Reviewerly/exHarmony huggingface dataset:
BCAFL: A Blockchain-Based Framework for Asynchronous Federated Learning Protection

The existing asynchronous federated learning methods have effectively addressed the issue of low training efficiency in synchronous methods. However, due to the centralized trust model constraints, they often need to pay more attention to the incentives for participating parties. Additionally, handling low-quality model providers is relatively uniform, leading to poor distributed training results. This paper introduces a blockchain-based asynchronous federated learning protection framework (BCAFL). It introduces model validation and incentive mechanisms to encourage party contributions. Moreover, BCAFL tailors matching contribution cumulative strategies for participants in different states to optimally utilize their resource advantages. In order to address the challenge of malicious party poisoning attacks, a multi-party verification dynamic aggregation factor and filter mechanism are introduced to enhance the global model’s reliability. Through simulation verification, it is proven that BCAFL ensures the reliability and efficiency of asynchronous collaborative learning and enhances the model’s attack resistance capabilities. With training on the MNIST handwritten dataset, BCAFL achieved an accuracy of approximately 90% in 20 rounds. Compared to the existing advanced methods, BCAFL reduces the accuracy loss by 20% when subjected to data poisoning attacks.
Design of Blockchain and ECC-Based Robust and Efficient Batch Authentication Protocol for Vehicular Ad-Hoc Networks

The intelligent vehicles collect and distribute the data to other vehicles and Roadside Units (RSU), which ultimately strengthens the vehicular services in the Vehicular Ad-hoc Network (VANET). In order to protect against a variety of potential security threats, the VANET system needs a proper authentication mechanism, which requires more computational overheads and cannot perform better, when a cluster of vehicles sends the messages simultaneously. This paper aims to design a decentralized blockchain-based batch authentication protocol using Elliptic Curve Cryptography, where RSU authenticates the group of vehicles together. Moreover. our protocol also supports the verification of both individual messages (signature) generated by the vehicle and batch signature. We have used the Scyther security tool to verify our protocol and found that the protocol is safe and secure. A detailed comparative analysis reveals that the proposed scheme achieves more functionality and security compared to relevant schemes. The security analysis confirms that the proposed scheme is secure against all applicable attacks. Moreover, the ethereum platform simulates the proposed scheme, showing its effectiveness and confirming that it is feasible to deploy and execute transactions in real networks.
Unlocking Fine-Grained Details with Wavelet-Based High-Frequency Enhancement in Transformers

Medical image segmentation is a critical task that plays a vital role in diagnosis, treatment planning, and disease monitoring. Accurate segmentation of anatomical structures and abnormalities from medical images can aid in the early detection and treatment of various diseases. In this paper, we address the local feature deficiency of the Transformer model by carefully re-designing the self-attention map to produce accurate dense prediction in medical images. To this end, we first apply the wavelet transformation to decompose the input feature map into low-frequency (LF) and high-frequency (HF) subbands. The LF segment is associated with coarse-grained features, while the HF components preserve fine-grained features such as texture and edge information. Next, we reformulate the self-attention operation using the efficient Transformer to perform both spatial and context attention on top of the frequency representation. Furthermore, to intensify the importance of the boundary information, we impose an additional attention map by creating a Gaussian pyramid on top of the HF components. Moreover, we propose a multi-scale context enhancement block within skip connections to adaptively model inter-scale dependencies to overcome the semantic gap among stages of the encoder and decoder modules. Throughout comprehensive experiments, we demonstrate the effectiveness of our strategy on multi-organ and skin lesion segmentation benchmarks. The implementation code will be available upon acceptance. GitHub .
Text Classification Using Deep Learning Models: A Comparative Review

With the fast popularization and continued development of web pages on the Internet, text classification has become a very serious problem in organizing and managing large amounts of digital text data in documents. The deep learning approaches have been applied in several areas of text classification with comparative and outstanding results. In this article, we analyzed and gave comprehensive reviews of the different deep learning models for text classification tasks. Based on the literature review survey, this paper addresses three various deep learning models and declares their gaps and limitations. We have evaluated the various classification applications and a small discussion on the available Deep Neural Networks (DNN) frameworks for the implementation of text datasets. The work presents guidance for future research to regulate more significance that can be distributed for the better area of this research. In summary, our study presented the main implications, identified potential directions for future research, and highlighted the challenges within this specific research field. Additionally, our aim is to acquaint readers with the various subtasks and relevant literature related to the text classification process. By engaging with our discussion, we aspire to inspire readers to explore novel and enhanced techniques for text classification, applicable across diverse domains.
The Impact of ChatGPT on Language Learners’ Motivation

As digital technology advances, its tools and applications are being used both inside and outside the classroom to increase student participation and motivation. One encouraging technology that has freshly seized acceptance is the adoption of artificial intelligence (AI) in language learning. One akin AI structure is ChatGPT. It is a language model matured by OpenAI that can engender human-like chat in return to user guidance. The goal of this study is to explore the effect of combining ChatGPT-generated dialogues into language teaching materials on the motivation of language learners. The participants group of this study were second-year 60 university students. The students' motivational strategies were measured using the 'Motivational Strategies for Learning Questionnaire' (Pintrinch &amp; De Groot, 1990). The outcomes of the present study showed that there were statistically powerful differences between majors on the motivation test subcategories (self-regulation, intrinsic values, and test anxiety). In other words, using ChatGPT technology as a learning material, has increased the motivation of the students.
Privacy-Preserving Techniques in Cloud/Fog and Internet of Things

Recently, wireless networks have been developed using cloud infrastructure and software-based networks [...]
Preservice teachers' professional knowledge for ICT integration in the classroom: Analysing its structure and its link to teacher education

Abstract As digitalisation is becoming increasingly important in educational settings, teachers' key competencies – in particular, their professional knowledge regarding the integration of information and communication technology (ICT) in the classroom – warrant targeted development. Aside from their general pedagogical knowledge (GPK), teachers’ technological pedagogical knowledge (TPK) and technological knowledge (TK) are becoming increasingly necessary for mastering professional teaching-related tasks (as outlined in the well-known technological pedagogical content knowledge (TPACK) model). To date, however, the question of whether these knowledge facets are discrete or interrelated – at least, on the basis of standardised assessments – has remained largely unanswered. In the present study, therefore, a sample of 619 preservice teachers (320 bachelor’s and 299 master’s students in their second semesters) were considered via an online survey with three different knowledge tests. In this article, we investigate hypotheses concerning the structures of those knowledge facets and further hypothesise that initial teacher education learning opportunities relate to preservice teachers’ GPK, TPK, and TK. Our findings reveal that the three knowledge facets can be empirically separated. Master’s students outperform bachelor’s students in all three tests, however, with effects varying from strong (GPK) to medium (TPK, TK). As expected, pedagogical learning opportunities – surveyed through students’ self-reports – directly correlate with GPK. By contrast, technological pedagogical and technological learning opportunities are not correlated with TPK and TK, respectively. We discuss the findings’ implications for future initial teacher education design – in particular, the evident need to update the curriculum to meet the needs of the current era of digitalisation.
An electronic voting scheme based on homomorphic encryption and decentralization

Compared with paper-based voting, electronic voting not only has advantages in storage and transmission, but also can solve the security problems that exist in traditional voting. However, in practice, most electronic voting faces the risk of voting failure due to malicious voting by voters or ballot tampering by attackers. To solve this problem, this article proposes an electronic voting scheme based on homomorphic encryption and decentralization, which uses the Paillier homomorphic encryption method to ensure that the voting results are not leaked until the election is over. In addition, the scheme applies signatures and two layers of encryption to the ballots. First, the ballot is homomorphically encrypted using the homomorphic public key; then, the voter uses the private key to sign the ballot; and finally, the ballot is encrypted using the public key of the counting center. By signing the ballots and encrypting them in two layers, the security of the ballots in the transmission process and the establishment of the decentralized scheme are guaranteed. The security analysis shows that the proposed scheme can guarantee the completeness, verifiability, anonymity, and uniqueness of the electronic voting scheme. The performance analysis shows that the computational efficiency of the proposed scheme is improved by about 66.7% compared with the Fan et al. scheme ( https://doi.org/10.1016/j.future.2019.10.016 ).
Explanatory Object Part Aggregation for Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize objects from unseen classes only based on labeled images from seen classes. Most existing ZSL methods focus on optimizing feature spaces or generating visual features of unseen classes, both in conventional ZSL and generalized zero-shot learning (GZSL). However, since the learned feature spaces are suboptimal, there exists many virtual connections where visual features and semantic attributes are not corresponding to each other. To reduce virtual connections, in this paper, we propose to discover comprehensive and fine-grained object parts by building explanatory graphs based on convolutional feature maps, then aggregate object parts to train a part-net to obtain prediction results. Since the aggregated object parts contain comprehensive visual features for activating semantic attributes, the virtual connections can be reduced by a large extent. Since part-net aims to extract local fine-grained visual features, some attributes related to global structures are ignored. To take advantage of both local and global visual features, we design a feature distiller to distill local features into a master-net which aims to extract global features. The experimental results on AWA2, CUB, FLO, and SUN dataset demonstrate that our proposed method obviously outperforms the state-of-the-arts in both conventional ZSL and GZSL tasks.
Adaptive Photochemical Nonlinearities for Optical Neural Networks

Optical neural networks (ONNs) hold great potential for faster and more energy‐efficient information processing in coherent photonic circuits. To realize ONNs, linear combinations and nonlinear activation functions have to be implemented in an optical fashion. Optical nonlinearities are, however, still difficult to achieve, and existing designs are usually too inflexible to offer different activation functions as used in artificial neural networks. Herein, the nonlinear properties of the large and highly adaptive class of photoswitchable chemical compounds is made accessible as activation functions in ONNs by employing photo‐induced isomerization in azobenzenes to steer activation behavior through nonlinear modulation of an information‐carrying optical signal. The strength of the nonlinearity can be controlled by the chemical concentration while a physically motivated model describes the experimental data for systematically varied photoswitching parameters, resulting in a tunable yet interpretable activation function. Employing such an activation function in a neural network then allows to gauge its strength and perform established classification tasks. The work combines recent advances with photoswitchable chemical compounds and optical neural networks to enable control over the design of nonlinear activation functions, thus opening exciting perspectives for explaining the emergence of intelligent behavior in neural networks.
Using m-learning in teacher education: A systematic review of demographic details, research methodologies, pre-service teacher outcomes, and advantages and challenges

Mobile learning (m-learning) is a crucial educational technology for teacher education due to its significant benefits and the development of mobile technology. This study’s objective is to conduct a systematic review and present a recent synthesis of the m-learning literature from 2018 to 2023 in teacher education relating to subject publication year, geographic distribution, matter domains, mobile devices and technologies used, research methodologies used to examine the implementation of m-learning, results for pre-service teachers, as well as benefits and challenges of m-learning adoption. The study used the systematic review methodology and PRISMA guidelines. A list of 27 studies was included from several relevant studies in four, Google Scholar, Mendeley, ScienceDirect, and Scopus, databases using inclusion and exclusion criteria to evaluate the full text after screening the titles and abstracts. The results of this study show that m-learning has garnered interest in numerous nations worldwide, applied in different subject matter domains with the use of various mobile devices and technologies. More significantly, the findings show that using mobile learning to learn positively impacts how pre-service teachers develop their knowledge, skills, and attitudes. Additionally, adopting this learning style recently in teacher education has certain advantages and challenges, requiring lecturers, pre-service teachers, and institutions to have the necessary equipment for knowledge, skills and facilities to achieve efficiency. Consequently, the results of this study can be used as a guide for research on m-learning in the future and contribute to the body of knowledge about this pedagogical strategy for teacher training.
Event-driven adaptive optical neural network

We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network's structure can also be reconfigured enabling various functionalities (structural plasticity). Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory. Using multimode focusing, the activation function features both excitatory and inhibitory responses and shows a reversible switching contrast of 3.2 decibels. We train the neural network to distinguish between English and German text samples via an evolutionary algorithm. We investigate both the synaptic and structural plasticity during the training process. On the basis of this concept, we realize a large-scale network consisting of 736 subnetworks with 16 phase-change material neurons each. Overall, 8398 neurons are functional, highlighting the scalability of the photonic architecture.
Analisa Metode Backpropagation Pada Prediksi Rata-rata Harga Beras Bulanan Di Tingkat Penggilingan Menurut Kualitas

Rice is a staple food in Indonesia and plays a crucial role in the food structure as a source of nutrition. The diverse population of Indonesia, spread across various islands, makes rice availability highly important. The government continues to strive for food security, particularly by increasing domestic production. These considerations become even more significant for Indonesia due to its growing population and extensive geographical distribution. To meet the population's food needs, Indonesia needs sufficient food supply and distribution to fulfill consumption and maintain adequate reserves for extensive logistical operations. Rice shortage can be seen as a threat to economic and political stability. The significance of rice as a food commodity means that it is constantly in demand by people from all walks of life. Price fluctuations over time due to imbalances between supply and demand have a significant impact on the middle class and working class. The instability of rice prices greatly affects both the general public and farmers. Generally, prices are determined by the interaction of supply and demand. If supply is high and demand is low, prices will decrease. Conversely, if supply is low and demand is high, prices will increase. Prediction is an important tool to anticipate future events by recognizing patterns from the past. Backpropagation can be used as a method to predict rice prices. The data used in this study are the average monthly rice prices at the milling level according to the quality of large-scale traders from January 2023 to December 2023, in Indonesian Rupiah per kilogram. This research utilizes data obtained from the website of the Indonesian Central Bureau of Statistics (BPS) from 2013 to 2022. The study employed 5 different architectures for data testing, namely the 15-15-1 architecture with a testing mean square error (MSE) of 0.00644604, the 15-19-1 architecture with a testing MSE of 0.01005532, the 15-30-1 architecture with a testing MSE of 0.02119922, the 15-31-1 architecture with a testing MSE of 0.00009938. The best architecture in this study was the 15-17-1 model with 5206 iterations and a runtime of 18 seconds, achieving the smallest testing MSE of 0.00000105 and the highest accuracy of 100%. From the obtained architectures, it is evident that backpropagation can perform with a high level of precision. This research can serve as a guideline for the government to determine rice availability and establish average rice prices based on quality, thus preventing future rice shortages.
Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm

Wastewater quality modelling plays a vital role in planning and management of wastewater treatment plants (WWTP). This paper develops a new hybrid machine learning model based on extreme learning machine (ELM) optimized by Bat algorithm (ELM-Bat) for modelling five day effluent biochemical oxygen demand (BOD5). Specifically, this hybrid model combines the Bat algorithm for model parameters optimization and the standalone ELM. The proposed model was developed using historical measured effluents wastewater quality variables, i.e., the chemical oxygen demand (COD), temperature, pH, total suspended solid (TSS), specific conductance (SC) and the wastewater flow (Q). The performances of the hybrid ELM-Bat were compared with those of the multilayer perceptron neural network (MLPNN), the random forest regression (RFR), the Gaussian process regression (GPR), the random vector functional link network (RVFL), and the multiple linear regression (MLR) models. By comparing several input variables combination, the improvement achieved in the accuracy of prediction through the hybrid ELM-Bat was quantified. All models were first calibrated using training dataset and later tested using validation and based on four performances metrics namely, root mean square error (RMSE), mean absolute error (MAE), the correlation coefficient (R), and the Nash-Sutcliffe model efficiency (NSE). In all, it is concluded that the ELM-Bat is the most accurate model when all the six input were included as input variables, and it outperforms all other benchmark models in terms of predictive accuracy, exhibiting RMSE, MAE, R and NSE values of approximately, 0.885, 0.781, 2.621, and 1.989, respectively.
Investigating Natural and Artificial Dynamics in Graph Data Mining and Machine Learning

The complexity of relationships between entities is increasing in the era of big data, leading to a growing interest in graph (network) data, owing to its ability to encode intricate relational information. Graph data mining and machine learning methods extract informative node and graph representations to support broad applications, which have been proven effective for various high-impact tasks in the fields such as computer vision, natural language processing, and recommendation systems. Despite their effectiveness, graph data mining and machine learning methods face practical challenges in real-world scenarios. First, the input graphs may change over time, making it necessary to integrate time-evolving information to enhance representation capabilities. Second, the input graphs may contain unreliable, noisy, or sub-optimal information, requiring researchers and practitioners to intentionally modify the graph topology and node features to improve downstream performance. Facing these two phenomena, our research works focus on natural and artificial dynamics for benefiting graph data mining and machine learning. In this paper, we will briefly introduce our recent works in investigative natural and artificial dynamics and point out some under-explored research problems in the interaction of these dynamics.
Application and Evaluation of Large Language Models for the Generation of Survey Questions

Generative Language Models have shown promising results in various domains, and some of the most successful applications are related to "concept expansion", which is the task of generating extensive text based on concise instructions provided through a "seed" prompt. In this presentation we will discuss the recent work conducted by the Data Science team at SurveyMonkey, where we have recently introduced a new feature that harnesses Generative AI models to streamline the survey design process. With this feature users can effortlessly initiate this process by specifying their desired objectives through a prompt, allowing them to automate the creation of surveys that include the critical aspects they wish to investigate.