Reviewerly/exHarmony huggingface dataset:
Unpacking the Emergence of Digital Entrepreneurship Ecosystems: Qualitative Evidence from Malanshan

Digital entrepreneurial ecosystems (DEE) command increasing attention from policy makers and scholars, yet DEE in peripheral area is underdeveloped and extant studies about the emergence of DEE is insufficient. We adopt the actor–network theory as our theoretical lens and conduct an in-depth case study at Malanshan, China. Case analysis reveals that the quadruple-E framework is formulated to demonstrate the Malanshan DEE emergence, in each mechanism digital elements and space providing different degrees of affordances with different contents and completing translation process by resolving controversies. Our study contributes to the DEE literature by splitting digital elements from organizations and providing a temporal perspective for the emergence of DEEs. It also contributes to the digital transformation on the ecosystem level, which has mainly focused on the individual firm, network, and business ecosystem levels. Regarding practical implications, policymakers can use our findings to develop their local DEEs.
Human resources development strategy use Backpropagation Artificial Neural Networks

The strategy for developing human resources for recruitment so that at the same time becoming a reliable workforce as expected is the goal of personnel in certain offices or agencies. This step must be taken by the management of companies or institutions, both public and private, in order to improve human resources (HR). Until now, there has never been any research on the conventional acceptance of prospective employees to test how accurate their performance is. In this study the conventional selection system for prospective employees will be used as a basic concept to find methods for analyzing the performance of prospective employees using computer media with an artificial neural network system approach with the backpropagation method. So that the accuracy of the predictive patterns of prospective new employees is obtained. So that finally the personnel of government and private agencies obtain actual information about the performance of prospective employees who will be accepted as workers. The results of the study using hidden layers and learning constants obtained the fastest convergent value at 3377, and the final results of this study will be published in a national journal accredited SINTA 4 or better.
Gesture retrieval and its application to the study of multimodal communication

Abstract Comprehending communication is dependent on analyzing the different modalities of conversation, including audio, visual, and others. This is a natural process for humans, but in digital libraries, where preservation and dissemination of digital information are crucial, it is a complex task. A rich conversational model, encompassing all modalities and their co-occurrences, is required to effectively analyze and interact with digital information. Currently, the analysis of co-speech gestures in videos is done through manual annotation by linguistic experts based on textual searches. However, this approach is limited and does not fully utilize the visual modality of gestures. This paper proposes a visual gesture retrieval method using a deep learning architecture to extend current research in this area. The method is based on body keypoints and uses an attention mechanism to focus on specific groups. Experiments were conducted on a subset of the NewsScape dataset, which presents challenges such as multiple people, camera perspective changes, and occlusions. A user study was conducted to assess the usability of the results, establishing a baseline for future gesture retrieval methods in real-world video collections. The results of the experiment demonstrate the high potential of the proposed method in multimodal communication research and highlight the significance of visual gesture retrieval in enhancing interaction with video content. The integration of visual similarity search for gestures in the open-source multimedia retrieval stack, vitrivr , can greatly contribute to the field of computational linguistics. This research advances the understanding of the role of the visual modality in co-speech gestures and highlights the need for further development in this area.
Key Frame Extraction from Videos Based on SIFT and Structural Similarity

With the advancement of high-resolution cameras, a large number of videos are captured using mobile devices. Extracting meaningful information from such videos is important for video summarization, fast archival, and storage. Key frames are the basic elements of videos that represent the salient features of a video stream. Due to the large volume, complex structure, and redundancy in the frames, key frame extraction is applied as a pre-processing work for analyzing any video content and compressing a video. In this work, a new key frame extraction technique is presented that uses the scale-invariant feature transform (SIFT) along with local features of the image like frame difference, and structural similarity (SSIM) of two consecutive frames. Candidate key frames are computed independently using the SSIM and SIFT techniques. Using suitable thresholds on SSIM and SIFT-based matching techniques, the key frames are computed. The qualitative and quantitative analysis has been carried out to evaluate the proposed algorithm on a state-of-the-art video dataset, and the experimental results show the efficacy of the algorithm proposed.
Estimating Job Satisfaction and Commitment Correlate of Senior Staff Members of the University for Development Studies, Tamale, Ghana

Purpose: Employees’ Job Satisfaction (JS) is becoming an important asset to an organisation, especially in the recent past. All other things being equal, satisfied employees are likely to be more productive as a result of citizenship and/or loyalty, decreased-absenteeism, high-commitment and so on. With the knowledge that a positive relationship may exist between employees JS and their commitment, it becomes imperative that management of universities institute measures to regularly collect data on employees’ JS. Thus, this study examined Senior Staff (SS) JS and their commitment relationship in the University for Development Studies (UDS), Tamale.&#x0D; Methodology: A structured questionnaire was administered to 231 Senior Staff (SS) members of the University. Of this number, which was determined using the Taro Yamane’s formula for sample size determination, valid data retrieved and analysed represented a response rate of 92 per cent (212). Whilst the study employs One-Way ANOVA to compare the JS and the commitment level among the SS employee group to the University, Correlation and Regression Analyses were performed to establish associations and to determine the cause-effect relationship between the two variables.&#x0D; Findings: The results of the study indicated that 74 per cent of SS commitment is influenced by their satisfaction level. Thus, the study concluded that the management of the University should priorities staff JS in almost all its corporate decisions, as this will engender their commitment to the University.&#x0D; Unique Contribution to Practice and Policy: Organisational Commitment (OC) with all its dimensions (affective, continuance and normative) were confirmed to have a positive relationship with the JS of SS of a multi-campus public university; suggesting that OC does not only predicts JS in terms of teaching staff, as indicated by previous studies, but it also does so even with SS (who are in the non-teaching category). It was, therefore, recommend, based on the findings of this study, that the management of the UDS should priorities staff JS in almost all its corporate decisions, as this will engender their commitment to the University.&#x0D;
Empowering the Worker in Industry 4.0: A Study on Redefining Talent, and Evolving Competencies

Talent, the intangible asset of an organization, is the source of competitive advantage, making talent management (TM) practices more prevalent across industries. The business environment of the global economy has entirely transformed since the call of the
Retweet Prediction Based on User-Based, Content-Based, Time-Based Features Using ANN Classification Optimized with the Bat Algorithm

Twitter is one of the most popular social media platforms today for information dissemination. It is favored by the public due to its real-time information sharing capabilities. Twitter provides two important features for information dissemination: Tweets and Retweets. Tweets allow users to write messages that can be instantly shared. Each tweet can contain text, media such as images, videos, or URLs. Retweets allow users to repost someone else's tweet and distribute it to their own followers. The Retweet feature is considered an effective way to spread information, as a high number of retweets indicates that the information in the tweet is spreading quickly and widely. This research aims to predict retweets based on several features: User-Based Feature, Content-Based Feature, and Time-Based Feature. The classification method used is Artificial Neural Network, which is optimized using a Nature-Inspired Algorithm called Bat Algorithm. The evaluation results of this study show an accuracy of 86%, precision of 87.8%, recall of 93.6%, and F1-score of 90.6% without imbalance class handling. Under Undersampling condition, the accuracy is 80.8%, precision is 91.0%, recall is 81.4%, and F1-score is 85.9%. Under Oversampling condition, the accuracy is 82.4%, precision is 89.6%, recall is 85.6%, and F1-score is 87.5%. These results indicate that using user-based, content-based, and time-based features, applying Artificial Neural Network classification method, and optimizing hyperparameters using Bat Algorithm are effective in predicting retweets.
Technological Solutions to Governance Problems in International Business - A Blockchain Perspective

The complexity of business relationships has made governance issues critical for their success. Governance issues have become especially critical for Multinational Corporations (MNCs) as they navigate the complex landscape of different cultures, laws, and regulations in the countries in which they operate. Further, technologies like blockchain have disrupted traditional models of business, creating new challenges and opportunities for governance. This paper presents a technological solution to the governance challenges faced by MNCs from a blockchain technology perspective. We highlight the way blockchain technology can help in overcoming different inter and intra-organization governance costs and thus offer itself as an alternate mechanism for the governance of international business relations. Specifically, blockchain features like immutability and peer-to-peer sharing offer the mechanism to bypass the need for trust in international business relations, thus the opportunity to overcome different governance costs. Taking this further, this paper presents a blockchain-business governance framework, which organizations can use to navigate different governance challenges in the international business context.
An Analysis of College Student Experience on the Use of Metaverse Space in Liberal Arts Lecture

Objectives This study investigated university students' experiences with the use of metaverse in humanities lectures, utilizing the Gather town platform.&#x0D; Methods 15 university students who had taken humanities courses participated in the in-depth interviews by constructing a semi-structured questionnaire, and the interview data was transcribed verbatim and analyzed using a continuous comparative analysis method.&#x0D; Results The analysis resulted in 5 categories, 14 subcategories, and 37 concepts were derived, and the 5 categories were 1)expectations and concerns before metaverse classes, 2)limitations of relaxed communication versus exchange, 3)reflection and application of communication, 4)new identity through avatars, and 5)relationship formation through presence. The subcategories of ‘expectations and concerns before metaverse class’ were the expectation of a new class space and the burden of new attempts. The subcategories of ‘limitations of relaxed communication versus exchange’ were more comfortable than face-to-face meetings, honest conversation, and deep communication limitations due to the absence of non-verbal information. The subcategories of ‘reflection and application of communication’ were self-recognition of communication methods, application of communication skills acquired through experience, and attempts to communicate more actively than usual. The subcategories of the ‘new identity through avatars’ were free conversations that do not reveal themselves, doubts and concerns in relationships, encounters without prejudice.&#x0D; Conclusions Through this study, by deeply exploring college students' experiences of using the metaverse space in liberal arts lectures, it provided implications for the development of education and counseling programs using the metaerse space in the future.
Deep Learning-Based Approach for Automatic Detection of Malaria in Peripheral Blood Smear Images

Malaria is a deadly disease that affects millions of people around the world every year. An accurate and timely diagnosis of malaria is essential for effective treatment and control of the disease. In this study, we propose a deep learning-based approach for automatic detection of malaria in peripheral blood smear images. Our approach consists of two stages: object detection & binary classification using Faster R-CNN, and multi-class classification using EfficientNetv2-L with SVM as the head. We evaluate the performance of our approach using the mean average precision at IoU = 0.5 (mAP@0.5) metric. Our approach achieves an overall performance of 88.7%, demonstrating the potential of deep learning-based approaches for accurate and efficient detection of malaria in peripheral blood smear images. Our study has several implications for the field of malaria diagnosis and treatment. The use of deep learning-based approaches for malaria detection could significantly improve the accuracy and speed of diagnosis, leading to earlier and more effective treatment of the disease.
Survey Study on the Increase of Arabic Learning Due to Smartphones

Background. The Covid-19 pandemic is growing and spreading in various countries, changing various fields of people's lives, especially in Indonesia for some time. Purpose. The purpose of this observation is to find out the use of smartphones for students of public madarasah tsanawiyah (mtsn) 11 tanah datar when studying online. this observation is through a quantitative approach through a survey study method and selecting a sample by randomly determining 10 sources who live in the dormitory, 7th grade mtsn 11 tanah datar students have given questionnaire answers using Google from. Method. The research tool was adapted from several writings, the tool made 5 questions. Results. The results of the observation found that there were 7 people who had their own smartphones and 3 students used their parents' smartphones, relatives and other people in online learning, the time used to study via smartphones was between 5 to 6 hours. mtsn 11 flatland students using smartphones are classified as good categories in learning, doing and collecting assignments. So that students are able to discuss with their friends and all feel satisfied in using smartphones in online learning activities. Conclusion. suggestions for further researchers to use attitude variables, behavior and the impact of using smartphones for mtsn 11 flatland students who have their own smartphones.
The Influence of Snakes and Ladders Game Media, Concrete Media and Initial Skills on Mathematics Learning Outcomes

This study aims to determine the use of snakes and ladders game media, concrete media, and initial skills can improve student learning outcomes. This methode f research is experimental research using a positivism-quantitative approach. The subjects of this study were students at SDN Jumputrejo class VB and class VC and SDN Sukodono I in-class VA and VB, amount 80 students. The data collection technique in this study is a questionnaire and the test technique is a questionnaire. Technical data analysis using SPSS. Based on the results of the study it was concluded that there were differences in the average score of mathematics learning outcomes in classes taught using snakes and ladders learning media and concrete media with the initial skills of students in the experimental group, students who were taught with the snakes and ladders game media obtained higher learning outcomes compared to the control group or the group taught with concrete media and there was an interaction effect between the snakes and ladders game media and concrete media and initial abilities in mathematics learning outcomes.
CPU Benchmarking of the Scalability and Power Consumption of Virtualized Edge Devices

Where use cases demand lower latency analysis of streaming video data, the move from traditional cloud-based infrastructure toward computing platforms closer to the edge of the network. Hardware manufacturers are releasing more powerful enterprise-grade server platforms designed to operate in edge environments. Alongside the hardware, the use of virtualization software enables multiple use cases to run concurrently on one physical platform. This paper examines the CPU performance of a physical unit, and the CPU performance of virtual devices, running on the same physical device and demonstrates the benefits virtualization can offer when delivering workloads requiring high CPU workloads, and can also offer benefits in power utilization compared with discrete individual devices.
IoT-Enabled Smart Bike Helmet with an AI-Driven Collision Avoidance System

This paper presents a system design for a smart bike helmet with multiple safety features that are intended to empower bicycle riders to proactively avoid potential sources of danger or injury. A Smart Sensor/Actuator Node (SSAN), driven by an Arduino Uno single-board microcontroller, contains input sensors and actuators to provide riders the ability to send and receive warnings promptly on their helmet. A Vision Node, driven by an NVIDIA Jetson Nano and a cable pin-connected camera, executes AI object detection algorithms for any dangerous objects that are out of sight of the rider and sends alerts to the SSAN as needed. By combining safety features of the SSAN and Vision Node while continuously sending data to an IoT-enabled backend web server, the safety operation of a typical bike ride can be substantially improved.
Regional Traditional Painting Generation Based on Controllable Disentanglement Model

Automatic generation of painting images is an interesting and difficult task, especially for regional traditional paintings with unique cultural styles while lacking large-scale training sets. In this paper, a hierarchical painting generation method is proposed, which can disentangle the generation of content and style. By mimicking the human painting process, the proposed method introduces multiple content blocks first and gradually generates image contents. In each block, a spatial self-modulation module is proposed to inject local details while preserving the global layout. After the preliminary generation of contents, a series of style blocks are presented to gradually adjust the artistic style. In the style block, an edge-oriented style-modulation module is proposed, which focuses on the lines and edges. In addition, edge adversarial training is used to further improve the quality of generated lines. To train and evaluate the proposed method, we construct datasets for five types of Chinese folk paintings. Experimental results demonstrate that the proposed method can generate high-quality and diverse painting images. More importantly, it can disentangle content and style sufficiently, so that the generation of specific contents or styles can be controlled freely. The datasets and source codes is available at https://github.com/Ritsu-mio/HPGN.
Daily Conversations on the Application “Hallo”: A New Approach to Assign Speaking Homework to Students

Utilizing mobile phone apps to enhance students’ English-speaking skills is not novel to teachers. Hallo is an app on which users make random audio calls with several callers from different countries in the world. Based on one feature of Hallo that for every 10-minute conversation with any speaker counts towards their consecutive use of the application over ten weeks. The case study was carried out to investigate the effectiveness of practicing English speaking on Hallo as daily at-home exercises. Ten English major sophomores at Hong Duc University were chosen to participate in the study lasting ten weeks. These students were required to make daily conversations about assigned topics on Hallo and then video-record them to send to the teacher for regular checking. The comparisons of pre-test and post-test results, interviews and classroom observations indicate that students made significant progress in English speaking skills and could communicate with others more fluently. The study suggests that the teacher should give detailed guidelines to help low-level students maximize the benefits of this app in mastering English speaking.
Analysis of Senior High Schools Quality in Banda Aceh Using Cluster and Correspondence Analysis

The purpose of this research is to analyze the relationship between teachers and school conditions on school test scores. Correspondence and cluster analysis are the analytical methods used. For school conditions to be classified into 3 clusters, 4 clusters, and 5 clusters, cluster analysis is used. The variable is tested using the G test to determine the affect on school test scores. This study resulted in a variable that has an impact on school test scores, school conditions with a p-value &lt; α, so a correspondence analysis plot was formed to show the correlation between school conditions and school test scores. Good school conditions will result in good school test scores. Meanwhile, a reasonably good school conditions will result in fairly good school test scores. The correlation of school test scores with teachers shows that good school test scores are resulted by teachers with very good quality. Fairly good school test scores are resulted by good and fairly good teachers. Meanwhile, poor school test scores are resulted by teachers with poor quality.
A 3D Implementation of Convolutional Neural Network for Fast Inference

Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge machine learning. By implementing a 5-stage pipelined CNN inference model in a 3D ASIC technology, we demonstrate that the model distributed on two dies utilizing face-to-face (F2F) 3D integration achieves superior performance. Our experimental results show that the design based on 3D integration achieves 43% better energy-delay product when compared to the traditional 2D technology.
SISTEM REKOMENDASI PEMBELIAN SMARTPHONE

The development of communication technology allows people to interconnected with each other without being limited by distance, space and time. Smartphone is one of the communication tools that is now widely used by everyone as a communication tool between people. And now many companies offer a variety of smartphone products, but it provides difficulties to everyone in choosing the most suitable smartphone for them. Therefore a recommendation system is needed to overcome these difficulties. In this study a smartphone purchase recommendation system was made based on other user ratings, a smartphone brand that uses the collaborative filtering method. This system can recommend a smartphone brand based on the results of the similarity calculation of the users who later one user will be compared to another user then from the comparison results between users searched for the Similarity value that is closest to 1 which means similar.