Reviewerly/exHarmony huggingface dataset:
A Robust Multi-Keyword Text Content Retrieval by Utilizing Hash Indexing

Digital content on servers increase the storage and fetching issues. So, researcher works in this field to organize content for fast retrieval with data security. This paper has worked on text digital content retrieval available in form of documents, files. User can search a desired file by test query and relevant list of files get appeared. Keywords were fetched from the text content by removing noisy data during pre-processing. Pre-processed keywords are identified by the number known as term ID. As per the term-ID each text content got a Hash Index which was termed as key numbers in document index. Each term or word has its own identification number known as term Id , so privacy of comparing content terms and user query maintain by hash based searching. As document identification done by hash index key, so storage of text content was done in encrypted numbers once document select for reading then decryption of document applied for a particular user. Experiment was done on real and artificial text content dataset files on different topics. It was obtained that proposed model of Hash indexing and tem based retrieval has improved the privacy with relevancy of as per query.
Fungsi dan Aplikasi Motivasi dalam Pembelajaran

The purpose of this research is to identify the factors that drive motivation, as well as the role of motivation in teaching and learning. This is a qualitative study conducted using library research methods. Work motivation is a driving force and source of encouragement that can elicit a sense of zeal, as well as modify human or individual behavior to lead to improved outcomes. Motivation is defined as the overarching driving force within the instructor that initiates and directs instructional actions in order to attain the objectives set forth by the teaching subject. So that every teacher is diligent in completing assignments, tenacious in overcoming obstacles (not easily discouraged), shows interest in a variety of problems, prefers to work independently, is not easily bored with routine tasks, is able to defend his or her point of view, and enjoys finding and solving problems.
A study on Decline and Development Strategy of Local Universities

The core of the university policy pursued by the Moon Jae-in administration is to strengthen university competencies through the diagnosis of universities. To this end, the word structural reform evaluation is changed to strengthening the basic competencies of universities, and is trying to escape from the past reduction of pro-class capacity. Deputy Prime Minister Kim Sang-gon insisted, We will improve the university structure evaluation, which is biased toward quantitative quota adjustment, with a diagnosis of basic university competency to support the autonomous development of the university. The Ministry of Education expressed its intention to strengthen the capacity of local universities by saying that it would also consider regional conditions in reducing the university quota due to the decrease in the school-age population so far. In the meantime, as the school-age population declined, the Ministry of Education promoted the reform of the university structure by dividing it into three cycles from 2014. This paper focuses on the problems of diagnosis of education reform and improvement measures, and tries to find out what is the desirable direction for education innovation. This paper studied the structural reform of universities following the decline of local universities. The policy alternatives in this paper are as follows. First, the contents of the government's push for university structural reform were analyzed. Second, we considered the problems of university structural reform. Third, they sought ways to develop local universities and enhance their competitiveness.
LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation

Unsupervised Domain Adaptation (UDA) for semantic segmentation has been actively studied to mitigate the domain gap between label-rich source data and unlabeled target data. Despite these efforts, UDA still has a long way to go to reach the fully supervised performance. To this end, we propose a Labeling Only if Required strategy, LabOR, where we introduce a human-in-the-loop approach to adaptively give scarce labels to points that a UDA model is uncertain about. In order to find the uncertain points, we generate an inconsistency mask using the proposed adaptive pixel selector and we label these segment-based regions to achieve near supervised performance with only a small fraction (about 2.2%) ground truth points, which we call "Segment based Pixel-Labeling (SPL)". To further reduce the efforts of the human annotator, we also propose "Point-based Pixel-Labeling (PPL)", which finds the most representative points for labeling within the generated inconsistency mask. This reduces efforts from 2.2% segment label to 40 points label while minimizing performance degradation. Through extensive experimentation, we show the advantages of this new framework for domain adaptive semantic segmentation while minimizing human labor costs.
Development of online learning media based on Telegram Chatbot (Case studies: Programming courses)

Abstract The Covid-19 pandemic has affected various aspects of life, one of which is education. In some certain areas, at this time the government has issued an interim policy that education provider institutions such as college, schools and its kind to conduct learning activities from home. The existence of this policy makes a change in learning patterns that are usually face-to-face to being forced to apply distance learning methods (online learnings). The limited internet quota, space and time during online learning, as well as different levels of understanding of each student make teaching methods with question and answer also hampered. The solution to that problem, we propose online learning media using Telegram Chatbot. The purpose of this research is to make the Telegram Chatbot as an online learning media with case studies of programming courses, so that be expected students can get learning with the way questions and answers about programming that can be accessed anytime and anywhere, without having to always tied to the educator. The Telegram Chatbot development method using the Prototype Method which is divided into three main stages, which are (1) system requirements analysis, (2) system design and development, and (3) evaluation. The result of our propose development can be an alternative as media learning.
Students’ Difficulties in Comprehending English Reading Text at SMPN 2 Bangkinang Kota

This research aimed to know students' difficulties in comprehending English reading text at SMPN 2 Bangkinang Kota. The design of this research is quantitative research with the use of descriptive quantitative study which focused on one variable. The population of this research was the eighth grade of SMPN 2 Bangkinang Kota. The total population was 112 students. To select the sample, the researcher uses simple random sampling. There were 28 students as the sample. In collecting data, the researcher distributed the questionnaire to the respondents consist of 15 items and used the documentation of the students' final task made by the English teacher. The data was analyzed by using Scale Likert through SPSS.16. The result of data analysis showed the lowest percentage of categories students' difficulties. The research found students' difficulties in comprehending English reading text at SMPN 2 Bangkinang Kota.
Comparative Study on Performance of Discrete Wavelength Transform and Huffman Compression Technique on 2D Signal

Nowadays, the development of technology which involves multimedia data is widely used to help better understanding in spreading information. Image is known as 2D signal which contain huge data especially a high resolution image. This paper shows the comparison of applying lossy and lossless compression on the image data. Image compression is necessary in reducing the size of image for storage or transmission purpose to support most of the application nowadays. The technique applied in this paper is the hybrid of Discrete Wavelet Transform (DWT) technique and Huffman coding technique which are classified as lossy and lossless compression, respectively. The performance of image compression are evaluated in terms of compression ratio, Mean Square Error (MSE), Power Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and computing time. Several types of evaluation can determine better technique to apply on specific type of application. The stand-alone of each DWT and Huffman technique are evaluated before applying hybrid of DWT and Huffman technique. After conducting a comprehensive observation, the hybrid technique can compress with ratio about 1:17 to 1:27 due to the support from DWT technique that apply filter concept. The MSE value is high with the average about 69 which contributes to low PSNR value with about 29 to 30 dB due to the relation of PSNR equation with MSE value. Besides, the SSIM value is 0.6 or about 40% far from the original image that affect the output image. Despite of that, the computing time is fast with about 3 to 4 seconds which has been improved from stand-alone Huffman technique. Therefore, hybrid compression is capable of supporting each other techniques in stand-alone technique.
Dual Graph Convolutional Networks with Transformer and Curriculum Learning for Image Captioning

Existing image captioning methods just focus on understanding the relationship between objects or instances in a single image, without exploring the contextual correlation existed among contextual image. In this paper, we propose Dual Graph Convolutional Networks (Dual-GCN) with transformer and curriculum learning for image captioning. In particular, we not only use an object-level GCN to capture the object to object spatial relation within a single image, but also adopt an image-level GCN to capture the feature information provided by similar images. With the well-designed Dual-GCN, we can make the linguistic transformer better understand the relationship between different objects in a single image and make full use of similar images as auxiliary information to generate a reasonable caption description for a single image. Meanwhile, with a cross-review strategy introduced to determine difficulty levels, we adopt curriculum learning as the training strategy to increase the robustness and generalization of our proposed model. We conduct extensive experiments on the large-scale MS COCO dataset, and the experimental results powerfully demonstrate that our proposed method outperforms recent state-of-the-art approaches. It achieves a BLEU-1 score of 82.2 and a BLEU-2 score of 67.6. Our source code is available at {\em \color{magenta}{\url{https://github.com/Unbear430/DGCN-for-image-captioning}}}.
Real-Time Eye Tracking For Password Authentication

For user authentication and security, many techniques are being used. In static authentication, password authentication is done using Personal Identification Numbers (PINs) which require users to input the pin physically, which are prone to attacks via shoulder-surfing. In order to avoid this problem, we can make use of hands-off blink-based PIN entry techniques. This paper focuses on designing a highly secured embedded smart card for the confidentiality of password followed by face detection and eye blinking method for initial authentication process using smart camera for multiple account access through a single swipe of smart card.
PELATIHAN METODE PEMBELAJARAN FUN ENGLISH GAMES USING APPLICATION BAGI GURU BAHASA INGGRIS MAS NU BATAHAN

This article deals with the training of Fun English Games Learning Method for English Teacher in MAS NU Batahan, This article shows several learning methods by using English Games funny and easy to understand, such as, Word Connect Application, Spelling Bee application, Stop the Clock Application, Typing Paragraphs for speed. This activity intends to enlarge some learning methods by using English Games for fun, and to give comprehending that English learning’s easy and enjoying. The method of this study used community development that collaborating theory and practicing in which using fun English games learning method in teaching English for English teacher MAS NU Batahan. The result shows that Fun English Games learning Method’s successfully applied in the classroom, the students are happy and can understand English learning easily and quickly through Fun English Games Method. It has benefit for teachers in teaching 4 skill, reading, writing, speaking and listening that must be mastered by students. In addition, School can use this method to improve the quality of learning in achieving very satisfactory results. Based on the explanation above can be concluded that Fun English Games learning Method for English teachers is very useful dan helpful in teaching and learning process.
Self-supervised Answer Retrieval on Clinical Notes

Retrieving answer passages from long documents is a complex task requiring semantic understanding of both discourse and document context. We approach this challenge specifically in a clinical scenario, where doctors retrieve cohorts of patients based on diagnoses and other latent medical aspects. We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. In addition, we contribute a novel retrieval dataset based on clinical notes to simulate this scenario on a large corpus of clinical notes. We apply our objective in four Transformer-based architectures: Contextual Document Vectors, Bi-, Poly- and Cross-encoders. From our extensive evaluation on MIMIC-III and three other healthcare datasets, we report that CAPR outperforms strong baselines in the retrieval of domain-specific passages and effectively generalizes across rule-based and human-labeled passages. This makes the model powerful especially in zero-shot scenarios where only limited training data is available.
Formation of the future philologists’ professional competence of as a pedagogical problem

In recent decades in Ukraine, in the context of changing social priorities, the introduction of the humanistic paradigm has changed the demands for education, in particular, higher education. The reason for this is the growing understanding that the main value of modern society is a professionally and socially competent person, capable for effective activity in various fields.
Seven challenges for harmonizing explainability requirements

Regulators have signalled an interest in adopting explainable AI(XAI) techniques to handle the diverse needs for model governance, operational servicing, and compliance in the financial services industry. In this short overview, we review the recent technical literature in XAI and argue that based on our current understanding of the field, the use of XAI techniques in practice necessitate a highly contextualized approach considering the specific needs of stakeholders for particular business applications.
Adversarial Robustness of Deep Code Comment Generation

Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, or natural language processing. Recently they also have been applied to various software engineering tasks, typically involving processing source code. DNNs are well-known to be vulnerable to adversarial examples, i.e., fabricated inputs that could lead to various misbehaviors of the DNN model while being perceived as benign by humans. In this paper, we focus on the code comment generation task in software engineering and study the robustness issue of the DNNs when they are applied to this task. We propose ACCENT, an identifier substitution approach to craft adversarial code snippets, which are syntactically correct and semantically close to the original code snippet, but may mislead the DNNs to produce completely irrelevant code comments. In order to improve the robustness, ACCENT also incorporates a novel training method, which can be applied to existing code comment generation models. We conduct comprehensive experiments to evaluate our approach by attacking the mainstream encoder-decoder architectures on two large-scale publicly available datasets. The results show that ACCENT efficiently produces stable attacks with functionality-preserving adversarial examples, and the generated examples have better transferability compared with baselines. We also confirm, via experiments, the effectiveness in improving model robustness with our training method.
Qubit Quantum Metrology with Limited Measurement Resources

Quantum resources, such as entanglement, can decrease the uncertainty of a parameter-estimation procedure beyond what is classically possible. This phenomenon is well described for noiseless systems with asymptotically many measurement resources by the Quantum Cramer-Rao Bound, but no general description exists for the regime of limited measurement resources. We address this problem by defining a Bayesian quantifier for uncertainty suitable for the regime of limited resources, and by developing a mathematical description for a parameter-estimation procedure which uses qubit probes to estimate a rotation angle induced on them. We simulate the qubit estimation scheme in the regime of limited resources using a single class of probe states. We find that, in noiseless systems, entanglement between qubits always decreases the uncertainty of the estimation; however, the quantum advantage decreases as fewer qubits are used in the estimation. We also find that the presence of strong dephasing noise removes the quantum advantage completely, regardless of the number of qubit probes used.
PyNose: A Test Smell Detector For Python

Similarly to production code, code smells also occur in test code, where they are called test smells. Test smells have a detrimental effect not only on test code but also on the production code that is being tested. To date, the majority of the research on test smells has been focusing on programming languages such as Java and Scala. However, there are no available automated tools to support the identification of test smells for Python, despite its rapid growth in popularity in recent years. In this paper, we strive to extend the research to Python, build a tool for detecting test smells in this language, and conduct an empirical analysis of test smells in Python projects. We started by gathering a list of test smells from existing research and selecting test smells that can be considered language-agnostic or have similar functionality in Python's standard Unittest framework. In total, we identified 17 diverse test smells. Additionally, we searched for Python-specific test smells by mining frequent code change patterns that can be considered as either fixing or introducing test smells. Based on these changes, we proposed our own test smell called Suboptimal assert. To detect all these test smells, we developed a tool called PyNose in the form of a plugin to PyCharm, a popular Python IDE. Finally, we conducted a large-scale empirical investigation aimed at analyzing the prevalence of test smells in Python code. Our results show that 98% of the projects and 84% of the test suites in the studied dataset contain at least one test smell. Our proposed Suboptimal assert smell was detected in as much as 70.6% of the projects, making it a valuable addition to the list.
Analysis of Requirements in the Context of Multi-Platform Dropshipping Application Using Activity Theory

Nowadays, a change has taken place in organizations that have significantly transformed the conduct of business. This impact on the business model resulted in a huge growth in e-commerce and brings numerous values to the customers, such as the data access of products at the cheapest cost, in a short amount of time and with a variety of choices. The main factor behind the growth of e-commerce is the Internet, where the expansion into this sector has helped many established and start-up companies. This paper proposes a method that uses activity theory to analyze the requirements of multi-platform system applications. It then explores the case study of the dropshipping sales management application and shows the results of the study of its requirements analysis using activity theory.
Eer Tech Ventures : Eer Power Bank / Muhammad Aidil Akmal Mazlan...[et al.]

This business proposal presents an overview of business description, activities and goals for EER Power Bank within three (3) years forecasted business period. EER Tech Ventures start up the business in Shah Alam, which is a developing city. Our mission is to invent productsthat captures and stores kinetic energy created from movements like walking, running and cycling and uses it to recharge our mobile devices. Our company guarantees that our product is an eco-friendly product as our vision is not only to provide technology to people but alsocreating technology that could conserve energy. EER Tech Ventures operating at Taman Perindustrian Bukit Raja Selatan, Shah Alam from Monday to Friday for 12 hours (10 a.m – 10 p.m). Our product which is EER Power Bank can ensure that the power bank can be more helpful for people in a critical moment.EER Tech Ventures aims to penetrate the market with 15% of market share among its main competitors which are Xiomi, Pineng and Qualcomm with averaging the forecast sales of RM96,070.00 monthly and it would generate about RM 1,152,840.00 sales revenue in the first year. The business will experience 5% and 10% increment in sales revenue and working capital for the second and third year of business. The total project implementation cost for RM36,951.00. The financial resources will consist of a ten – year small medium ventures loan equal to RM 36,951.00 and the remaining will acquire from business equity (partner’s contribution). In order to ensure the business will experience sustainable growth, EER Tech Ventures plans to develop its new product which is an automatic dustbin robot.
Working the Speaking Skill by Using a Web Page in English Classes

Information and communication technologies, or ICTs, have gained importance in the globalized world. Meanwhile, they are also a challenge for teachers, who might be reflecting on their teaching practices. The following paper reports on a study developed with sixth graders at a public institution in Tunja, Colombia. The study was implemented during the first semester of 2016, and its goal was to develop speaking skills through workshops organized on a web page. Data was collected through field notes, students’ artifacts, recordings, and questionnaires. The results showed that the use of ICTs caught the students’ attention, encouraged them to speak English, and increased their vocabulary. Being able to speak and understand more vocabulary could help students use the language in real life situations, as well as reinforce their learning autonomy.
Photonic neuromorphic computing

Photonic integrated circuits allow for designing computing architectures which process optical signals in analogy to electronic integrated circuits. Therein electrical connections are replaced with photonic waveguides which guide light to desired locations on chip. Through near-field coupling, such waveguides enable interactions with functional materials placed very close to the waveguide surface. This way, photonic circuits which are normally passive in their response are able to display active functionality and thus provide the means to build reconfigurable systems. By integrating phase-change materials nonvolatile components can be devised which allow for implementing hardware mimics of neural tissue. Here I will present our efforts on using such a platform for developing optical non-von Neumann computing devices.