Reviewerly/exHarmony huggingface dataset:
Heterogeneous Multi-Behavior Recommendation Based on Graph Convolutional Networks

Traditional recommendation systems usually use single user-item interaction information, which ignore the multiple relationships that exist for other interactions (e.g., likes, clicks).Multi-behavioral recommendation models compensate for the shortcomings of traditional models.The existing multibehavior recommendation models focus on obtaining behavioral information by distinguishing the interaction differences of multiple user behaviors but ignore the common preferences of items related to user different interactions.In this paper, we propose a Multi-Behavior Heterogeneous Contrastive learning Recommendation (MBHCR) model.Specifically, MBHCR stresses the information fusion between different interaction behaviors and differences in the behavior view.Firstly, we design a unified heterogeneous graph based on the complexity of user multi-behavioral interaction information to distinguish behavioral differences while preserving their complete semantic information.Secondly, we propose a multi-behavior relational aggregator component to model the unified heterogeneous graph only once to capture the potential common representations of users and their different interactions and to mitigate user sparsity.Thirdly, we also design a behavior comparison learning enhancer to complement the interaction differences between the user's target behavior and auxiliary behaviors on the behavior view and extract valid information.Experiments were conducted on two real-world datasets to demonstrate the validity of MBHCR and designed ablation experiments to verify the contribution of model components.
Open science–related policies in Europe

Abstract This study aims to review the open science (OS) policy documents, identify their subject areas, and distinguish the topics of OS support policies in seven European countries, providing a platform for practical cooperation between countries in science popularization. With a qualitative–inductive approach, all pertinent policy documents in OS were collected through documentary study, and thematic analysis was conducted to identify OS policies for each country. Finally, forty-six policy documents extracted up to December 2020 were thematically analyzed through a qualitative–inductive case study. All selected countries had developed OS policies, and these supportive policies were generally related to the three dimensions of ‘open input, open process, and open output’. In ‘open input’, recommendations for the performance of research data repositories, as well as management criteria, are considered. Most countries adopted ‘open output’ protectionist policies. Multiple policies in the ‘open process’ indicate the need for an appropriate OS platform.
Two new classes of projective two-weight linear codes

In this paper, for an odd prime p, several classes of two-weight linear codes over the finite field Fp are constructed from the defining sets, and then their complete weight enumerators are determined by employing character sums. These codes can be suitable for applications in secret sharing schemes. Furthermore, two new classes of projective two-weight codes are obtained, and then two new classes of strongly regular graphs are given.
Parallel tomography of quantum non-demolition measurements in multi-qubit devices

An efficient characterization of QND measurements is an important ingredient towards certifying and improving the performance and scalability of quantum processors. In this work, we introduce a parallel tomography of QND measurements that addresses single- and two-qubit readout on a multi-qubit quantum processor. We provide an experimental demonstration of the tomographic protocol on a 7-qubit IBM-Q device, characterizing the quality of conventional qubit readout as well as generalized measurements such as parity or measurement-and-reset schemes. Our protocol reconstructs the Choi matrices of the measurement processes, extracts relevant quantifiers -- fidelity, QND-ness, destructiveness -- and identifies sources of errors that limit the performance of the device for repeated QND measurements. We also show how to quantify measurement cross-talk and use it to certify the quality of simultaneous readout on multiple qubits.
Flexible Hybrid Table Recognition and Semantic Interpretation System

Abstract Extracting information from documents containing quantitative data in tabular format is an important but still unsolved task due to the heterogeneity of document layouts. This work aims to take a step toward developing a solution to this problem. This paper proposes a flexible, hybrid table extraction system consisting of a deep learning-based table detection module, a heuristic-based structure recognition method, and a graph-based semantic interpretation component. The proposed system is modular and supports the most frequent table layouts. Moreover, it handles both the documents in image format and PDF files with embedded text. The proposed system outperforms the baseline method and achieves results on par with state-of-the-art approaches on the challenging benchmarks from ICDAR 2013 and ICDAR 2019 table interpretation competitions. Moreover, we correct an issue with the evaluation script used in the latter competition and report extended results of the proposed method in comparison with a leading commercial product. Finally, our table extraction system achieves a high F $$_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mn>1</mml:mn> </mml:msub> </mml:math> score in the scenario where raw documents are given as input and the targeted information is contained in a subset of table columns. The presented system achieves results competitive with leading methods in the field. It has already been evaluated on general-purpose data and biomedical benchmarks. We intend to continuously improve our approach and process data from other domains, e.g., financial documents. To support future research on information extraction from documents, we make the evaluation scripts and results from our experiments publicly available at https://github.com/mnamysl/tabrec-sncs .
Improving voice detection in real life scenarios: differentiating television and human speech at older adults’ houses

The use of voice-operated robots in real-life settings introduces multiple issues as opposed to the use of them in controlled, laboratory conditions. In our study, we introduced conversation robots in the homes of 18 older adults’ homes to increase the conversation activities of the participants. A manual examination of the audio data the robot considered a human voice showed that a considerable amount was from television sounds present in the participants’ homes. We used this data to train a neural network that can differentiate between human speech and speech-like sounds from television, achieving high metrics. We extended our analysis into how the voices of the participants contain inherent patterns that can be general or uncommon and how this affects performance of our algorithm in our attempts to identify human speech with or without these patterns.
Smart glove for sign language translation

The development of a wearable device-based sign language translator is discussed in this study. It can translate sign language into both speech and text. The glove-based gadget can read the motions of one arm and five fingers. The system consists of an accelerometer and five flex sensors to measure finger bending and arm motions, respectively. Based on the interaction of these sensors, the gadget can recognize specific motions that in American Sign Language (ASL) correspond to the alphabet and convert them into speech and text using a mobile phone application. The hardware design of the device is mostly explained in this publication. The gadget displayed an average value of 0.6 s to translate a sign language into speech and text based on early trial results on six simple sign languages, demonstrating the applicability of the suggested device.
Online - Education and training in higher educational institutions of Ukraine: Challenges and benefits

This publication is devoted to the peculiarities of distance learning during the COVID-19 pandemic from the point of view of students of Ukrainian educational institutions. The study involved 110 participants from two educational institutions in Ukraine. In the course of the research, it turned out that the students highlighted the partial advantages of distance learning (free time for personal development and gaining new experience), as well as the disadvantages - there are no people around who admire you. They also emphasize the features of psychological comfort outside the university classrooms, but only in front of a computer monitor.
HNTSumm: Hybrid text summarization of transliterated news articles

Data generated from social networking sites, blogs, digital magazines, and news websites is the largest human-generated data. Summarization is the process of extracting the crux of a document which when done manually can be tedious and deluging. Automatic text summarization is an approach that encapsulates long documents into a few sentences or words by enwrapping the gist and the principal information of the document. With the growth of social networking sites, eBooks, and e-Papers, the prevalence of transliterated words in text corpora is also on the rise. In this paper, we propose a word embeddings-based algorithm called HNTSumm by combining the advantages of unsupervised and supervised learning methods. The proposed algorithm HNTSumm algorithm is an imminent method for automatic text summarization of huge volumes of data that can learn word embeddings for words transliterated from other languages to English by utilizing weighted word embeddings from a Neural Embedding Model. Further, the amalgamation of extractive and abstractive approaches yields a concise and unambiguous summary of the text documents as the extractive approach eliminates redundant information. We employ a hybrid version of the Sequence-to-sequence models to generate an abstractive summary for the transliterated words. The feasibility of this algorithm was evaluated using two different news summary datasets and the accuracy scores were computed with the ROUGE evaluation metric. Experimental results corroborate the higher performance of the proposed algorithm and show HNTSumm outperforms relevant state-of-the-art algorithms for datasets with transliterated words.
Urdu Short Paraphrase Detection at Sentence Level

Paraphrase detection systems uncover the relationship between two text fragments and classify them as paraphrased when they convey the same idea; otherwise non-paraphrased. Previously, the researchers have mainly focused on developing resources for the English language for paraphrase detection. There have been very few efforts for paraphrase detection in South Asian languages. However, no research has been conducted on sentence-level paraphrase detection in Urdu, a low-resourced language. It is mainly due to the unavailability of the corpora that focus on the sentence level. The available related studies on the Urdu language only focus on text reuse detection tasks at the passage and document levels. Therefore, this study aims to develop a large-scale manually annotated benchmark Urdu paraphrase detection corpus at the sentence level, based on real cases from journalism. The proposed Urdu Sentential Paraphrases (USP) corpus contains 4,900 sentences (2,941 paraphrased and 1,959 non-paraphrased), manually collected from the Urdu newspapers. Moreover, several techniques were proposed, developed, and compared as a secondary contribution, including Word Embedding (WE), Sentence Transformers (ST), and feature-fusion techniques. N-gram is treated as the baseline technique for our research. The experimental results indicate that our proposed feature-fusion technique is the most suitable for the Urdu paraphrase detection task. Furthermore, the performance increases when features of the proposed (ST) and baseline (N-gram) are combined for the classification task. In addition, The proposed techniques have also been applied to the UPPC corpus to check their performance at the document level. The best result we obtained using the feature fusion technique ( F 1 = 0.855). Our corpus is available and free to download for research purposes.
Categorizing affective response of customer with novel explainable clustering algorithm: The case study of Amazon reviews

Electronic word of mouth (e-WOM) influences consumer decision-making. Since consumers' affective experiences for products are vast, research is needed to understand and categorize them accurately. In this paper, we developed a deep learning-based clustering algorithm for categorizing consumer sentiment in product reviews and explored the applicability of this algorithm. A Deep Attentive Self-Organizing Map (DASOM) was created by noting individualized sentimental characteristics of each review and interpreting why each review was included in a particular cluster. As a result of analyzing 4941 reviews of Amazon, one of online commerce platforms, it was confirmed that sentiment classification through DASOM could be effectively used to categorize implicit affective experiences of consumers. DASOM was effective in identifying the relationship between multi-dimensional affective elements that were difficult to derive from TF-IDF. Using the proposed methodology, it is expected to provide practical information for companies that design products considering consumer affection.
Computing word meanings by aggregating individualized distributional models: Wisdom of the crowds in lexical semantic memory

Linguistic experience varies across individuals and is impacted by both demography and personal preferences, leading to differences in word meanings across languages (Thompson et al., 2020) and people (Johns, 2022). An active area of study in the cognitive sciences that examines the impact of varied knowledge across individuals is the wisdom of the crowd effect, where it is found that the aggregate judgement of a group of individuals is often better than the judgement of the best individual in the group (Surowiecki, 2004). The goal of this article was to determine if there is a wisdom of the crowd effect in lexical semantic memory, such that the aggregated word similarity values from many individual language users exceeds the fit of the best fitting individual. This was accomplished by training 500 different distributional models from 500 high-level commenters on the internet forum Reddit. By deriving aggregated word similarity values from these individuals, a strong wisdom of the crowd effect was found where the aggregated similarity values far exceeded the performance of the best fitting individual for each dataset tested. Additionally, it was found that even aggregating only a small number of users provided a large increase in fit relative to the individual corpora, but with the best fitting measure including word similarity values from all possible users. The results of this article provide an avenue for future distributional model development by demonstrating that the best pathway towards better distributional models may lie in the aggregation of multiple representations attained from individual users of a language.
Blind matching algorithm based proxy distribution against internet censorship

Abstract Proxy based censorship circumvention technique is treated as one of the most promising approaches to fighting against Internet censorship, and its practical applications like the onion router and Lantern have been widely utilized. However, the proxy distribution, as a basic and core issue, still remains unsolved due to (i) the finiteness and non‐renewable nature of proxy resources, (ii) the heterogeneity of clients and proxies requirements, and (iii) the existence of secondary censorship. In this paper, a win‐win driven proxy distribution mechanism for proxy based censorship circumvention systems is designed by leveraging the blind matching theory. Specifically, a combinational manner is developed to measure the quality of service (QoS) requirements of clients in terms of normalized bandwidth and reliability, and security requirements of proxies in terms of credits and suspicion. Then, by introducing the concept of virtual currency, the proxy distribution issue is formulated as two‐sided combinatorial problems from the perspective of clients and proxies, respectively. To solve the complex NP‐hard combinatorial problems in a win‐win manner without unilateral dominance, a two‐sided many‐to‐many context‐free matching game is constructed, in which the aspiration levels and agreement functions are defined to capture the security and QoS concerns of clients and proxies. Further, a modified notion of pairwise stability is exploited as the solution concept, and a client‐proxy blind matching algorithm with market and information decentralization is proposed to obtain the ε‐pairwise stable solution, where clients and proxies make encounters randomly and update their aspiration levels through a stochastic learning process. Finally, theoretical analysis and numerical results are carried out to validate the properties and performance of the proposed algorithm including stability, complexity, and convergence.
Localized curvature-based combinatorial subgraph sampling for large-scale graphs

This paper introduces a subgraph sampling method based on curvature to train large-scale graphs via mini-batch training. Owing to the difficulty in sampling globally optimal subgraphs from large graphs, we sample the subgraphs to minimize the distributional metric with combinatorial sampling. In particular, we define a combinatorial metric that distributionally measures the similarity between an original graph and all possible node and edge combinations of the subgraphs. Further, we prove that the subgraphs sampled using the probability model proportional to the discrete Ricci curvature (i.e., Ollivier-Ricci curvatures) of the edges can minimize the proposed metric. Moreover, as accurate calculation of the curvature on a large graph is challenging, we propose to use a localized curvature considering only 3-cycles on the graph, suggesting that this is a sufficiently approximated curvature on a sparse graph. We also show that the probability models of conventional sampling methods are related to coarsely approximated curvatures with no cycles, implying that the curvature is closely related to subgraph sampling. The experimental results confirm the feasibility of integrating the proposed curvature-based sampling method into existing graph neural networks to improve performance.
Characterisation of coal using hyperspectral core scanning systems

Commercially available hyperspectral drill core scanning systems in the Visible and Near-Infrared (VNIR), Short-Wave Infrared (SWIR) and Long-Wave Infrared (LWIR) regions, along with a handheld spectrometer for the Middle-Wave Infrared (MWIR) region, were used to characterise a rank suite of coal samples (0.45% to 1.76% Rr) with different preparations (slabbed surface, outer core and compressed powder pellets). The samples used in this study came from Australian basins of Permian and Jurassic ages. The spectral characteristics of the coals measured using these scanning systems were compared to those obtained using well-established spectroscopy techniques that operate in the same region of the electromagnetic spectrum, such as Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy. The observed spectral features of the coals changed with increasing coalification. The features occurring at 1910 nm in the SWIR region and 2940 nm in the MWIR region tended to disappear as rank increased; these features arise from OH bonds related to structural water and hence correlate with the moisture content. The CH stretching fundamentals at 3280 nm correspond to the aromatic fraction of the coal, while the spectral region between 3380 nm and 3500 nm corresponds to the CH2 and CH3 stretching fundamentals occurring in the aliphatic fraction of the coal. The ratio of the area of the aromatic absorption feature to that of the aliphatic absorption features, herein called Rank Spectral Index, displayed a strong linear correlation with coal rank parameters, such as vitrinite reflectance. Other spectral features in the SWIR and MWIR regions also correlated with coal rank parameters. In addition to rank, trends in the magnitudes of the spectral features were influenced by maceral composition, particularly in low rank samples with abundant liptinite or inertinite group macerals. The variable spectral features related to composition were mapped onto the slabbed core surface, discriminating bright and dull bands and fusain lenses enabling the classification of coal lithotypes, regardless of rank. Among the less banded lithotypes, it was established that high liptinite content coals can be distinguished by their aliphatic spectral features in core samples. Overall, this study has shown that hyperspectral drill core scanners can be used to characterise coal and non-destructively classify rank and lithotype.
Fast and accurate interpretation of workload classification model

How can we interpret predictions of a workload classification model? A workload is a sequence of operations executed in DRAM, where each operation contains a command and an address. Classifying a given sequence into a correct workload type is important for verifying the quality of DRAM. Although a previous model achieves a reasonable accuracy on workload classification, it is challenging to interpret the prediction results since it is a black box model. A promising direction is to exploit interpretation models which compute the amount of attribution each feature gives to the prediction. However, none of the existing interpretable models are tailored for workload classification. The main challenges to be addressed are to 1) provide interpretable features for further improving interpretability, 2) measure the similarity of features for constructing the interpretable super features, and 3) provide consistent interpretations over all instances. In this paper, we propose INFO (INterpretable model For wOrkload classification), a model-agnostic interpretable model which analyzes workload classification results. INFO provides interpretable results while producing accurate predictions. We design super features to enhance interpretability by hierarchically clustering original features used for the classifier. To generate the super features, we define and measure the interpretability-friendly similarity, a variant of Jaccard similarity between original features. Then, INFO globally explains the workload classification model by generalizing super features over all instances. Experiments show that INFO provides intuitive interpretations which are faithful to the original non-interpretable model. INFO also shows up to 2.0× faster running time than the competitor while having comparable accuracies for real-world workload datasets.