Reviewerly/exHarmony huggingface dataset:
A focused crawler based on semantic disambiguation vector space model

Abstract The focused crawler grabs continuously web pages related to the given topic according to priorities of unvisited hyperlinks. In many previous studies, the focused crawlers predict priorities of unvisited hyperlinks based on the text similarity models. However, the representation terms of the web page ignore the phenomenon of polysemy, and the topic similarity of the text cannot combine the cosine similarity and the semantic similarity effectively. To address these problems, this paper proposes a focused crawler based on semantic disambiguation vector space model (SDVSM). The SDVSM method combines the semantic disambiguation graph (SDG) and the semantic vector space model (SVSM). The SDG is used to remove the ambiguation terms irrelevant to the given topic from representation terms of retrieved web pages. The SVSM is used to calculate the topic similarity of the text by constructing text and topic semantic vectors based on TF × IDF weights of terms and semantic similarities between terms. The experiment results indicate that the SDVSM method can improve the performance of the focused crawler by comparing different evaluation indicators for four focused crawlers. In conclusion, the proposed method can make the focused crawler grab the higher quality and more quantity web pages related to the given topic from the Internet.
Rancang Bangun Sistem Informasi Profil TK Al Hidayah Kebumen dengan Metode Waterfall Berbasis Website

Al Hidayah Kindergarten does not yet have an information system that can be accessed through the website so the presentation of information is still done conventionally. This study aims to build a website-based school profile information system. This information system can be used by schools to improve the quality of information services to the community. The method used in this research is the waterfall model system development method because this method is the most suitable development model used by developers, system project work is easy to control and can be timely. The programming language that the author uses is PHP (Hypertext Preprocessor) and uses a MySQL database. Testing the feasibility of the system using blackbox testing. The functions tested are admin and user pages. The test results on the admin page obtained that 90% were declared successful, 10% were declared slow loading, and no failed test items were found. While on the user page 95% was declared successful, 5% was declared slow loading, and no failed test items were found. Keywords: information system, website, waterfall model, PHP, blackbox testing
EXTRACURRICULAR ACTIVITIES IN PROFESSIONAL EDUCATIONAL INSTITUTIONS

This article is devoted to the methods of organizing and conducting extracurricular activities in vocational education in vocational education.
Development of an attitude assessment instrument pertaining to the thematic learning of second grade elementary students

This study aimed to develop an instrument for assessing politeness in the thematic learning of students in elementary schools. The type of research and development used refers to the R&amp;D theory of Borg and Gall. This study included three schools in the Melati cluster, Banjit district. The sample of this study were second grade students of State Elementary School 01 Bonglai. Data collection techniques used were non-test in the form of questionnaires, interviews and documentation. Data analysis used qualitative analysis with descriptive percentage for expert &amp; practitioner validation, and quantitative analysis with Rasch program for validity and reliability. The results of this study indicated that the instrument for assessing politeness in thematic learning in elementary schools that was developed was theoretically and practically feasible.
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval

Recently, the cross-modal pre-training task has been a hotspot because of its wide application in various down-streaming researches including retrieval, captioning, question answering and so on. However, exiting methods adopt a one-stream pre-training model to explore the united vision-language representation for conducting cross-modal retrieval, which easily suffer from the calculation explosion. Moreover, although the conventional double-stream structures are quite efficient, they still lack the vital cross-modal interactions, resulting in low performances. Motivated by these challenges, we put forward a Contrastive Cross-Modal Knowledge Sharing Pre-training (COOKIE) to grasp the joint text-image representations. Structurally, COOKIE adopts the traditional double-stream structure because of the acceptable time consumption. To overcome the inherent defects of double-stream structure as mentioned above, we elaborately design two effective modules. Concretely, the first module is a weight-sharing transformer that builds on the head of the visual and textual encoders, aiming to semantically align text and image. This design enables visual and textual paths focus on the same semantics. The other one is three specially designed contrastive learning, aiming to share knowledge between different models. The shared cross-modal knowledge develops the study of unimodal representation greatly, promoting the single-modal retrieval tasks. Extensive experimental results on multi-modal matching researches that includes cross-modal retrieval, text matching, and image retrieval reveal the superiors in calculation efficiency and statistical indicators of our pre-training model.
Algebraic approach for investigation of a multi-mode quantum system dynamics

We introduce algebraic approach for superoperators that might be useful tool for investigation of quantum (bosonic) multi-mode systems and its dynamics. In order to demonstrate potential of proposed method we consider multi-mode Liouvillian superoperator that describes relaxation dynamics of a quantum system (including thermalization and intermode coupling). Considered algebraic structure of superoperators that form Liouvillian and their algebraic properties allows us to diagonilize multi-mode Liouvillian to find its spectrum. Also it allows to derive linear by mean number of thermal (environmental) photons approximation for time-evolution superoperator that keeps amount of considered dimensions in Fock space finite (assuming initial amount of dimensions finite) that might be helpful regarding entanglement dynamics problems. Conjugate Liouvillian is considered as well in order to perform analysis in Heisenberg picture, it can be implemented for multi-time correlation functions derivation.
On Increasing the Role of Rao and Regional Scientific Centers of Rao in Training Personnel for Regional Educational and Scientific Organizations

The modern stage of the development of the national education system and Russian science has actualized a number of important issues requiring systematic analysis and comprehensive solutions presented in the chapter of the monograph. The priorities in training personnel for educational and scientific organizations in the regions of the Russian Federation are a fundamental approach to the study of disciplines, practical orientation, education of respect for national history, the state language and traditions, the personality of the teacher and the student; awareness of the responsibility and importance of Russian education, consideration of advanced ideas and demands of society. The fundamental nature of personnel training has always been combined with high requirements for the mission of teaching staff – people of high scientific, methodological, moral and universal culture.
Meetings and Mood – Related or Not? Insights from Student Software Projects

[Background:] Teamwork, coordination, and communication are a prerequisite for the timely completion of a software project. Meetings as a facilitator for coordination and communication are an established medium for information exchange. Analyses of meetings in software projects have shown that certain interactions in these meetings, such as proactive statements followed by supportive ones, influence the mood and motivation of a team, which in turn affects its productivity. So far, however, research has focused only on certain interactions at a detailed level, requiring a complex and fine-grained analysis of a meeting itself. [Aim:] In this paper, we investigate meetings from a more abstract perspective, focusing on the polarity of the statements, i.e., whether they appear to be positive, negative, or neutral. [Method:] We analyze the relationship between the polarity of statements in meetings and different social aspects, including conflicts as well as the mood before and after a meeting. [Results:] Our results emerge from 21 student software project meetings and show some interesting insights: (1) Positive mood before a meeting is both related to the amount of positive statements in the beginning, as well as throughout the whole meeting, (2) negative mood before the meeting only influences the amount of negative statements in the first quarter of the meeting, but not the whole meeting, and (3) the amount of positive and negative statements during the meeting has no influence on the mood afterwards. [Conclusions:] We conclude that the behaviour in meetings might rather influence short-term emotional states (feelings) than long-term emotional states (mood), which are more important for the project.
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios

Pool-based Active Learning (AL) has achieved great success in minimizing labeling cost by sequentially selecting informative unlabeled samples from a large unlabeled data pool and querying their labels from oracle/annotators. However, existing AL sampling strategies might not work well in out-of-distribution (OOD) data scenarios, where the unlabeled data pool contains some data samples that do not belong to the classes of the target task. Achieving good AL performance under OOD data scenarios is a challenging task due to the natural conflict between AL sampling strategies and OOD sample detection. AL selects data that are hard to be classified by the current basic classifier (e.g., samples whose predicted class probabilities have high entropy), while OOD samples tend to have more uniform predicted class probabilities (i.e., high entropy) than in-distribution (ID) data. In this paper, we propose a sampling scheme, Monte-Carlo Pareto Optimization for Active Learning (POAL), which selects optimal subsets of unlabeled samples with fixed batch size from the unlabeled data pool. We cast the AL sampling task as a multi-objective optimization problem, and thus we utilize Pareto optimization based on two conflicting objectives: (1) the normal AL data sampling scheme (e.g., maximum entropy), and (2) the confidence of not being an OOD sample. Experimental results show its effectiveness on both classical Machine Learning (ML) and Deep Learning (DL) tasks.
Machine Learning of Average Non-Markovianity from Randomized Benchmarking

The presence of correlations in noisy quantum circuits will be an inevitable side effect as quantum devices continue to grow in size and depth. Randomized Benchmarking (RB) is arguably the simplest method to initially assess the overall performance of a quantum device, as well as to pinpoint the presence of temporal-correlations, so-called non-Markovianity; however, when such presence is detected, it hitherto remains a challenge to operationally quantify its features. Here, we demonstrate a method exploiting the power of machine learning with matrix product operators to deduce the minimal average non-Markovianity displayed by the data of a RB experiment, arguing that this can be achieved for any suitable gate set, as well as tailored for most specific-purpose RB techniques.
Attribute Based Signature Architecture for Transferring Digital Document

Abstract Authentication and cryptosystems are two examples of methods for ensuring user identification and data confidentiality. Because of its one-way nature, hashing is used to verify the identity and accuracy of data travelling across an open medium. It detects unlawful modifications and changes made to transmissions. Digital signature, which deals with document uniqueness and creator oneness, are a well-known application of hashing. It is updated by adding more information about the users' attributes and is classified as an attribute-based signature (ABS). We investigated various past approaches to the ABS phenomena and discovered certain unsolved challenges, resulting in a solution that decreases the difficulty level, utilization, and operating expense coupled with signature creation and confirmation. For focusing the method of digital signatures using elements and their first order logic, we suggested a unique Attribute Based Signature Architecture for Transferring Digital Document. The system includes flexible primitives that give signing parties complete control over signature generation as well as policy-based access security constraints. It has a message-based first order logic and protects the secrecy of signatures. After examining the technique logically, various benefits were discovered, which will be confirmed soon by the implementation solution over the recommended framework under the specified key.
Speeding Up Semantic Instance Segmentation by Using Motion Information

Environment perception and understanding represent critical aspects in most computer vision systems and/or applications. State-of-the-art techniques to solve this vision task (e.g., semantic instance segmentation) require either dedicated hardware resources to run or a longer execution time. Generally, the main efforts were to improve the accuracy of these methods rather than make them faster. This paper presents a novel solution to speed up the semantic instance segmentation task. The solution combines two state-of-the-art methods from semantic instance segmentation and optical flow fields. To reduce the inference time, the proposed framework (i) runs the inference on every 5th frame, and (ii) for the remaining four frames, it uses the motion map computed by optical flow to warp the instance segmentation output. Using this strategy, the execution time is strongly reduced while preserving the accuracy at state-of-the-art levels. We evaluate our solution on two datasets using available benchmarks. Then, we conclude on the results obtained, highlighting the accuracy of the solution and the real-time operation capability.
Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning

In recent decades, science and engineering have been revolutionized by a momentous growth in the amount of available data. However, despite the unprecedented ease with which data are now collected and stored, labeling data by supplementing each feature with an informative tag remains to be challenging. Illustrative tasks where the labeling process requires expert knowledge or is tedious and time-consuming include labeling X-rays with a diagnosis, protein sequences with a protein type, texts by their topic, tweets by their sentiment, or videos by their genre. In these and numerous other examples, only a few features may be manually labeled due to cost and time constraints. How can we best propagate label information from a small number of expensive labeled features to a vast number of unlabeled ones? This is the question addressed by semi-supervised learning (SSL). This article overviews recent foundational developments on graph-based Bayesian SSL, a probabilistic framework for label propagation using similarities between features. SSL is an active research area and a thorough review of the extant literature is beyond the scope of this article. Our focus will be on topics drawn from our own research that illustrate the wide range of mathematical tools and ideas that underlie the rigorous study of the statistical accuracy and computational efficiency of graph-based Bayesian SSL.
The Development of Research-Based Learning Tools with a STEM Approach to Improve Metaliteracy Skills to Solve Rainbow Vertex Anti-magic Coloring Problems

Metaliteracy is the framing and strengthening of information literacy based on critical and creative thinking. Metaliteracy indicators include generating, combining, using, participating, sharing, and collaborating. To implement higher-order thinking skills, we implement research-based learning with a STEM approach. To develop the students’ metaliteracy, educators should also equip the learning process by learning materials. In this study, we developed RBL-STEM learning materials to improve students' metaliteracy skills. The learning materials that have been developed are considered to be valid, effective, and practical. The validity score obtained in each materials is 3.5 for the Lesson Plan, 3.54 for the Student Worksheet, and 3.63 for the Learning Outcomes Test.
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e.g., hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
Divert More Attention to Vision-Language Tracking

Relying on Transformer for complex visual feature learning, object tracking has witnessed the new standard for state-of-the-arts (SOTAs). However, this advancement accompanies by larger training data and longer training period, making tracking increasingly expensive. In this paper, we demonstrate that the Transformer-reliance is not necessary and the pure ConvNets are still competitive and even better yet more economical and friendly in achieving SOTA tracking. Our solution is to unleash the power of multimodal vision-language (VL) tracking, simply using ConvNets. The essence lies in learning novel unified-adaptive VL representations with our modality mixer (ModaMixer) and asymmetrical ConvNet search. We show that our unified-adaptive VL representation, learned purely with the ConvNets, is a simple yet strong alternative to Transformer visual features, by unbelievably improving a CNN-based Siamese tracker by 14.5% in SUC on challenging LaSOT (50.7% > 65.2%), even outperforming several Transformer-based SOTA trackers. Besides empirical results, we theoretically analyze our approach to evidence its effectiveness. By revealing the potential of VL representation, we expect the community to divert more attention to VL tracking and hope to open more possibilities for future tracking beyond Transformer. Code and models will be released at https://github.com/JudasDie/SOTS.
A Classy Memory Management System (CyM2S) using an Isolated Dynamic Two-Level Memory Allocation (ID2LMA) Algorithm for the Real Time Embedded Systems

Due to an increased scalability, flexibility, and reduced cost complexity, the dynamic memory allocation models are highly preferred for the real-time embedded systems. For this purpose, the different types of dynamic models have been developed in the conventional works, which are highly focused on allocating the memory blocks with increased searching capability. However, it faced some of the problems and issues related to the factors of complex operations, high time consumption, memory overhead, and reduced speed of processing. Thus, this research work objects to design an advanced and intelligent dynamic memory allocation mechanism for the real-time embedded systems. Here, a Classy Memory Management System (CyM2S) is developed by using an Isolated Dynamic Two-Level Memory Allocation (ID2LMA) algorithm for efficiently allocating the memory blocks with simple searching. The CyM2S helps to reduce the fragmentation rate and time consumption by optimally allocating the memory blocks. In this model, the small buffer has been maintained for surplus pointers, and the allocated blocks comprise the metadata and payload data. During evaluation, the performance of the proposed CyM2S- ID2LMA technique is validated and compared by using the measures of memory allocation time, release time, execution, and processing speed.
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space

The generation of feasible adversarial examples is necessary for properly assessing models that work in constrained feature space. However, it remains a challenging task to enforce constraints into attacks that were designed for computer vision. We propose a unified framework to generate feasible adversarial examples that satisfy given domain constraints. Our framework can handle both linear and non-linear constraints. We instantiate our framework into two algorithms: a gradient-based attack that introduces constraints in the loss function to maximize, and a multi-objective search algorithm that aims for misclassification, perturbation minimization, and constraint satisfaction. We show that our approach is effective in four different domains, with a success rate of up to 100%, where state-of-the-art attacks fail to generate a single feasible example. In addition to adversarial retraining, we propose to introduce engineered non-convex constraints to improve model adversarial robustness. We demonstrate that this new defense is as effective as adversarial retraining. Our framework forms the starting point for research on constrained adversarial attacks and provides relevant baselines and datasets that future research can exploit.
How centralized is decentralized? Comparison of wealth distribution in coins and tokens

Rapidly growing distributed ledger technologies (DLTs) have recently received attention among researchers in both industry and academia. While a lot of existing analysis (mainly) of the Bitcoin and Ethereum networks is available, the lack of measurements for other crypto projects is observed. This article addresses questions about tokenomics and wealth distributions in cryptocurrencies. We analyze the time-dependent statistical properties of top cryptocurrency holders for 14 different distributed ledger projects. The provided metrics include approximated Zipf coefficient, Shannon entropy, Gini coefficient, and Nakamoto coefficient. We show that there are quantitative differences between the coins (cryptocurrencies operating on their own independent network) and tokens (which operate on top of a smart contract platform). Presented results show that coins and tokens have different values of approximated Zipf coefficient and centralization levels. This work is relevant for DLTs as it might be useful in modeling and improving the committee selection process, especially in decentralized autonomous organizations (DAOs) and delegated proof of stake (DPoS) blockchains.