Reviewerly/exHarmony huggingface dataset:
BULLYING IN SCHOOLS: DOES EMPATHY HELP

Bullying is a universal problem, affecting students in schools all around the world. It makes life miserable for those who are bullied, and some victims have been so upset that they committed suicide. Bullying is an act of intentional harm, carried out repeatedly. It can take the form of physical, verbal, social or emotional and cyber bullying. The bullies are aggressive in nature and the emotion they harbor is anger. Their home environment is one of conflict, and lack of warmth. The victims of bullies are sensitive and lack social and problem-solving skills and easily fall prey to the bully. They are anxious, depressive and lonely. As a result of bullying, their school attendance falls, and academic results are low. To tackle the bullying problem in schools, many programs have been introduced which have reported to increase empathy in students, and consequently, reducing bullying. This article emphasizes the importance of bullying prevention programs in schools with empathy as a core component, because it is impossible to eradicate bullying by enforcing policies and rules only. The solution of bullying is to develop empathy which will help students to appreciate differences, and to develop a sense of community in students. Keywords: Intervention Program, Reducing Bullying, Schoolchildren and Empathy
Chapter Six. Open Science

Abstract The success of GenBank, and more generally of any community database, hinged on finding a solution to the problem of data acquisition. A few researchers, such as molecular biologists Richard J. Roberts, went on a campaign to convince scientific journal editors to help databases collect data from researchers. By the end of the 1990s, a number of leading journals had, often reluctantly, adopted some form of mandatory data submission policy, which tied the publication of an article to sharing data with a database. As the size of databases grew exponentially, computer scientists developed new algorithms to analyze sequence data, even on small personal computers. But tensions grew, between researchers determining sequences and bioinformaticians analyzing them, over who should own the intellectual credit for a resulting discovery. These tensions became particularly strong in the context of the Human Genome Project and remain unsolved today. The impact of the sequence database goes beyond the issue of access to scientific data, since it served as a general model for promoting open access to published scientific knowledge.
Legislation on Access to Information: The Perspective of Azad Jammu and Kashmir Right to Information Act

Information encourages active participation of public in affairs of state. Keeping in view the importance of Right to Information (RTI), the United Nations (UN) has termed it as "touchstone of all the freedoms to which the UN is consecrated". The government introduced 13th Constitutional amendment in 2018 by which RTI was incorporated in Azad Jammu and Kashmir Interim Constitution, 1974. The fact that RTI has been explicitly protected by the constitution can't be undermined however this constitutional provision needs assistance by an ordinary legislation. Because, only an Act can prescribe the scope of right,responsibility of officials, kinds of information to be made available proactively, procedure to be followed to request and the mechanism to grant remedies in case of denial or unsatisfactory response to requested information etc. To resolve the issue of lack of RTI Act in AJK the study shows that there is no legal or constitutional barrier for the enactment of RTI Act, besides it is an immense need in the territory. The study foresees RTI Act as significant mean to bring democratic reforms and to bridge the gap among the individuals and the government.
MultPAX: Keyphrase Extraction Using Language Models and Knowledge Graphs

Keyphrase extraction aims to identify a small set of phrases that best describe the content of text. The automatic generation of keyphrases has become essential for many natural language applications such as text categorization, indexing, and summarization. In this paper, we propose MultPAX, a multitask framework for extracting present and absent keyphrases using pre-trained language models and knowledge graphs. In particular, our framework contains three components: first, MultPAX identifies present keyphrases from an input document. Then, MultPAX links with external knowledge graphs to get more relevant phrases. Finally, MultPAX ranks the extracted phrases based on their semantic relatedness to the input document and return top-k phrases as a final output. We conducted several experiments on four benchmark datasets to evaluate the performance of MultPAX against different state-of-the-art baselines. The evaluation results demonstrate that our approach significantly outperforms the state-of-the-art baselines, with a significance t-test $$p < 0.041$$ . Our source code and datasets are public available at https://github.com/dice-group/MultPAX .
Chapter 2: Methods Using First Derivatives

This chapter explores the worst-case complexity of obtaining an ϵ-approximate first-order minimizer of a nonconvex, unconstrained optimization problem, that is, a point at which the norm of the gradient of the objective function is below a specified threshold ϵ. Naturally this presupposes that the gradient exists, but, as we see first, this alone is not strong enough to derive useful complexity results. Consequently, we strengthen our assumptions to require that the gradient be Lip-schitz continuous. This then enables us to investigate the worst-case complexity of three well-known classes of first-order algorithms: linesearch methods, trust-region schemes, and adaptive-regularization algorithms. We show that to obtain an ϵ-approximate first-order minimizer with first-order algorithms, that is, algorithms that only use oracles of degree at most one, requires at most O(ϵ−2) evaluations of the objective function and its gradient, irrespective of the class of algorithms considered. Moreover, we show that such a bound is sharp for functions that do not have differentiable gradients.
Environment risk assessment and issues with hydrocarbon and geotoxicological factors

Our scientists recognize a disaster ecosystem as a result of changes in The geochemical composition of the Earth's biosphere. The paper aims to figure out What are issues of hydrocarbon and Geotoxicology and environment risk. By using descriptive method for primary model, synthesis methods and process analysis and analysis of difficulties and discussion, The study of this problem to some extent facilitates the extensive, although scattered and extremely heterogeneous analytical material in terms of the content of impurity elements in crude oil, refined products and gases accumulated over the years of exploration, production and disposal of hydrocarbons.
The Equivalence in The Translation of English Idiomatic Expression into Indonesian by Students of English Education Department University of Mataram

The aims of this study are: 1) to find out frequently used types of equivalence by the seventh semester students of English Education Department at University of Mataram in translating English idiomatic expression into Indonesia according to Nida’s equivalence theory, and 2) to find out the problem faced by the English students in the translating of the idiomatic expression of English into Indonesia. This study used mixed-method. The participant of this study were from the seventh semester English students of University of Mataram. To collect the data, this study used two types of instruments: translation test and questionnaires. The research findings showed that among 240 datas of students translation result, 73,64% of the datas categorized as dynamic equivalence and only 5,85% of the datas categorized as formal equivalence. Furthermore, based on close-ended and open-ended questionnaires answers about students dificulty in the translating of the idiomatic expressions, it reveals that students faced difficulty in translating process for the cultural background diferences, minim exposure to idioms, and how the idioms were used. three strategies were available to students to use in encountering the difficulties in translating English idioms into Indonesian: 1) giving their selves more familiar to idioms through reading and using various medias, 2) paying more attention to the context which the idioms being served, 3) and looking for the meaning in dictionary or google. It could be infered that the most frequently used types of equivalence by the students was dynamic equivalence and the students still faced some problem in translating English idiomatic expression into Indonesia.Keywords: idiomatic expression; translation equivalence; difficulties
Improving Virus Colony Search Performance on Travelling Salesman Problem Case

The Virus Colony Search algorithm is a nature-inspired algorithm, simple yet effective enough to solve optimization problems. The algorithm has three main parts, Viruses Diffusion, Host Infection, and Immune Response. The Viruses Diffusion's part uses the Gaussian Random Walk as the main component to calculate. In this paper, it is using the implementation of the Global Random Walk in the Viruses Diffusion part and also to prove that The Virus Colony Search Algorithm is able to solve the Travelling Salesman Problem, in this case by using the Symmetric-TSP. The conducted experiments are divided into two parts, experiments by the number of populations or different alternative virus populations and experiments by the size of the problem dimensions or the number of nodes. As the result of this research, it is proven that The Virus Colony Search algorithm was able to solve the Travelling Salesman Problem and also by using the Global Random Walk on the Search Colony Virus Algorithm, it can improve performance, especially on the aspect of the required iterations.
Research on Holographic Communication System Based on 3D Face Reconstruction

Holographic communication, as one of the main communication modes in the future, has attracted extensive attention from all walks of life with high immersion and high naturalness interactive experience. The traditional holographic communication system relies too much on the optical principle, which is easy to be disturbed by external light and reduce the resolution of the picture. Therefore, we propose a holographic communication system based on 3D face reconstruction algorithm. The system collects the images and audio data of both sides of communication through the input module, and takes the 3D face reconstruction module as the core to realize the mapping from 2D face data to 3D image. The reconstruction results are transmitted to the rendering module to complete the 3D face rendering and display, so as to realize the transformation from plane graphics to stereo images. At the same time, it realizes real-time voice transmission combined with audio and video synchronization technology, which can meet the needs of formal communication meetings.
Research on mathematical expressions matching algorithms for computer algebra systems

For the interactive computer algebra system (CAS), to solve the matching problem of different mathematical expressions between students and CAS, this paper proposes a mathematical expression matching algorithm to achieve exact matching of different mathematical expressions between students and CAS. First, according to the MathML representation markup coding of the mathematical expression, the markup coding is parsed into the linear string representation of the expression by the back-end program, and the binary tree is generated after preprocessing it and then normalizing its tree structure and calculating the structured coding of the binary tree. For the two mathematical expressions to be matched, if their structure codings are the same, then compare their postorder and in-order traversal sequences. If they are the same, the two expressions are matched in the structure; go to the next step of the algorithm: matching the content of the mathematical expressions. For the binary tree of expressions with the same structure, the above traversal sequence is repeated to compare the obtained traversal sequence. The two expressions match if they are the same; otherwise, they do not match. The algorithm can realize exact matching between two mathematical expressions from the experimental results. When faced with mathematical expressions containing a large number of large integers, the matching of its structure is judged first. If it does not match, it can interrupt and exit and save time. It improves the running efficiency of the algorithm and is a convenient, fast, and practical algorithm.
Dynamical Scene Representation and Control with Keypoint-Conditioned Neural Radiance Field

In this work, we present a method that can learn to model dynamic and arbitrary 3D scenes, purely from 2D visual observations. Our approach uses a keypoint-conditioned Neural Radiance Field (KP-NeRF) to capture and model these scenes with the overarching goal of supporting image-based robot manipulation. Differentiating this from previous methods, which typically condition the model on generic embedding vectors for representation, our implicit neural radiance function is conditioned on a set of keypoints that are inferred from a learned encoder given imagery observations. This implicitly separates the visual modeling components into object appearances and object pose configurations. Such inductive bias built into the architecture encourages discovered keypoints to capture state transitions in the robot’s environment across time and space. We then learn a forward prediction model of the encoded keypoints, constructed over the keypoint representation space, and perform MPC control for challenging manipulation tasks including block pushing and door closing. We evaluate the performance of our method through various tasks: novel scene view synthesis, action-conditioned forward prediction, and robot manipulation tasks.
Efficient Resource Allocation using a Multi-criteria approach and nodes Clustering for Heterogeneous Hadoop Cluster

Hadoop is an open-source framework that is widely used to store and process large amounts of data. Its core component is called YARN that is responsible for resource management and job scheduling for Big Data applications. Although YARN has very advanced processing performance. It suffers from resource allocation problems when used in a heterogeneous cluster serving multiple users. To deal with this heterogeneity and overcome the degradation of cluster, load balancing has become a major challenge for Hadoop run-time management. In this paper, an efficient resource allocation strategy combining multi-criteria decision-making and accurate clustering of nodes in a heterogeneous Hadoop environment is proposed. A multi-criteria decision approach relying on a modified analytical hierarchy process method is applied to assign a score to jobs depending on the resources requirements at run time. First, the proposed system profiles the available nodes by grouping them into clusters with similar performance. Second, the system performs a dynamic multi-criteria selection on resource requests by assigning scores to jobs according to their resource usage. Third, node groups and job scores are used to dynamically allocate jobs to the most appropriate resources in real time while keeping load balance in the heterogeneous cluster. The aims of this present paper are to optimize the resource utilization and achieve a high performance of the Yarn resource management by considering a heterogeneous environment. The experimental results demonstrate that the proposed algorithm provides the best utilization of available resources by 45% and 12% compared to fair and H-fair respectively, which implies a minimum job execution time in a heterogeneous cluster.
Weakly Supervised Video Anomaly Detection with Temporal and Abnormal Information

Weakly supervised video anomaly detection is to distinguish anomalies from normal scenes and events in videos, under the setting that we only know whether there are abnormal events in a video, but the specific occurrence time of abnormal events is unknown. It is generally modeled as a MIL (multiple instance learning) problem, where video-level labels are provided to train an anomaly detector to obtain frame-level labels for videos. However, most existing methods generally overlook temporal information in abnormal videos (positive bags), and only use one sample (snippet) in the positive bag to train. The positive bag may include more useful information with high possibility. Therefore, we propose the Weakly Supervised Video Anomaly Detection Approach with Temporal and Positive Features, which consider both the temporal information and more positive samples for video anomaly detection. Its contributions can be summarized as follows: (1) we consider more temporal information and introduced the attention mechanism in our network, we use both local and global snippets’ features to enhance the temporal representation ability of these features. (2) We use more positive (abnormal) samples and its features in bags to train our model, so that more complementary and relevant information will make our model more robust and effective. (3) We consider not only the differences between normal samples and abnormal samples but also between abnormal samples and abnormal samples, which can help our proposed approach to excavate positive (abnormal) samples’ information more efficiently and adequately. Experimental results demonstrate the effectiveness of our proposed methods in the UCF-Crime and ShanghaiTech dataset.
MapReduce Indexes

The previous chapter demonstrated complete control over an index’s definition and life cycle. Map and MultiMap indexes were introduced, along with various ways to compute fields that can be used to filter and sort documents. This chapter will show how to perform grouping and aggregation in RavenDB. Concepts of MapReduce and MultiMapReduce indexes will be introduced, along with a way to materialize the content of the index into a new collection.
CRCTTS: Convolution-Recurrent-Convolution Text-to-Speech System

End-to-end speech synthesis technology has already replaced the positions of Statistical Parametric Speech Synthesis (SPSS) in text-to-speech (TTS) field. The end-to-end model based on neural network, does not require a lot of domain knowledge but synthesize more natural speeches. Tacotron is the first model that can synthesize speeches which even human is hard to distinguish. We propose a new end-to-end speech synthesis system which is called Convolution-Recurrent-Convolution Text-to-Speech (CRCTTS). We chose Tacotron as our baseline model and adjust the architecture through fully Convolution Neural Network (CNN) module and Dynamic Convolution Attention (DCA). Besides, we also introduce the attention guided mechanism to our model for accelerating the attention alignment in the decoder module. The model we proposed has been proved that can synthesis speech with better quality and cost less time in terms of training stage and synthesis stage than the baseline model with these technologies.
Metaverse: Homogenization experience and cultural illusion

&lt;p align="justify"&gt;Although the metaverse may bring technological and consumer revolution, from the perspective of cultural model, the metaverse does not lead to significant changes in social emotional structure. The metaverse is still a new form of network technology development. It strengthens visual experience and weakens tactile, taste and auditory experience from this perspective, the biggest feature of the metaverse is the violence of visual culture, which makes the “light rhyme” disappear and makes people form a homogeneous experience in the simulated world. Therefore, we should not superstitious and blindly follow the “meta cosmic culture”.&lt;/p&gt;
Designing Codes for Making AI Robots that Accompany English Learning

Robots with human movements and facial expressions are easy to approach for children. If such a robot has some artificial intelligence, it can help children learn a variety of things. Therefore, by interacting with this robot, children can learn English and practice daily conversations in English, even learning how to get along with people and develop good social skills. Hence, the purpose of this research is to use an online graphical programming tool -CodeLab- to create a good interaction mode between humans and Kebbi Air robots, and help children learn and talk in English in an interactive way. By using this tool, this research created a function so that users can recognize English Words in a period of time. In this way, preschoolers can easily learn basic English Words and simple daily conversations to lay a good foundation for language education. In addition, parents can remotely observe their children's learning through the AI robot camera, and use the robot to discuss learning content with their children. The research results showed that artificial intelligence robots are beneficial to improve children's learning enthusiasm and attention, build their self-confidence, and encourage them to learn, listen and speak English.
HFine - Your Language, Your Diagnosis

India has a population of around 1400 million people and a varied mix of urban and rural areas. In comparison to medical advancements in the urban sector, the rural sector lags far beyond. Technology advancements in the twenty-first century have brought many sectors closer together. We can overcome the gap between the two medical sectors with the help of this technology. Regardless of how advanced technology has become, the medical industry still requires human intervention. To have a steady quantity of technology and human intermediary, and to use both of their expertise coherently and realistically, we devised a system that incorporates both of these features and attempts to provide the most correct solution possible. Our module provides a possible diagnosis based on symptoms, which aids in the doctor’s analysis. It also considers India’s multiplicity of languages and ensures that communication with the patient is conducted in a language that he/she understands.
Generation and Implementation of Random Number Architecture using Difference Expansion for Digital Circuits

Unified logic and very large-scale integration (VLSI) circuitry have been created to generate random numbers from various distributions such as uniform, exponential, and Gauss distributions. An arithmetic iteration notion is used to implement several architectural levels in the proposed system. Fast speed, low power consumption, and great output accuracy make this random number generator ideal for communications systems and other models that need multi-distribution random numbers. The difference expansion approach and linear feedback shift register (LFSR) bit switching are recommended to generate random numbers. Besides this, the randomization is unpredictably random. Predictable binary sequences have been a major drawback of earlier randomization approaches. Differential characteristics construct the sequence and keep things simple in this technique. It also aims to simplify the circuit and use less energy. Power, delay, and a lookup table are used to make performance comparisons. The proposed architecture was tested and evaluated using Xilinx. Simulation results demonstrate that the proposed model outperforms standard random generators using Slices, Area, and Look Up Tables. According to the research findings, the proposed Difference Expansion-based Random Generation (DERG) has reduced latency and consumes less power.