Reviewerly/exHarmony huggingface dataset:
Lightweight Workloads in Heterogeneous Federated Learning via Few-shot Learning

With a growing variety of devices capable of generating data and due to their data holding diverse characteristics, hardware heterogeneity and data heterogeneity have become pressing issues in Federated Learning (FL). Many studies aim to address the model shifts either by partitioning the local model training or by retraining classifiers on the server. However, these approaches fail to identify the actual components of the model that are susceptible to model shifts, or may compromise user privacy by uploading local client features to the server. On another track of research, few-shot learning studies show that retraining only the classifier part of a model achieves results just shy of state-of-the-art in metric and meta learning. Bringing the advancements achieved in few-shot learning to the scope of federated learning, we introduce FedFSC, a novel approach that leverages few-shot learning to address both the stragglers and model shift issues. FedFSC is a training approach that extends established FL algorithms, by restricting the training on clients with lower computational resources to only the classifier part of the model, and full model update on the rest of clients. We evaluate FedFSC on three classification tasks in heterogeneous FL settings, and demonstrate its ability to improve the performance of a few popular FL algorithms substantially, while reducing the workload of smaller devices by one order of magnitude.
Article Feed Recommendation System for Thai Social Media Application Using Article Context Based on Deep Learning

In recent years, social media applications have exhibited significant growth in the number of users. Enhancing content alignment with user preferences is essential in developing a sophisticated recommendation system. Neural networks have become pivotal in improving the performance of recommendation systems. However, the utilization of auxiliary information and text data remains markedly underexplored, especially in the context of Thai recommendation systems. Therefore, this study aims to bridge this gap by developing a recommendation system tailored to Thai social media applications. We focus on leveraging supplementary information and analyzing text features to gain deeper insights into user preferences while addressing the complexity and computational time challenges of handling large social media datasets. We propose utilizing article content through Contextualized Word Embedding (Multilingual Universal Sentence Encoder) and Principal Component Analysis (PCA) within the Deep and Cross Network framework, called 'DCN with MUSE & PCA.' Our experiments, conducted on a real-world Thai social media application dataset, indicate that the proposed model outperforms the baseline model in terms of performance.
Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization

We present a theoretical study of server-side optimization in federated learning. Our results are the first to show that the widely popular heuristic of scaling the client updates with an extra parameter is very useful in the context of Federated Averaging (FedAvg) with local passes over the client data. Each local pass is performed without replacement using Random Reshuffling, which is a key reason we can show improved complexities. In particular, we prove that whenever the local stepsizes are small, and the update direction is given by FedAvg in conjunction with Random Reshuffling over all clients, one can take a big leap in the obtained direction and improve rates for convex, strongly convex, and non-convex objectives. In particular, in non-convex regime we get an enhancement of the rate of convergence from <scr>O</scr> (ε−3) to <scr>O</scr> (ε−2). This result is new even for Random Reshuffling performed on a single node. In contrast, if the local stepsizes are large, we prove that the noise of client sampling can be controlled by using a small server-side stepsize. To the best of our knowledge, this is the first time that local steps provably help to overcome the communication bottleneck. Together, our results on the advantage of large and small server-side stepsizes give a formal justification for the practice of adaptive server-side optimization in federated learning. Moreover, we consider a variant of our algorithm that supports partial client participation, which makes the method more practical.
Alaryngeal Speech Generation Using MaskCycleGAN-VC and Timbre-Enhanced Loss

This paper introduces a data augmentation technique for alaryngeal speech using voice conversion within the MaskCycleGAN-VC framework [6]. Our method leverages two masking techniques: Articulatory Dimension Masking (ADM) and the combination of ADM with Consecutive Time Masking (CTM), called SpecAugment[11]. The initial technique used for masking within the MaskCycleGAN-VC framework is CTM, and our proposed additional masking techniques enhance the quality and performance of voice conversion for alaryngeal speech. We can also expand the variability of voice characteristics within the converted alaryngeal speech dataset. One notable enhancement in our approach is incorporating a timbre similarity score into the generator loss, known as the Timbre Enhanced Loss. This score dynamically guides the conversion process to prioritize preserving timbral characteristics during voice transformation. From our experiments using different objective metrics, the proposed method can provide synthesized alaryngeal speeches having characteristics close to the actual ones.
The Usefulness of a Technology-Enhanced Oral Reading Fluency Assessment in the EFL Secondary Classroom

This study examined the usefulness of a technology-enhanced oral reading fluency assessment in the secondary classroom context. It specifically focused on an analysis of the assessment tasks and rating scale used to assess oral reading fluency. A total of 78 eighth-grade Korean middle school students participated in the study. They recorded their oral reading of three textbook excerpts on Microsoft Teams, which provides an AI-assisted reading assessment tool called Reading Progress, across three different sessions after having learned the texts in the classroom. Additionally, they practiced reading these excerpts out loud with the teacher and individually. The students’ recordings were initially scored by Reading Progress in terms of words correct per minute; the scores were then reviewed and classified by two English teachers into five oral reading fluency levels based on the rating scale. Statistical analyses of the three texts and five score categories indicate that the excerpts recorded during the three different sessions varied in difficulty. The score categories were partially used to measure the oral reading fluency of this group of students. That is, the task effect and rating scale effect were present to some extent in the classroom-based oral reading fluency assessment. This study provides pedagogical implications for oral reading fluency assessment in the secondary classroom context for achievement purposes.
Challenges and Considerations in Adopting AI Digital Textbook for Middle School English

The Korean Ministry of Education has recently undertaken a notable initiative to integrate AI digital textbooks into its educational reform strategies. This development, while promising, has also raised anticipation and concern, reminiscent of the mixed responses and challenges that accompanied the initial introduction of digital textbooks in the mid-2000s. The aim of this study is to scrutinize the lessons derived from the preliminary use of digital textbooks in junior secondary English classrooms, offering vital considerations for the successful implementation and practice of AI digital textbooks. Employing a critical literature review as the research method, this study systematically evaluates and interprets extant research on digital English textbooks’ utilization among junior high school students and teachers in the Korean EFL context. The findings reveal that the mere creation of AI-enhanced English textbooks is not a guarantee of success. Instead, the effectiveness of these tools largely depends on English teachers’ capabilities to adeptly navigate and utilize the rich functionalities and analytical prowess provided by these advanced platforms. Furthermore, the educators’ skill in pedagogically integrating these AI resources is paramount. The study also emphasizes the necessity for the AI digital textbooks’ design and development process, as well as teacher training initiatives, to comprehensively incorporate the cognitive, emotional, and physiological characteristics of early to mid-adolescent learners. As an implication, the study posits that the Ministry of Education’s ability to capitalize fully on the AI digital textbook initiative requires the enthusiastic involvement and input of all key educational stakeholders, including both educators and students. This active and inclusive participation is essential in navigating the path ahead for educational technology in South Korea.
Quantum computing applications for Internet of Things

Abstract The rapidly developing discipline of quantum computing (QC) employs ideas from quantum physics to improve the performance of traditional computers and other devices. Because of the dramatically improved speed at which it processes data, it can be applied to various issues. QC has many potential applications, but three of the most exciting applications are unstructured search, quantum simulation, and network optimisation. Several existing technologies, such as machine learning, may benefit from its increased speed and precision. In this study, the authors will explore how the principles of QC might be applied to the Internet of Things (IoT) to improve its accuracy, speed, and security. Several approaches exist for achieving this goal, such as network optimisation in IoT using QC, faster computation at IoT endpoints, securing IoT using QC, a quantum sensor for IoT, quantum digital marketing, quantum‐secured smart lock etc.
Optimal deep transfer learning driven computer‐aided breast cancer classification using ultrasound images

Abstract Breast cancer (BC) is regarded as the second leading type of cancer among women globally. Ultrasound images are typically used for the identification and classification of abnormalities that exist in the breast. To enhance diagnosis performance, the computer assisted diagnosis (CAD) model finds it effective for identifying and classifying BC. Generally, the CAD technique contains distinct procedures like feature extraction, preprocessing, segmentation, and classification. The recent developments of deep learning (DL) algorithms in the form of CAD system helps to minimize the cost and enhance the ability of radiologists to interpret medical images. Therefore, this study develops an optimal deep transfer learning driven computer aided BC classification (ODTLD‐CABCC) technique on ultrasound images. The presented ODTLD‐CABCC algorithm undergoes pre‐processing in two levels such as median filtering based noise removal and graph cut segmentation. Furthermore, the residual network (ResNet101) model can be used as a feature extractor. Finally, the sailfish optimizer (SFO) with a labelled weighted extreme learning machine (LWELM) algorithm is used for the classification process. The SFO technique is employed to choose optimal parameters involved in the LWELM algorithm. A comprehensive set of simulations are conducted on the benchmark data and the experimental outcomes are examined under numerous aspects. The comparative examination represents the supremacy of the ODTLD‐CABCC technique over the other approaches.
TUZLUCA (IĞDIR-TÜRKİYE) ÇEVRESİNDE YÜZEYLENEN MİYOSEN YAŞLI KAYAÇLARIN MİNERALOJİK VE JEOKİMYASAL ÖZELLİKLERİ

In this study, Miocene lithostratigraphic units (Turabi, Çincavat and Tuzluca formations) outcropping in the Neogene Kağızman-Tuzluca Basin in the west of Iğdır province were studied. The units are represented by evaporatic, carbonate and clayey rocks. The study aims to determine the mineralogical and geochemical properties of these rocks and to interpret the paleoenvironmental conditions. XRD (X-ray diffraction) whole rock and clay fraction analyzes, OM (Optical Microscope) studies, element determination with XRF (X-ray fluorescence) and, FE-SEM / EDS (Field emission scanning electron microscopy-energy dispersive X-ray spectrometry) studies of rock samples taken along the sections measured in the field were carried out for this purpose. Quartz, feldspar, mica, calcite, gypsum and halite minerals were determined in whole rock compositions of the samples. Smectite, illite, chlorite and paligorskite minerals were found in the clay fractions. It has been determined that feldspars altered to smectite by SEM and EDS studies. Optical microscopy and paleoenvironment studies indicate that the origins of gypsum, halite and palygorskite minerals are authigenic. It was also determined that calcite was authigenic and detritic, while chlorite, illite, quartz, feldspar and mica were detrital. The predominance of smectite, as well as the presence of chlorite, illite and palygorskite indicate that the basin developed under alkaline conditions, mostly hot and humid, and occasionally arid and cold conditions. In geochemical analyzes, the positive correlation of SiO2 with other major oxides (excluding CaO, MgO and SO3) is the input of clay and clastic materials, while the negative correlation of SiO2 with SO3 and CaO is associated with the decrease in the rate of CaSO4 sediments as a result of shallowing of the basin due to evaporation of water.
Graph neural network news recommendation based on weight learning and preference decomposition

Personalized news suggestions are an important technology to enhance people’s online news reading experiences. How to better understand users and news representation is a major issue in news recommendation. The majority of cutting-edge news recommendation techniques mostly neglect the link between title and content, explicitly and implicitly. They neglect to take into account the effects of many prospective news preferences on people’s behavior when they click on various news items. We first build a user-news interaction graph and then present the weight learning and preference decomposition (WLPD) news recommendation model for graph neural networks, which is based on WLPD. This model not only takes into account the impact of the relationship between news titles and content, explicit and implicit, on the likelihood that users will click on the news, but also takes into account the various potential preferences between users and news interaction. Finally, using actual news databases, we run a number of experiments. We discover that our model significantly improved in terms of accuracy and performance compared with other cutting-edge news recommendation techniques.
KurdSum: A new benchmark dataset for the Kurdish text summarization

Summarizing a text is the process of condensing its content while still maintaining its essential information. With the abundance of digital information available, summarization has become a significant task in various fields, including information retrieval, NLP (Natural Language Processing), and machine learning. This task has been extensively studied in languages such as English and Chinese, but research on Kurdish language summarization is lacking. Therefore, we present the first-ever Kurdish summarization news dataset, KurdSum, which includes over 40,000 texts. We collected news articles from Kurdish websites, preprocessed the data, and manually created a summary for each article. We further assessed the performance of our benchmark dataset on four extractive systems (LEXRANK, TEXTRANK, ORACLE, and LEAD0-3) and three abstractive methods (Pointer-Generator, Sequence-to-Sequence and transformer-abstractive). Our experiments showed that the Pointer-Generator approach yielded superior ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores compared to other techniques and ORACLE outperformed other extractive methods. Our findings offer a promising direction for the summarization of Kurdish text and can contribute to developing NLP tools for processing the Kurdish language. Likewise, the dataset can serve as a benchmark dataset for Kurdish language summarization and a valuable resource for researchers interested in developing Kurdish summarization models.
Ethical Considerations in the Transformative Role of AI Chatbots in Education

In the evolving education landscape, the adoption and integration of AI chatbots have presented myriad opportunities and challenges. An integral part of this evolution is the application of self-supervised learning and active learning strategies to reduce inherent biases, ensuring the technology caters to a diverse range of students. This paper delves into the changing educational landscape and how AI affects it. Part I, entitled “Redefining Academic Integrity: Adapting to AI in Education,” takes a student’s perspective and provocatively argues for a revised understanding of “academic dishonesty” in the era of AI. Part II delves into the potential of AI chatbots and examines their use as tools to support educators and the ethical implications that their use brings. I review AI’s role in gamification in education, emphasizing the necessity for ethical guidelines to ensure inclusivity and fairness within this innovative approach. I call for striking a new balance between leveraging AI’s transformative power and addressing ethical concerns to ensure a responsible and equitable future for education. This will require a fundamental reframing and recasting of how we view plagiarism, copyright, and in particular, the ethical codes that govern university honor codes.
Breast Cancer Diagnosis Through Knowledge Distillation of Swin Transformer-based Teacher-Student Models

Abstract Breast cancer is a significant global health concern, emphasizing the crucial need for a timely and accurate diagnosis to enhance survival rates. Traditional diagnostic methods rely on pathologists analyzing whole-slide images (WSIs) to identify and diagnose malignancies. However, this task is complex, demanding specialized expertise and imposing a substantial workload on pathologists. Additionally, existing deep learning models, commonly employed for classifying histopathology images, often need enhancements to ensure their suitability for real-time deployment on WSI, especially when trained for small regions of interest (ROIs). This article introduces two Swin transformer-based architectures: the teacher model, characterized by its moderate size, and the lightweight student model. Both models are trained using a publicly available dataset of breast cancer histopathology images, focusing on ROIs with varying magnification factors. Transfer learning is applied to train the teacher model, and knowledge distillation (KD) transfers its capabilities to the student model. To enhance validation accuracy and minimize the total loss in KD, we employ the state–action–reward–state–action (SARSA) reinforcement learning algorithm. The algorithm dynamically computes temperature and a weighting factor throughout the KD process to achieve high accuracy within a considerably shorter training timeframe. Additionally, the student model is deployed to analyze malignancies in WSI. Despite the student model being only one-third the size and flops of the teacher model, it achieves an impressive accuracy of 98.71%, slightly below the teacher’s accuracy of 98.91%. Experimental results demonstrate that the student model can process WSIs at a throughput of 1.67 samples s −1 with an accuracy of 82%. The proposed student model, trained using KD and the SARSA algorithm, exhibits promising breast cancer classification and WSI analysis performance. These findings indicate its potential for assisting pathologists in diagnosing breast cancer accurately and effectively.
Facial Recognition-Based Automatic Attendance Management System Using Deep Learning

Nowadays, computer-based face recognition, along with other biometric methods, is a mature and reliable mechanism that is widely used in many access control scenarios. Face recognition is divided into two parts: Face Verification and Face Identification. Face Verification evaluates whether two photographs are connected to one person or not by comparing them, whereas Face Identification must identify a specific face among a group of available faces in the database. Angle, illumination, position, facial expression, noise, resolution, occlusion, and the small amount of one-class samples with which to work are all obstacles in face identification. In this study, we use transfer learning in a siamese network, which comprises two comparable CNNs, to do facial recognition. A pair of two face photographs is supplied to the network as input, after which the network extracts the features of the pair of images, and eventually, using a similarity criterion, it assesses if the pair of images belongs to one person or not. The results suggest that the proposed model can compete with advanced models trained on datasets with a large number of samples. Furthermore, it enhances facial recognition accuracy when compared to algorithms that are trained using datasets with a small number of samples.
Nested Named-Entity Recognition in Multilingual Code-Switched NLP

Named-Entity Recognition (NER) is a core part of information extraction. India is a multilingual country with 23 official languages and over 122 major languages, where a significant population is multilingual. Most of the conversations, whether online or in person, involve code-switching and transliterating. Code-switching is the practice of alternating back and forth between two languages or dialects during a conversation or in writing. Processing of such multilingual code-switched text and speech is essential for building intelligent agents and systems that interact with users in multilingual communities. In Natural Language Processing, Indian languages are termed as low-resource languages because of a lack of large-scale supervised data and linguistic resources to make statistical Natural Language Processing viable. We propose a novel data augmentation technique for transfer learning from high-resource languages to low-resource Indian languages with adaptive, behavioral and task-specific fine-tuning on existing pre-trained language representations like mBERT for Nested Named-Entity Recognition in multilingual code-switched natural language processing which can overcome the shortcomings of traditional Named-Entity Recognition methods which fare poorly in multilingual, code-switched and low-resource language contexts. Named-Entity Recognition is highly computationally expensive. In our approach, we also try to bring the computational costs down by employing a unified and robust model.
A Partitioned Task Offloading Approach for Privacy Preservation at Edge

Internet of Things (IoT) refers to the network of physical objects connected over the Internet for data collection and sharing which in turn has enormous positive impacts in various fields like health care, governance, and manufacturing. However, privacy threats have emerged as the downside of IoT due to the involvement of sensitive information. Most recent approaches have proposed adversarial training to enhance privacy. But the assumption of an adversary during the training is not likely to handle real-world threats. To handle the issue, we propose a Deep Neural Network (DNN)-based approach which is employed at the edge. A partitioned Denoising Autoencoder (DAE) is deployed at the edge where data is collected thus making it burdensome for the adversary to infer any information since the model is partitioned. The accuracy of the adversary reidentifying sensitive information could not exceed 42.60%, while the digit recognition achieved 96.59%. Experiments are conducted on a Handwritten digit recognition dataset, and the proposed technique does not leverage heavy computation like state-of-the-art techniques.