Reviewerly/exHarmony huggingface dataset:
ICE: Information coverage estimate for automatic evaluation abstractive summaries

Summaries are expected to relay the most amount of information in the least amount of words. Summary assessment tools such as Rouge, METEOR, and BLEU fail to explain the factual consistency of summaries with source documents and also disregard the synonymy between expressions. Information-coverage is a measure of the amount of important information retained in the summary. We propose ICE and ICE-T metrics that employ pre-trained word embeddings, Part-Of-Speech or POS-based keyword extraction, cosine-similarity, source length, and target length, for gauging the information-coverage in automatically shaped abstracts. We propose five POS-based keyword sampling techniques (NN, NN-VB, NN-JJ, NN-VB-JJ, and NN-VB-JJ-CD) for efficient retrieval of predominant information from the source text. Experiments show that: (1) ICE very strongly correlates with human-judgments w.r.t NN-VB-JJ-CD sampling (Pearson (0.989), Spearman rank (0.987) and Kendall rank (0.919)); and NN-VB-JJ sampling (Pearson (0.978), Spearman rank (0.979) and Kendall rank (0.899)) (2) ICE-T strongly correlates with human-judgments w.r.t NN-VB-JJ-CD sampling (Pearson (0.904), Spearman rank (0.846) and Kendall rank (0.702)); and NN-VB-JJ sampling (Pearson (0.877) and Spearman rank (0.832)). ICE and ICE-T are reliable information-coverage estimation tools as they equate eminently with human evaluations compared to Rouge (Rouge-1, Rouge-2, Rouge-L, Rouge-WE), and BLEU.
SELF-EDUCATIONAL ACTIVITY OF STUDENTS AS ONE OF THE CONDITIONS FOR THE DEVELOPMENT OF CREATIVE PERSONAL POTENTIAL

The article considers the independent work of students under the guidance of a teacher and the independent work of students as a way to a deeper and more conscious assimilation of educational material.
Non-parametric Semi-supervised Learning by Bayesian Label Distribution Propagation

Semi-supervised classification methods are specialized to use a very limited amount of labelled data for training and ultimately for assigning labels to the vast majority of unlabelled data. Label propagation is such a technique that assigns labels to those parts of unlabelled data that are in some sense close to labelled examples and then uses these predicted labels in turn to predict labels of more remote data. Here we propose to not propagate an immediate label decision to neighbors but to propagate the label probability distribution. This way we keep more information and take into account the remaining uncertainty of the classifier. We employ a Bayesian schema that is simpler and more straightforward than existing methods. As a consequence we avoid to propagate errors by decisions taken too early. A crisp decision can be derived from the propagated label distributions at will. We implement and test this strategy with a probabilistic k-nearest neighbor classifier, proving competitive with several state-of-the-art competitors in quality and more efficient in terms of computational resources.
A Weak Supervision Approach with Adversarial Training for Named Entity Recognition

Named entity recognition (NER) is a basic task of natural language processing (NLP), whose purpose is to identify named entities such as the names of persons, places, and organizations in the corpus. Utilizing neural networks for feature extraction, followed by conditional random field (CRF) layer decoding, is effective for the NER task. However, achieving reliable results using neural networks generally requires a large amount of labeled data and the acquisition of high-quality labeled data is costly. To obtain a better NER effect without labeled data, we propose a weak supervision approach with adversarial training (WSAT). WSAT obtains supervised information and domain knowledge through labeling functions, including external knowledge bases, heuristic functions, and generic entity recognition tools. The labeled results are aggregated through the linked hidden Markov model (linked HMM), and adversarial training strategies are added when using the aggregated results for training. We evaluate WSAT on two real-world datasets. When compared to rival algorithms, the F1 values are improved by approximately 2% and 1% on the MSRA and Resume NER datasets, respectively.
Compression of Deep Learning Models for NLP

In recent years, the fields of NLP and information retrieval have made tremendous progress thanks to deep learning models like RNNs and LSTMs, and Transformer [36] based models like BERT [9]. But these models are humongous in size. Real world applications however demand small model size, low response times and low computational power wattage. We will discuss six different types of methods (pruning, quantization, knowledge distillation, parameter sharing, matrix decomposition, and other Transformer based methods) for compression of such models to enable their deployment in real industry NLP projects. Given the critical need of building applications with efficient and small models, and the large amount of recently published work in this area, we believe that this tutorial is very timely. We will organize related work done by the ‘deep learning for NLP’ community in the past few years and present it as a coherent story.
Pedagogical Creativity as a Means of Inclusion in Primary School

This chapter presents some creative pedagogical strategies used during the distance or remote learning period due to the COVID-19 pandemic from March to May 2020. The chapter explores the use of coding in a transdisciplinary way. Strategies for online tools and their specific use both in remote and in face-to-face learning are presented. The role of hands-on learning as a process of learning-by-doing and how to involve pupils using the methods of a flipped classroom are also presented. The chapter concludes with the importance of games to keep the class group united and cohesive in order to develop a healthy sense of competitiveness and collaboration among the pupils.
Uniaxial microscale 3D surface shape measurement based on π phase-shifting method

In this paper, π phase-shifting method is proposed in the uniaxial microscopic 3D profilometry. Specifically, the π phase-shifting method uses two fringes with a phase difference of π , whose subtraction can effectively eliminate the background information and provide a more accurate modulation distribution. Compared with the ten-step phase-shifting method (PSM) and the Fourier transform method (FTM), with only one-fifth of the data acquisition of PSM, the proposed method (PM) can achieve almost the same measurement accuracy as that, but has higher measurement accuracy than FTM.
Geometric Transformation Image Registration Based on Discrete Feature Point Fitting

Image registration is the premise of image fusion, it is important to find a high-precision registration method to make the multi-modal medical images align between the coordinate points of the same particle in the space. Previous researchers studied multi-modal medical image registration, which is based on the three-dimensional contour points and the largest common area of the images. Although the final experimental results are acceptable, part of the original image data is lost. Instead of intercepting the maximum common region, all layers of the two modes are involved in the calculation of the registration algorithm in this paper. The feature points of each layer are extracted, and the feature axis is obtained by linear fitting. The rotation operator is constructed based on this characteristic axis, then the 3D models of the two modes are registered in space.
Comparison Of Classification Method On Lombok Songket Woven Fabric Based On Histogram Feature

Songket fabric has repeated geometric shapes in one type of fabric; some parts of the forming motif. Can distinguish motifs based on the shape of the motif, the density of the motif, the number of colors, and the position of the motif on the songket cloth. One area that is famous for its songket woven fabric is Lombok. Lombok people's awareness of the songket motif is still minimal, and the difference between one motif and another is still unknown. Previous research only used one algorithm. This study uses a method by applying four classification algorithms, where the image is enlarged and a comparison is made of each pixel size used. The results obtained from each image size produce the other highest accuracy values, where the highest product in the Naive Bayes algorithm is 90% with a pixel size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$100\times 100$</tex> . The size is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$300\times 300$</tex> pixels, the Naive Bayes algorithm for the highest accuracy value is 80%, while for the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$400\times 400$</tex> size the highest value is the Decision Tree algorithm at 90%. Where from this comparison, the algorithm with the highest accuracy value is Naive Bayes. The results given are expected to help and facilitate the community in recognizing songket motifs.
Biometrics: succeeding or falling short?

For years biometrics were touted as the solution to the challenge of user authentication – but until relatively recently, they were something that more people had heard about than actually used. Affordable commercial biometric devices have been around in some form since the 1990s, but it is only in the last 10 years that they have really become commonplace, thanks to their widespread adoption in mobile devices like smartphones and tablets 1 , and to a lesser extent laptop and desktop PCs.
Person Counting Based On Graph Relation Network Using UAV

Person counting at the construction site is of great significance to person scheduling and progress supervision. This work aims to develop a person counting framework, which is composed of three modules: person detection network, person re-identification module and person counting module. In order to solve the problem of repeated counting caused by Unmanned Aerial Vehicles (UAVs) taking multiple shots of the same person, we propose a Graph Similarity-based Person Counting Network (GSPCN), which can re-identify persons and avoid counting the same person multiple times. We tested the proposed framework both on simulation environment and real datasets (our data was collected from multiple construction sites in Shenzhen), It is empirically superior to the most advanced methods available.
THE PRINCIPLE OF EQUIVALENCE OF ENERGY SPENT ON PROCESS AND OF STEP OF TIME

It was formulated the physical principle of the equivalence of the energy expense during the process of the Universe spreading and of the step of time. It was also shown that physical time is a material quantity connected with expending our Universe at constant velocity. A parallel was carried out between the increase of the size of the drops and bubbles and the expending of the Universe. The above principle is in full agreement with SR and GTR. It was shown as well that physical time could quantize.
A Novel Framework of CNN for Image Super-Resolution Based on Attention Module

Because the convolutional neural network only captures the inherent size feature of a single image in the research of image super-resolution process, a framework based on the attention module and multi-dimension feature merge is proposed. Using the attention module, the network can validly conform non-local information, thus improving the network's feature expression ability. Meanwhile, the convolution kernels of different dimensions are used to extract the multi-dimension intelligence of the image to maintain the intact information of distinguishing feature under the different scales. Experimental results demonstrate that this method is advantageous than some super-resolution reconstruction alagorithms in objective quantitative indicators.
Off-Line Recognition System for Handwritten Arabic Words Using Artificial Intelligence

Arabic alphabets are used in more than 25 languages such as Arabic, Persian, Kurdish, Urdu etc. In this research work it is planned to build a computer system for recognizing handwritten Arabic words. We used sequence vector technique to recognize the Arabic words. Multi-layer Networks structure and Back-propagation Training are used as tools to decide. Also, for special cases of handling identicalness vectors, special feature extraction technique is applied. Experiments were performed by writing code in MATLAB, which achieved average accuracy of more than 92%.
Design and implementation of aspect Based Sentiment Analysis of Hotel Reviews

Over the past 2 years, in India, a significant rise in the number of people having access to the internet has been observed. Owing to its extensive fame, there is a huge quantity of user reviews or thoughts produced and shared every day. In this project hotel and product reviews will be collected, pre-processed, scrutinized and visualized using open-source tools to achieve text mining and sentiment analysis for examining user contributed online reviews. Collecting customer opinions manually using conventional methods such as surveys can be expensive, cumbersome and time consuming and a lot of constraints will come into picture. Which is why it is used text mining from online websites. The sentiment analysis of customer opinions will assist industries in appreciating their competitive worth in a changing market and understanding their clients' opinions about their products and services, which will also provide a vision into future promotion strategies and real-time decision making policies.
Image Decomposition for Helping Diagnosis of Covid-19 Lung Injury

The importance of biomedical image processing operations increases day by day. Different methods are used for biomedical image and signal applications. Image segmentation, wavelet transformation, filtering, and classification form a fundamental way of solution in many applications of biomedical image processing. Wavelet is a type of frequency analysis technique. Decomposition and reconstruction are two of the significant wavelet operations. The paper is devoted to the segmentation of Covid-19 patients' X-ray images by using wavelet decomposition and reconstruction with morphological operations.
Deep Neural Network to Detect Gender Violence on Mexican Tweets

During COVID-19 quarantine, in online sites such as social networks, Gender-Based Violence has alarmingly increased. Online platforms have taken various measures to regulate and prevent broadcasting of violence messages. Multiple proposals based on machine learning and deep learning approaches have been used to address this problem. This work presents an improvement in implementation of a deep learning neural network for detection of Gender-Based Violence in Twitter messages. A total of 32,500 tweets were downloaded from Mexican Twitter accounts and human volunteers manually tagged the tweets as violent and non-violent to be used as training and testing data sets. Experimental results show the effectiveness of the deep neural network (about 90% of the Area Under the Receiver Operating Characteristic) to detect gender violence in Twitter messages using a simple Natural Language Processing approach.
Offline or Online?: How Should Biology Be Taught in a Flexible Learning Modality in the Philippines

How should Biology be taught during a pandemic in Higher Education Institutions? This study explored on the instructional delivery preferences of pre-service science major students in a public higher education in the Philippines, and identified its correlates to gender, year level, and post-secondary strand type, using validated instruments. Salient findings showed that majority of the respondents prefer the delivery instruction delivery of Cell and Molecular Biology course in a partially-online/offline mode throughout the semester, a scheme which they believe when implemented will enable 100% of them to pass the course. If partial scheme is not an option, more respondents prefer the instructional delivery of CeMBio as offline, but ironically, there are more of them who believe they have higher chances of passing the subject if it is delivered online. Among the variables studied for correlates, gender did not appear to influence the delivery preferences of the respondents. Further, a significant difference is reported in terms of year level and strand type, along selected chapter segments of the CeMBio course only, with year level appearing to influence preferences at the first few chapters of the course and strand type in the middle topics of the course. Gender, year level, and strand type did not significantly differ along the last few chapter topics of the CeMBio syllabus content. The delivery of CemBio in a partial-offline-online modality is recommended, not only because of the respondents’ 100%-passing belief, but because this scheme will reduce instructional loads among the teachers, allowing flexibility in instruction, research, extension, and production activities of the faculty. Finally, should the option to decide which topics are offline or online, the researchers recommend that topics without significant differences reported, will be delivered offline while those with reported significance will be delivered online with the hope that the teacher could possibly address during instruction the cause of these differences.
Semantic-Aware Deep Neural Attention Network for Machine Translation Detection

Web crawling is an important way to collect a massive training corpus for building a high-quality machine translation system. However, a large amount of data collected comes from machine-translated texts rather than native speakers or professional translators, severely reducing the benefit of data scale. Traditional machine translation detection methods generally require human-crafted feature engineering and are difficult to distinguish the fine-grained semantic difference between real text and pseudo text from a modern neural machine translation system. To address this problem, we propose two semantic-aware models based on the deep neural network to automatically learn semantic features of text for monolingual scenarios and bilingual scenarios, respectively. Specifically, our models incorporate the global semantic from BERT and the local semantic from convolutional neural network together for monolingual detection and further explores the semantic consistency relationship for bilingual detection. The experimental results on the Chinese-English machine translation detection task show that our models achieve 83.12% \(F_{1}\) in the monolingual detection and 85.53% \(F_{1}\) in the bilingual detection respectively, which is better than the strong BERT baselines by 2.2–3.2%.
Robust Symmetric Encryption for Public Key Infrastructure

Mattoo, Anubhav Samleti, Nikhil Aher, Puja Patil, Shubham Jarali, AshwiniIn every encryption suite, symmetric ciphers are an essential tool. They provide safe data transmission with minimal processing effort for both encryption and decryption. The randomness supplied by a decent cipher makes it impractical to reverse by brute force attacks. This paper describes a method of using Double-Ratchet algorithm, error-correcting codes and existing symmetric encryption algorithms to form a robust symmetric cipher. This can be used to verify transferred data, detect modifications in the data and correct modifications done to the data to a certain tolerance level, whereby improving the robustness of a cipher suite in case of modification or mutilation (such as minor bit-flip attacks and insertion or deletion errors). Applications of these ciphers are in the field of public key infrastructure.
Quantum and Classical Hybrid Generations for Classical Correlations

We consider two-stage hybrid protocols that combine quantum resources and classical resources to generate classical correlations shared by two separated players. Our motivation is twofold. First, in the near future, the scale of quantum information processing is quite limited, and when quantum resource available is not sufficient for certain tasks, a possible way to strengthen the capability of quantum schemes is introducing extra classical resources. We analyze the mathematical structures of these hybrid protocols, and characterize the relation between the amount of quantum resources and classical resources needed. Second, a fundamental open problem in communication complexity theory is to describe the advantage of sharing prior quantum entanglement over sharing prior randomness, which is still widely open. It turns out that our quantum and classical hybrid protocols provide new insight into this important problem.
SI-RCNN: A Shape-Invariant Set-Abstraction for 3D Object detection

Recent years have witnessed more attention being paid to 3D object detection, due to its indispensable roles in autonomous driving and intelligent transportation systems. The accuracy of 3D object detection heavily depends on sufficient and robust feature extractors. However, most feature extractors adopt fixed perceptive fields or fixed spatial locations, which could not efficiently model geometric variations or geometric transformations in object scale, pose, and part deformation. To fill this gap, this paper proposes a novel Shape-Invariant Set-Abstraction network (SI-RCNN) for 3D object detection in point clouds, which can extract shape-adaptive features. Specifically, we first propose an Offsets Generation Module to learn a set of offsets, where the shape of the offsets can adapt to the changes of features. Then, trilinear interpolation is applied to assign features to each offset, since it might be empty due to discrete points in the cloud data. Finally, we further exploit the ground-truth box to supervise the offsets. Extensive experiments demonstrate the effectiveness of our proposed SI-RCNN on the competitive KITTI 3D detection benchmark, as compared to several state-of-the-art methods.
BlockForest: A Distributed Tree Structure

Blockchain has broadened its boundaries from being a cryptocurrency application to a technology that comprises of varied domains and fields under its hood. However, applications are demanding quick services, faster processing, prompt verification and with the increase in data size associated with blockchain, the overall processes are bound to become slow till the point its speed feels arrested. The blockchain obligates the nodes to store the entire data onto their systems, with such replication, only increasing the scalability issues of the system. To address these issues we introduce a concept of BlockForest, which is based on the tree structure instead of the linked-list (chain) structure. BlockForest would be capable of handling large amounts of data and address the scalability issues without any compromise in its speed and overall efficiency.
Automatic Facial Expression Detection Using Genetic Algorithm with SVM

The discovery of photo designs within a set of photos is known as face mining. Vision, image management, knowledge mining, artificial intelligence, AI, database, and reasoning for people are all used in this operation, including information computers (PC) (personal computer). Facial recognition deconstructs and takes account of examples from facial photographs. Extracting facial features is identifying by a computer programme of highlights on human faces, such as brows ,lip and also eye. Here analysis of PCA results, GMM,GLCM and SVM (Support Vector Machines), in which the outward appearances of seven different persons are found in a database, including rage, sadness, happiness, disgust, neutrality, fear and surprise. The following are described in this paper: The purpose of this debate is to address the most successful facial recognition systems. The current research demonstrates the feasibility of awareness of physical appearance monitoring and human-computer communication.