Reviewerly/exHarmony huggingface dataset:
Model of Development of Teacher’s Readiness to Work for Inclusive Education

The study was conducted to create a model of development of teacher's readiness to work for inclusive education in general secondary education.This model consists of four blocks: 1) a conceptual one (defines methodological approaches and principles); 2) a semantic one (provides a set of knowledge, skills and competencies that are acquired, developed and mastered in higher education, as well as in non-formal and informal education; it provides development of a system of methodological, theoretical, practical and technological knowledge, along with design, gnostic, organizational, communicative, control and assesment skills); 3) a procedural one (is considered to be a pedagogical system which includes diagnostic, motivational, activating and analytical stages, along with a set of forms, methods, technologies and teaching aids); 4) an effective one (determines the success of the model on the basis of scientifically sound criteria and corresponding indicators of teacher's readiness to work for inclusive education).The effectiveness of this model has been tested on the basis of the center for postgraduate education and pre-university training at Drohobych Ivan Franko State Pedagogical University.The data of a final section of a quantitative distribution of the teachers (in %) by the levels of their readiness to work for inclusive education according to the criteria of motivational, valuable, cognitive, activating, emotional, volitional and reflexive components confirm the effectiveness of this model.
The impact of transformational leadership on individual academy performance through knowledge sharing

The objective of this paper is to evaluate the impact of transformational leadership on individual academy performance through knowledge sharing, organizational learning, organizational commitment in higher education Vietnam. The study conducts the research on 500 lecturers at 10 universities in Vietnam. The study uses Smart pls 3.6 software to analyze the data. The results show that transformational leadership had a positive effect on knowledge sharing, organizational learning and organizational commitment. Ultimately, employee engagement and social support play a moderate role in the relationship between transformational leadership and knowledge sharing statistically. However, organizational learning and organizational commitment did not play any mediate role on the relationship between transformational leadership and knowledge sharing.
Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using Genetic Network Programming Algorithms

This paper presents a filter generating method that modifies sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. For our earlier study, we developed a prototype that incorporates bed-leaving detection sensors using piezoelectric films and a machine-learning-based behavior recognition method using counter-propagation networks (CPNs). Our method learns topology and relations between input features and teaching signals. Nevertheless, CPNs have been insufficient to address individual differences in parameters such as weight and height used for bed-learning behavior recognition. For this study, we actualize automatic calibration of sensor signals for invariance relative to these body parameters. This paper presents two experimentally obtained results from our earlier study. They were obtained using low-accuracy sensor signals. For the preliminary experiment, we optimized the original sensor signals to approximate high-accuracy ideal sensor signals using generated filters. We used fitness to assess differences between the original signal patterns and ideal signal patterns. For application experiments, we used fitness calculated from the recognition accuracy obtained using CPNs. The experimentally obtained results reveal that our method improved the mean accuracies for three datasets.
Automatic White Blood Cell Classification Using the Combination of Convolution Neural Network and Support Vector Machine

Leukocyte tests can help diagnose several diseases related to blood disorders such as acute leukemia, chronic myeloid leukemia. Manual blood cell classification is commonly used in clinics and hospitals which, although is accurate, is not time-effective and affects clinical workflow. Automated cell classification systems might assist clinicians to increase the efficiency and accuracy of diagnosis. However, most of the published papers classify only non-pathological cells while applying a complex segmentation process. To overcome this problem, we proposed Convolutional Neural Network and Support Vector Machine (CNN-SVM) where Convolutional Neural Network model is used to extract features directly from the images. The obtained features will then be used by a following Support Vector Machine for classifying white blood cells into 5 different types include eosinophil, lymphocyte, monocyte, neutrophil, and pathological white blood cell (leukemia). Three different CNN models including Alexnet, Resnet-101, VGG-19 were tested on 15764 enhanced images to pick the best CNN model for extracting the features. Resnet-101 was chosen for its best average accuracy (97.8%) when combining with SVM to classify 5 white blood cell types. This average accuracy was also the second-best compared to other published methods from 2016 up to now.
Recommender Systems for the Social Networking Context for Collaborative Filtering and Content-Based Approaches

In helping internet users to locate useful information by recommending possible interesting information, Recommender Systems play an important role. Due to the perceived significance of social interactions in proposed systems, in recent years social guidelines have gained increasing popularity. There has been a substantial increase in the number of network sciences and a huge increase in the number of social networking sites, which can have a direct effect on different aspects of everyday life. Since the advent of the World Wide Web, Recommender Systems have been a further fast-growing market, which also has a proven financial significance because a well-focused online proposal often results in actual purchases. While at the outset, both the websites and the Recommender Networks had very different routes, as well as the groups of researchers working with it, in a variety of real-world networks, almost immediate synergies, have recently started. This is just the beginning, however: there is still a range of potentially valuable synergies to explore. This chapter describes the concept of the recommended systems in the social networking context and addresses both traditional Recommendation Systems and the new Recommendation Systems, mostly through collaborative filtering and content-based approaches. They also describe a thorough analysis of the need to implement the social networking structure, the problems and challenges of the Recommender Systems, and the creation of capacity-building systems for social recommendations.
An $L^2$ Analysis of Reinforcement Learning in High Dimensions with Kernel and Neural Network Approximation

Reinforcement learning (RL) algorithms based on high-dimensional function approximation have achieved tremendous empirical success in large-scale problems with an enormous number of states. However, most analysis of such algorithms gives rise to error bounds that involve either the number of states or the number of features. This paper considers the situation where the function approximation is made either using the kernel method or the two-layer neural network model, in the context of a fitted Q-iteration algorithm with explicit regularization. We establish an $\tilde{O}(H^3|\mathcal {A}|^{\frac14}n^{-\frac14})$ bound for the optimal policy with $Hn$ samples, where $H$ is the length of each episode and $|\mathcal {A}|$ is the size of action space. Our analysis hinges on analyzing the $L^2$ error of the approximated Q-function using $n$ data points. Even though this result still requires a finite-sized action space, the error bound is independent of the dimensionality of the state space.
Application of exploration geochemistry data to identify anomalies in the plateau region: a case study from the Xiongcun district in the Gangdese metallogenic belt, Tibet, China

This paper describes the discovery of porphyry Cu–Au ore bodies (No. 2 and No. 3) in Xiongcun, Tibet, China, through the investigation of stream sediments, soils and rocks. The study area (Xiongcun), located in the city of Xigaze, has a complex topography due to its complicated geological background. The Xiongcun No. 1 ore body will soon be mined, 15 years after its discovery. However, recent research on stream sediments, soils and rocks around the No. 1 ore body has revealed notable Cu anomalies on the periphery. With the successful application of exploratory geochemistry data in the anomaly locations, the No. 2 and No. 3 ore bodies have been revealed. The ore reserves of the No. 2 and No. 3 ore bodies are estimated to reach 1.64 million tons of Cu, more than 80 tons of Au and 200 tons of Ag. We expect that our findings will not only enhance the understanding of the reanalysis of anomalies surrounding the discovered deposits but also contribute to the evaluation of the ore-prospecting potential of this plateau region.
Dimension-Preserving Reductions Between SVP and CVP in Different <i>p</i>-Norms

We show a number of reductions between the Shortest Vector Problem and the Closest Vector Problem over lattices in different ℓp norms (SVPp and CVPp respectively). Specifically, we present the following 2∊m-time reductions for 1 ≤ p ≤ q ≤ ∞, which all increase the rank n and dimension m of the input lattice by at most one:•a reduction from Õ(1/∊1/p)γ-approximate SVPq to γ-approximate SVPp;•a reduction from Õ(1/∊1/p)γ-approximate CVPp to γ-approximate CVPq; and•a reduction from Õ(1/∊1+1/p)-CVPq to (1 + ∊)-unique SVPp (which in turn trivially reduces to (1 + ∊)-approximate SVPp).The last reduction is interesting even in the case p = q. In particular, this special case subsumes much prior work adapting 2O(m)-time SVPp algorithms to solve O(1)-approximate CVPp. In fact, we show a stronger result in the special case when 1 ≤ p = q ≤ 2 and the SVPp oracle is exact: a reduction from O(1/∊1/p)-CVPp to (exact) SVPp in 2∊m time. For example, taking ∊ = log m/m and p = 2 gives a slight improvement over Kannan's celebrated polynomial-time reduction from to SVP2. We also note that the last two reductions can be combined to give a reduction from approximate-CVPp to SVPq for any p and q, regardless of whether p ≤ q or p > q.Our techniques combine those from the recent breakthrough work of Eisenbrand and Venzin [21] (which showed how to adapt the current fastest known algorithm for these problems in the ℓ2 norm to all ℓp norms) together with sparsification-based techniques.
Leveraging knowledge sources for detecting self-reports of particular health issues on social media

This paper investigates incorporating quality knowledge sources developed by experts for the medical domain as well as syntactic information for classification of tweets into four different health oriented categories. We claim that resources such as the MeSH hierarchy and currently available parse information are effective extensions of moderately sized training datasets for various fine-grained tweet classification tasks of self-reported health issues.
A Reversible Data Hiding Scheme through Encryption using Rotated Stream Cipher

The research in the domain of reversible data hiding got much attention in recent years due to its wide applications in medical image transmission and cloud computing. Reversible data hiding during image encryption is a recently emerged framework for hiding secret data into an image during the image encryption process. In this manuscript, we propose a new reversible data hiding through encryption scheme which will ensure a high embedding rate without bringing any additional overhead of key handling. The proposed algorithm can use any secure symmetric encryption scheme, and the encryption and/or decryption key should be shared with the receiver for data extraction and image recovery. As per the proposed scheme, the data hider can hide three-bits of secret message in an image block of size $B\times B$ pixels. The data extraction image recovery will be carried out by analyzing the closeness between adjacent pixels. The simulation of the new scheme on the USC-SIPI dataset shows that the proposed scheme outperforms the well-known existing schemes in embedding rate and bit error rate.
A novel image quality assessment method and coefficient of quality for digital solutions of colour blindness

Eyesight is one of the primary senses that human beings have. Reports show that colour blindness, a form of colour vision deficiency (CVD), affects about 8% of the male population and 0.5% of female population. The Assistive Technology Act of 2004 lays focus on technologies that help individuals with disabilities and deficiencies. With the rapid advancement in technologies, several assistive solutions are available for visually impaired or CVD patients. Such solutions involve simulation and compensation of conflicting colours to help the colour blind in the visual perception of colours. Given the increased usage of the web, post the pandemic, these solutions improve the quality of life for the colour blind. Defining the image quality assessment criteria for such digital solutions becomes imperative. The study proposes a novel method for image quality assessment of digital solutions aimed at assisting the colour blind users. The proposed coefficient of quality (CQ) would be useful to rank colour compensation and recolouring algorithms. Experiments were conducted with a novel questionnaire set designed for this quality measurement. The results affirm the efficiency of the assessment method proposed. This will also provide objective feedback to the researchers and experts in this area to improve their solutions for CVD patients.
Genetic Algorithm and Fisher Discriminant Analysis based Wavelet Thresholding for Speckle Noise Filtering in Ultrasound Images

Speckle noise is a significant property of medical ultrasound imaging, and it typically degrades the resolution and contrast of images, sinking the diagnostic importance of the imaging modality.As a consequence, filtering speckle noise in the ultrasound images is a critical step for further analysis by the medical experts.In this paper, a speckle noise filtering technique have been suggested via wavelet thresholding for denosing ultrasound images.For each wavelet coefficient, in the first step, two optimal threshold parameters are estimated through the genetic algorithm and fisher discriminant analysis respectively.In the second step, thresholding of wavelet coefficient is performed by both threshold parameters.Finally, thresholded coefficient which corresponds to lowest mean square error is selected for obtaining the denoised ultrasound image.Results show that, the proposed technique outperforms different existing denoising techniques.
PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation

Cross-lingual transfer is a leading technique for parsing low-resource languages in the absence of explicit supervision.Simple 'direct transfer' of a learned model based on a multilingual input encoding has provided a strong benchmark.This paper presents a method for unsupervised cross-lingual transfer that improves over direct transfer systems by using their output as implicit supervision as part of self-training on unlabelled text in the target language.The method assumes minimal resources and provides maximal flexibility by (a) accepting any pre-trained arc-factored dependency parser; (b) assuming no access to source language data; (c) supporting both projective and non-projective parsing; and (d) supporting multi-source transfer.With English as the source language, we show significant improvements over state-of-the-art transfer models on both distant and nearby languages, despite our conceptually simpler approach.We provide analyses of the choice of source languages for multi-source transfer, and the advantage of non-projective parsing.Our code is available online.
A Comprehensive Survey on Movie Recommendation Systems

Internet technology has occupied an important part of human lives. Users often face the problem of the available excessive information. Recommandation system (RS) are deployed to help users cope up with the information explosion. RS is mostly used in digital entertainment, such as Netflix, prime video, and IMDB, and e-commerce portals such as Amazon, Flipkart, and eBay. The two traditional methods namely, collaborative filtering (CF) and content-based approaches consist of few limitations individually. However, any hybrid system, which utilizes the advantage of both the systems to leverage better results. Some fundamental issues faced by movie recommendation systems such as scalability, cold start problem, data sparsity and practical usage feedback and verification based on real implementation are still neglected. Other issues that require significant research attention are accuracy and time complexity problem, which could make RS, a bad candidate for real-world recommendation systems. This literature survey aims to consolidate and structurally categorize all the major drawbacks present in the most common and popular commercial movie recommendation systems.
Image Deblurring Algorithm Using Block Augmented Lagrangian with Low Rank Gradients

The proposed paper focuses on using Enhanced Augmented Lagrangian for image deblurring with some additional performance-enhancing parameters. In recent literature, high performance and several benefits have been demonstrated by restoring blurred images using Augmented Lagrangian methods. This paper proposes BLOCK AUGMENTED LAGRANGIAN WITH LOW RANK GRADIENTS (BALLORG) which is novel in the following ways: (i) faster convergence which leads to faster execution times. For most images the minimum is achieved with as little as 20 iterations, (ii) the use of derivatives and low rank together as regularization priors. The BALLORG begins with the lowest rank matrix, which is also the sparsest matrix available, and the final deblurred result is very successful in achieving good dB improvements through rank regulation; (iii) The penalty and regularization weights ensure that each iteration hits a global minimum with steep descent. The simulation experiments demonstrate how well the proposed BALLORG works when opposed to the other state-of-the-art approaches.
Dynamic Graph Transformer for Implicit Tag Recognition

Textual information extraction is a typical research topic in the NLP community.Several NLP tasks such as named entity recognition and relation extraction between entities have been well-studied in previous work.However, few works pay their attention to the implicit information.For example, a financial news article mentioned "Apple Inc." may be also related to Samsung, even though Samsung is not explicitly mentioned in this article.This work presents a novel dynamic graph transformer that distills the textual information and the entity relations on the fly.Experimental results confirm the effectiveness of our approach to implicit tag recognition.
Improved privacy preserving method for periodical SRS publishing

Spontaneous reporting systems (SRSs) are used to collect adverse drug events (ADEs) for their evaluation and analysis. Periodical SRS data publication gives rise to a problem where sensitive, private data can be discovered through various attacks. The existing SRS data publishing methods are vulnerable to Medicine Discontinuation Attack(MD-attack) and Substantial symptoms-attack(SS-attack). To remedy this problem, an improved periodical SRS data publishing-PPMS(k, θ, ɑ)-bounding is proposed. This new method can recognize MD-attack by ensuring that each equivalence group contains at least k new medicine discontinuation records. The SS-attack can be thwarted using a heuristic algorithm. Theoretical analysis indicates that PPMS(k, θ, ɑ)-bounding can thwart the above-mentioned attacks. The experimental results also demonstrate that PPMS(k, θ, ɑ)-bounding can provide much better protection for privacy than the existing method and the new method dose not increase the information loss. PPMS(k, θ, ɑ)-bounding can improve the privacy, guaranteeing the information usability of the released tables.
Denoising of Video Frames Resulting From Video Interface Leakage Using Deep Learning for Efficient Optical Character Recognition

The present work shows Deep Neural Networks' application in the automatic recovery of information from unintended electromagnetic emanations emitted by video interfaces. A dataset of 18,194 captured frames is generated, which allows training two Convolutional Neural Networks for the denoising of captured video frames. After processing the noisy frames with the CNNs, a significant improvement is measured in the Peak Signal to Noise Ratio (PSNR). Consequently, text can be automatically extracted using Optical Character Recognition (OCR), allowing us to recover 68% of the text from our validation dataset. The proposed approach aims at evaluating the risk introduced by modern Deep Learning algorithms when applied to these captures, showing that compromising electromagnetic leakage represents a non-negligible threat to information security.
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic

We describe our submitted system to the 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic (Abu Farha et al., 2021). We tackled both subtasks, namely Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). We used state-of-the-art pretrained contextualized text representation models and fine-tuned them according to the downstream task in hand. As a first approach, we used Google’s multilingual BERT and then other Arabic variants: AraBERT, ARBERT and MARBERT. The results found show that MARBERT outperforms all of the previously mentioned models overall, either on Subtask 1 or Subtask 2.
Emoji-Based Transfer Learning for Sentiment Tasks

Sentiment tasks such as hate speech detection and sentiment analysis, especially when performed on languages other than English, are often low-resource.In this study, we exploit the emotional information encoded in emojis to enhance the performance on a variety of sentiment tasks.This is done using a transfer learning approach, where the parameters learned by an emoji-based source task are transferred to a sentiment target task.We analyse the efficacy of the transfer under three conditions, i.e. i) the emoji content and ii) label distribution of the target task as well as iii) the difference between monolingually and multilingually learned source tasks.We find i.a. that the transfer is most beneficial if the target task is balanced with high emoji content.Monolingually learned source tasks have the benefit of taking into account the culturally specific use of emojis and gain up to F1 +0.280 over the baseline.