Reviewerly/exHarmony huggingface dataset:
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI CABANG MINIMARKET TERBAIK MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING BERBASIS WEB

Authors:
raw_author_name: Aisyah Mutia Dawis
raw_affiliation_string: Universitas 'Aisyiyah Surakarta

Abstract: Minimarkets are shops that sell daily necessities. This mini market is located on Jalan Station Kauman, Krikilan Hamlet, Dawungan Village, Masaran District, Sragen Regency, Central Java Province. In developing inter-company leaders, it is difficult to make decisions about the location of new branches, because there are many criteria such as: strategic location, distance and population to facilitate decision making. The purpose of this study is that researchers assist company leaders in choosing the best new minimarket branch locations using the SAW algorithm method. This method was chosen because it is able to carry out the process of ranking and weighting the best alternatives by applying many criteria. The technique used in this research is observation (observation), interview (interview), and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables and database design. This application is made using the PHP programming language and the database uses MySQL. The final result is a report on the best location data. System testing is done by testing the functionality and testing the validity of the obtained results are 100% valid.
High accuracy, compact 3D face imaging method based on translational and online-switchable phase-shifting fringe projection

Authors:
raw_author_name: Yanqiong Guo
raw_affiliation_string: Sichuan University
raw_author_name: Zhisheng You
raw_affiliation_string: Sichuan University
raw_author_name: Wei Xiong
raw_affiliation_string: Sichuan University
raw_author_name: Di You
raw_affiliation_string: Chinese Academy of Sciences

Abstract: In this paper, a compact, cost-effective, and fast translational online-switchable phase-shifting fringe (TOPF) projector is designed and fabricated for high accuracy three-dimensional (3D) face imaging. Compared with the conventional mechanical projectors, the main difference is that it utilizes a translational approach instead of a rotational one to achieve a better balance in terms of size, speed, accuracy, and cost. To mitigate the inconsistency of the motor's step size and ensure the stability of phase-shifting, an optical encoder-based feedback control mechanism is employed. Additionally, to address the random phase shift errors induced by mechanical motion, a fast, generalized phase-shifting algorithm with unknown phase shifts (uPSAs) that can calculate arbitrary phase shifts is proposed. Finally, a 3D imaging system consisting of the TOPF projector and two cameras is constructed for experimental validation. The feasibility, effectiveness, and precision of our proposed method are substantiated through the reconstruction of a static facial model and a dynamic real face.
Analysis of Word Choice Errors in Saudi EFL Students’ Spoken Language

Authors:
raw_author_name: Fawzi Eltayeb Yousuf Ahmed
raw_affiliation_string: King Khalid University
raw_author_name: Elsadig Hussein Fadlalla Ali
raw_affiliation_string: King Khalid University

Abstract: The purpose of this research paper is to identify and analyze word choice errors committed in spoken English by Saudi EFL students at King Khalid University. It also seeks to identify the origins of the errors and to suggest relevant solutions to assist learners in developing lexical competence and reducing the occurrence of these errors. The research focuses on five different forms of word choice errors. These errors consist of errors of redundancy, literal translation, subject /verb agreement, collocation, and inappropriate usage of formal and informal words. To gather data, the study is based on conducting structured interviews with three EFL-speaking teachers. It also employs a speaking test as a data collection technique from fifteen EFL learners chosen at random, who are required to speak one by one in front of the class for 3-6 minutes. Their oral performance is recorded by mobile phones. According to data analysis, the most common error category is literal translation (33.97%), which is followed by collocation errors (24.88%) as well as subject/verb agreement (18.66%). Furthermore, there are redundancy errors with a percentage of (12.44%). The last type of error committed is inappropriate use of formal and informal language, which has a percentage of (10.04%). The study attributes these errors to a variety of causes, including the Arabic language's impact as well as ineffective teaching techniques and learning strategies. The study offers several recommendations and pedagogical implications to help EFL learners overcome and minimize word choice errors.
Knowledge distillation under ideal joint classifier assumption

Authors:
raw_author_name: Xiwen Chen
raw_affiliation_string: School of Computing at Clemson University, Clemson, 29634, SC, USA
raw_author_name: Gregory Ditzler
raw_affiliation_string: EpiSys Science, Philadelphia, PA, 19128, USA
raw_author_name: Janet Roveda
raw_affiliation_string: BIO5 Institute, The University of Arizona, Tucson, 85721, AZ, USA

Abstract: Knowledge distillation constitutes a potent methodology for condensing substantial neural networks into more compact and efficient counterparts. Within this context, softmax regression representation learning serves as a widely embraced approach, leveraging a pre-established teacher network to guide the learning process of a diminutive student network. Notably, despite the extensive inquiry into the efficacy of softmax regression representation learning, the intricate underpinnings governing the knowledge transfer mechanism remain inadequately elucidated. This study introduces the ‘Ideal Joint Classifier Knowledge Distillation’ (IJCKD) framework, an overarching paradigm that not only furnishes a lucid and exhaustive comprehension of prevailing knowledge distillation techniques but also establishes a theoretical underpinning for prospective investigations. Employing mathematical methodologies derived from domain adaptation theory, this investigation conducts a comprehensive examination of the error boundary of the student network contingent upon the teacher network. Consequently, our framework facilitates efficient knowledge transference between teacher and student networks, thereby accommodating a diverse spectrum of applications.
An optical authentication scheme based on all-optical neural network

Authors:
raw_author_name: Linfei Chen
raw_author_name: Jianping Wang

Abstract: Diffractive deep neural network is architectural designs based on the principles of neural networks, which consists of multiple diffraction layers and has the remarkable ability to perform machine learning tasks at the speed of light. In this paper, a novel optical authentication system was presented that utilizes the diffractive deep neural network principle. By carefully manipulating a light beam with both a public key and a private key, we are able to generate a unique and secure image representation at a precise distance. The generated image can undergo authentication by being processed through the proposed authentication system. Leveraging the utilization of invisible terahertz light, the certification system possesses inherent characteristics of concealment and enhanced security. Additionally, the entire certification process operates solely through the manipulation of the light beam, eliminating the need for electronic calculations. As a result, the system offers rapid certification speed. The proposed optical authentication scheme is further validated through computer simulations, which showcase its robust security and high precision. This method holds immense potential for diverse applications in optical neural network authentication, warranting a broad scope of future prospects.
Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection

Authors:
raw_author_name: Weiming Tong
raw_affiliation_string: Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Luyao Yang
raw_affiliation_string: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Zhongwei Li
raw_affiliation_string: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Xianji Jin
raw_affiliation_string: School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
raw_author_name: Liguo Tan
raw_affiliation_string: Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China

Abstract: To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.
Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms

Authors:
raw_author_name: Jhansi Lakshmi Potharlanka
raw_affiliation_string: Department of Computer Science and Engineering, Vignan’s Foundation for Science Technology and Research, Guntur, 522213, India

Abstract: Abstract Feature selection is an indispensable aspect of modern machine learning, especially for high-dimensional datasets where overfitting and computational inefficiencies are common concerns. Traditional methods often employ either filter, wrapper, or embedded approaches, which have limitations in terms of robustness, computational load, or capability to capture complex interactions among features. Despite the utility of metaheuristic algorithms like Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization (WOA) in feature selection, there still exists a gap in efficiently incorporating feature importance feedback into these processes. This paper presents a novel approach that integrates the strengths of PSO, FA, and WOA algorithms into an ensemble model and further enhances its performance by incorporating a Deep Q-Learning framework for relevance feedbacks. The Deep Q-Learning module intelligently updates feature importance based on model performance, thereby fine-tuning the selection process iteratively. Our ensemble model demonstrates substantial gains in effectiveness over traditional and individual metaheuristic approaches. Specifically, the proposed model achieved a 9.5% higher precision, an 8.5% higher accuracy, an 8.3% higher recall, a 4.9% higher AUC, and a 5.9% higher specificity across multiple software bug prediction datasets and samples. By resolving some of the key issues in existing feature selection methods and achieving superior performance metrics, this work paves the way for more robust and efficient machine learning models in various applications, from healthcare to natural language processing scenarios. This research provides an innovative framework for feature selection that promises not only superior performance but also offers a flexible architecture that can be adapted for a variety of machine learning challenges.
The use of deep learning integrating image recognition in language analysis technology in secondary school education

Authors:
raw_author_name: Liqing Chu
raw_affiliation_string: School of Foreign Studies, Tangshan Normal University, Tangshan City, 063000, China
raw_author_name: Yixi Zhai
raw_affiliation_string: School of Foreign Studies, Tangshan Normal University, Tangshan City, 063000, China
raw_author_name: Dandan Wang
raw_affiliation_string: School of Foreign Studies, Tangshan Normal University, Tangshan City, 063000, China
raw_author_name: Yufei Wu
raw_affiliation_string: School of Foreign Studies, Tangshan Normal University, Tangshan City, 063000, China

Abstract: This work aims to investigate the application of advanced deep learning algorithms and image recognition technologies to enhance language analysis tools in secondary education, with the goal of providing educators with more effective resources and support. Based on artificial intelligence, this work integrates data mining techniques related to deep learning to analyze and study language behavior in secondary school education. Initially, a framework for analyzing language behavior in secondary school education is constructed. This involves evaluating the current state of language behavior, establishing a framework based on evaluation comments, and defining indicators for analyzing language behavior in online secondary school education. Subsequently, data mining technology and image and character recognition technology are employed to conduct data mining for online courses in secondary schools, encompassing the processing of teaching video images and character recognition. Finally, an experiment is designed to validate the proposed framework for analyzing language behavior in secondary school education. The results indicate specific differences among the grouped evaluation scores for each analysis indicator. The significance p values for the online classroom discourse's speaking rate, speech intelligibility, average sentence length, and content similarity are -0.56, -0.71, -0.71, and -0.74, respectively. The aim is to identify the most effective teaching behaviors for learners and enhance the support for online course instruction.
Respecting Partial Privacy of Unstructured Data via Spectrum-Based Encoder

Authors:
raw_author_name: Qingcai Luo
raw_affiliation_string: School of Cyber Engineering, Xidian University, Xi’an 710126, China
raw_author_name: Hui Li
raw_affiliation_string: School of Computer Science and Technology, Xidian University, Xi’an 710071, China

Abstract: Since the popularity of Machine Learning as a Service (MLaaS) has been increasing significantly, users are facing the risk of exposing sensitive information that is not task-related. The reason is that the data uploaded by users may include some information that is not useful for inference but can lead to privacy leakage. One straightforward approach to mitigate this issue is to filter out task-independent information to protect user privacy. However, this method is feasible for structured data with naturally independent entries, but it is challenging for unstructured data. Therefore, we propose a novel framework, which employs a spectrum-based encoder to transform unstructured data into the latent space and a task-specific model to identify the essential information for the target task. Our system has been comprehensively evaluated on three benchmark visual datasets and compared to previous works. The results demonstrate that our framework offers superior protection for task-independent information and maintains the usefulness of task-related information.
Model-Agnostic Binary Patch Grouping for Bone Marrow WSI Representation

Authors:
raw_author_name: Youqing Mu
raw_affiliation_string: McMaster University, Hamilton, Canada; University of Toronto, Toronto, Canada
raw_author_name: Hamid R. Tizhoosh
raw_affiliation_string: Rhazes Lab, Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
raw_author_name: Taher Dehkharghanian
raw_affiliation_string: McMaster University, Hamilton, Canada; University Health Network, Toronto, Canada
raw_author_name: Saghir Alfasly
raw_affiliation_string: Rhazes Lab, Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA
raw_author_name: Clinton JV. Campbell
raw_affiliation_string: McMaster University, Hamilton, Canada; University of Toronto, Toronto, Canada; William Osler Health System, Brampton, Canada

Abstract: Histopathology is the reference standard for pathology diagnosis, and has evolved with the digitization of glass slides, i.e. whole slide images (WSIs). Trained histopathologists can help to diagnose disease by examining WSIs visually, but this process is time-consuming and prone to variability. To address these issues, AI models are being developed to create slide-level representations of WSIs, summarizing the entire slide as a single vector. This enables various computational pathology applications, including inter-slide search, multi-modal training, and slide-level classification. Achieving expressive and robust slide-level representations hinges on patch feature extraction and aggregation steps. We propose integrating an additional Binary Patch Grouping (BPG) step, a plugin that can be integrated into various slide-level representation pipelines to enhance the quality of slide-level representation in bone marrow histopathology. BPG excludes patches with less clinical relevance through minimal interaction with the pathologist: a one-time human intervention for the entire process. We further investigated domain-general versus domain-specific feature extraction models based on convolution and attention and examined two different feature aggregation methods, with and without BPG, showing BPG’s generalizability. We show that BPG boosts the performance of WSI retrieval (mAP@10) by 4% and improves WSI classification (weighted-F1) by 5% relative to not using BPG. Additionally, we found that the pipeline with BPG, domain-general large models and parameterized pooling produced the best-quality slide-level representations.
Touch or click friendly: Towards adaptive user interfaces for complex applications

Authors:
raw_author_name: Ibrar Hussain
raw_affiliation_string: Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus TOBE Camp, Abbottabad, Pakistan
raw_author_name: Iftikhar Ahmed Khan
raw_affiliation_string: Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus TOBE Camp, Abbottabad, Pakistan
raw_author_name: Waqas Jadoon
raw_affiliation_string: Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus TOBE Camp, Abbottabad, Pakistan
raw_author_name: Rab Nawaz Jadoon
raw_affiliation_string: Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus TOBE Camp, Abbottabad, Pakistan
raw_author_name: Abdul Nasir Khan
raw_affiliation_string: Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus TOBE Camp, Abbottabad, Pakistan
raw_author_name: Muhammad Shafi
raw_affiliation_string: Faculty of Computing and Information Technology, Sohar University, Sohar, Oman

Abstract: This study evaluated the usability of a direct manipulation device (touchscreen) vs. indirect manipulation devices (mouse and touchpad) on the selected Microsoft (MS) Word tasks as per ISO-9241-11 standard. MS Word was taken as an example of a complex application. The tasks were evaluated in terms of touch-friendly or click-friendly using efficiency, effectiveness, and satisfaction parameters to propose a customized task menu. The experiment was conducted with fifty-four participants, divided into three MS Word usage-based expertise groups. Each participant performed fifty-six tasks using a mouse, a touchpad, and a touchscreen. To assess task-level usability, individual one-way ANOVAs were performed for each task to gauge both efficiency and effectiveness. It’s worth noting that the touchscreen significantly outperformed other input methods in just one specific task regarding effectiveness. Consequently, an ANCOVA was employed, with task completion time as the independent variable and the number of errors as a covariate, to further investigate effectiveness. A total of 19 (34%) of the total tasks were found to be significantly efficient with a mouse, while 21 (37.5%) were significantly efficient with a touchscreen. Based on the results, a customized menu is recommended for MS Word-like applications that combine actions in touch-friendly tasks and mouse-friendly tasks separately.
Demystifying Compiler Unstable Feature Usage and Impacts in the Rust Ecosystem

Authors:
raw_author_name: Chenghao Li
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Yifei Wu
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Wenbo Shen
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Zichen Zhao
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Rui Chang
raw_affiliation_string: Zhejiang University, Hangzhou, China
raw_author_name: Chengwei Liu
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore
raw_author_name: Yang Liu
raw_affiliation_string: Nanyang Technological University, Singapore, Singapore
raw_author_name: Kui Ren
raw_affiliation_string: Zhejiang University, Hangzhou, China

Abstract: Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to extend compiler functionality, syntax, and standard library support. However, these features are unstable and may get removed, introducing compilation failures to dependent packages. Even worse, their impacts propagate through transitive dependencies, causing large-scale failures in the whole ecosystem. Although RUF is widely used in Rust, previous research has primarily concentrated on Rust code safety, with the usage and impacts of RUF from the Rust compiler remaining unexplored. Therefore, we aim to bridge this gap by systematically analyzing the RUF usage and impacts in the Rust ecosystem. We propose novel techniques for extracting RUF precisely, and to assess its impact on the entire ecosystem quantitatively, we accurately resolve package dependencies. We have analyzed the whole Rust ecosystem with 590K package versions and 140M transitive dependencies. Our study shows that the Rust ecosystem uses 1000 different RUF, and at most 44% of package versions are affected by RUF, causing compiling failures for at most 12% of package versions. To mitigate wide RUF impacts, we further design and implement a RUF-compilation-failure recovery tool that can recover up to 90% of the failure. We believe our techniques, findings, and tools can help stabilize the Rust compiler, ultimately enhancing the security and reliability of the Rust ecosystem.