Reviewerly/exHarmony huggingface dataset:
Research into the Application Effect of Basketball Teaching Method in the Multimedia Environment

Under the traditional teaching mode, in order to enable students to master the correct basketball technology and sports skills, teachers usually demonstrate and let students practice through repeated actions.However, it is inevitable that students will get bored when they repeat a lot of exercises.Multimedia embodies pictures, texts and sounds, so it can vividly show the dynamic change process of technical actions and effectively stimulate the enthusiasm of students in learning basketball techniques.Therefore, some multimedia content such as micro lesson explanation and game video can be added in basketball teaching to solve the difficult display of complex technical actions; and we can also record videos of the teaching process to clarify the teaching deficiencies and adjust teaching strategies in time, effectively promoting the innovative development of basketball teaching.
Pre-service teachers’ evaluation of the student internship program: Basis for design improvement

This descriptive-survey study aimed to evaluate the implementation of the institute at City College of Angeles.Obtained data for this study were solicited via judgment sampling from 84 pre-service teachers from the Bachelor of Physical Education and Bachelor of Performing Arts programs who had their internship last academic year 2021-2022.Descriptive statistics such as frequency, mean and standard deviation were used to describe the quality of the internship program based on institutional and company evaluation, program effectiveness, and thematic analysis for the comments and suggestions of students on the internship program offered.Based on the results, the internship program of the institute is very effective since it is responsive to the community's needs, and students develop skills needed for their careers.On the other hand, the deployment of students to various public schools emerged as the central theme based on the results.On a positive note, pre-service teachers highly appreciate the internship program.The results of this study are expected to improve the program design of the pre-service teachers of the institute.Regular review of the curriculum and offered courses matching the needs of the industry should be performed.Additionally, recommendation on the possibility of forging linkages with other private schools and industries related to the program is a result of this study.Also, formulation and development of the institutional policies and guidelines of the program or deployment manual, which may serve as a reference for pre-service teachers, is highly suggested.
DEVELOPMENT OF NUMERATION LITERACY MODULE BASED ON REALISTIC APPROACH FOR ELEMENTARY SCHOOL

This research aims to expand module study literacy numeration based on approach realistic. Supported from the PISA survey, TIMSS test, and based on the results assessment national student ability in literacy numeration under competence minimum. Type this research is research and development using the ADDIE model. subject in this research students classes IV SDN 104223 Bingkawan. Research results show module study literacy numeration based on realistic approach from the results validation of expert material, expert design and expert language show this module is “Very Eligible” for participants to use in learning education.
The instrument development to measure the verbal ability of prospective high school students

An alternative for determining an accurate major for prospective high school students is not only based on academic scores but also on the results of the scholastic aptitude test (SAT). Verbal ability is an SAT subtest that assesses language management, vocabulary, and problem-solving abilities through a complete language study. This study developed a verbal ability test instrument for junior high school students consisting of the ability of synonyms, antonyms, and analogies. The data was collected from 300 junior high school students in grade nine who took a test with dichotomous data. The data analysis approach used one-order confirmatory factor analysis (CFA) with correlation factors. The results showed that CFA with correlation factors indicated the construct validity of the instrument was valid with the index criteria value =446.80, &lt;em&gt;df&lt;/em&gt;=389, &lt;em&gt;p&lt;/em&gt;-value=0.02267, root mean squared error of approximation (RMSEA)=0.022, goodness of fit index (GFI)=0.91, adjusted goodness-of-fit (AGFI)=0.89, and comparative fit index (CFI)=0.98. Then, construct reliability has good reliability with coefficient values for each dimension of 0.93, 0.95, and 0.84. As for the composite reliability of 0.88. It shows that using the verbal ability test instrument is feasible and has a reliable scale to measure the ability of junior high school students.
Cyber Risks in 3D Printing

With the increasing use of 3D printing, Corey Nachreiner, CTO at WatchGuard Technologies, looks at the potential cyber risks and how to keep this fast-growing technology safe.
Machine Learning to Improve Software Quality

CODE DEFECT AI TOOL IS ABLE TO PREDICT LIKELIHOOD OF BUGS IN SOURCE CODE.
Stronger 3SUM-Indexing Lower Bounds

The $3$SUM-Indexing problem was introduced as a data structure version of the $3$SUM problem, with the goal of proving strong conditional lower bounds for static data structures via reductions. Ideally, the conjectured hardness of $3$SUM-Indexing should be replaced by an unconditional lower bound. Unfortunately, we are far from proving this, with the strongest current lower bound being a logarithmic query time lower bound by Golovnev et al. from STOC'20. Moreover, their lower bound holds only for non-adaptive data structures and they explicitly asked for a lower bound for adaptive data structures. Our main contribution is precisely such a lower bound against adaptive data structures. As a secondary result, we also strengthen the non-adaptive lower bound of Golovnev et al. and prove strong lower bounds for $2$-bit-probe non-adaptive $3$SUM-Indexing data structures via a completely new approach that we find interesting in its own right.
Improved Pattern-Avoidance Bounds for Greedy BSTs via Matrix Decomposition

Greedy BST (or simply Greedy) is an online self-adjusting binary search tree defined in the geometric view ([Lucas, 1988; Munro, 2000; Demaine, Harmon, Iacono, Kane, Patrascu, SODA 2009). Along with Splay trees (Sleator, Tarjan 1985), Greedy is considered the most promising candidate for being dynamically optimal, i.e., starting with any initial tree, their access costs on any sequence is conjectured to be within O(1) factor of the offline optimal. However, despite having received a lot of attention in the past four decades, the question has remained elusive even for highly restricted input.In this paper, we prove new bounds on the cost of Greedy in the “pattern avoidance” regime. Our new results include:• The (preorder) traversal conjecture for Greedy holds up to a factor of O(2α(n)), improving upon the bound of 2α(n)O(1) in (Chalermsook et al., FOCS 2015) where α(n) is the inverse Ackermann function of n. This is the best known bound obtained by any online BSTs.• We settle the postorder traversal conjecture for Greedy. Previously this was shown for Splay trees only in certain special cases (Levy and Tarjan, WADS 2019).• The deque conjecture for Greedy holds up to a factor of O(α(n)), improving upon the bound 2O(α(n)) in (Chalermsook, et al., WADS 2015). This is arguably “one step away” from the bound O(α*(n)) for Splay trees (Pettie, SODA 2010).• The split conjecture holds for Greedy up to a factor of O(2α(n)). Previously the factor of O(α(n)) was shown for Splay trees only in a special case (Lucas, 1988).The input sequences in traversal and deque conjectures are perhaps “easiest” in the pattern-avoiding input classes and yet among the most notorious special cases of the dynamic optimality conjecture. Key to all these results is to partition (based on the input structures) the execution log of Greedy into several simpler-to-analyze subsets for which classical forbidden submatrix bounds can be leveraged. We believe that this simple method will find further applications in doing amortized analysis of data structures via extremal combinatorics. Finally, we show the applicability of this technique to handle a class of increasingly complex pattern-avoiding input sequences, called k-increasing sequences.As a bonus, we discover a new class of permutation matrices whose extremal bounds are polynomially bounded. This gives a partial progress on an open question by Jacob Fox (2013).* The full version of the paper can be accessed at https://arxiv.org/abs/2211.04112
Fully Homomorphic Encryption for Data Security Over Cloud

From the past few years cloud services are so popular and are being used by many people from various domains for various purposes such as data storage, e-mails, backing up data and much more. While there were many options to perform such things why did people choose cloud? The answer is clouds are more flexible, convenient, reliable and efficient. Coming to security of data over cloud, it is secure to store data over cloud rather than storing data locally as there is chance of some computer breakdown or any natural disaster may also occur. There are also many threats for data security over cloud namely data breaching, lack of access-key management and much more. As the data has been processed and being stored online for various purposes, there is a clear requirement for data security. Many organizations face various challenges while storing their data over cloud such as data leakages, account hijacking, insufficient credentials and so on. So to overcome these challenges and safeguard the data, various encryption techniques were implemented. However, even though encryption is used, the data still needs to be decrypted in order to do any type of operation. As a result, we must choose a manner in which the data can be analyzed, searched for, or used in any other way without needing to be decoded. So, the objective is to introduce a technique that goes right for the above conditions mentioned and for data security over cloud.
KOMPARASI METODE SVM, K-NN DAN NBC PADA ANALISIS SENTIMEN

The beauty of Bali raises many comments about how a trip to Indonesia is not complete without going to Bali. In the tourism industry the application of tourist satisfaction and perspective is very important, but most of them still apply surveys. The survey-based approach has weaknesses such as operational costs, the potential for data duplication, and a lack of comprehensiveness. Sentiment analysis of natural tourism objects is performed by classifying positive and negative comments in the Jatiluwih tourist comment dataset. The focus of this panel's sentiment analysis is on comments related to the Natural Tourism Attractiveness Criteria. According to the Directorate General of Forest Protection and Nature Conservation in 2003, the criteria for natural tourism objects are tourist attraction, market potential, accessibility, socio-economic environmental conditions, public services, climate conditions, supporting facilities and infrastructure, and the availability and safety of clean water. This study compares the SVM, K-NN, and NBC methods. This study aims to provide a comprehensive analysis of the performance of each method using the confusion matrix. The results showed that the K-NN method was superior to SVM and NBC in terms of testing accuracy and precision, where accuracy on K-NN gave a value of 93.4%, SVM 93.1%, and NBC 87.9%.
Histopathological Image Classification Methods and Techniques in Deep Learning Field

A cancerous tumour in a woman's breast, Histopathology detects breast cancer. Histopathological images are a hotspot for medical study since they are difficult to judge manually. In addition to helping doctors identify and treat patients, this image classification can boost patient survival. This research addresses the merits and downsides of deep learning methods for histopathology imaging of breast cancer. The study's histopathology image classification and future directions are reviewed. Automatic histopathological image analysis often uses complete supervised learning where we can feed the labeled dataset to model for the classification. The research methods are frequentlytrust on feature extraction techniques tailored to specific challenges, such as texture, spatial, graph-based, and morphological features. Many deep learning models are also created for picture classification. There are various deep learning methods for classifying histopathology images.
A New Verification Process for Assessing Intended Learning Outcomes

Continuance and accurate quality assessment of any academic program is the primary indicator of its progress and success. Quality assessment processes are mainly conducted to improve students’ learning outcomes. Successful quality assessment processes should demonstrate clearly how an educational program had achieved its intended learning objectives. To validate the accomplishment of these intended objectives by a course instructor, students’ assessments are usually used to demonstrate that the instructor had covered all relevant contents and material within his lectures. The main drawback of this approach is that it cannot prove that students had really attained the required knowledge and skills for each intended learning objective. In this paper we are proposing a general model and a tool that supports the evaluation of each intended learning objective within a course based on the actual students’ performance on that objective. It measures the accomplishment of an objective not only based on instructor’s opinion but also on the actual students’ assessments results related to that specific objective. For abstraction, the model is consisted of a several layered architecture and a main assessment process. A specialized tool which follows this proposed model has been also developed. It supports a course reviewer to accurately evaluate the accomplishment of each and every indented learning objective within a course. The reviewer’s evaluation will be basically based on the averaged students’ scores and the instructor’s own rating. In addition, this tool will provide the reviewer with the ability to back-track that averaged score to its original assessment objects’ scores
Concerns from Cloud Security Issues: Challenges and Open Problems

The Cloud has become a significant topic in computing; however, the trend has established a new range of security issues that need to be addressed. For example, the owners of data might be worried because the data and associated software are not under their control but rather possessed by the Cloud. In addition, the data owner may not be aware of where the data is geographically located at any particular time. So a question arises as to how secure is the data contained in the Cloud. To help address this question, clients of the Cloud should be given the ability to assess the effectiveness of the Cloud’s security measures. Other concerns include unwarranted and unauthorized stoppage of clients’ services in the Cloud, breach of security measures, and disruption to service availability. In this article, perspectives from Cloud computing practitioners are shown to help address clients concerns and bring about awareness of the measures put in place to ensure the security of the client services running in the Cloud. In addition, we have reviewed a number of the existing Cloud security approaches and techniques to put a systematic survey of the current security issues in the Cloud environment. Furthermore, we discuss challenges and open problems that these mechanisms face to be effective and efficient.
TRENDS IN CIRCULATION SERVICES OF THE UNIVERSITY LIBRARIES

Purpose: The primary purpose of this study was to examine the current practices of circulation service and its’ effectiveness in the University libraries. Design: The present study has applied the descriptive research method using literature analysis along with structured questionnaire as a part of primary data collection tools. The research sample was 27 university libraries spread in different domain and ownerships such as government funded and private universities. Findings: The finding of the study revealed that the majority of the university libraries are extensively using technology for circulation such as barcodes, QR codes, 26% universities libraries are having RFID systems and self-circulation kiosks. Originality: The preliminary investigation and research gap based on the review of literature has revealed that no attempts have been made to study the circulation services and methods comprehensively in the university libraries of Gujarat state, however, it is seen that very a few studies have been conducted on different aspects of circulation service in India. University libraries in Gujarat are using different library software and universities are offering different courses in a variety of domains
A Blockchain-Based Tamper-Resistant Logging Framework

Abstract Since its introduction in Bitcoin, the blockchain has proven to be a versatile data structure. In its role as an immutable ledger, it has grown beyond its initial use in financial transactions to be used in recording a wide variety of other useful information. In this paper, we explore the application of the blockchain outside of its traditional decentralized, financial domain. We show how, even with only a single “mining” node, a proof-of-work blockchain can be the cornerstone of a tamper resistant logging framework. By attaching a proof-of-work to blocks of logging messages, we make it increasingly difficult for an attacker to modify those logs even after totally compromising the system. Furthermore, we discuss various strategies an attacker might take to modify the logs without detection and show how effective those evasion techniques are against statistical analysis.
Low-light image enhancement model based on improved SCT in indoor scenes

For the low-light enhancement model SCT, lowlight enhancement in an indoor environment, there will be some textures that are usually not characteristics of the object. These fake textures can generate misunderstandings. An improved SCT model is proposed in response to this problem, called SCT++. The improved model adds the spatial uniform loss function, the color constant loss function, and the brightness smoothing loss function. Comparing the SCT model and the SCT++ model through the LOL dataset, it is found that the convergence rate of the SCT++ model is greater than that of the SCT model, and the convergence rate is improved. Finally, SCT++, SCT, DCE++, GAN, LIME, LLVE, and RUAS were compared, and PSNR, SSIM, NIQE, LPIPS, MSE, and other image evaluation methods were selected. It is concluded that the SCT++ model is better than the SCT model in terms of PSNR, LPIPS, and MSE.
Global Optimization of Plane-transition Locations in Biped Wall-climbing Robots

It is important and challenging for biped wall-climbing robots to plan the plane-transition locations which satisfy the positional and kinematic constraints in a complex 3D environment. In this paper, we propose a plane-transition location optimization model and a projection-based plane-transition location planner to optimize the plane-transition locations. The core idea behind is that point-to-point projection generates the approximately shortest surface path because of its straight-line properties. Therefore, there are a series of project line segments that are translated on the wall. To deal with the positional and kinematic constraints of the absorption points, the plane-transition locations were transformed into the intersection points of the project translation line segment and the common vertical plane formed by two neighboring via walls. The simulation results with W-Climbot show that the proposed algorithm can efficiently and robustly plan the plane-transition locations of biped wall-climbing robots. It has a solving success rate of 98%. In the cases where the number of via walls is less than 10, the proposed algorithm solved the problem within 0.5s, which is significantly lower than particle swarm optimization(PSO). And the minimum climbing steps obtained are also significantly lower than PSO.
Chatbot using NLP

A chatbot is a application which is mainly used to provide text-based or text-to-speech online chat conversations in place of live human chat operators. The chatbot aims to establish connection between and a machine and a human. The machine incorporates data so that it can recognize the sentences and come to a conclusion in response to an inquiry. The response principle is the matching of the user's input phrase. The current technological effort involves establishing a chatbot-based system for college inquiries that uses Natural Language Processing to send natural language to a server. In this project, we're creating a chatbot that can answer users' questions about the college. The user will be able to ask the chatbot different questions about the college, including inquiries about the infrastructure and courses. The user will able to ask various questions to the chatbot about the college, the questions may be concerned with the infrastructure and courses.
WEBSITE BASED EMPLOYEE ACCEPTANCE INFORMATION SYSTEM AT PT NOTO TEKNOLOGI INDONESIA

The development of information technology is currently growing very rapidly, many companies want to improve their work processes by using information systems to process company data more quickly and efficiently. The process of accepting prospective employees at PT Noto Teknologi Indonesia is still done manually, i.e. prospective employees must register and deliver job application files to the office. To find out the results of the selection of employee recruitment, prospective employees still get information via WhatsApp. This makes the acceptance of new employee candidates less effective and efficient because it will take a lot of time and the possibility of data errors. So in this study a website based recruitment information system was created to facilitate the process of accepting new employees. The method used in making this system is the waterfall method where this method has 5 stages, namely user needs analysis, system design, implementation, testing and maintenance by PT Noto Teknologi Indonesia. The results of the black-box test show valid results for all features which indicate that this system is as expected. From the results of black-box testing, it can be concluded that this system is feasible to use as a tool to facilitate the process of accepting new employees at PT Noto Teknologi Indonesia.
Automated ethnicity recognition using equilibrium optimizer with machine learning on facial images

In recent times, computer vision related face image analysis has gained significant attention in various applications namely biometrics, surveillance, security, data retrieval, informatics, etc. The main objective of the facial analysis is to extract facial soft biometrics like expression, identity, age, ethnicity, gender, etc. Of these, ethnicity recognition is considered a hot search topic, a major part of community with deep connections to many social and ecological concerns. The deep learning and machine learning methods is merit for effective ethnicity classification and recognition. This study develops a facial imaging based ethnicity recognition using equilibrium optimizer with machine learning (FIER-EOML) model. The goal of the FIER-EOML technique is to detect and classify different kinds of ethnicities on facial images. To accomplish this, the presented FIER-EOML technique applies an EfficientNet model to generate a set of feature vectors. For ethnicity recognition, the presented model uses long short-term memory method. To improve the recognition performance, the FIER-EOML technique utilizes EO algorithm for hyperparameter tuning process. The performance validation of the FIER-EOML technique is tested on BUPT-GLOBALFACE dataset and the results are examined under several measures. The comprehensive comparison study reported the enhanced performance of the FIER-EOML technique over other recent approaches.