Reviewerly/exHarmony huggingface dataset:
Analysis of YouTube video contents on Koha and DSpace, and sentiment analysis of viewers' comments

Purpose The purpose of the study is to evaluate certain characteristics of the videos of the software Koha and DSpace posted on YouTube. Since YouTube has the potential to provide the content creator to share their knowledge and experience through their content which has become much more beneficial to the information seeker. Nowadays, people search for queries or tutorial videos on YouTube very often to earn a better understanding of the term. Sentiment analysis of the viewers' opinion of the videos is another purpose of this study. Design/methodology/approach Dataset for evaluating the characteristic of the videos of Koha and DSpace was extracted by using Webometric Analyst by creating YouTube API. Once retrieval of data was completed, a manual verification was enhanced to filter out spam videos unrelated to the scope. After the confirmation of authentic relatable videos, seeking the video's id as query, the comments per video were extracted using Webometric Analyst. For opinion mining, the Parallel Dots API web service was used in Google Sheets as an addon function. The sentiment, multilingual sentiment, emotion, intention and word frequency of the viewers' opinion was examined with the help of certain default functionalities. Findings Webometric Analyst extracted a total of 461 and 397 videos of Koha and DSpace, respectively, uploaded on the YouTube platform. The findings of the study indicate that the growth rate of videos on Koha is decreasing, while the number of videos uploaded on DSpace is gradually increased in the last 10 years. The highest number of videos posted in 1–20 min duration category with mostly high definition (HD) with standard YouTube license and prominently in the English language. The sentiment analysis of the total extracted comments on Koha and DSpace videos found to be 2043 and 862 comments, respectively, among whom “Positive” comments are mostly found and with “Happy” emotion can be highly detected with most supportive “Feedback” intention on both Koha and DSpace videos. The top word frequency signifies that the users of both the software are using the comments section of the videos on YouTube to ask and provide troubleshooting help to each other. Research limitations/implications The present study has some limitations too; the dataset for the study includes only those videos whose title, description or keywords sections had the query terms “Koha” or “DSpace” there are chances that some videos would have been left out from the dataset related to these software. Originality/value This is the first paper to evaluate the characteristics and sentiment of both the videos Koha and DSpace. Through this, the popularity, likeness and dislike and the impact of the contents of the videos uploaded will be disclosed, and creators can make an improvement by referring this, and the seekers will adapt to the use of correct and authentic information.
Sample-Efficient Device-Independent Quantum State Verification and Certification

Authentication of quantum sources is a crucial task in building reliable and efficient protocols for quantum-information processing. Steady progress vis-\`{a}-vis verification of quantum devices in the scenario with fully characterized measurement devices has been observed in recent years. When it comes to the scenario with uncharacterized measurements, the so-called black-box scenario, practical verification methods are still rather scarce. Development of self-testing methods is an important step forward, but these results so far have been used for reliable verification only by considering the asymptotic behavior of large, identically and independently distributed (IID) samples of a quantum resource. Such strong assumptions deprive the verification procedure of its truly device-independent character. In this paper, we develop a systematic approach to device-independent verification of quantum states free of IID assumptions in the finite copy regime. Remarkably, we show that device-independent verification can be performed with optimal sample efficiency. Finally, for the case of independent copies, we develop a device-independent protocol for quantum state certification: a protocol in which a fragment of the resource copies is measured to warrant the rest of the copies to be close to some target state.
Real-time implementation of fast discriminative scale space tracking algorithm

Abstract Real-time object tracking is an important step of many modern image processing applications. The efficient hardware design of real-time object tracker must achieve the desired accuracy while satisfying the frame rate requirements for a variety of image sizes. The existing methods of visual tracking employ sophisticated algorithms and challenge the capabilities of most embedded architectures. Discriminative scale space tracking is one algorithm that is capable of demonstrating good performance with affordable complexity. It has a high degree of parallelism which can be exploited for efficient implementation of reconfigurable hardware architectures. This paper proposes a real-time implementation of the discriminative scale-space tracker on FPGA for the major blocks. A careful design exploration of core mathematical operations of the tracking algorithm is performed to improve their hardware utilization and timing performance. Among the core functional units optimized in this work, the discrete Fourier transform achieves a computational time improvement of 92% relative to existing works, QR factorization achieves a 2.3 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> reduction in resource utilization, and singular value decomposition yields a 3.8 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> improvement in processing time. The proposed data path architecture is designed using Vivado HLS tool set and implemented for Zync Zed Board (xc7z020clg484-1). For an input image size of 320 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> 240, the proposed architecture achieves a mean 25.38 fps.
Generative Imagination Elevates Machine Translation

There are common semantics shared across text and images.Given a sentence in a source language, whether depicting the visual scene helps translation into a target language?Existing multimodal neural machine translation methods (MNMT) require triplets of bilingual sentence -image for training and tuples of source sentence -image for inference.In this paper, we propose ImagiT, a novel machine translation method via visual imagination.ImagiT first learns to generate visual representation from the source sentence, and then utilizes both source sentence and the "imagined representation" to produce a target translation.Unlike previous methods, it only needs the source sentence at the inference time.Experiments demonstrate that ImagiT benefits from visual imagination and significantly outperforms the text-only neural machine translation baselines.Further analysis reveals that the imagination process in ImagiT helps fill in missing information when performing the degradation strategy.
ICT’s System for AutoSimTrans 2021: Robust Char-Level Simultaneous Translation

Simultaneous translation (ST) outputs the translation simultaneously while reading the input sentence, which is an important component of simultaneous interpretation.In this paper, we describe our submitted ST system, which won the first place in the streaming transcription input track of the Chinese-English translation task of AutoSimTrans 2021.Aiming at the robustness of ST, we first propose char-level simultaneous translation and applied wait-k policy on it.Meanwhile, we apply two data processing methods and combine two training methods for domain adaptation.Our method enhance the ST model with stronger robustness and domain adaptability.Experiments on streaming transcription show that our method outperforms the baseline at all latency, especially at low latency, the proposed method improves about 6 BLEU.Besides, ablation studies we conduct verify the effectiveness of each module in the proposed method.
Stable fuzzy controllers via LMI approach for non-linear systems described by type-2 T–S fuzzy model

Purpose The purpose of this paper is to stabilize the type-2 Takagi–Sugeno (T–S) fuzzy systems with the sufficient and guaranteed stability conditions. The given conditions efficaciously handle parameter uncertainties by the upper and lower membership functions of the type-2 fuzzy sets (FSs). Design/methodology/approach This paper reports on a relevant study of stable fuzzy controllers and type-2 T–S fuzzy systems and reported that the synthesis of controller for nonlinear systems described by the type-2 T–S fuzzy model is a key problem and it can be resolve to convex problems via linear matrix inequalities (LMIs). Findings The multigain fuzzy controllers are established to improve the solvability of the stability conditions, and the authors design multigain fuzzy controllers which have extensive information of upper and lower membership grades. Consequently, the authors derive the traditional stability condition in terms of LMIs. One simulation examples illustrate the effectiveness and robustness of the derived stabilization conditions. Originality/value The uncertain MIMO nonlinear system described by Type-2 Takagi-Sugeno (T-S) fuzzy model, and successively LMI approach used to determine the system stability conditions. The proposed control approach will give superior fault-tolerant control permanence under the actuator fault [partial loss of effectiveness (LOE)]. Also the controller robust against the unmeasurable process disturbances. Additionally, the statistical z -test are carried out to validate the proposed control approach against the control approach proposed by Himanshukumar and Vipul (2019a).
Сoronavirus Zigzags of Electronic Distance Learning

The coronavirus pandemic in 2020, which has led to a massive and urgent transition to remote forms of learning, has drawn the attention of almost all of humanity to the issue of e-distance learning (EDL). The purpose of this article is to analyze the didactic aspects of the experience of using EDL methods and technological tools during a pandemic, to assess the potential positive and real negativity of this process, to develop recommendations on the rational use of modern virtual learning environments. The study is based on methods of systemic analysis, pedagogical psychology and didacticism, long-term experience of authors in the field of EDL.The analysis of two main didactic approaches in the modern EDL was carried out: classes in a virtual classroom, most of which copy traditional methods of classroom learning, and predominantly independent cognitive activities of students in learning management systems using specially trained digital educational resources. It has been shown that much of the negative aspects of EDL during the coronavirus pandemic are due to attempts to implement rudiments of the usual classroom learning system with the help of innovative electronic technologies.Recommendations have been made on the structure of the electronic distance course in modern learning management systems, which provide for the freedom to choose the time and place of study with them with the necessary regulation of the content and the learning process inherent in formal education institutions.A number of positive aspects of EDL related to its quality, ever-widening application boundaries, educational potential and economic feasibility have been considered. The importance of properly assessing possible negative aspects of EDL is shown. The reason for which is not in electronic tools, but in didacticly incorrect methods of training with them. To create new teaching methods focused on innovative electronic tools, we need research of psychological and didactic aspects of EDL in the areas of student motivation, digital educational resources development, gamification, teacher training, etc.
Effectiveness of the Augmented Reality on Improving the Visual Thinking in Mathematics and Academic Motivation for Middle School Students

This research aimed to identify the effectiveness of augmented reality technology on improving visual thinking in mathematics and academic motivation among middle school learners in Saudi Arabia. To accomplish the research objectives, the experimental method with a quasi-experimental design was adopted and it included an experimental group and a control group with the pre-and post-test of both groups. The research was implemented on a random sample of (76) students and the research tools included the Visual Thinking Test and Academic Motivation Scale. The tools were pre-and post-tested to the groups of the research in the second semester of the academic year 2019/2020AD. The results concluded that the experimental group surpassed the control group in visual thinking and academic motivation. Based on the results, both researchers recommended the involvement of augmented reality technology in mathematics instruction at different levels of education. They also recommend the inclusion of this technology in mathematics in overall, and Geometry in specific.
Violence behavior recognition of two-cascade temporal shift module with attention mechanism

Violence behavior recognition is an important research scenario in behavior recognition and has broad application prospects in the field of network information review and intelligent security. Inspired by the long-short-term memory network, we estimate that temporal shift module (TSM) may have more room for improvement in the feature extraction ability of long-term information. In order to verify the above conjecture, we explored based on TSM. After many attempts, it was finally proposed to connect the two TSMs in a cascaded manner, which can expand the receptive field of the model. In addition, an efficient channel attention module was introduced at the front end of the network, which strengthened the model's spatial feature extraction capabilities. At the same time due to behavior recognition prone to over-fitting, we extended and processed on the basis of some open-source datasets to form a larger violence dataset and solved the problem of over-fitting. The final experimental results show that the algorithm proposed can improve the model's feature extraction ability of violent behavior in the space and temporal dimension and realize the recognition of violent behavior, which verified the above point of view.
Formally Validating a Practical Verification Condition Generator

Abstract A program verifier produces reliable results only if both the logic used to justify the program’s correctness is sound, and the implementation of the program verifier is itself correct. Whereas it is common to formally prove soundness of the logic, the implementation of a verifier typically remains unverified. Bugs in verifier implementations may compromise the trustworthiness of successful verification results. Since program verifiers used in practice are complex, evolving software systems, it is generally not feasible to formally verify their implementation. In this paper, we present an alternative approach: we validate successful runs of the widely-used Boogie verifier by producing a certificate which proves correctness of the obtained verification result. Boogie performs a complex series of program translations before ultimately generating a verification condition whose validity should imply the correctness of the input program. We show how to certify three of Boogie’s core transformation phases: the elimination of cyclic control flow paths, the (SSA-like) replacement of assignments by assumptions using fresh variables (passification), and the final generation of verification conditions. Similar translations are employed by other verifiers. Our implementation produces certificates in Isabelle, based on a novel formalisation of the Boogie language.
Cycle Structure and Illumination Constrained GAN for Medical Image Enhancement

The non-uniform illumination or imbalanced intensity in medical images brings challenges for automated screening, examination and diagnosis of diseases. Previously, CycleGAN was proposed to transform input images into enhanced ones without paired images. However, it did not consider many local details of the structures, which are essential for medical images. In this paper, we propose a Cycle Structure and Illumination constrained GAN (CSI-GAN), for medical image enhancement. Inspired by CycleGAN based on the global constraints of the adversarial loss and cycle consistency, the proposed CSI-GAN treats low and high quality images as those in two domains and computes local structure and illumination constraints for learning both overall characteristics and local details. To evaluate the effectiveness of CSI-GAN, we have conducted experiments over two medical image datasets: corneal confocal microscopy (CCM) and endoscopic images. The experimental results show that our method yields better performance than both conventional methods and other deep learning based methods. As a complementary output, we will release the CCM dataset to the public in the future.
MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction

Neural relation extraction models have shown promising results in recent years; however, the model performance drops dramatically given only a few training samples.Recent works try leveraging the advance in few-shot learning to solve the low resource problem, where they train label-agnostic models to directly compare the semantic similarities among context sentences in the embedding space.However, the label-aware information, i.e., the relation label that contains the semantic knowledge of the relation itself, is often neglected for prediction.In this work, we propose a framework considering both label-agnostic and label-aware semantic mapping information for low resource relation extraction.We show that incorporating the above two types of mapping information in both pretraining and fine-tuning can significantly improve the model performance on low-resource relation extraction tasks.
How do I update my model? On the resilience of Predictive Process Monitoring models to change

Existing well-investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions and then use this model to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make Predictive Process Monitoring too rigid to deal with the variability of processes working in real environments that continuously evolve and/or exhibit new variant behaviours over time. As a solution to this problem, we evaluate the use of three different strategies that allow the periodic rediscovery or incremental construction of the predictive model so as to exploit new available data. The evaluation focuses on the performance of the new learned predictive models, in terms of accuracy and time, against the original one, and uses a number of real and synthetic datasets with and without explicit Concept Drift. The results provide an evidence of the potential of incremental learning algorithms for predicting process monitoring in real environments.
Online Handwriting, Signature and Touch Dynamics: Tasks and Potential Applications in the Field of Security and Health

Background: An advantageous property of behavioural signals ,e.g. handwriting, in contrast to morphological ones, such as iris, fingerprint, hand geometry, etc., is the possibility to ask a user for a very rich amount of different tasks. Methods: This article summarises recent findings and applications of different handwriting and drawing tasks in the field of security and health. More specifically, it is focused on on-line handwriting and hand-based interaction, i.e. signals that utilise a digitizing device (specific devoted or general-purpose tablet/smartphone) during the realization of the tasks. Such devices permit the acquisition of on-surface dynamics as well as in-air movements in time, thus providing complex and richer information when compared to the conventional pen and paper method. Conclusions: Although the scientific literature reports a wide range of tasks and applications, in this paper, we summarize only those providing competitive results (e.g. in terms of discrimination power) and having a significant impact in the field.
RobeCzech: Czech RoBERTa, a Monolingual Contextualized Language Representation Model

We present RobeCzech, a monolingual RoBERTa language representation model trained on Czech data. RoBERTa is a robustly optimized Transformer-based pretraining approach. We show that RobeCzech considerably outperforms equally-sized multilingual and Czech-trained contextualized language representation models, surpasses current state of the art in all five evaluated NLP tasks and reaches state-of-the-art results in four of them. The RobeCzech model is released publicly at https://hdl.handle.net/11234/1-3691 and https://huggingface.co/ufal/robeczech-base.
Optimal keyframe selection-based lossless video-watermarking technique using IGSA in LWT domain for copyright protection

Abstract Video piracy is a challenging issue in the modern world. Approximately $$90\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>90</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> of newly released films were illegally distributed around the world via the Internet. To overcome this issue, video watermarking is an effective process that integrates a logo in video frames as a watermark. Therefore, this paper presents an efficient lossless video-watermarking scheme based on optimal keyframe selection using an intelligent gravitational search algorithm in linear wavelet transform. This technique obtains color motion and motionless frames from the cover video by the histogram difference method. One-level linear wavelet transform is performed on the chrominance channel of motion frames and a low-frequency sub-band LL opts for watermark embedding. The performance of the proposed technique has been evaluated against 12 video processing attacks in terms of imperceptibility and robustness. Experiments demonstrate that the proposed technique outperforms five state-of-the-art schemes on the considered attacks.
Characterizing response quantity on academic social Q&amp;A sites: a multidiscipline comparison of linguistic characteristics of questions

Purpose As academic social Q&amp;A networking websites become more popular, scholars are increasingly using them to meet their information needs by asking academic questions. However, compared with other types of social media, scholars are less active on these sites, resulting in a lower response quantity for some questions. This paper explores the factors that help explain how to ask questions that generate more responses and examines the impact of different disciplines on response quantity. Design/methodology/approach The study examines 1,968 questions in five disciplines on the academic social Q&amp;A platform ResearchGate Q&amp;A and explores how the linguistic characteristics of these questions affect the number of responses. It uses a range of methods to statistically analyze the relationship between these linguistic characteristics and the number of responses, and conducts comparisons between disciplines. Findings The findings indicate that some linguistic characteristics, such as sadness, positive emotion and second-person pronouns, have a positive effect on response quantity; conversely, a high level of function words and first-person pronouns has a negative effect. However, the impacts of these linguistic characteristics vary across disciplines. Originality/value This study provides support for academic social Q&amp;A platforms to assist scholars in asking richer questions that are likely to generate more answers across disciplines, thereby promoting improved academic communication among scholars.