Reviewerly/exHarmony huggingface dataset:
Revolutionizing EFL special education: how ChatGPT is transforming the way teachers approach language learning

This mixed-methods study explored the attitudes of 199 English as a Foreign Language (EFL) special education teachers towards using ChatGPT for language learning. The survey questionnaire, consisting of 21 items, examined attitudes, effectiveness, barriers, and the future use of ChatGPT. The results revealed that participants held moderate attitudes, perceiving ChatGPT as moderately effective with moderate barriers. While no significant differences were found between male and female teachers in attitudes and effectiveness, significant gender differences emerged in the future use of ChatGPT, with female teachers exhibiting a greater willingness to embrace it. Follow-up email interviews with five participants provided valuable insights into strategies, effectiveness, challenges, and inclusivity when using ChatGPT in language instruction for special education students. These findings contribute to implementing and developing ChatGPT as a language learning tool for EFL special education students, emphasizing the importance of gender-inclusive approaches and practical considerations to enhance its efficacy.
PiLike: Post-Quantum Identity-Based Lightweight Authenticated Key Exchange Protocol for IIoT Environments

As an imperative division of the Internet of Things (IoT), Industrial IoT (IIoT) has also attracted full attention. IIoT networks are equipped with various authenticated key exchange techniques under the cumbersome Diffie–Hellman (DH) problems and certificate management overheads. However, DH-type problems are vulnerable to quantum cryptanalysis. In this article, a novel identity-based authenticated key exchange protocol is designed using the concept of lattice cryptography, which enables the different security features in the IIoT in open wireless communication. Due to the lattice hard assumptions, the proposed protocol can withstand a quantum attack. Rigorous formal security analysis has been made, which shows the ability of the proposed protocol to resist existing as well as quantum attacks. Performance results demonstrate that the proposed protocol exhibits better communication, energy consumption, cryptographic key storage, and computation costs than other similar state of the arts.
A COMPARATIVE STUDY USING 2D CNN AND TRANSFER LEARNING TO DETECT AND CLASSIFY ARABIC-SCRIPT-BASED SIGN LANGUAGE

Nowadays, Sign Language Recognition (SLR) plays a significant impact in the disabled community because it is utilized as a learning tool for everyday tasks like interaction, education, training, and human activities. All three of these languages—Arabic, Persian, and Kurdish—share the same writing system, called the Arabic script. In order to categorize sign languages written in the Arabic alphabet, this article employs convolutional neural networks (CNN) and transfer learning (mobileNet) methods. The study’s primary goal is to develop a common standard for alphabetic sign language in Arabic, Persian, and Kurdish. Different activation functions were used throughout the model’s extensive training on the ASSL2022 dataset. There are a total of 81857 images included in the collection, gathered from two sources and representing the 40 Arabic-script-based alphabets. As can be seen from the data obtained, the proposed models perform well, with an average training accuracy of 99.7% for CNN and 99.32% for transfer learning. When compared to other research involving languages written in the Arabic script, this one achieves better detection and identification accuracy.
Implicit Neural Representation for Cooperative Low-light Image Enhancement

The following three factors restrict the application of existing low-light image enhancement methods: unpredictable brightness degradation and noise, inherent gap between metric-favorable and visual-friendly versions, and the limited paired training data. To address these limitations, we propose an implicit Neural Representation method for Cooperative low-light image enhancement, dubbed NeRCo. It robustly recovers perceptual-friendly results in an unsupervised manner Concretely, NeRCo unifies the diverse degradation factors of real-world scenes with a controllable fitting function, leading to better robustness. In addition, for the output results, we introduce semantic-oriented supervision with priors from the pretrained vision-language model. Instead of merely following reference images, it encourages results to meet subjective expectations, finding more visual-friendly solutions. Further, to ease the reliance on paired data and reduce solution space, we develop a dual-closed-loop constrained enhancement module. It is trained cooperatively with other affiliated modules in a self-supervised manner. Finally, extensive experiments demonstrate the robustness and superior effectiveness of our proposed NeRCo. Our code is available at https://glthub.com/Ysz2022/NeRCo.
Part-Aware Transformer for Generalizable Person Re-identification

Domain generalization person re-identification (DG-ReID) aims to train a model on source domains and generalize well on unseen domains. Vision Transformer usually yields better generalization ability than common CNN networks under distribution shifts. However, Transformer-based ReID models inevitably over-fit to domain-specific biases due to the supervised learning strategy on the source domain. We observe that while the global images of different IDs should have different features, their similar local parts (e.g., black backpack) are not bounded by this constraint. Motivated by this, we propose a pure Transformer model (termed Part-aware Transformer) for DG-ReID by designing a proxy task, named Cross-ID Similarity Learning (CSL), to mine local visual information shared by different IDs. This proxy task allows the model to learn generic features because it only cares about the visual similarity of the parts regardless of the ID labels, thus alleviating the side effect of domain-specific biases. Based on the local similarity obtained in CSL, a Part-guided Self-Distillation (PSD) is proposed to further improve the generalization of global features. Our method achieves state-of-the-art performance under most DG ReID settings. The code is available at https://github.com/liyuke65535/Part-Aware-Transformer.
Adaptive Fuzzy-PID Controller for Liquid Flow Control in theHeating Tank System

Liquid flow control systems are often used in some industrial processes. One of theproblems is the existence of disturbance that can cause the flow response to become unstable. Thus, it is necessary to re-tuning the controller when the disturbance occurs. This study aims to design and implement an Adaptive Fuzzy-PID (AF-PID) controller for the liquid flow control in the heating tank system. We develop an industrial plant prototype of a heating tank process to test the designed controller on a laboratory scale. AF-PID controller is used to controlling the flow rate when the disturbance occurs. This controller was chosen because it has the ability to adapt PID parameters when a disturbance occurs based on fuzzy logic.The nominal PID controller constants will adjust by additional PID parameters when there is a disturbance based on the Mamdani type fuzzy logic rule. The hardware experimental result shows that the designed controller can maintain the stability of the liquid flow when given 50% and 100% pipe leakages with maximum undershot by 3.33% and 24% respectively
DEEP REINFORCEMENT LEARNING WITH HIDDEN MARKOV MODEL FOR SPEECH RECOGNITION

Nowadays, many applications uses speech recognition especially the field of computer science and electronics, Speech Recognition (SR) is the interpretation of words spoken into a text. It is also known as Speech-To-Text (STT) or Automatic-Speech-Recognition(ASR), or just Word-Recognition(WR). The Hidden-Markov-Model (HMM) is a type of Markov model, which means that the future state of the model depends on the current state, not on the entire history of the system and the goal of HMM is to learn a sequence of hidden states from a set of known states. The Long-Short-Time-Memory (LSTM) network is a type of Recurrent Neural Network (RNN) that can learn long-term dependencies between time steps of sequence data. The LSTM network is trained by the network in order to predict the values of subsequent time steps in a series-to-series regression. Deep Neural Network (DNN) models are better classifiers than Gaussian Mixture Models (GMMs), they can generalize much better with a smaller number of parameters over complex distributions. They model distributions of different classes jointly, called “distributed” learning, or, more properly “tied” learning. This work is aimed at developing a speech recognition model that will predict isolated speech of some selected fruits in Hausa, Igbo and Yoruba language by using the predicting power of Mel-Frequency-Cepstral-Coefficient (MFCC), LSTM and HMM algorithms. The findings of the study would improve the development of better automatic speech applications systems and would benefit the academic and research community in the field of Natural Language Processing.
Using A Product Line Approach To Develop Course Projects

Product Line and Product Family approaches are development techniques that take advantage of commonalities that exist among a set of current or planned products.The use of a product line approach allows for speedier integration of new requirements and capabilities to account for product variation without the timely process of reorganizing and reengineering an entire product.While the product line approach is an emerging paradigm in the software development research and industrial communities, little attention has been placed on its use as a methodology for developing and maintaining course projects in an engineering curriculum.As part of an Embedded Systems concentration at Arizona State University we are creating a course in Embedded Systems Engineering that focuses on systems integration and applications development.For this course we are developing a home automation product line.By using a product line approach, students can gain exposure to new technologies in successive offerings while still receiving instruction on core concepts.Furthermore, instructors can easily provide a wide variety of experiences for students with a minimal amount of incremental course refinements.
Design of Information-Intensive Systems Involving Cognitive Aspects: An Emerging Opportunity for Transdisciplinary Cooperation

With the rise of smart systems, ubiquitous computing and cyber-physical systems, information-intensiveness of products increases and users become challenged—possibly even overloaded—with expanding options and possible interactions. The number of possible variations of user-operation sequences can rapidly escalate and for designers it becomes difficult to foresee all possible outcomes, which might include unacceptable performance, failure, and even fatalities. With the objective to reduce the risk of unwanted cognitive effects and to realize a more symbiotic relationship between users and systems, we show how two model-based theories from cognitive science, i.e., cognitive architectures and mental models, can be deployed in the design of these systems. We argue that the deployment of such models requires a transdisciplinary approach in which designers intensively cooperate with cognitive scientists and end users.
A Knowledge Media Approach Using Associative Representation for Constructing Information Bases

In this paper, we present a new approach based on knowledge medium using associative representation as a framework of information representation to gather raw information from vast information sources and to integrate it into information bases cost-effectively. We then present a knowledge media information base system called CM-2 which provides users with a means of accumulating, sharing, exploring and refining conceptually diverse information gathered from vast information sources. We describe the system’s four major facilities; (a) an information capture facility, (b) an information integration facility, (d) an information retrieval facility and (d) an information refinement facility. We discuss the strength and weakness of our approach by analyzing results of experiments.
Enforcing Social Laws in Goal Directed Communication

Multi-Agent systems are virtual representations of social systems consisting of coarse grained intelligent entities, called agents. The motivation behind considering such paradigms is to emulate social behaviour among agents. Since an agent may not have the capabilities to accomplish all its tasks, it seeks cooperation of other agents in its society. Such goal directed cooperative efforts require communication among agents. In this paper, we present a scheme to manage communication in a large society of agents where there could be heavy communication traffic load due to all possible interactions among agents. The purpose of our communication scheme is two fold; (1) to manage communication traffic load by employing an agent partitioning strategy, (2) to maintain a behavioral discipline among agents by enforcing certain social laws during communication. Enforcement of social laws also ensures cooperative attitude among agents. The applicability of the proposed communication scheme is shown by taking an example from corporate sector.
Connectionist Learning to Read Aloud and Comparison to Human Data

Research on connectionist mapping from written to spoken forms in natural language is presented. For this task, the more plausible Simple Recurrent Networks were used instead of static Neural Networks. The model was trained on a Dutch monosyllabic corpus. The effects of frequency, length and consistency were examined and were found similar to reported data in psycholinguistic experiments.
Fuzzy Reasoning Method Based on Distance Measure and Its Reductive Property

This paper shows a basic and original fuzzy reasoning method that can draw a novel study direction of the approximate inference in fuzzy systems with uncertainty. Firstly we propose a criterion function for checking of the reductive property about fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT). Secondly unlike fuzzy reasoning methods based on the similarity measure, we propose a principle of new fuzzy reasoning method based on distance measure and then present two theorems for FMP and FMT. Thirdly through the several computational experiments, we show that proposed method is simple and effective, and in accordance with human thinking. Finally we pointed out conclusion that proposed method does satisfy the convergence of the fuzzy control and has not information loss.
Experimental Evidence Against the Dual-Route Account of Inflectional Morphology

Inflectional morphology has figured prominently not only in debate about the nature of linguistic knowledge, but also in the foundational debate between proponents of symbolic and of connectionist accounts of cognition. We present two experiments designed to test predictions of Pinker’s (1991) dualroute account of inflection, the central component of which is a symbolic rule. Contrary to the predictions of the dual-route account, we find evidence of both frequency and similarity effects on the regularization of novel items (i.e., pseudo words).
Chemical analysis of rocks and soils

Information on the chemical composition of geological materials is often of fundamental importance within many branches of modern geoscience, and applies to geomaterials in their widest sense, as solid rocks and ores, sediments, water and air. Analytical chemistry is therefore an important element within all fields of geology and related activities. Basic research within many fields of geology and geochemistry has created an extended interest for chemical data, not only with regard to element coverage and concentration level, but also concerning the type of geological material. In the early days of the development of mineralogy and petrology, geological materials like rocks, ores and minerals were exclusively analysed with gravimetric and titrimetric wet chemical methods. X-ray fluorescence represents one of the few instrumental analytical methods which normally uses solid samples.
Topic Model Kernel Classification with Probabilistically Reduced Features

Probabilistic topic models have become a standard in modern machine learning to deal with a wide range of applications. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics interpretation, but could also be informative for classification tasks. In this paper, we describe the Topic Model Kernel (TMK), a topicbased kernel for Support Vector Machine classification on data being processed by probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks with real world datasets. TMK outperforms existing kernels on the distributional features and give comparative results on nonprobabilistic data types.
Misère domineering on 2 × n boards

Domineering is a well-studied tiling game, in which one player places vertical dominoes, and a second places horizontal dominoes, alternating turns until someone cannot place on their turn. Previous research has found game outcomes and values for certain rectangular boards under normal play (last move wins); however, nothing has been published about domineering under misère play (last move loses). We find optimal-play outcomes for all 2 × n boards under misère play: these games are Right-win for n ⩾ 12. We also present algebraic results including sums, inverses, and comparisons in misère domineering.
PuzzleFixer: A Visual Reassembly System for Immersive Fragments Restoration

We present PuzzleFixer, an immersive interactive system for experts to rectify defective reassembled 3D objects. Re- assembling the fragments of a broken object to restore its original state is the prerequisite of many analytical tasks such as cultural relics analysis and forensics reasoning. While existing computer-aided methods can automatically reassemble fragments, they often derive incorrect objects due to the complex and ambiguous fragment shapes. Thus, experts usually need to refine the object manually. Prior advances in immersive technologies provide benefits for realistic perception and direct interactions to visualize and interact with 3D fragments. However, few studies have investigated the reassembled object refinement. The specific challenges include: 1) the fragment combination set is too large to determine the correct matches, and 2) the geometry of the fragments is too complex to align them properly. To tackle the first challenge, PuzzleFixer leverages dimensionality reduction and clustering techniques, allowing users to review possible match categories, select the matches with reasonable shapes, and drill down to shapes to correct the corresponding faces. For the second challenge, PuzzleFixer embeds the object with node-link networks to augment the perception of match relations. Specifically, it instantly visualizes matches with graph edges and provides force feedback to facilitate the efficiency of alignment interactions. To demonstrate the effectiveness of PuzzleFixer, we conducted an expert evaluation based on two cases on real-world artifacts and collected feedback through post-study interviews. The results suggest that our system is suitable and efficient for experts to refine incorrect reassembled objects.
Methodological culture and teacher's world view: gnoseological aspects of actualization

The purpose of the study is to substantiate the need for a logical-epistemological construction of a conceptual model of the relationship between methodological culture and the worldview of a university teacher. The novelty of the work lies in identifying the typological and epistemological construct as the basis of the understanding thinking of the teacher, actively influencing the essence of his educational activity. As a result of the study, the psychological and pedagogical intentionality of the construct is specified in the dialectic of methodological form and worldview content. It is proved that in the intellectual discourse of thought-activity practice, the teacher's professional competence is expressed by the educational alloy of his methodological knowledge and ideological and moral conviction.
Tomorrow’s Headlines

This is good technique to do, having created a shared understanding of the product vision.
Effect of Market Orientation on Performance of Small and Medium Enterprise (SMEs) in Kirkos Sub-City

The aim of this study was to investigate the effect of market orientations on performance of SMEs in Kirkos Sub City, Addis Ababa.The conceptual framework was designed by treating market orientations as an independent variable and business performance as dependent variable.Market orientation was further expressed using specific dimensions such as customer orientation, competitor orientation, and interfunctional orientation.Explanatory survey design was used while a questionnaire was used to gather primary data.The study sample in terms of the respondents covered randomly selected SMEs in Kirkos Sub City and a sample of 139 was administered with the questionnaire and 70.5% response rate was achieved.The data collected was analyzed with the aid of descriptive statistical techniques such as mean score.More so, multiple linear regressions were used to establish the relationship between study variables using Statistical Package of Social Sciences Version 22.The result of regression indicates that all three predictor variables (customer orientation, competitor orientation, and inter-functional orientation) have statistically significant influence on business performance of SMEs in Kirkos Sub City.The adjusted R² of 0.585 indicates 58.5% of the variance in business performance measured in terms of growth and profitability can be predicted by market orientations.Therefore, the study concluded that market orientations have a positive and significant effect on business performance of SMEs in Kirkos Sub City.Therefore, the managers/owners of SMEs should improve their market orientation practices as a way of improving their business performance.
Gamification as a means of activating the cognitive interest of schoolchildren in the process of integrating mathematics with other subjects

The article considers the use of gamified educational tasks in mathematics with the content of an integrated nature as a means of activating the cognitive interest of students in a general education school. The purpose of the study is to present a model of activating cognitive interest among schoolchildren in the process of integrating mathematics with various fields of knowledge, and to show its effectiveness in teaching mathematics in specialized (engineering) classes, based on scientific data and our own methodological experience. Generalization and concretization of information from scientific sources on the research topic were carried out to create the author's model. The specified model is developed, presented and described. Scientific novelty lies in: problem statement, scientific and methodological analysis of literature, development and testing of models, games and analysis of the results of the experiment. Based on the results of the study, the experience of using this model by the authors in specialized (engineering) classes of a general education school is shown, and the results of testing are presented and analyzed. The work with students of the Pedagogical University in this direction is described and its results are given. Prospects are outlined.