Reviewerly/exHarmony huggingface dataset:
Research on Target Tracking Algorithm Based on context Multi-feature Fusion

Abstract In traditional target tracking algorithms, cosine window is often used to suppress the boundary effect and expand the search range when the target is located, which will reduce the available background information. Besides, when a single feature is used, the representation of the target appearance is not strong. When the challenge factors such as background clutter and occlusion occur to the target, the target will drift, which will reduce the accuracy of target tracking and even lead to tracking failure. To solve this problem, this paper proposes a context-based target tracking algorithm that fuses multiple features, using directional gradient histogram features and color name features as input features. To solve the occlusion problem, the occlusion mechanism of average peak correlation energy and maximum response fraction value is designed to judge the updating of the model. When both values are higher than a certain historical mean, the model is updated; Otherwise, stop updating. Finally, experiments show that the tracking algorithm proposed in this paper is more robust than some traditional correlation filtering algorithms.
Encoding Syntactic Constituency Paths for Frame-Semantic Parsing with Graph Convolutional Networks

We study the problem of integrating syntactic information from constituency trees into a neural model in Frame-semantic parsing sub-tasks, namely Target Identification (TI), FrameIdentification (FI), and Semantic Role Labeling (SRL). We use a Graph Convolutional Network to learn specific representations of constituents, such that each constituent is profiled as the production grammar rule it corresponds to. We leverage these representations to build syntactic features for each word in a sentence, computed as the sum of all the constituents on the path between a word and a task-specific node in the tree, e.g. the target predicate for SRL. Our approach improves state-of-the-art results on the TI and SRL of ~1%and~3.5% points, respectively (+2.5% additional points are gained with BERT as input), when tested on FrameNet 1.5, while yielding comparable results on the CoNLL05 dataset to other syntax-aware systems.
Chinese Shuyuan: A Legacy in Chinese Education History, or a Solution for Modern Undergraduates in China?

The paper looked into concepts claimed to be essence of Chinese residential college, an on-going institution presumed to be a solution towards undergraduates&amp;rsquo; issues in some pioneer universities in China. It&amp;rsquo;s analyzed that Chinese residential college today in China is not a Shuyuan that was ever striving as a unique education mode in ancient China, even if it&amp;rsquo;s named after Shuyuan in Chinese, concerning on its nature, function and goal, while it&amp;rsquo;s not a conventional residential college in English speaking countries neither. By investigation and comparison of its origin, function and features among Shuyuan and Chinese residential college, the spirit of development of a human with goodness and well-being through pursuit of knowledge and culture inherited and transmitted in Shuyuan is unearthed, which is supposed to be the resource of inspiration when the pioneer universities and educators designed and operate residential college on Chinese campus, though the effects couldn&amp;rsquo;t be accounted as appealing as what Shuyuan produced in ancient China. The research aims to depict a real Shuyuan, an education legacy every being alive in Chinese history, which were featured with paramount concepts and values that should be correctly recognized and implemented when there is great concern around undergraduates.
The Harmful Effects of Online and Offline Anti LGBTI Hate Speech

Lesbian, gay, bisexual, transgender and intersex people (LGBTI) are discriminated and subjected to violence in societies across the world, and ensuring their rights is on the international agenda. ...
Design of Sales Information Systems in Stock Point Lily Shop is Based on PHP MySQL

Stock Point Lily shop is a shop that specializes in selling Ice Cream Carts, which have been established since July 22, 2013 to the present. The system of recording the sale, purchase and stock of Stock Point Lily shop is still done by manual recording. Recording manually can take a relatively long time in terms of the handover between sales and shop owners, errors in the amount of stock often occur at the time of sale or stock addition. Store owners can only even guess the profits and sales they get every month. The problem discussed is how to design and implement the design of a sales information system at the Stock Point Lily Shop in Pekanbaru. This design uses UML (Unified Modeling Language) using diagrams including use case diagrams, activity diagrams, class diagrams. The programming languages ​​used are PHP and MySQL as the database. From this research resulted in a sales information system design at the Stock Point Lily Shop that serves to facilitate and assist the Shop owner in managing, informing and searching for data relating to data Sales of goods and inventory reports at the Stock Point Lily Store in increasing efficiency and effectiveness in working so that work that was previously done manually can be easier and computerized properly.
Pixel Grouping of Digital Images for Reversible Data Hiding

Pixel Grouping (PG) of digital images has been a key consideration in recent development of the Reversible Data Hiding (RDH) schemes. While a PG kernel with neighborhood pixels helps compute image groups for better embedding rate-distortion performance, only horizontal neighborhood pixel group of size 1×3 has so far been considered. In this paper, we formulate PG kernels of sizes 3×1, 2×3 and 3×2 and investigate their effect on the rate-distortion performance of a prominent PG-based RDH scheme. Specially, a kernel of size 3×2 (or 2×3) that creates a pair of pixel-trios having triangular shape and offers a greater possible correlation among the pixels. This kernel thus can be better utilized for improving a PG-based RDH scheme. Considering this, we develop and present an improved PG-based RDH scheme and the computational models of its key processes. Experimental results demonstrated that our proposed RDH scheme offers reasonably better embedding rate-distortion performance than the original scheme.
Improving Signature Forgery Analysis through Deep Learning Classification Algorithms

This project seeks to develop a machine learning algorithm to identify a forgery from a legitimate signature, for use in signature verification in low profile financial crimes. This model will be trained on data collected on specific handwriting characteristics used by professional document analysis experts. Signature forgery in financial institutions was recently brought to light in the Wells Fargo fake accounts scandal, where employees opened 3.5 million unauthorized accounts, for which 190,000 customers were unwittingly charged fees. This product will help protect individuals from exploitation by providing a verification tool for company managers and executives. The hypothesis predicted if characteristics such as length-to-height ratio and relative slant were taken into account, then the accuracy of the model would be greater than 90%. Each data point consisted of 6 signatures, of which 5 were ‘true’ (produced by the same person) and the final was forged. The data was fed to a processing program that used mathematical formulae (standard deviation from the mean) to account for, and negate human error. Finally, using the python Scikit machine learning library, multiple models were trained on the data sample, using k-fold analysis. The most successful model, XGBoost Classifier, had an accuracy rate of 94.55%.
Multi-Task Convolutional Neural Network Leading to High Performance and Interpretability via Attribute Estimation

A multi-task convolutional neural network leading to high performance and interpretability via attribute estimation is presented in this letter. Our method can provide interpretation of the classification results of CNNs by outputting attributes that explain elements of objects as a judgement reason of CNNs in the middle layer. Furthermore, the proposed network uses the estimated attributes for the following prediction of classes. Consequently, construction of a novel multi-task CNN with improvements in both of the interpretability and classification performance is realized.
Sources Of Foreign Language Education Content In The Information Age

The article examines the modern context that has led to the expansion of the functions of a foreign language as a tool for interaction between people, a means of intercultural dialogue and as a means of education, learning, and achieving personal goals in various spheres of life. In this regard, we consider the concept of "information and educational environment", which provides access to the content of teaching a foreign language, taking into account the emerging opportunities for teaching in a secondary school; describes the generally recognized levels of information and educational environment and proposes a new subject level; establishes and justifies the correlation of these levels of the information and educational environment. The article gives a deep analysis of the information and educational environment from the point of view of the subject content, defining each level, clarifying the key tasks relevant to the implementation of the main functions of a foreign language. It provides information about modern educational technologies, current training sites, and describes the capabilities of electronic systems used in schools and other educational institutions. In order to develop students' reflexive skills, it is proposed to use specialized databases for self-control and self-correction in the course of mastering a foreign language in the student's personal information and educational environment. Besides, the article focuses on the role of the teacher in the modern information and educational environment, as well as the importance of interaction between the subjects of the educational process at all stages of the hierarchy.
E-Learning Recommended System

In Hybrid e-learning recommender frameworks, to share the data between researcher is deficient, which makes it difficult to apply cross breed filtering (HF) proposal framework. We propose a communitarian filtering (CF) methods utilizing memory based separating to beat the issues which we experienced on crossover sifting strategies. Memory-based separating procedures utilize the whole client thing dataset to make various neighborhoods of clients. Neighborhood-based calculation decide the likeness between two clients or things, and produces an expectation for the client by taking the weighted normal of the considerable number of appraisals. Influence engendering implies that a student can advance toward dynamic students, and such practices can invigorate the moving practices of his neighbors. This self-association based suggestion approach accomplishes a steady structure dependent on circulated and base up practices of people. The trial results exhibit that memory based sifting strategies to give customized and diversified suggestions in e-learning situations.
The LDBC Graphalytics Benchmark

In this document, we describe LDBC Graphalytics, an industrial-grade benchmark for graph analysis platforms. The main goal of Graphalytics is to enable the fair and objective comparison of graph analysis platforms. Due to the diversity of bottlenecks and performance issues such platforms need to address, Graphalytics consists of a set of selected deterministic algorithms for full-graph analysis, standard graph datasets, synthetic dataset generators, and reference output for validation purposes. Its test harness produces deep metrics that quantify multiple kinds of systems scalability, weak and strong, and robustness, such as failures and performance variability. The benchmark also balances comprehensiveness with runtime necessary to obtain the deep metrics. The benchmark comes with open-source software for generating performance data, for validating algorithm results, for monitoring and sharing performance data, and for obtaining the final benchmark result as a standard performance report.
AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection

Few-shot object detection (FSOD) aims at learning a detector that can fast adapt to previously unseen objects with scarce annotated examples, which is challenging and demanding. Existing methods solve this problem by performing subtasks of classification and localization utilizing a shared component (e.g., RoI head) in the detector, yet few of them take the distinct preferences of two subtasks towards feature embedding into consideration. In this paper, we carefully analyze the characteristics of FSOD, and present that a general few-shot detector should consider the explicit decomposition of two subtasks, as well as leveraging information from both of them to enhance feature representations. To the end, we propose a simple yet effective Adaptive Fully-Dual Network (AFD-Net). Specifically, we extend Faster R-CNN by introducing Dual Query Encoder and Dual Attention Generator for separate feature extraction, and Dual Aggregator for separate model reweighting. Spontaneously, separate state estimation is achieved by the R-CNN detector. Besides, for the acquisition of enhanced feature representations, we further introduce Adaptive Fusion Mechanism to adaptively perform feature fusion in different subtasks. Extensive experiments on PASCAL VOC and MS COCO in various settings show that, our method achieves new state-of-the-art performance by a large margin, demonstrating its effectiveness and generalization ability.
Philosophical and Aesthetic Attitude of the French Symbolists of the Turn of the XIX-XX Centuries as Means of Artistic Cognition

The paper justifies the thesis that ideological and artistic attitudes that determined the subject and morphology of creative work of the French symbolist painters of the turn of the XIX-XX centuries had ancient non-European roots. The author advances a well-grounded hypothesis that symbolist painters’ esoteric worldview was based on ancient gnostic doctrines, which constitutes scientific originality of the study. The conducted research allows concluding that, having followed the gnostic tradition, symbolist painters explored different aspects of a symbol as an art object, which implements a certain ideological and artistic conception and serves as an ethical ideal.
Selectable Heaps and Optimal Lazy Search Trees

We show the $O(\log n)$ time extract minimum function of efficient priority queues can be generalized to the extraction of the $k$ smallest elements in $O(k \log(n/k))$ time (we define $\log(x)$ as $\max(\log_2(x), 1)$.), which we prove optimal for comparison-based priority queues with $o(\log n)$ time insertion. We show heap-ordered tree selection (Kaplan et al., SOSA '19) can be applied on the heap-ordered trees of the classic Fibonacci heap and Brodal queue, in $O(k \log(n/k))$ amortized and worst-case time, respectively. We additionally show the deletion of $k$ elements or selection without extraction can be performed on both heaps, also in $O(k \log(n/k))$ time. Surprisingly, all operations are possible with no modifications to the original Fibonacci heap and Brodal queue data structures. We then apply the result to lazy search trees (Sandlund & Wild, FOCS '20), creating a new interval data structure based on selectable heaps. This gives optimal $O(B+n)$ time lazy search tree performance, lowering insertion complexity into a gap $\Delta_i$ from $O(\log(n/|\Delta_i|) + \log \log n)$ to $O(\log(n/|\Delta_i|))$ time. An $O(1)$ time merge operation is also made possible when used as a priority queue, among other situations. If Brodal queues are used, all runtimes of the lazy search tree can be made worst-case.
Inferring Students’ Tracing Behaviors from Interaction Logs of a Learning Environment for Software Design Comprehension

VeriSIM is a learning environment which helps software engineering students comprehend a given design, by enabling them to trace different scenarios in the design. Learners trace different scenarios by constructing a state diagram. In VeriSIM, learners go through four challenges which help them progressively trace scenarios in the design. VeriSIM provides learners affordances to add, edit, delete states, as well as “run” a given state diagram. All such learner interactions are logged in the system. In this paper, we analyze the interaction logs of 12 students, in order to infer certain behavior patterns. The preliminary analysis of these patterns shows that the order of the challenges help learners effectively trace scenarios.
Data rescue: An assessment framework for legacy research collections

This white paper is the product of research conducted under a Non-Assistance Cooperative Agreement #58-8260-6-003 between the University of Maryland and the United States Department of Agriculture (USDA), Agricultural Research Service (ARS), National Agricultural Library with funding provided by the USDA, ARS, Office of National Programs.
Sustainable education in Russia

In the modern world, many countries recognize that education should act in the interests and for the benefit of the ideas of sustainable development. At the same time, sustainable development itself becomes an integral element of quality education and its key factor. In Russia, the “national strategy of education for sustainable development” has been formed, which provides for a reorientation from ensuring that students have certain knowledge to the ability to analyse real problems and find possible solutions for them. At the same time, the traditional approach to teaching based on the study of specific subjects remains, but it is important to ensure that each student is able to carry out an interdisciplinary analysis of real-life situations. Currently, a project on “Key areas of development of Russian education for achieving the goals and objectives of sustainable development in the education system” until 2035 has been developed and published, which contains indicators that allow us to assess the achievement of the goals set out in it. However, this document hardly presents the level of higher professional education and its contribution to sustainable development. The article will examine the role of Russian higher education in the concept of sustainable development.
Efficient Method for Mining Maximal Inter-transaction Patterns

Frequent Maximal Pattern (FMP) mining is an interesting topic in data mining. So far, many algorithms have been proposed for mining frequent maximal patterns of items occurring within transactions, but there is no method for mining FMPs across transactions in the database. In this study, we propose an efficient algorithm, namely tMITP-Miner, for mining frequent maximal inter-transaction patterns (FMITPs). The proposed method uses tidset to store the information of patterns for efficient mining FMITPs. Besides, we also proposed effective pruning strategies help reducing the search space to speed up the runtime and to cut down the memory usage. Experiments have been conducted to compare the effectiveness between the tMITP-Miner and the post-processing method in terms of runtime and memory usage.
Risk Evaluation in Software Project Using Bayesian Network Modeling

Project risk events are often influenced by each other and rarely act independently. In this context, effective methods to identify, model and analyze these risks are necessary. The objective of this article is to apply the risk analysis in a software development project, based on the model of the Software Engineering Institute (SEI), using the Bayes model to calculate the event probabilities and also the Noisy-OR calculation structure to assign the initial weights of the network of factors that influence the project. In this way, it is expected to increase the chances of success of the risk analysis process. The results obtained by the techniques adopted prove to be promising in assisting the process of decision making by the managers of software development projects.
Teachers’ perception of educational challenges in the face of the COVID 19

The health context related to the COVID-19 virus has implied challenges for various areas in which the educational sphere stands out. Preventive measures of social distancing and quarantine have led to classes being given virtually (online), and in this scenario research is emerging that focuses on the educational challenges of teachers. The DIFPRORET project, educational difficulties, activities and challenges in confinement is applied to a sample of 200 teachers, with a qualitative method and methodological strategy of content analysis. The findings show the difficulty in the use of platforms, understanding of distance education, creative action and the generation gap with 64.26%. With this, a hierarchical map is expressed that gives account of three significant constructs: didactic operation, progressive learning and curricular innovation, which allows us to conclude that under these constructs translate the perceptions of teachers on educational challenges. Keywords: Digital strategy, COVID-19.
A Value Token Transfer Protocol (VTTP) for Decentralized Finance

We present Value Token Transfer Protocol (VTTP), a decentralized finance protocol for exchange of value or tokens within and between participating blockchain networks, fiat bank accounts and fiat wallets.The protocol allows intra-chain or inter-chain transfers of cryptocurrencies or tokens.VTTP works in both client-server and peer-to-peer models.The protocol comprises receiving from a client a transfer request to transfer value in a form of a cryptocurrency or a token, determining if the transfer request is intra-chain or interchain, transmitting to the client a response to the transfer request, the response comprising a raw transaction, receiving from the client a response to the raw transaction wherein a private key of a user is used to sign the raw transaction, defining a signed transaction, verifying a signature of the signed transaction and broadcasting the signed transaction to the sending and receiving blockchain networks.
Google Meet Application and Media Power Points to Improve IPA Learning Results

&lt;p&gt;&lt;em&gt;The background of this research is the low courage of the science learning outcomes in grade V SDN 02 Pekuncen. The purpose of this research is to improve the learning outcomes of students in the science muple. The method used in learning is through the Google Meet application using Power Point media. Classroom Action Research is carried out in three cycles, namely planning, implementing, observing, and reflecting. In cycle I there is an increase in learning outcomes from cycle as much as 38%, from cycle 1 to cycle II experienced a 53% increase in learning outcomes and cycle II to cycle III as much as 97%. This shows that the use of the Google application to meet the Power Point media can improve student learning outcomes, especially in Class V science muple at SDN 02 Pekuncen.&lt;/em&gt;&lt;/p&gt;
Elimination of flow rate restriction for system of storm water sewage with the help of drag-reducing polymers

The flow-rate restriction for storm sewage network is substantiated. Possible causes of flooding of territories by storm water in the case of emergency and methods of storm waters management are considered. The article is devoted to an increase in throughput of storm sewage networks with the help of in-line storm water detention tank installed at the beginning of storm sewage network and dragreducing polymers (DRP). It is proposed to introduce DRPs in the form of solution directly into the sewage network through a storm-water inlet or through a sewer manhole. The introduction is conducted from a tank (cistern) in which there is a device for preparing an aqueous solution from the raw materials of DRP. For a square (in horizontal plane) catchment, in the case of point-type water drainage, the numerical simulation of the work of a system of storm water sewage with the help of DRP has been carried out.