g., educational and health records and wellbeing data from wearable devices) by returning its control to both data subjects/data providers throughout the entire data lifecycle. The project is providing a data marketplace which will allow the sharing of personal data and its usage for research and business purposes, by using privacy-preserving cryptographic tools. KRAKEN is developing an advanced platform to share certified information between users and organizations by leveraging on distributed ledger technology, promoting the vision of self-sovereign identity solutions (ensuring users’ consent and data control in a privacy-friendly way), preserving security, privacy, and the protection of personal data in compliance with EU regulations (e.g., GDPR). The feasibility of the KRAKEN solution will be tested through two high-impact pilots in the education and healthcare fields.
Changes in the system of postgraduate training

The development and establishment of postgraduate training in the Russian Federation has a long history. A retrospective comparison of all periods of postgraduate training showed that, as a training program, postgraduate studies have always been such, but with regard to the tool for preparing a qualified scientist, the question still hovers among the professional community - what is the educational component and what is the scientific component in the specified educational program. New regulations that came into force in 2022 that change the system for training scientific (scientific and pedagogical) personnel are the result of a search for a solution to create an optimal system for training postgraduate students in the Russian Federation.
ANTI FUZZY SUBSEMIRING

When the first operation’s inverse axiom is deleted from the ring, an algebraic structure, the semiring, is generated. Subsemiring is one of the subjects covered in semiring. The concepts of fuzzy subsemiring, anti subsemiring fuzzy semiring, and complement are introduced in this paper. In addition, the anti-subsemiring fuzzy semiring, a wedge, or a combination of two or more fuzzy anti-subsemiring associated with a non-empty subset of the semiring whose membership criteria are defined by the membership value of the zero elements will be discussed.
From legacy to next generation: a story of collaboration to push the boundaries of the open source Haplo repository from Cayuse

This article describes the development of the Haplo standards-based, open source repository software, from Cayuse, that meets the findable, accessible, interoperable and reusable (FAIR) principles, and which captures all research, regardless of what it is, with a focus on prioritizing the capture of ‘practice research’ – ‘… an umbrella term that describes all manners of research where practice is the significant method of research conveyed in a research output.’ in the arts and architecture. This research has been neglected by the repository landscape and surrounding discoverability infrastructure, which has traditionally focused on text-based research publications in the STEM disciplines where there is a policy mandate (and funding) for open access. As practice research has not been captured effectively in repositories, it has not been possible for it to be preserved for long-term access via appropriate integrated digital preservation solutions. This story of collaboration between the University of Westminster and Haplo (now Cayuse), puts researchers at the centre of development, using a co-design approach, while ensuring the Research and Scholarly Communications team (then based within the Library and Archives Service) at the University were driving this work in alignment with sector-wide standards. The role of user engagement, advocacy and inclusive policy development is highlighted and illustrates that this underpins, and is crucial to, successful software development. While the successes are documented and celebrated, the challenges are acknowledged and the lessons learned are shared.