Reviewerly/exHarmony huggingface dataset:
Reform of Undergraduate Education with Chat-Bots for Microelectronics Specialty

Microelectronics is an important field that has the potential to revolutionize many industries, offering numerous career opportunities, including research and development, design, manufacturing, and testing. The current state of undergraduate education in this field is outdated and inadequate to meet the demands of the modern industry. Despite the importance of this field, there are several challenges facing undergraduate courses in this area. In this part, we will discuss the current state and urgent necessity of reformation in microelectrics undergrad uate education. We will also discuss the advantages and disadvantages of incorporating Chat-Bots into microectronics education.
A Design of Artificial Intelligence Education

The fast development and use of artificial intelligence (AI) and its penetration to almost every profession in the Age of AI need support in popularizing AI education to nurture many professionals. The design includes a hierarchy of education in AI from graduate school level to undergraduate level and then to middle and elementary schools need to be established. It first builds up AI experts in a supportive environment for both teaching and conducting research at the university level with enough critical mass to provide leadership in AI education in a degree program of AI on its own. The AI degree program nurtures AI professionals as well as future AI schoolteachers and may also provide AI training to existing schoolteachers. Having AI schoolteachers, AI education programs may be implemented in all schools starting from an early age at the 3rd grade through 12th grade. Here, AI experts from the university and experienced schoolteachers may collaborate to design the curriculum. One may develop an AI learning and teaching management system to support self-directed learning as well as collaborative learning to rapidly popularize AI education. In addition, learning through projects from an early age will nurture future innovators. Also, competitions and student presentations at conferences provide much motivation and training for the young learners.
Implementation of Efficient Serial Architecture for Prince Block Cipher with Enhanced Security

The Internet of Things (IoT) has seen a tremendous rise in usage over the past few years in a variety of fields. Many restrictions, including power consumption and resource scarcity, have evolved as a result of the proliferation of IoT applications. An increasingly important requirement for system dependability is the security of linked devices.. They were created and proposed a number of light-weight cryptography techniques for these. In this paper PRINCE cipher is implemented in serial approach to replace the PRESENT cipher which uses less power when compared with PRESENT cipher along with the use of DLFSR for generating the key and s-box values. The PRINCE cipher using serial approach is Implemented Utilizing the Vivado 16.2 design package from Xilinx in Verilog level along with the PRESENT cipher. The PRINCE cipher is the improvement in terms of power consumption of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$195{{\ mW}}$</tex> when compared with the PRESENT cipher which uses 242 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mW}$</tex> . The serial approach in PRINCE cipher uses the 16 bits of data path encryption instead of all the 64 bits at a time in this paper by efficient hardware serial architecture approach. Comparatively to other hardware applications already in use, PRINCE provides faster throughput of 572 Mbps when compared with 338 Mbps from PRESENT cipher. The keys generation and s-box values are generated with Dynamic LFSR(linear feedback shift register) to enhance the security of PRINCE cipher from brute force attacks..
A Joint BERT-based Approach with Explicitly Incorporated Slot into Intent

Intent detection and slot filling are two prominent tasks to capture the conversational utterance. Extant studies have shown that joint learning models can promote the two tasks mutually. However, the previous joint models mainly think over the guideline of intent detection for slot filling and show little improvement about the opposite way. This study fully considers the guidance of slot filling applying on intent detection and proposes a joint approach on the basis of BERT (Bidirectional Encoder Representation from Transformers) with max pooling layer and CRF (Conditional Random Field) for intent detection and slot filling. The joint approach fuses the max-pooled slot embedding representation with the beginning of the sentence token vector for intent detection and replaces the softmax layer with CRF for slot filling. The comparative experiments by using two common public datasets demonstrate that the proposed joint model achieve competitive performance for both tasks.
Cloud Storage For Privacy-Confidential Social Media Data Using Blockchain

The utilization of cloud figuring is extending rapidly across many administrations, and it is normal to generally change how IT associations lead business. The prevalence and spread of the cloud figuring climate has been worked with by late improvements in portable, social media, and online undertakings. In any case, Significant issues emerge while moving client data from a nearby gadget to the cloud, which is considered an outsider. Issues with privacy and security, as well as dangers to data security and trustworthiness, are raised by the cloud registering idea. Data security and reliable registering stay the critical troubles in current cloud figuring applications, notwithstanding cloud processing's gigantic advancement throughout the long term. Numerous scholastics have led broad examination into this issue with an end goal to track down an answer, and numerous techniques, for example, secure multi-party computation and data respectability test, have been introduced. Notwithstanding, most of these frameworks generally dislike versatility or unreasonable processing intricacy. This article researches how to improve this situation by using blockchain innovation.
Two-step heterogeneous fusion hashing network for unpaired cross-modal retrieval

Most existing cross-modal hashing methods take advantage of pairwise relationships to establish correlations be-tween multimodal data. However, the majority of methods employing cross-modal hashing ignore the incompleteness and imbalance of multimodal data. To cope with this issue, we propose a Two-Step Heterogeneous Fusion hashing network for unpaired cross-modal retrieval (TSHF). In the first step, we utilize zero padding to enable image-text stitching of unpaired data and construct a triple network to learn the stitching features together. In the second step, we deploy an attention fusion network in order to explore the hidden information in the data through the inter-modal enhancement and the intra-modal complementation. In addition, manifold learning is adopted to preserve semantic similarity of original features; adversarial learning is introduced to deal imbalance in the features; label prediction ensures that discrimination is preserved. Extensive experiments on WikiData and MIRFlickr datasets indicate that TSHF has excellent performance in paired and unpaired modal retrieval.
Real-time Streaming Reliability and Performance Optimization Using Content Steering

Media streaming over the Internet is dominated by HTTP adaptive streaming solutions such as DASH and HLS. These solutions typically host the required manifest files and media segments on multiple content delivery networks (CDNs). To avoid outages and performance problems and for scalability reasons, selecting the optimal CDN is an essential step in the media streaming workflow. Content steering provides a deterministic capability for a content distributor to switch a player's CDN source, both at startup and in the middle of a session, through a remote out-of-band steering service. This paper focuses on the theoretical and practical aspects of content steering for DASH. We highlight the important features of the DASH-IF Content Steering specification and demonstrate our implementation in an open-source media player (dash.js). Moreover, we illustrate how streaming metrics based on the CTA-5004 (Common Media Client Data, CMCD) specification and information about the Internet service provider can drive the selection mechanism on the steering server. Our results show that content steering enables a seamless transition between different CDNs during a running streaming session and has the potential to significantly improve the quality of experience for the end-user.
The Influence of Learning Motivation and Learning Facilities on The Learning Presetation of Mambaul Ulum Mts Mambaul Ulum Mts Students Margo Mulyo Belitang 2 Oku Timur

The purpose of this study was to determine whether there is an effect of learning motivation and social studies learning facilities either partially or simultaneously on social studies learning achievement. The population of this study were students of class VII A and VII B of MTS MAMBAUL ULUM Margo Mulyo Belitang 2 Oku Timur, whose number was 56. The variables studied were learning motivation and learning facilities as independent variables and learning achievement as related variables. The data collection method uses direct observation at school. The results of this study indicate 1) there is an effect of learning motivation on learning achievement, 2) there is an influence of learning facilities on learning achievement 3) there is a simultaneous influence of learning motivation and learning facilities on social studies learning achievement.
INTEGRATING POWTOON VIDEO INTO WHATSAPP GROUP CLASS IN TEACHING READING

This research integrates Powtoon videos and WhatsApp group classes to teach reading in Intensive reading subjects. It describes the implementation of Powtoon Video and Whatsapp Group Classes in Intensive Reading classes for English Teaching Department students of Universitas PGRI Madiun during the pandemic era. Descriptive qualitative was chosen as a research method. The subject of this research is the first-semester students of the English Teaching Department consisting of 16 students. The research data were collected through observation and interviews. The collected data was then analyzed through data reduction, data display, and conclusion. The result of this research shows that, in general, the process of online teaching by using Whatsapp Group Class and Powtoon video is conducted into two sections. The first is preparation and the second is implementation. In preparation, the lecturers have to prepare some teaching devices, such as lesson plans, Powtoon video material, YouTube channel, and some rules for conducting the class. The class implementation consists of pre, main /breaking time, and closing activities. The different things in this research are found in the main activities or it’s called breaking time. It’s different from real classroom activities. The class was conducted with extraordinary treatments and in boundless time caused of the low access to internet signal and most of the students live in rural areas. This type of combination makes the students feel comfortable because they can access the material at every time and everywhere. By this situation, it will create a meaningful teaching-learning process, the student’s motivation will appear automatically, and they become a more active and self-individual learner. &#x0D;
Peningkatan kesadaran kritis melawan berita palsu dan ujaran kebencian melalui literasi digital

Most villages in Indonesia, such as Bugel Village, are vulnerable to the spread of hate speech and fake news online. This fake news often contains elements of hate speech against specific groups, such as ethnicity, religion, race, or class (referred to as SARA), which can trigger division and conflict within society. Therefore, it is important for the community to have a critical awareness strengthened through digital literacy, so that they can counter hate speech and fake news. The community service activity carried out by the Universitas Pendidikan Indonesia in Bugel Village, Tasikmalaya District, aimed to enhance digital literacy among the community. This activity involved 30 participants from various social strata, including village heads and farmers, focusing on digital literacy education to identify and counter the spread of fake news and hate speech online. Lecture and demonstration methods were used, and participants underwent pretests and posttests to measure improvements in understanding and application of digital literacy. The results showed significant improvement; the average pretest score for understanding increased from 55.8 to 85.8, and for application, from 65 to 86.1 in the posttest. Participants' satisfaction with the training was quite high, averaging 4.09. This activity successfully demonstrated the importance of digital literacy in the community, especially in facing digital information challenges such as fake news and hate speech, and emphasized the need for ongoing efforts to develop digital literacy in the community.
ON SOME ISSUES OF INTRODUCTION OF THE APPLIED BACHELOR'S DEGREE IN KAZAKHSTAN

To tackle the challenge of bridging the gap between theoretical and practical education of Kazakhstan graduates, a potential solution involves establishing and introducing applied bachelor's degree programs. The article analyses foreign and domestic experience of introducing applied bachelor's degree programs. The authors also consider the possibilities and prospects of introducing the applied bachelor's program at the higher education level. According to the authors, although the legislative framework includes the introduction of applied bachelor's degree programs in technical and vocational education, challenges persist in meeting the labor market's demand for highly skilled professionals with advanced practical abilities and the opportunity for program graduates to continue their education. The authors reviewed and compared the applied bachelor's degree programs and curricula within the technical and vocational education system versus those within the higher education system (in pilot higher education institutions). Introduction of applied bachelor's programs in universities requires the determination of the optimal ratio of courses that cultivate cross-cultural and professional skills, while considering the program's practice-oriented approach. The issue of the subsequent educational scenario for graduates of applied bachelor's programs necessitates further research and, in in the long view, justification to establish regulatory provisions.
Innovation Adoption of Household-Scale Portable Biodigester to Reduce the Impact of Food Waste in Bangunjiwo Village, Bantul District

Abstract. This community service activity is the implementation of a portable biodigester in Bangunjiwo Village, Bantul Regency. The activity took place from February to March 2023 in Ngentak Hamlet, Bangunjiwo Village, Kasihan Subdistrict, Bantul Regency, and involved participants consisting of representatives from the Neighborhood Association (RT) and mosque administrators. The guidance on the use and maintenance education of biodigester was conducted by a team consisting of lecturers from Agribusiness Study Program from Universitas Muhammadiyah Yogyakarta and Creavill Indonesia, who have expertise in biodigester technology. The activity was conducted in several sessions where the participants were provided with an in-depth understanding of the principles and benefits of biodigester and the necessary steps for routine maintenance. In conclusion, the implementation of a portable biodigester in Bangunjiwo Village, Bantul Regency has brought significant benefits in reducing the impact of food waste, generating renewable energy, improving agricultural productivity, protecting the environment, and saving costs. This activity actively involved the participations of the Bangunjiwo Village community, and strengthened awareness of the importance of utilizing environmentally friendly technologies such as biodigester.
SENTIMENT ANALYSIS OF PUBLIC OPINIONS TOWARDS TELKOM UNIVERSITY POST PANDEMIC

Abstract: Twitter, as a social media platform, has rapidly grown as a means for people to express their opinions and thoughts on various topics, including education. The number of Twitter users surged to 10.645.000 in 2020, with a significant increase during the pandemic. Telkom University, as a private institution of higher education in Indonesia, has become one of the topics of discussion on Twitter. Users’ opinions about Telkom University vary, ranging from positive to negative. To gain deeper insights into public view, sentiment analysis is essential. The analysis follows the Knowledge Discovery in Databases (KDD) process, utilizing the Naive Bayes classification algorithm. The evaluation results indicate the best accuracy achieved with an 80:20 data split, resulting in an accuracy rate of 82.05%, precision of 82.3%, recall of 82.05%, and F1-Score of 82.08%. The Naïve Bayes model demonstrates good performance for sentiment analysis of public views regarding Telkom University on Twitter. Keywords: naïve bayes; sentiment analysis; twitter; telkom university. Abstrak: Media sosial Twitter berkembang pesat sebagai sarana masyarakat berekspresi untuk menuangkan opini dan pikiran mereka mengenai topik apapun, termasuk pendidikan. Pengguna Twitter meningkat tajam hingga 10.645.00 pengguna pada tahun 2020 dan terus meningkat selama pandemi. Telkom University sebagai perguruan tinggi menjadi salah satu topik yang dibicarakan yang berkaitan dengan pendidikan. Pendapat mengenai Telkom University yang diungkapkan oleh pengguna Twitter beragam, baik positif maupun negatif. Analisis sentimen diperlukan untuk memahami pandangan publik lebih mendalam. Digunakan tahapan Knowledge Discovery in Databases dan algoritma klasifikasi Naïve Bayes dalam analisis ini. Hasil evaluasi menunjukkan akurasi paling baik dicapai dengan rasio data 80:20, dengan nilai akurasi sebesar 82.05%, nilai presisi sebesar 82.3%, nilai recall sebesar 82.05%, dan nilai F1-Score sebesar 82.08%. Model klasifikasi Naïve Bayes memiliki performa baik untuk analisis sentimen pandangan publik di Twitter mengenai Telkom University. Kata kunci: analisis sentimen; naïve bayes; twitter; telkom university.
The learning costs of Federated Learning in constrained scenarios

Recently, machine-learning model sizes have grown considerably. Consequently, training the model on a single high-end machine can take up to several months. Since waiting such a long time is not feasible for most practical applications, a new approach was proposed. This approach, named distributed learning, leverages multiple computing nodes on a single machine or distributed across several to reduce the training times and is usually applied in high-performance computing clusters with centralized data. However, one of its implementations, federated learning, aims to take the training closer to the data sources. This approach applies the same algorithms as distributed learning but uses machines with limited hardware resources and communication capabilities. Although the advantages of these algorithms are well known, such as increased data privacy, they also have costs, as they create network overhead and require synchronization between devices. To that extent, understanding the trade-offs of federated learning is necessary to enable its correct deployment. This paper analyzes the trade-offs of implementing federated learning by deploying a small test bed of Raspberry Pis connected over a gigabit network. The results show that training on a single device is, on average, 2.99% faster when compared with distributed approaches.
Design and Development of a Digital Health Application for Lifelong Medical History Visualization

Half the global population lacks access to essential healthcare, with those fortunate enough to see certified physicians often receiving consultations of 5 minutes or less due to overwhelming demand. This study presents the Smart Health Gantt Chart (SHGC) system, aiding physicians in time and resource-limited developing countries to provide efficient services. SHGC also enables patients to manage digital medical records and share them with doctors. User feedback from both doctors and patients demonstrates positive interest, though further improvements are required for full usability.Clinical Relevance: The SHGC system can assist both doctors and patients in maintaining lifelong medical history and enhance quality healthcare services within limited time and resources.
Underwater Refractive Stereo Vision Using Ray Tracing for Dome Port Camera

Stereo vision is one of the most important ways to explore the underwater world. However, the refraction occurs on the dome port camera housing, resulting in distorted images and errors in the computed 3D information. In this paper, we proposed an underwater refractive stereo vision method that can calibrate the camera’s parameters and obtain 3D information underwater. First, a dome port refractive ray tracing model is proposed to describe the projection relation between the object point and the image point. Then, a refractive calibration method is proposed to calibrate the camera’s intrinsic and extrinsic parameters without pre-calibration in air. Finally, a refractive rectification method is proposed for disparity search along polar lines and calculating 3D information. Experimental results show that the proposed method outperforms existing methods in both simulation and real imaging scenarios.
Density-aware Swin Transformer for Compressed Point Cloud Geometry Artifacts Removal

Geometry-based point cloud compression (G-PCC), as a prevalent compression technique, has achieved remarkable compression efficiency, thereby significantly reducing the cost of transmission and storage. However, the compressed point clouds inevitably suffer from severe compression artifacts, i.e., geometry distortion, when the compression ratio increases. To address this, we propose the DensityFormer, the first transformer-based network to restore the geometry distortion in the compressed point cloud. Particularly, our approach focuses on two prominent challenges for this: i) the discrete points demand more stringent requirements for long-range contextual information modeling and ii) the point cloud exhibits a non-uniformed point distribution. For the first challenge, our DensityFormer introduce the Swin Transformer-based hierarchical encoder-decoder architecture, intending to model the multi-grained global contextual information for geometric restoration, based on the superior long-range dependency modeling capability of 3D Swin Transformer block. To solve the second challenge, we propose the density-aware Swin Transformer block on the basis of the intuition that the local density of the point cloud can identify the distribution of points, thereby enabling the adaptive non-uniformed restoration for compressed point clouds. By incorporating the above two advanced techniques, our DensityFormer has shown superior restoration capability on multiple typical benchmark datasets, which outperforms existing state-of-the-art (SOTA) methods by an average of 0.55 dB.