Reviewerly/exHarmony huggingface dataset:
Universal Linear Transformations of Intensity Patterns under Spatially Incoherent Illumination

We demonstrate that, under spatially incoherent illumination, a diffractive optical network can perform an arbitrary linear transformation of intensity patterns between an input and an output field-of-view.
Time-lapse Image Classification through a Diffractive Optical Network

We introduce a time-lapse optical image classification scheme, which substantially enhances the inference and generalization of a diffractive optical network on the classification of complex input objects.
High-Speed Multidimensional Optical Computing

A coherent multi-dimensional photonic tensor accelerator performing high-speed matrix-matrix multiplication is proposed and demonstrated. A pattern recognition experiment is demonstrated at a 25Gbps modulation speed exploiting orthogonal dimensions of light including time, wavelength, and spatial mode.
Pembuatan Portabel Energi Berbasis Panel Surya Sebagai Sumber Energi Listrik Alternatif Saat Darurat Bencana untuk Bandan Penanggulangan Bencana Daerah Yogyakarta (BPBD DIY)

Natural disasters are quite common in Indonesia, such as volcanic eruptions, tsunamis, earthquakes, and floods, this is because Indonesia is located between the three tectonic plates of the world (Indo-Australia, Eurasia and the Pacific). Disasters that often occur cause material losses such as damage to buildings and hamper the supply of primary needs, for example the need for electrical energy. The delay in the need for electrical energy after a natural disaster has caused the mobility of the community to be disrupted, for example lighting.The portable energy system that has been designed has proven to be quite effective in supplying electrical energy for the evacuation of victims of natural disasters. The test results show that portable energy can produce energy of 720Wh with a load scheme of using five 10Watt LED lights that operate for 12 hours/day and three charger stations operating for 4 hours/day. In the charging test using a solar panel with 120 Wp the device can be fully charged for 9 hours, but if the weather is cloudy, the solar panel cannot produce maximum power and results in less than optimal battery charge.Portable energy can have positive social and environmental impacts. Positive impact on the social sector, because it can provide a temporary supply of electrical power so that the electricity supply to the affected areas can operate normally.
Emotion-Cause Pair Extraction in Conversations Based on Multi-Turn MRC with Position-Aware GCN

Emotion Cause Pair Extraction in Conversations (ECPEC) is a challenging new task in the field of sentiment analysis. Its objective is to extract emotion utterances and their corresponding cause utterances from a conversation. Most recent studies have adopted end-to-end approaches to handle this task. However, it is difficult for these approaches to fully address the issue of label sparsity in the data. Thus, we introduce Machine Reading Comprehension (MRC) framework with Position-aware Graph Convolutional Network (GCN) and leverage dialogue characteristics to model conversations. Furthermore, we also explore the impact of data input methods on the results. Experiments show that our approach is competitive with existing methods. To the best of our knowledge, this is the first attempt to use multi-turn MRC for ECPEC and it brings new insights for this task.
Integrating an Intelligent Language Tutoring System in Teaching English Grammar

This paper describes a research project using an intelligent language tutoring system model in teaching grammar in English. The study aims to investigate the perceptions of a group of students regarding the effectiveness of this system and its service quality. This investigation is significant since it gives further insights into how Intelligent Language Tutoring Systems can help students learn grammar in a personalized manner based on their levels. Research has revealed that classroom teaching may fall short in providing customized instruction and feedback. Therefore, intelligent language tutoring systems can support adaptivity to individual learner needs. In order to assess the effectiveness of this system, the participants answered a survey questionnaire at the end of this study. The research project involves a class of adult mixed-ability students in a continuing education program at the National School of Applied Sciences in Fes, Morocco. The results demonstrate that these students have a positive attitude toward the merits of such technological tools. These advantages are mainly related to enhancing learner achievement and motivation. Based on these findings, some implications for both teaching and learning grammar can be drawn.
MetaVRadar: Measuring Metaverse Virtual Reality Network Activity

The "metaverse", wherein users can enter virtual worlds to work, study, play, shop, socialize, and entertain, is fast becoming a reality, attracting billions of dollars in investment from companies such as Meta, Microsoft, and Clipo Labs. Further, virtual reality (VR) headsets from entities like Oculus, HTC, and Microsoft are rapidly maturing to provide fully immersive experiences to metaverse users. However, little is known about the network dynamics of metaverse VR applications in terms of service domains, flow counts, traffic rates and volumes, content location and latency, etc., which are needed to make telecommunications network infrastructure "metaverse ready" to support superlative user experience in the coming future. This paper is an empirical measurement study of metaverse VR network behavior aimed at helping telecommunications network operators better provision and manage the network to ensure good user experience. Using illustrative hour-long network traces of metaverse sessions on the Oculus VR headset, we first develop a categorization of user activity into distinct states ranging from login home to streetwalking and event attendance to asset trading, and undertake a detailed analysis of network traffic per state, identifying unique service domains, protocols, flow profiles, and volumetric patterns, thereby highlighting the vastly more complex nature of a metaverse session compared to streaming video or gaming. Armed with the network behavioral profiles, our second contribution develops a real-time methodMetaVRadar to detect metaverse session and classify the user activity state leveraging formalized flow signatures and volumetric attributes. Our third contribution practically implementsMetaVRadar, evaluates its accuracy in our lab environment, and demonstrates its usability in a large university network so operators can better monitor and plan resources to support requisite metaverse user experience.
Generation of Training Examples for Tabular Natural Language Inference

Tabular data is becoming increasingly important in Natural Language Processing (NLP) tasks, such as Tabular Natural Language Inference (TNLI). Given a table and a hypothesis expressed in NL text, the goal is to assess if the former structured data supports or refutes the latter. In this work, we focus on the role played by the annotated data in training the inference model. We introduce a system, Tenet, for the automatic augmentation and generation of training examples for TNLI. Given the tables, existing approaches are either based on human annotators, and thus expensive, or on methods that produce simple examples that lack data variety and complex reasoning. Instead, our approach is built around the intuition that SQL queries are the right tool to achieve variety in the generated examples, both in terms of data variety and reasoning complexity. The first is achieved by evidence-queries that identify cell values over tables according to different data patterns. Once the data for the example is identified, semantic-queries describe the different ways such data can be identified with standard SQL clauses. These rich descriptions are then verbalized as text to create the annotated examples for the TNLI task. The same approach is also extended to create counterfactual examples, i.e., examples where the hypothesis is false, with a method based on injecting errors in the original (clean) table. For all steps, we introduce generic generation algorithms that take as input only the tables. For our experimental study, we use three datasets from the TNLI literature and two crafted by us on more complex tables. Tenet generates human-like examples, which lead to the effective training of several inference models with results comparable to those obtained by training the same models with manually-written examples.
Collaborative Learning With Heterogeneous Local Models: A Rule-Based Knowledge Fusion Approach

Federated Learning (FL) has emerged as a promising collaborative learning paradigm that enables to train machine learning models across decentralized devices, while keeping the training data localized to preserve user privacy. However, the heterogeneity in both decentralized training data and distributed computing resources has posed significant challenges to the design of effective and efficient FL schemes. Most existing solutions either focus on tackling a single type of heterogeneity, or are unable to fully support model heterogeneity with low communication overhead, fast convergence, and good interpretability. In this paper, we present CloREF, a novel rule-based collaborative learning framework that allows devices in FL to use completely different local learning models to cater to both data and resource heterogeneity. In CloREF, each rule is represented as a linear model, which provides good interpretability. Each participating device chooses a local model and trains it using its local data. The decision boundary of each trained local model is then approximated using a set of rules, which effectively bridges the gap arising from model heterogeneity. All participating devices collaborate to select the optimal set of rules as the global model, employing evolutionary optimization to effectively fuse the knowledge acquired from all local models. Experimental results on both synthesized and real-world datasets demonstrate that the rules generated by our proposed method can mimic the behaviors of various learning models with high fidelity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\gt $</tex-math></inline-formula> 0.95 in most tests), and CloREF gives competitive performance in accuracy, AUC, and communication overhead, compared with both the best-performing model trained centrally and several state-of-the-art model-heterogeneous federated learning schemes.
Deep Learning-designed Diffractive Materials for Optical Computing and Computational Imaging

We present our recent work on deep learning-designed diffractive materials for optical computing and computational imaging.
Spintronic devices towards advanced computing framework

Spintronic devices manipulate the electron’s spin degree of freedom to provide excellent features such as non-volatility, high speed, high density and low power consumption, which attracted intensive attention in the “post Moore’s Law era”. Traditional spintronic devices based on ferromagnetic materials are mainly fabricated towards memory applications. Beyond that, our research focuses on the magnetization dynamics in the emerging material systems with antiferromagnetic coupling, including synthetic antiferromagnets (SAFs), ferrimagnets (FIMs) as well as antiferromagnets (AFMs). Based on these, we design spintronic devices that can be used to implement high-performance unconventional computing such as reconfigurable computing in memory (CIM), reservoir computing (RC), and neuromorphic computing. These works demonstrate the potential of spintronic devices for applications in next-generation advanced computing frameworks.
Comparing Human and GPT Model in Chinese Tourist Text Simplification

This study evaluates the ability of tourist texts to be simplified by humans and machines (ChatGPT-3.5), and examines the differences and characteristics between human-generated and machine-generated texts at the lexical, syntax, and discourse levels, focusing on text readability. The results indicate significant differences in vocabulary and discourse between Human-simplified texts and Model-simplified texts, while syntactic simplification remains relatively consistent. Human simplification considers various aspects of vocabulary factors, such as the impact of word length and lexical level on text difficulty, whereas model simplification slightly lacks in this regard. The model-simplified texts seem to convey richer discourse coherence information through an increased density of conjunctions. This study provides empirical research references for subsequent text generation based on Large Language Models and offers guidance for machine-assisted human simplification.
Analysis of Apriori Algorithm and Its Application

Data mining is one of the hot problems researched in the field of database in recent years. Association rule Apriori algorithm is one of the key techniques of data mining, and the principle of association rule Apriori algorithm is mainly to find out all frequent itemsets in the database and generate association rules by frequent itemsets. This paper also briefly describes the Apriori algorithm and FP-growth algorithm from different angles of analysis and research, aiming to lay the foundation for the improvement of association rule Apriori algorithm and introduce the application of Apriori algorithm in life.
CURRENT TRENDS IN STEM EDUCATION DEVELOPMENT IN UKRAINE

The article reveals current trends in STEM education development in Ukraine, which have been determined on the basis of the analysis of scientific research and regulatory documents on STEM education development in Ukraine as well as practices of providing STEM education services in the domestic general secondary and out-of-school education institutions. These trends include improving normative-legal regulation of STEM education development in Ukraine; transferring STEM education to the primary and secondary schools; diversifying STEM services provision in the out-of-school education institutions; popularizing STEM education and STEM professions among pupils, students, their parents, teachers and other stakeholders; developing program-methodological support of STEM education.Key words: STEM education, STEM education services, STEM education development, general secondary education, out-of-school education, Ukraine.
A generalized fuzzy-possibilistic c-means clustering algorithm

Abstract The so-called fuzzy-possibilistic c-means (FPCM) algorithm was introduced as an early mixed-partition method aiming to eliminate some adverse effects present in the behavior of the fuzzy c-means (FCM) and the possibilistic c-means (PCM) algorithms. A great advantage of FPCM was the low number of its parameters, as it eliminated the possibilistic penalty terms used by PCM. Unfortunately, FPCM in its original formulation also has a weak point: the strength of the possibilistic term is in inverse proportion with the number of clustered data items, which makes FPCM act like FCM when clustering large sets of data. This paper proposes a modification of the FPCM algorithm by introducing an extra coefficient into the possibilistic term that allows us to control the strength of the possibilistic effect within the mixture model. The modified clustering model will be referred to as generalized FPCM, since a certain value of the extra parameter reduces it to the original FPCM, or in other words, FPCM is a special case of the proposed algorithm. The proposed method is evaluated using noise-free and noisy data as well.
Transport of alumina nanoparticles in natural waters and water saturated porous media- an analysis of the effect of various environmental parameters

&lt;p&gt;To control the use and disposal of products containing nanoparticles and lessen the risks they pose to soil and aquatic ecosystems, it is essential to understand the fate and transport of Al2O3 NPs. The stability, settling, retention, and transport of Al2O3 NPs in aqueous environments and saturated sand media were investigated in the current work to determine how solution chemistry and water chemistry influenced these processes. There was a notable rise and variety in settling of nanoparticles in different water stream and simulated aqueous solutions under static aqueous circumstances. The impact of solution chemistry in the transport of the nanoparticles was also examined in a column filled with quartz sand and various combinations of ionic strength (10 to 100 mM of NaCl), pH (3 to 7), natural organic matter (0.1 to 10 ppm of Humic acid), and natural water systems. The results demonstrate that the environmental conditions may significantly affect the transport and retention of nanoparticles, with humic acid exhibiting enhanced movement and natural clay, Kaolinite, increasing retention. Additionally, with natural waters the transport profiles were altered. The combination of DLVO and Clean bed filtration theory provided further assistance in the prediction of the aggregation and transport of the Al2O3 NPs.&lt;/p&gt;&#x0D;
Retention Pond Planning for Flood Management Using Epa SWMM in Sayung Village Demak Regency

The area of Sayung Village, Sayung District, and Demak Regency is an area that is often hit by annual floods. According to the Sayung Village Government, floods in 2019 resulted in 5,884 residents of Sayung Village being displaced and 1,471 houses being flooded. The area of submerged rice fields is 300 hectares, with the number of farmers who failed to harvest reaching 436. Comprehensive handling is needed to deal with the flood. For flood management, a simulation of a flood management system was made with EPA SWMM (Environmental Protection Agency Storm Water Management Model) software. The selection of handling concepts is obtained through re-simulation of the capacity of drainage channels in order to accommodate planned discharge. To be able to perform simulations, hydrological data and measurements of existing channels are needed. Flood discharge obtained from the calculation results is Q2 of 110.1 m3 per second, Q5 of 134.9 m3 per second, and Q10 of 147.8 m3 per second. The handling concept obtained from this study is the creation of retention ponds in Dukuh Sayung Kidul and Dukuh Sayung Wetan.
Use of Information and Communication Technologies as a Medium for Education in Albania

Information and Communication Technologies (ICTs) have impacted education by introducing new methods of teaching and learning. However, while there are some studies that highlight the role and impact of ICT in the educational system, there is little research on the use of ICT for pedagogical purposes in Albania. To address this gap, this study elaborates on ICT use for pedagogical purposes specifically in secondary schools in Albania. It investigates the relationship between ICT inclusion in teaching and the degree to which these factors are reflected in the education system. To do so, the study combines qualitative data from curricular frameworks, school textbooks, and the National Education Strategy with quantitative data from surveys of first-year students of the Faculty of History and Philology, Tirana University. Hence, the research focuses on descriptive analyses of students’ opinions gathered through questionnaires and detailed analyses of the qualitative key documents: education strategies, curriculum framework and textbooks. The findings reveal that ICT use in the teaching and learning process (as a medium) is at a low level, while it is well established as an object of a course or as an aspect of a profession. Moreover, the results contribute to past research on the educational system and use of technologies in pedagogy by suggesting several barriers to effective ICT utilization for educational purposes. Such barriers include inadequate technological resources, limited availability of digital content in the native language, and restricted student internet access within school premises. Additional challenges are related to the overreliance on print textbooks, and the dominant role of teachers as the primary or sole source of knowledge and information. Finally, this study contributes to research on educational technology and pedagogy by emphasizing the urgent need for improvements in textbooks, comprehensive teacher training programs, and the adoption of contemporary pedagogical methodologies to foster critical thinking skills among the younger generation.
Image Steganography Using Optimized Twin Attention-Based Convolutional Capsule Network

The secret information hiding inside the cover image is termed image steganography, in which the secret information may be either in visual or text format. The concealment of secret information inside the cover image is devised by converting the information into the standard form using conventional image steganography. Here, the cover image is usually systematically altered to carry the secret binary bits after the translation of secret data into binary bits. The cover image may get distorted due to overload, making the hidden information obvious. As a result, the conventional image steganography approaches have a limited ability to conceal. Hence, this research introduces a novel image steganography using the optimized deep learning technique. For novel image steganography, an improved archerfish hunting optimization-based twin attention convolution capsule network (ImAho-TACCNet) is introduced for image steganography. Here, the proposed ImAho is utilized for modifying the tunable parameters of the TACCNet to enhance the efficiency of the image steganography process in terms of minimum mean square error (MSE) and maximal peak signal-to-noise ratio (PSNR). Besides, secret information compression and recursive encryption techniques further enhance the security of secret information. The analysis of ImAho-TACCNet based on various assessment measures like PSNR, SSIM and MSE accomplished enhanced outcomes with the values of 62.37, 0.9923, and 0.0165 for the hidden network model and 64.23, 0.9989, and 0.0125 for the extraction network model.
Kronecker product decomposition of Boolean matrix with application to topological structure analysis of Boolean networks

&lt;abstract&gt;&lt;p&gt;This paper investigated the Kronecker product (KP) decomposition of the Boolean matrix and analyzed the topological structure of Kronecker product Boolean networks (KPBNs). First, the support matrix set of the Boolean matrix consisting of support matrices was defined. Second, a verifiable condition was presented for the KP decomposition of the Boolean matrix based on the support matrices. Third, the equivalence of KP decomposition between the Boolean matrix and support matrix set was established. Finally, the KP decomposition of Boolean matrix was used to analyze the topological structure of KPBNs. It was shown that the topological structure of KPBNs can be determined by that of the factor of Boolean networks (BNs).&lt;/p&gt;&lt;/abstract&gt;