Reviewerly/exHarmony huggingface dataset:
Z-Transform-Based Profile Matching to Develop a Learning-Free Keyword Spotting Method for Handwritten Document Images

Abstract For easy accessibility of the information from the digitized document images, optical character recognition (OCR)-based software can be used. But in the case of handwritten documents, the performance of the state-of-the-art OCR systems is not satisfactory owing to the complexity of the unconstrained handwriting. Hence, research affinity comes up with an alternative solution for this problem called keyword spotting (KWS) which is much more practical than an OCR-based solution. This work proposes a novel learning-free KWS method that can be applied to a heterogeneous collection of handwritten documents. In this work, we introduce a new way of profile matching to compare the query word profiles (i.e., both upper and lower) with the target words’ profiles. At first, both query and target words are binarized, and then two profiles from each such word are generated. Next, we formulate rules to filter out the irrelevant words concerning the query word and obtain the probable candidate query (i.e., target) words. Then we compare the profiles of the query and candidate query words in the Z-transform domain using the condition of resonance for the damped oscillator. However, before the match, we perform an affine transformation on the Bezier curve representation of the profiles of the candidate query words to reduce the effects like scaling, rotation, and shearing which might occur due to the variant writing styles of individuals. The proposed method achieves satisfactory performance compared to state-of-the-art learning-free methods when applied to four publicly available standard datasets namely ICFHR 2014 H-KWS competition Modern, IAM, ICFHR 2016 H-KWS competition Botany and ICFHR 2016 H-KWS competition Konzilsprotokolle datasets.
Study Skills, Writing Strategies and Reading Habits: A Causal Model in Motivation in Learning a Language

Aims: To determine the best fit model of motivation in learning the Filipino language anong senior high school students.&#x0D; Study Design: Descriptive-causal design.&#x0D; Place and Duration of Study: The study was conducted among private high schools located in Davao City during the school year 2021-2022.&#x0D; Methodology: The respondents were 400 senior high school students from private educational institutions in Davao region, Philippines. The respondents were chosen using stratified random sampling. The data were collected using the online platform.&#x0D; Results: The overall results showed that the respondents had a high level of study skills, writing strategies, reading habits, and motivation in learning the Filipino language. It also showed a significant relationship between the three variables, the study skills, writing strategies, and reading habits in the motivation in learning the Filipino language. Of the five models, model 5 fits best.&#x0D; Conclusion: The high level of study skills, writing strategies, reading habits, and motivation in learning the Filipino language, suggests that the students have time management. As a strategy, it suggests that students read different writings by well-known writers to gather good writing methods, ask for help or consult a teacher, friend, or family member so that their writing will be done well. In reading habits, it suggests that students spend time reading different books. Lastly, on motivation, it suggests that students use the Filipino language daily so that they become more fluent. The significant relationship between the three variables means that the three variables are of great help to motivate the students in learning the Filipino language. Model 5 acceptable because all the indices met the set criteria against the obtained model fit value. This study proves that a high level of ability in study skills, writing strategies, and reading habits contributes to the motivation in learning the Filipino language.
Analisis Kinerja Load Balancing Terhadap Jaringan Internet Menggunakan Metode Equal Cost Multi Path (ECMP)

Load balancing on Mikrotik is a technique for distributing loads network traffic on two or more connection lines in a balanced way, so that network traffic can run optimally, maximize transfer speed, reduce response time and avoid excessive capacity usage on one connection line. There are various methods that can be used in load balancing, one of which is the ECMP method. ECMP is a load balancing method that is used to divide the traffic of two or more internet connections with the same load on each line. The application of load balancing on the internet network using the ECMP method has good performance as evidenced by the distribution of a balanced number of connection loads on each gateway, and if one source of the internet connection dies or a fileover occurs, the backup will automatically run itself by taking connections from active internet resources. The QoS measurement results in this study produce good quality because the value meets the THIPON (Telecomunications and Internet Protocol Harmonization Over Network) standard where the average throughput value is more than 2 Mbps, the average delay value is less than 150 ms, the average packet loss value is less than 3% while the mean value of jitter is less than 125 ms. &#x0D; &#x0D; Keywords: Load Balancing, ECMP, Internet Network, QoS,
A Pair of Coupled Waveguides as a Classical Analogue for a Solid-State Qubit

We have determined conditions when a pair of coupled waveguides, a common element for integrated room-temperature photonics, can act as a qubit based on a system with a double-well potential. Moreover, we have used slow-varying amplitude approximation (SVA) for the "classical" wave equation to study the propagation of electromagnetic beams in a couple of dielectric waveguides both analytically and numerically. As a part of an extension of the optical-mechanical analogy, we have considered examples of "quantum operations" on the electromagnetic wave state in a pair of waveguides. Furthermore, we have provided examples of "quantum-mechanical" calculations of nonlinear transfer functions for the implementation of the considered element in optical neural networks.
Med-Tree: A Medical Ontology Tree Combined with the Graph Attention Networks for Medication Recommendation

Medication recommendation based on Electronic Health Records (EHRs) is a significant research direction in the field of intelligent medicine, which aims to recommend personalized medication combinations for patients based on their historical and current physical conditions. However, since the structural and temporal characteristics of medical records are affected by many uncertain factors, there are many limitations in medication recommendation methods based on EHRs. Specifically, most existing works either fail to adequately assess the structural correlation and temporal dependency among various medical entities or ignore existing knowledge of Drug–Drug Interactions (DDI), which could lead to adverse outcomes. These factors contribute to poor recommendation quality. Therefore, we propose a medical ontology tree model combined with the Graph Attention Networks (GAT) for medication recommendations. First, the class hierarchy extracted from the medical ontology and the GAT model is used to learn the ICD-9 codes of diagnoses and procedures, which enriches the semantic representation of medical entities. Secondly, Gate Recurrent Units (GRU) are used to learn the temporal characteristics of medical entities. Finally, memory bank, dynamic memory and DDI graph are used to optimize the hidden layer results, which improve the accuracy of the model. Experimental results show that the proposed model is superior to the previous methods in all evaluation indicators, and the recommended results have a lower DDI rate.
Composite Spatial Manipulation Framework for Redirected Walking

In this study, we present a composite spatial manipulation framework for the redirected walking technique. The proposed framework focuses on utilizing two different approaches simultaneously to manipulate the user’s position and orientation in the physical space, aiming to substantially improve their redirection in a confined physical space and reduce the special requirements for the RDW technique. Each approach utilizes different perceptual processes. The first is a discrete spatial manipulation approach that introduces translation and/or rotation gains to the user’s virtual perspective in the immersive virtual environment (IVE) during temporal events such as eyeblinks. The second approach is the continuous spatial manipulation approach, which continuously introduces (with each frame) translation and/or rotation gains below the user’s perception threshold to their virtual perspective in the IVE. Two simulation experiments were conducted to investigate the feasibility of adopting the composite spatial manipulation framework for RDW without considering the user’s walking behavior or the impact of the proposed approach on user performance in the immersive virtual environment. In the second simulation experiment we aimed to investigate the performance of the proposed approach while considering the user’s walking behavior and performance in the IVE. Finally, a user experiment was conducted to validate the proposed framework and its impact on the user’s performance in the IVE. The findings revealed a significant improvement in the redirection performance of the proposed controller when it was compared to the classical RDW controller. Additionally, there was significant improvement in the user’s performance when the composite RDW controller was utilized.
The Use of ICT for the Teaching and Learning of English as a Second Language in Nigeria: The "What", "Why", and "How"

In language teaching, the use of computers has been on since before the coming of the internet. Later computer assisted language learning (CALL) developed and concerned itself with the pedagogical applications of the technology. Information Communication Technology (IT) which is an offshoot of science through its internet and computer-mediated communication (CMC) has gone beyond science to broaden the lexical inventories of the English language. It is against this backdrop that this paper tends to conduct a study on computer-mediated communication with a focus on the teaching and learning of the English language in Nigeria. The study aims to bring to the fore the issues and challenges associated with this new normal. The study also examines the benefits associated with the use of this technology in the teaching and learning of the English language. The study also employs Direct Participatory Observation and a questionnaire as means of gathering data for the research. Famous Mclunnham’s Technological Determinism Theory was employed as the theoretical framework of analysis. On the whole, the paper discovered among other things that the use of CMC made students confident and gives them the zeal of being online and wanting to discover and learn via the medium of computer-mediated communication. The study concludes that the use of this method of teaching and learning English in Nigeria has to be encouraged for efficiency and exactness for both in the teaching and learning of English in Nigeria. Solutions and recommendations were proffered to ameliorate the challenges associated with the use of these technological devices.
Sul concetto di Intelligenza Artificiale

In the last few years, because of its spread and economic importance, Artificial Intelligence (AI) has taken a leading role in legal studies. This essay investigates the definition of the concept of AI from multiple point of views: firstly, from a technological and terminological perspective; secondly, analyzing and comparing the definitions given by the US legislation, the forthcoming EU AI Act and the Council of State (decision n. 7891/2021).
Analisis Sirkumstan dalam Teks Terjemahan dengan Pendekatan Sistemic Functional Linguistics (SFL)

This research is a descriptive research. In this study, researchers analyzed the circumstantial elements contained in a children's story book entitled The 7 Habits of Happy Kids by Sean Covey and its translation. The approach used in this research is Systemic Functional Linguistic (SFL). The method used in this study is the results showed that the types of circumtances that appear in the bilingual children's story book are 7 types of circumtances consisting of: location, extension, role, matter, manner, accompaniment and cause circumtances. The seven circumstantials are formed by words, groups of words and prepositional phrases. These forms can be grouped into: (1) accompaniment circumstantial formed by prepositional phrases, (2) circumstantial ways include 4 types, namely tools, quality, comparison and degree. The method circumstantial (tool and comparison) is formed by prepositional phrases and word groups while the manner (quality and degree) circumstantial is formed by words and word groups, (3) the location circumstantial includes 3 types, namely time, place and space. Time and place circumstantials are formed by words, groups of words and prepositional phrases while space circumstantials are only formed by prepositional phrases, (4) role circumstantials are formed by prepositional phrases and word groups, (5) external circumstantials are formed by words, groups of words and prepositional phrases, (6) circumstantial matter is formed by prepositional phrases, and (7) circumstantial cause includes 3 types, namely behalf, purpose and reason. The three types of circumstantial cause are only formed by prepositional phrases. Children's stories in the books studied are dominated by circumstantial time and place locations.&#x0D; &#x0D; Keywords: transivity, circumtances, translated text
Analysis of Additional Teaching Materials for 8th Grade Junior High School

This study aims to examine the need for additional teaching materials for science learning among students at MTs Negeri 2 Ngawi. This study uses a research and development method known as Research and Development (RnD). This research development model was adapted from the development of 4D-Models (define, design, development, and dissemination). The subjects in this study were class VIII students of MTs Negeri 2 Ngawi. Observations on science learning, interviews with teachers and students, and the distribution of questionnaires on the need for additional teaching materials indicate that in addition to the textbooks already owned by students, additional teaching materials are also needed according to the characteristics of students. The textbooks owned by students also have several shortcomings, ranging from the limited material presented and the lack of experimental exercises. This research was conducted to explore the analysis of student needs and classroom conditions to find solutions for the development of teaching materials to be used. The results of this study can be concluded that students need teaching materials that allow them to conduct direct experiments to receive the material effectively.
The Study of Flood Discharge in Ujong Krueng in the Tripa Watershed

The Krueng Tripa River is located Nagan Raya Regency that is vunerability to flooding. Administratively Krueng Tripa flows through two regencies in Aceh Province, namely Gayo Lues Regency in the upper reaches of the river and Nagan Raya Regency in its downstream with an area of watershed covering 2953,457 km². The rainfall in this watershed is around 2,197 mm per year. High rainfall has a major impact on flood disasters and has a negative impact on the surrounding community.The purpose and benefits of this study are to determine the magnitude of the flood discharge of the Krueng Tripa River and as input for related darta in flood mitigation problems. Analysis of the flood discharge using the Synthesis Unit Hydrograph (HSS) Gama I method. From the analysis using the distribution of log person III with return periods of 2, 5, 10, 25, 50, and 100 years, it were obtained respectively 141,520 mm, 192,349 mm; 227.094 mm; 272.270 mm; 306,569 mm; 341,869 mm. The results of the flood analysis in the Krueng Tripa River using the HSS Gama I method with return periods of 2, 5, 10, 25, 50, and 100 years in a row are 3419,276 m³/s; 4647.384 m³/s; 5486.740 m³/s; 6578,338 m³/s; 6702.133 m³/s; 8259.949 m³/s.
Analysis of the Effectiveness of Taxpayer Data Security in Implementing Tax Obligations at the Directorate General of Taxes

Taxpayer data security in carrying out tax obligations at the Directorate General of Taxes has experienced several problems of taxpayer data leakage because the security system is still weak, so that it is infected with malware that is able to steal taxpayers' personal data. This study aims to evaluate the effectiveness of taxpayer data security in carrying out tax obligations with case studies at the Directorate General of Taxes, knowing the obstacles faced by the Directorate General of Taxes in building a taxpayer data security information system and analyzing the efforts made by the Directorate General of Taxes regarding the development taxpayer data security system in carrying out tax obligations. This study uses a descriptive qualitative approach. Data collection techniques through observation, interviews, and documentation studies as well as data analysis techniques using content analysis. The results of this study indicate that after the taxpayer data leak occurred, the Directorate General of Taxes carried out a comprehensive system improvement so that the level of taxpayer data security became better. The participation of taxpayers in using a strong username and password is an important factor that determines the taxpayer's own data. The Directorate General of Taxes seeks to improve the security system and provide socialization to taxpayers to always use usernames and passwords that are not easily guessed and to periodically change E-filing passwords.
Kurdish Language Sentiment Analysis: Problems and Challenges

The increasing usage of blogs, social networks, and forums for sharing opinions on a certain topic has created vast amounts of internet data. Therefore, Sentiment Analysis has gained great popularity among researchers and industry for analyzing the polarity of users' opinions. In recent years, Sentiment Analysis has been applied to various languages using machine learning-approach, corpus-based approach, and deep learning techniques since it is beneficial for creating an effective recommender system. The Kurdish Language is an Indo-European language, one of the official languages in Iraq, and it is also widely used in Turkey, Iran, and Syria. Although the importance of this Language is spoken by over 40 million people, to the best of our knowledge, no research has been done regarding the challenges and problems of Kurdish sentiment analysis. Our research aims to highlight the latest studies and examine the most critical challenges of applying sentiment analysis approaches to the Kurdish Language. The study includes determining each challenge in each step of sentiment analysis processing in the Kurdish Language. In addition, our proposed methodology that could help address most of these challenges is implementing a hybrid approach by combining machine learning and lexicon-based approaches to improve the proficiency of sentiment classification in the Kurdish Language.
Analysis of The Influence of Instructional Design and Usability on Student Motivation in Online Tutoring

Learning in the online era is not a barrier for tutoring institutions in providing online services. However, from the user's perspective, there is an inhibiting factor, namely decreased learning motivation when using online learning platforms. One of the aspects is usability and instructional design. In particular, students' approaches to learning through online tutoring systems were examined. Students from various types of education participate in this course namely junior high students, high school students, vocational high school students, universities and the general public. Using a qualitative research approach that examines data from the perspective of student experience. The study found that the completeness and clarity of instructional design had a dominant factor in influencing students' learning motivation. While the usability aspect is not significant enough in influencing the learning motivation of tutoring
UniASM: Binary Code Similarity Detection without Fine-tuning

Binary code similarity detection (BCSD) is widely used in various binary analysis tasks such as vulnerability search, malware detection, clone detection, and patch analysis. Recent studies have shown that the learning-based binary code embedding models perform better than the traditional feature-based approaches. In this paper, we propose a novel transformer-based binary code embedding model named UniASM to learn representations of the binary functions. We design two new training tasks to make the spatial distribution of the generated vectors more uniform, which can be used directly in BCSD without any fine-tuning. In addition, we present a new tokenization approach for binary functions, which increases the token's semantic information and mitigates the out-of-vocabulary (OOV) problem. We conduct an in-depth analysis of the factors affecting model performance through ablation experiments and obtain some new and valuable findings. The experimental results show that UniASM outperforms the state-of-the-art (SOTA) approach on the evaluation dataset. The average scores of Recall@1 on cross-compilers, cross-optimization levels, and cross-obfuscations are 0.77, 0.72, and 0.72. Besides, in the real-world task of known vulnerability search, UniASM outperforms all the current baselines.
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild

Recent advances in self-supervised learning (SSL) using large models to learn visual representations from natural images are rapidly closing the gap between the results produced by fully supervised learning and those produced by SSL on downstream vision tasks. Inspired by this advancement and primarily motivated by the emergence of tabular and structured document image applications, we investigate which self-supervised pretraining objectives, architectures, and fine-tuning strategies are most effective. To address these questions, we introduce RegCLR, a new self-supervised framework that combines contrastive and regularized methods and is compatible with the standard Vision Transformer architecture. Then, RegCLR is instantiated by integrating masked autoencoders as a representative example of a contrastive method and enhanced Barlow Twins as a representative example of a regularized method with configurable input image augmentations in both branches. Several real-world table recognition scenarios (e.g., extracting tables from document images), ranging from standard Word and Latex documents to even more challenging electronic health records (EHR) computer screen images, have been shown to benefit greatly from the representations learned from this new framework, with detection average-precision (AP) improving relatively by 4.8% for Table, 11.8% for Column, and 11.1% for GUI objects over a previous fully supervised baseline on real-world EHR screen images.
Entropic Neural Optimal Transport via Diffusion Processes

We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal transport (EOT) plan between continuous probability distributions which are accessible by samples. Our algorithm is based on the saddle point reformulation of the dynamic version of EOT which is known as the Schr\"odinger Bridge problem. In contrast to the prior methods for large-scale EOT, our algorithm is end-to-end and consists of a single learning step, has fast inference procedure, and allows handling small values of the entropy regularization coefficient which is of particular importance in some applied problems. Empirically, we show the performance of the method on several large-scale EOT tasks. https://github.com/ngushchin/EntropicNeuralOptimalTransport