Reviewerly/exHarmony huggingface dataset:
Teacher Collaboration: Its Effect on the Instructional Teaching Effectiveness of Faculty Members

Teachers collaborate in a variety of ways when they are connected. They talk about the education of the students, exchange concepts and materials, collaborate on projects, and produce new knowledge. This study looked at the degree of teacher collaboration on the Sanchez Mira campus of Cagayan State University in the Philippines. It also examined the respondents’ profiles, including their level of effectiveness as teachers. The degree of teacher collaboration and instructional effectiveness were also assessed for significant associations among the profile factors. The study’s research methodology was descriptive correlational, having the campus faculty members as the respondents. Frequency, percentage, and chi-square tests were used to analyze the data. The level of teacher collaboration acquired an overall mean of 3.72. With a focus on the level of teaching effectiveness, the faculty members manifested an outstanding performance, as exemplified by a mean of 96.53 or 97. Educational attainment is significantly related to collaboration. Trainings in teacher collaboration are also significantly related to teacher collaboration on dialogue. There exists a significant relationship between trainings on teacher collaboration and the level of teacher collaboration. A highly significant correlation exists between teacher collaboration training and teaching effectiveness rating. A highly significant correlation was also established between the factors of teacher collaboration and the instructional effectiveness of the faculty members.
Kekeliruan Penggunaan Bahasa Indonesia dalam Peraturan Daerah Provinsi Sumatra Barat

This article based on research about the mistakes of the Indonesian language in the Local Government Regulations of West Sumatra Province. This article explained the mistakes of Indonesian language in local government regulation were designed, discussed, and decided by the West Sumatra Provincial Government and the Assembly at West Sumatra Province. This research is important because the Indonesian language mistakes in local government regulations are related to the implementation of Presidential Regulation Number 63 of 2019 Part 3 which is “that Indonesian language should be used in grammar regulations which are using sentences, writing techniques, and spelling. Local government regulation is a product of laws and regulations that should use the Indonesian language correctly. However, some mistakes Indonesian languages still found in the local government regulation. This research is qualitative and the method used in data collection is the observation method, the method of analyzing data used the distributional method, and the method of presenting the results of the analysis is an oral presentation. Several mistakes in the Indonesian language was found in the local government regulation of West Sumatra Province that are 1) the mistake of used punctuation, 2) the mistake of words and spelling, 3) the mistake of used words from local language and foreign language, 4) the mistake of used compound, 5) the sentences that are not effective, 6)using of non-standard words, 7) the mistype the words, and 8) the mistake used preposition.&#x0D; Keywords: Mistakes, Indonesian Language, Local Government Regulation, West Sumatra
Sequence Generation Model Integrating Domain Ontology for Mathematical question tagging

In online learning systems, tagging knowledge points for questions is a fundamental task. Automatic tagging technology uses intelligent algorithms to automatically tag knowledge points for questions to reduce manpower and time costs. However, the current knowledge point tagging technology cannot satisfy the situation that mathematics questions often involve a variable number of knowledge points, lacks the consideration of the characteristics of the mathematics field, and ignores the internal connection between knowledge points. To address the above issues, we propose a Sequence Generation Model Integrating Domain Ontology for Mathematical question tagging (SOMPT). SOMPT performs data augmentation for text and then obtains intermediate text based on domain ontology replacement to facilitate deep learning model to understand mathematical question text. SOMPT is able to obtain dynamic word vector embedding to optimize the textual representation for math questions. What’s more, our model can capture the relationship between tags to generate knowledge points more accurately in the way of sequence generation. The comparative experimental results show that our proposed model has an excellent tagging ability for mathematical questions. Moreover, the sequence generation module in SOMPT can be applied on other multi-label classification tasks and be on par with the state-of-the-art performance models.
Research and Design of LIMS in the Field of Metal Deep Processing

This paper mainly studies the current situation and characteristics of LIMS in the field of metal deep processing, designs and implements a LIMS system suitable for the needs of a local metal material processing enterprise . In the implementation process, the Spire component newly released by Microsoft is used to solve the problem of operating different file formats under terminal conditions, ensuring the security of data; At the same time, a method of complex trigger is designed to solve the problem that the system data can be consistent after sub-item detection, which ultimately improves the production efficiency and management efficiency of the enterprise.
Semantic Dependency Analysis of Special Sentence Patterns in Ancient Chinese

With the development of natural language processing, automatic semantic analysis has attracted more and more attention. This research delineates the semantic dependency labeling rules of several kinds of special sentence patterns in ancient Chinese. And we compare the result of manual annotation with automatic semantic parser and put forward improvement methods to those poor results. This research can help to improve the semantic understanding of ancient Chinese, the translation of ancient Chinese and the perfomance of parser to a certain extent.
Research on Safety Management and Control of Power Enterprises Based on AI and Edge Computing Technology

In the last decade, a significant amount of worker were died due to all kinds of fatalities. Accident report shows that a large majority incidents were issued by the lack of suitable training on electrical risks on construction site or workers had not been training adequately to avoid electric shock. In this paper, we study a IOT equipment implementation in practical to unfold precaution control measures in a construction site. The IOT equipment is composed by a mobile camera, a wireless router, intelligent safety hats, a pad which used by the live guarder, an artificial intelligence Image Identification machine which focus on live recognize Electrical Safety Signs on Construction Sites/Security Rules violation. Additionally, our team have been done amount of practical research on safety regulation based on worker’s pin-location, qualification verify to eliminate hazards.
Performance Analysis of User Behavior Pattern Mining Using Web Log Database for User Identification

User behavior analytics is a progressive research domain. Understanding the user’s behavior patterns and identifying their behavior patterns will provide solutions to many issues like identity theft and user authentication. So many research works are done in analyzing the frequent access patterns of the users by pre-processing access logs and applying various algorithms to understand the frequent access behavior of the user. From the literature, it founds that the frequent user access pattern identification needs improvement on prediction accuracy and the minimal false positives. To accomplish these, three different approaches were proposed to overcome the existing issues and intended to reduce false positives and improve the frequent pattern mining accuracy based on web access logs. Proposed methods were found to be good while compared with the existing works.
THE MULTI-STEP RANDOMIZED KACZMARZ ALGORITHMS FOR SOLVING LARGE CONSISTENT LINEAR EQUATIONS

In order to solve large scale consistent linear systems, based on the Kaczmarz algorithm, we propose two Multi-step Randomized Kaczmarz algorithms, denoted as MRK1 and MRK2, and give the corresponding convergence analysis. We consider using parameters to control the number of working rows. MRK1 algorithm uses a fixed parameter <i>m</i>, and the best result is that the parameter <i>m</i> is the number of rows of the coefficient matrix, while MRK2 algorithm uses a randomly generated parameter <i>m</i>, which is more general. Finally, we carry out the corresponding numerical simulation experiments, the experimental results show that, compared with the RK, GRK algorithm, the new algorithm is faster and more efficient on CPU in most cases, and the maximum CPU acceleration is 12.54. And the difference between MRK1(s), MRK2 and MGRK on CPU is not very significant according to experimental results.
Research on Intelligent Hierarchical Model of Online Course Learners Based on K-Means Algorithm

Due to personal defects in the learner population, the general teaching method can no longer meet the needs of learners' overall talent. The graded curriculum focuses on the different humanization and academic needs of primary and secondary school students, which requires feasible plans and overall objectives for the improvement of the overall effect of the school. Focusing on the hierarchical model in education, a hierarchical model is designed based on the K-means algorithm in teaching computer technology and network courses to divide learners' academic situations. The simulation test shows that the hierarchical model is more scientific and reasonable than the hierarchical model based on professional ranking and learners' total score ranking. The study result provides a reference for other disciplines.
Asynchronous Fully-Decentralized SGD in the Cluster-Based Model

This paper presents fault-tolerant asynchronous Stochastic Gradient Descent (SGD) algorithms. SGD is widely used for approximating the minimum of a cost function $Q$, as a core part of optimization and learning algorithms. Our algorithms are designed for the cluster-based model, which combines message-passing and shared-memory communication layers. Processes may fail by crashing, and the algorithm inside each cluster is wait-free, using only reads and writes. For a strongly convex function $Q$, our algorithm tolerates any number of failures, and provides convergence rate that yields the maximal distributed acceleration over the optimal convergence rate of sequential SGD. For arbitrary functions, the convergence rate has an additional term that depends on the maximal difference between the parameters at the same iteration. (This holds under standard assumptions on $Q$.) In this case, the algorithm obtains the same convergence rate as sequential SGD, up to a logarithmic factor. This is achieved by using, at each iteration, a multidimensional approximate agreement algorithm, tailored for the cluster-based model. The algorithm for arbitrary functions requires that at least a majority of the clusters contain at least one nonfaulty process. We prove that this condition is necessary when optimizing some non-convex functions.
A Frequent Subgraph Publishing Algorithm Based on Differential Privacy

Frequent subgraph mining is a common technology in data mining and has important application value. However, If graph data contains private information, publishing frequent subgraphs and support directly will lead to privacy leakage. Aiming at the problem of lower availability of the published results of the existing differential privacy frequent subgraphs publishing algorithms, this paper proposes a frequent subgraphs publishing algorithm based on differential privacy (DPFSG). First, in order to improve the availability of frequent subgraphs, we design an improved frequent subgraphs preprocessing strategy based on exponential mechanism. We reduce the scale of candidate subgraphs based on threshold, and mine frequent subgraphs combined with exponential mechanism. Secondly, in order to improve the availability of the published results of the frequent subgraphs support, we combine the hierarchical clustering method to cluster and group the support of each frequent subgraphs, and then use the Laplace mechanism to add noise to each group. Theoretical analysis and experimental results verify that the method improves the availability of published data while preserving privacy.
Entanglement generation through Markovian feed-back in open two-qubit systems

Abstract We discuss the generation and the long-time persistence of entanglement in open two-qubit systems whose reduced dissipative dynamics is not a priori engineered but is instead subjected to filtering and Markovian feedback. In particular, we analytically study (1) whether the latter operations may enhance the environment capability of generating entanglement at short times and (2) whether the generated entanglement survives in the long-time regime. We show that, in the case of particularly symmetric Gorini–Kossakowski–Sudarshan–Lindblad it is possible to fully control the convex set of stationary states of the two-qubit reduced dynamics, therefore the asymptotic behaviour of any initial two-qubit state. We then study the impact of a suitable class of feed-back operations on the considered dynamics.
A PRODUÇÃO DE CONTROVÉRSIAS E INCERTEZAS NA VACINAÇÃO CONTRA A COVID-19 EM GESTANTES: NECROPOLÍTICA EM AÇÃO

fi cial documents from government agencies, medical entities and women's health rights associations and media reports.It is concluded that vaccination against Covid-19 in Brazil for pregnant women did not happen without many doubts, fears and the dissemination of false and harmful information that led to preventable deaths.
On adaptive learning paths using hidden Markov models

Due to the global impact of COVID-19, the use of non-face-to-face learning is increasing. For non-face-to-face learning, it is important to create a learning path based on efficiency. This study introduces the hidden Markov model (HMM) as a method of creating a learning path known as a network in which knowledge concepts are arranged in order, that is, the path of experience that students may encounter in class. and it aims to improve the accuracy of path prediction by using a variable selection technique that includes least absolute shrinkage and selection operator (LASSO), and random forest (RF) before performing HMM. In addition, this study aims to show that the learning path based on higher-order concepts made of precedence relationships from HMM is more accurate than other candidate paths. As a result of using data shared by AI-hub (https://aihub.or.kr/), the performance of HMM when selecting relational pairs using LASSO, and RF was improved significantly, and the case of using HMM when evaluating the learning path consisting of higher concepts was excellent in terms of model goodness of fit compared to other models.
A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval

Content-based image retrieval is the process of retrieving a subset of images from an extensive image gallery based on visual contents, such as color, shape or spatial relations, and texture. In some applications, such as localization, image retrieval is employed as the initial step. In such cases, the accuracy of the top-retrieved images significantly affects the overall system accuracy. The current paper introduces a simple yet efficient image retrieval system with a fewer trainable parameters, which offers acceptable accuracy in top-retrieved images. The proposed method benefits from a dilated residual convolutional neural network with triplet loss. Experimental evaluations show that this model can extract richer information (i.e., high-resolution representations) by enlarging the receptive field, thus improving image retrieval accuracy without increasing the depth or complexity of the model. To enhance the extracted representations’ robustness, the current research obtains candidate regions of interest from each feature map and applies Generalized-Mean pooling to the regions. As the choice of triplets in a triplet-based network affects the model training, we employ a triplet online mining method. We test the performance of the proposed method under various configurations on two of the challenging image-retrieval datasets, namely Revisited Paris6k (RPar) and UKBench. The experimental results show an accuracy of 94.54 and 80.23 (mean precision at rank 10) in the RPar medium and hard modes and 3.86 (recall at rank 4) in the UKBench dataset, respectively.
Research on the Construction of Electronic Information Professional Groups in Higher Vocational Colleges Serving the Upgrading of Regional Information Industry

Through studying the current situation of the construction of electronic information professional groups in higher vocational colleges serving the upgrading of regional information industry, this paper proposes establishing inter-school professional groups from a horizontal perspective, making appropriate adjustments on the professional groups, and actively adapting to the industrial development. On this basis, the integration of industry-education and in-depth cooperation between schools and enterprises can be realized, and the path of realizing “three-sharing, three-integration” professional group service industry upgrading is innovatively proposed.
Data Leakage and Privacy Concerns in Public Bug Bounty Platforms

For long-term relationships, the websites of various businesses store the PII and PHI of customers, which are mainly targeted by hackers. Cyber breaches mainly result in lack of customer trust and downfall of the business reputation. As a result, the customers become reluctant to share PII and PHI with online businesses until provided with the protection of sensitive data. The online resources of a business need to be in compliance with GDPR and PCI DSS. Companies undergo penetration testing of the infrastructure; for this, paid white hat hackers are hired under a legal contract. The companies also adopt RVDP program, where the Bug Bounty Platforms is one of the variants of RVDP. Security researchers are rewarded with an amount of bounty in the form of money or name in the hall of fame at official website for bugs' identification. Ideally, the security researcher would perform cyber security assessment. The vulnerabilities would be reported to intended stakeholders and the remediation measures with great deal of care so that PII and PHI won't be exploited by anyone.
Forgery detection of low quality deepfake videos

The rapid growth of online media over different social media platforms or over the internet along with many benefits have some negative effects as well. Deep learning has many positive applications like medical, animations and cybersecurity etc. But over the past few years, it is observed that it is been used for negative aspects as well such as defaming, black-mailing and creating privacy concerns for the general public. Deepfake is common terminology used for facial forgery of a person in media like images or videos.The advancement in the forgery creation area have challenged the researchers to create and develop advance forgery detection systems capable to detect facial forgeries. Proposed forgery detection system works on the CNN-LSTM model in which we first extracted faces from the frames using MTCNN then performed spatial feature extraction using pretrained Xception network and then used LSTM for temporal feature extraction. At the end classification is performed to predict the video as real or fake. The system is capable to detect low quality videos. The current system has shown good accuracy results for detecting real or fake videos on the Google deepfake AI dataset.
Secure authentication framework based on one-time password for internet of things

Like other systems, the IoT field needs to be secured and efficient. IoT network knows as a complex network because of the number of devices it contains. An important part of the security is the authentication that allows different devices to identify each other and authenticate themselves. Authentication can be achieved using different methods from the simplest static pass-words to the encrypted dynamic password. Unlike other systems, IoT devices have less processing power capability and a short memory; therefore, authentication schemes should be suitable. The authentication field is the potential target for many attackers because it is designed to protect systems. Hence, attackers developed many ways to get into systems. On the other side, security researchers and specialists evolved accordingly to cope with attacks. Authentication begins with static passwords, then a one-time password, and a multi-factor. How-ever, a secure authentication framework based on a one-time password (OTP) is going to be proposed in this paper.
Enhancing citizens' well-being using a gait monitoring smart wearable device

Advances in technology have always been a determining factor that marked significant changes in the course of humanity with multiple effects on several different areas. From simple everyday things like a watch to entire cities tend to become smart and this way people&apos;s lives become safer and healthier. For this reason, Smart Insole was developed, an innovative, smart system that monitors the person&apos;s gait and is used to detect a fall and inform the appropriate authority. This paper presents the architecture of the system and emphasizes the laboratory tests that were conducted and emulated real-world use cases for the developed system.
A Study on Current Issues Related to the Anti-Corruption Act

This study aims to examine the current issues of the Anti-Corruption Act focusing on the revised bill. In Chapter 2, the legal issues related to the protection and compensation support for whistleblowers were reviewed following: first, vitalization of the corruption reporting system through the essential reduction of penalties; second, reinforcement of reporter protection strengthening protection procedures for reporting person, easing requirements for temporary suspension decision for ‘procedural pre-protection and post-review’ of reporting person); third, improving the effectiveness of subrogating the right to claim compensation for damages. Next, in Chapter 3, legal issues related to the strengthening of citizens' surveillance and participation functions were considered through securing the timeliness and effectiveness of the national audit request system. Lastly, in Chapter 4, legal issues related to strengthening the integrity of public institutions were reviewed as follows: first, investigation and evaluation of corruption in public institutions and imposition of administrative fines; second, the use of business secrets by employees of public institutions; third, the punishment on acquiring property by using undisclosed information acquired in official affairs unrelated to work.&#x0D; It is impossible to prevent massive corruption without aversion to small corruption. Moreover, The problems corruption are serious not only in the public sector, but also in the private sector. Therefore, I agree with the point that solution should be changed from public office-centered corruption to public interest-centered corruption in the future.&#x0D; The rule of law has developed as a principle of liberalism to guarantee the freedom of the people, and is recognized today as a component of liberal democracy along with separation of powers.&#x0D; Anti-corruption needs to be approached from the perspective of overcoming the crisis of the rule of law, because corruption has the meaning of eroding the rule of law.