Reviewerly/exHarmony huggingface dataset:
Sistem Pengendalian Persediaan Menggunakan Metode Moving Average dan Pengembangan Sistem Extreme Programming

Inventory of goods is one of several important factors in a company, whether engaged in trading or manufacturing. It is important for inventory to be controlled and supervised in the recording and calculation of inventory, this is because inventory can affect financial reporting. If the amount of inventory is too large (overstock), on the other hand, if the inventory is a little, it will have an impact on the lack of inventory (stockout). The purpose of this research is to develop an inventory control system using the moving average method and the development of an Extreme Programming (XP) system. The moving average method in inventory prediction is more effective because this method compares current and previous data. To develop the system, the Extreme Programming (XP) system development method is used. This method has advantages including: fast process, time and cost efficiency, low risk, flexibility and easy to implement. From the test results using black box testing by testing the functionality, the system shows the system has met expectations with a value reaching 100%. Meanwhile, for testing with the DeLone and McLean models which measure the success of information systems, the score reaches 81.37%.
QUTY: Towards Better Understanding and Optimization of Short Video Quality

Short video applications such as TikTok and Instagram have attracted tremendous attention recently. However, it is very limited for industry and academia to understand the user's Quality of Experience (QoE) on short video, let alone how to improve the QoE in short video streaming.
Deep Learning based Money Detection System for Visually Impaired Person

In recent times, visually challenged people are facing challenges in completing their daily tasks. One of the issues is to use bank money to make purchases and also to deal with financial fraud. In light of this, this study has developed a novel system to automatically identify the currency and provide output in audio format. The proposed system is composed of various modules. Using a camera, the data is collected and given as input to the first module. Following pre-processing, the deep learning model will determine whether the currency is genuine or not, and if it is, it will classify the currency. In order to do this, this study has examined various deep learning techniques. For this, many deep learning object identification and classification algorithms are examined before choosing the one that performs the task the best. In the following module, NLP is utilized to extract text from the currency and create the text. The created text will be converted into audio with the aid of the Text-to-Speech module, and the audio output will be provided to blind users via a Raspberry Pi connected audio device.
DIGITAL LAW AND THE IMPACTS OF GENETIC ENGINEERING ON INTERNATIONAL LAW

With the evolution of technology, social, economic, and political relationships undergo major changes, which directly impact the thinking and living of human beings.Nevertheless, the law follows social changes and alters itself throughout history, because its main goal and challenge is to organize society in the most fair and correct way possible.Meanwhile, the Digital Right is born, which encompasses all legal spheres, adapting them, however, to a new, previously unknown scenario.Thus, laws that govern the regulation of digital data have arisen in Brazil and around the world, which are essential to be studied and understood, because it is increasingly through the Internet that society relates, moving valuable information and large fortunes.Moreover, with the advent of new technologies, the human being itself is the object of study of some of them, which is important to the law.Scientists investigate genetic data in search of health improvements, fertilization clinics operate, and so many other procedures that, in the final analysis, bring into question the great question of the immeasurable value of life and of the human being itself.This technological, digital, and genetic advance is global, although more apparent in some countries than in others, which gives geopolitical power to the former, and must be analyzed and studied in light of domestic and international law.Therefore, this paper aims to study digital law and its new technologies, as well as the impact of genetic engineering, nationally and internationally, in defense of human rights, that is, the human being itself.
Knowledge-enhanced prototypical network with class cluster loss for few-shot relation classification

Few-shot Relation Classification identifies the relation between target entity pairs in unstructured natural language texts by training on a small number of labeled samples. Recent prototype network-based studies have focused on enhancing the prototype representation capability of models by incorporating external knowledge. However, the majority of these works constrain the representation of class prototypes implicitly through complex network structures, such as multi-attention mechanisms, graph neural networks, and contrastive learning, which constrict the model’s ability to generalize. In addition, most models with triplet loss disregard intra-class compactness during model training, thereby limiting the model’s ability to handle outlier samples with low semantic similarity. Therefore, this paper proposes a non-weighted prototype enhancement module that uses the feature-level similarity between prototypes and relation information as a gate to filter and complete features. Meanwhile, we design a class cluster loss that samples difficult positive and negative samples and explicitly constrains both intra-class compactness and inter-class separability to learn a metric space with high discriminability. Extensive experiments were done on the publicly available dataset FewRel 1.0 and 2.0, and the results show the effectiveness of the proposed model.
Learning in Higher Education Based on Artificial Intelligence (AI) with Case Based Reasoning (CBR)

Learning in Higher Education Based on Artificial Intelligence (AI) with Case Based Reasoning (CBR) is a teaching and learning process as part of artificial intelligence providing a problem solving learning model Learning is a process of transferring knowledge from teaching staff to students. Artificial Intelligence (AI) is present as a tool that can facilitate the teaching and learning process in tertiary institutions. This research aims to produce a learning model in higher education by utilizing AI as a learning tool. The method used is Case Based Reasoning (CBR). CBR is a computer reasoning way of thinking by utilizing past knowledge to handle new cases. Research subjects in universities. The implementation of this learning process can be carried out in various study programs, including: Accounting, Civil Engineering, Teaching and Education Faculty and all existing study programs in tertiary institutions. The results achieved by teachers and students will be easy to use technology in the teaching and learning process. The implication is that universities produce quality students in the application of information technology by implementing AI with CBR.
BPS ASM 2023 abstract supplement

Free accessAbstractFirst published online June 9, 2023BPS ASM 2023 abstract supplementVolume 17, Issue 1_supplhttps://doi.org/10.1177/20494637231177771
AWdpCNER: Automated Wdp Chinese Named Entity Recognition from Wheat Diseases and Pests Text

Chinese named entity recognition of wheat diseases and pests is an initial and key step in constructing knowledge graphs. In the field of wheat diseases and pests, there are problems, such as lack of training data, nested entities, fuzzy entity boundaries, diverse entity categories, and uneven entity distribution. To solve the above problems, two data augmentation methods were proposed to expand sentence semantic information on the premise of fully mining hidden knowledge. Then, a wheat diseases and pests dataset (WdpDs) for Chinese named entity recognition was constructed containing 21 types of entities and its domain dictionary (WdpDict), using a combination of manual and dictionary-based approaches, to better support the entity recognition task. Furthermore, an automated Wdp Chinese named entity recognition model (AWdpCNER) was proposed. This model was based on ALBERT-BiLSTM-CRF for entity recognition, and defined specific rules to calibrate entity boundaries after recognition. The model fusing ALBERT-BiLSTM-CRF and rules amendment achieved the best recognition results, with a precision of 94.76%, a recall of 95.64%, and an F1-score of 95.29%. Compared with the recognition results without rules amendment, the precision, recall, and F1-score was increased by 0.88 percentage points, 0.44 percentage points, and 0.75 percentage points, respectively. The experimental results showed that the proposed model could effectively identify Chinese named entities in the field of wheat diseases and pests, and this model achieved state-of-the-art recognition performance, outperforming several existing models, which provides a reference for other fields of named entities recognition such as food safety and biology.
One Gate Digital Archive for Elementary School Digitalisation

Archive management is an aspect that must be managed by schools, which still receive a lack of attention despite its importance. This lack of attention to management can be seen in elementary schools, which only rely on one school operator to manage the archives because of a lack of employees. As a result, a lot of data is not archived, damaged, lost, and unorganized. This study aims to produce a web-based one-gate digital archive by utilizing the learning.id account feature that is owned by educators and educational staff in Indonesia. The creation of a one-gate digital archive is carried out to improve the school’s archive management and to increase school digitalization, so that educational services increase. This research uses plomp’s model development research. The advantage of using a one-gate digital archive is that it allows schools to easily change the web's appearance and features needed with low-cost. The result showed that the developed one-gate digital archive was valid and reliable, with an average score of 4,7, which was included in the excellent category. The existence of one gate digital archive is expected to help the principal and teachers in managing the archive. This research still needs to examine the effectiveness of this one-gate digital archive, so further research needs to be done.
Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT

A fall is one of the most devastating events that aging people can experience. Fall-related physical injuries, hospital admission, or even mortality among the elderly are all critical health issues. As the population continues to age worldwide, there is an imperative need to develop fall detection systems. We propose a system for the recognition and verification of falls based on a chest-worn wearable device, which can be used for elderly health institutions or home care. The wearable device utilizes a built-in three-axis accelerometer and gyroscope in the nine-axis inertial sensor to determine the user’s postures, such as standing, sitting, and lying down. The resultant force was obtained by calculation with three-axis acceleration. Integration of three-axis acceleration and a three-axis gyroscope can obtain a pitch angle through the gradient descent algorithm. The height value was converted from a barometer. Integration of the pitch angle with the height value can determine the behavior state including sitting down, standing up, walking, lying down, and falling. In our study, we can clearly determine the direction of the fall. Acceleration changes during the fall can determine the force of the impact. Furthermore, with the IoT (Internet of Things) and smart speakers, we can verify whether the user has fallen by asking from smart speakers. In this study, posture determination is operated directly on the wearable device through the state machine. The ability to recognize and report a fall event in real-time can help to lessen the response time of a caregiver. The family members or care provider monitor, in real-time, the user’s current posture via a mobile device app or internet webpage. All collected data supports subsequent medical evaluation and further intervention.
Cache-Timing Attack Against HQC

In this paper, we present the first chosen-ciphertext (CC) cache-timing attacks on the reference implementation of HQC. We build a cache-timing based distinguisher for implementing a plaintext-checking (PC) oracle. The PC oracle uses side-channel information to check if a given ciphertext decrypts to a given message. This is done by identifying a vulnerability during the generating process of two vectors in the reference implementation of HQC. We also propose a new method of using PC oracles for chosen-ciphertext side-channel attacks against HQC, which may have independent interest.We show a general proof-of-concept attack, where we use the Flush+Reload technique and also derive, in more detail, a practical attack on an HQC execution on Intel SGX, where the Prime+Probe technique is used. We show the exact path to do key recovery by explaining the detailed steps, using the PC oracle. In both scenarios, the new attack requires 53, 857 traces on average with much fewer PC oracle calls than the timing attack of Guo et al. CHES 2022 on an HQC implementation.
Theoretical and methodological principles of the formation of professional culture and professional image among educators by means of the acmeological approach in war conditions

The relevance of the research is determined by the need for purposeful activity in the direction of the formation of professional culture and professional image of education seekers during their studies. It was analyzed the essence of professional culture and professional image, based on the analysis of psychological and pedagogical literature. There was revealed their role in the formation of a professional specialist. There are also analyzed innovations in the process of formation of professional image and professional culture. They are caused by wartime. We attribute the main role in the formation of professional culture and professional image to the acmeological approach. Considering this, it is highlighted the potential of the acmeological approach. There are analyzed its advantages in the organization of the educational process. There are proposed innovative changes that should be introduced to improve the educational process based on the acmeological approach.
The effectiveness of pop-up book media in learning reading skills of grade 2 elementary school

This quasi-experimental study aims to test the effectiveness of Pop Up Book media in learning reading skills in grade II Elementary School. Randomly class II B was assigned as an experimental class, taking reading lessons using Pop Up Book media, and class II-A, as a control class, participating in reading lessons using ordinary picture media. Data collection techniques use performance tests to assess 6 aspects of reading skills, namely accuracy in voicing writing, pronunciation reasonableness, intonation, fluency, voice clarity, and word understanding. The validity of the test items ranged from 0.65 – to 0.70, with a reliability index of 0.704. The average value of reading skills from the experimental class students' pre-test results was 63.96, and 62.29 in the control class students. From the different test results, the pretest score obtained a t value of 0.887 at sig. 0.380. It can be concluded that there is no difference in reading skills between the two groups before the treatment was carried out. After the learning was carried out, the average post-test scores of the experimental class students were 81.56 and 65.38. control class students. The results of hypothesis testing obtained a value of t 7,252 on s ig. 0.000. It can be concluded that there is a very significant difference in reading skills between the experimental class and the control class. The results of the study concluded that the Pop Up Book media was effective for improving the reading skills of second-grade elementary school students.
A2B: Anchor to Barycentric Coordinate for Robust Correspondence

There is a long-standing problem of repeated patterns in correspondence problems, where mismatches frequently occur because of inherent ambiguity. The unique position information associated with repeated patterns makes coordinate representations a useful supplement to appearance representations for improving feature correspondences. However, the issue of appropriate coordinate representation has remained unresolved. In this study, we demonstrate that geometric-invariant coordinate representations, such as barycentric coordinates, can significantly reduce mismatches between features. The first step is to establish a theoretical foundation for geometrically invariant coordinates. We present a seed matching and filtering network (SMFNet) that combines feature matching and consistency filtering with a coarse-to-fine matching strategy in order to acquire reliable sparse correspondences. We then introduce DEGREE, a novel anchor-to-barycentric (A2B) coordinate encoding approach, which generates multiple affine-invariant correspondence coordinates from paired images. DEGREE can be used as a plug-in with standard descriptors, feature matchers, and consistency filters to improve the matching quality. Extensive experiments in synthesized indoor and outdoor datasets demonstrate that DEGREE alleviates the problem of repeated patterns and helps achieve state-of-the-art performance. Furthermore, DEGREE also reports competitive performance in the third Image Matching Challenge at CVPR 2021. This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences.
The Crime of Creating Material and Their Danger to Society: Study in Arab Regulations

Praise be to Allah, the Lord of the Worlds, and prayers and peace be upon His last messenger, Prophet Mohammed, his family and companions, and those who follow his guidance until the Day of Judgment. The creation of materials related to pornography networks has become a major threat to social security, due to the risks it entails at various local and global levels, which are increasing day by day due to the development of modern technology, and have become one of the major issues that occupy the attention of the international community.In this research, the researcher tries to clarify the most important aspects related to the crime of establishing web pornographic material and their danger to society.Research Plan:To illustrate and clarify the subject of this research, the researcher divided it into an introductory topic and three topics and a conclusion.Introductory section: Explaining the most important terms contained in the study.The first Section: The causes of the crime of creating networks pornographic content, their danger to society.The second topic: The elements of the crime of creating networks pornographic content, and their danger to society in Islamic law and Arab laws.The third topic: The seriousness of the establishment of web pornographic material on the international community.
Design of High-Speed Area-Efficient VLSI Architecture of 32-Bit Three-Operand Binary Adder

Abstract: Adders are one of the most widely used digital components in digital integrated circuit design. With the advances in technology, the design that offers either high speed, low power consumption, less area, or a combination of them is designed. There are various processes performed by the digital circuits among which arithmetic operations are prominent. Three-operand binary adder is the basic functional unit to perform the modular arithmetic in various cryptography and pseudorandom bit generator (PRBG) algorithms. Carry save adder (CS3A) is the widely used technique to perform the three-operand addition. However, the ripple carry stage in the CS3A leads to a high propagation delay. Moreover, a parallel prefix two-operand adder such as Han-Carlson (HCA) can also be used for three-operand addition, significantly reducing the critical path delay at the cost of additional hardware. Hence, a new high-speed and area-efficient adder architecture is designed using pre-compute bitwise addition followed by carry prefix computation logic to perform the three-operand binary addition that consumes substantially less area, low power, and drastically reduces the adder delay. To design a faster computing system with less hardware we require some other architecture. The proposed architecture will reduce the area as well as the delay by replacing the Han-Carlson adder with the Ladner fisher adder in a Highspeed area-efficient three-operand binary adder. The proposed architecture is synthesized with the zynq-7000 library. The proposed architecture reduces the LUT count and delay by 20 and 0.4 nanoseconds respectively. By using these synthesis results, we noted the performance parameters like the number of LUTs and delay. We compared the adders in terms of LUTs (represents area) and delay value
Your Prompt is My Command: On Assessing the Human-Centred Generality of Multimodal Models

Even with obvious deficiencies, large prompt-commanded multimodal models are proving to be flexible cognitive tools representing an unprecedented generality. But the directness, diversity, and degree of user interaction create a distinctive “human-centred generality” (HCG), rather than a fully autonomous one. HCG implies that —for a specific user— a system is only as general as it is effective for the user’s relevant tasks and their prevalent ways of prompting. A human-centred evaluation of general-purpose AI systems therefore needs to reflect the personal nature of interaction, tasks and cognition. We argue that the best way to understand these systems is as highly-coupled cognitive extenders, and to analyse the bidirectional cognitive adaptations between them and humans. In this paper, we give a formulation of HCG, as well as a high-level overview of the elements and trade-offs involved in the prompting process. We end the paper by outlining some essential research questions and suggestions for improving evaluation practices, which we envision as characteristic for the evaluation of general artificial intelligence in the future. This paper appears in the AI &amp; Society track.
Analysis of Education Model for University Students' Psychological Fitness Using Machine Learning Under the Environment of Virtual Reality

An adaptive learning model for English vocabulary through a machine learning is proposed in this paper.The four main types of user information, including basic student information, quiz information, course video viewing information, and forum interaction information, are processed through feature engineering, and a better model on sparse data is proposed through comparison on different models, and the prediction accuracy of the model is improved through natural language processing techniques, to achieve feedback on user learning efficiency through user data and provide teachers and students with the corresponding teaching and learning suggestions for teachers and students.It is found that the quiz information has more influence than the course video viewing information, and the accuracy is improved by about 3% compared with TF-IDF after introducing word embedding.The use of mobile for English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus improving different aspects of learners' reading skills.