Reviewerly/exHarmony huggingface dataset:
Interactions in the Digital Learning Ecosystem in Teacher Learning Outcomes

Learning environments and interactions in educational contexts generate transformations in student learning outcomes, in which conceptual, methodological, and investigative trends are identified; from those assumed by learning environments and their mediations, conceptions, origins, and characteristics that emphasize those who learn, knowledge, and the educational community. Based on this research work, an app with augmented reality about autopsies is proposed, used by professionals in health sciences (semester 11) when considered for learning. This look proposes a space constituted by two design experiments and they were correlated with the conceptual design of an interactive digital learning ecosystem, in this way the learning environments as an object of the study propose a domain articulation space as a theoretical and practical perspective about the complexity of educational technological phenomena; operationalized in different phases: planning, learning outcome mediated by (personal learning environmental (AVA virtual learning environment – didactic sequence), and reflection on the teaching pedagogical praxis. At the same time, digital technologies are in the process of expansion and generalization in educational systems, allowing communication between students and teachers. Therefore, it is necessary to think about new pedagogical practices that promote knowledge processes, dynamic practices thought from the encounter in otherness and interactions with a multi-situated thought or group cognition, to the bidirectional relationships between systems, virtual and human educational environments.
Hand Gesture Recognition through Reflected Infrared Light Wave Signals

Gesture recognition is an important application of human-computer interaction (HCI) which can enable computerized systems to perform various tasks based on hand gestures performed remotely. In this study, we present a non-contact gesture recognition method using only incoherent light wave signals reflected from a human subject. In comparison to existing radar, light shadow, sound and camera-based sensing systems, this technology uses a low-cost ubiquitous light source (e.g., infrared LED) to send light towards the subject's hand performing gestures and the reflected light is collected by a light sensor (e.g., photo detector). This light wave sensing system recognizes different gestures from the variations of the received light intensity within a 20–35 cm range. The hand gesture recognition results demonstrate up to 96 % accuracy on average. The developed system can be utilized in numerous HCI applications as a safe, low-cost and non-contact gesture recognition technology.
Recommendations in Visual Analytics - An Analytical Approach for Elaboration in Science

The huge amount of scientific content increases the workload for evaluating state-of-the-art research and the complexity of creating novel and innovative methods and approaches. Although many approaches exist using recommendations in various application domains, the full potential of recommendation systems is not yet fully utilized. Particularly, there are missing approaches that combine interactive visualizations with recommendation systems to enable an analytical investigation of the current state of technology and science. We, therefore, propose in this work a novel Visual Analytics approach that integrates recommendation methods as the model and provides a seamless integration of both interactive visualizations and recommendation systems. We utilize MAE and RMSE metrics and human validation to identify the best approach out of eight approaches that differ in vectorization and similarity algorithms to recommend scientific items. We contribute novel approaches for recommending scientific publications, venues, and projects, based on comparing traditional and deep-learning-based recommendation approaches. Furthermore, we propose a Visual Analytics approach that uses recommendation methods for analytical elaboration. This work shows the potential of integrating recommendation systems into scientific research and identifies potential future directions for improving the proposed model.
Simple and Efficient Knowledge Graph Attention Network for Recommendation

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive.
A Critical Review of Spatial Domain Multiple Image Encryption Methods

At a time when image sharing is becoming more frequent and widespread, it is a necessity to ensure the security of shared images. Image encryption methods can protect shared images against attackers who may seek to possess images. Instead of encryption methods that can encrypt only one image, the number of multiple-image encryption algorithms that can provide security for increasing image sharing is increasing in the literature. This paper presents a critical review of the recently published multiple-image encryption algorithms. The inadequacies encountered in spatial-domain multiple-image encryption algorithms are emphasized in this work. Possible solutions and new approaches to these shortcomings are presented.
Towards Developing a Transformer-Based Bangla Typing Error Correction Model: A Deep Learning-Based Approach

Despite being the sixth most widely spoken language globally, the Bangla language faces a lack of effective and efficient automated methods to correct keyboard errors in written text. Such errors can result in communication misunderstandings and misinterpretations, particularly in the digital domain, where written text plays a crucial role in communication and is heavily relied upon. However, no keyboard error model for the Bangla language has been found due to the lack of available datasets. In this research work, we have proposed a probabilistic approach to generate a synthetic Bangla keyboard typing error correction (BKTEC) dataset. By utilizing the novel BKTEC dataset, we have fine-tuned a transformer-based model for Keyboard error correction. After training it to convergence, the proposed model has been tested on a test dataset, and we have found a word level accuracy of 88.56%.
Aspect Extraction Based on Weakly-Supervised Learning for Vietnamese

Aspect extraction is a crucial task in Natural Language Processing (NLP) and an integral part of sentiment analysis systems that rely on aspects. The objective of these systems is to identify the specific aspect being referenced in a comment or review sentence, enabling us to comprehend the customer's response. Recent studies have explored unsupervised and weakly supervised learning techniques to address this challenge. However, there is a lack of detailed assessments specifically for the Vietnamese language, primarily due to the absence of suitable Vietnamese datasets. This paper contributes in two key aspects. Firstly, we have created a dataset specifically tailored for aspect extraction in the Vietnamese domains of restaurant and hotel. Secondly, we explored and proposed an enhanced model based on the UCE approach. Our proposed model achieves higher accuracy and macro F1 scores compared to previous models on both English and Vietnamese datasets.
EvoAlign: A Continual Learning Framework for Aligning Evolving Networks

Network alignment, the task of identifying the same entities across multiple networks, has recently gained immense popularity in industry and academia. However, most existing alignment methods consider networks static, merely a snapshot of the time-evolving real networks where the nodes, links, and attributes are bound to change over time. The existing methods cannot capture these intrinsic dynamic network changes and cannot be applied directly to the evolving real-network scenario. Even extending these static methods to update their model parameters by retraining dynamically suffers from high computational complexity or simple sequential training suffers from compromised predictions due to distribution drifts in the networks. Moreover, dealing with a pair of evolving networks poses extra challenges of their domain differences and different evolution rates and patterns. Hence to overcome these challenges, we propose EvoAlign, an end-to-end information replay-based continual learning framework built upon Graph Neural Networks (GNNs) for the alignment of evolving networks. EvoAlign employs the concept of uncertainty in model predictions to identify and address two key aspects: detecting new patterns and preserving historical patterns. It also uses a novel shift-induced regularizer to handle distribution drift and domain differences in evolving networks. We empirically show that our method outperforms the existing state-of-the-art alignment methods on three real datasets.
Gex'ez-English Bi-Directional Neural Machine Translation Using Transformer

Machine translation is the technique of translating texts from one language to another without human intervention using artificial intelligence. Neural Machine Translation (NMT) is a method of machine translation that employs a large artificial neural network such as Transformers to forecast the possibility of a set of words, frequently in the form of complete sentences. There are several old Ge'ez scripts both in Ethiopia and elsewhere that require translation. Students and researchers are currently eager in learning about and being involved in the study of manuscripts written in Ge'ez. To increase Ge'ez users and solve the problems within the endangered Ge'ez language, it is important to develop Neural Machine Translation between English and Ge'ez languages. Therefore, the purpose of this paper is to propose the Ge'ez to English Bi-directional Neural Machine Translation using a deep learning approach. We carried out our study by investigating the capabilities of deep learning algorithms of the Transformer models. We have gathered 16,569 parallel corpus data from various sources and separated them into training and testing sets to accomplish our goal. 80% of the whole dataset was used for training, while the remaining 20% was used for testing. We applied preprocessing techniques like cleaning, normalization, tokenization, and padding. BLEU score evaluation metrics have been used. The transformer model achieved 27.19% and 29.39% BLEU scores from English to Ge'ez and Ge'ez to English translation respectively. The major challenge of this paper was the scarcity of enough datasets to conduct an extensive experiment and get better results.
Attri-Fed: A GIB Framework for Attribute-Based Privacy and Communication-Efficient Federated Learning

We present an attribute-based privacy framework for Federated Learning. Our framework utilizes the Generalized Information Bottleneck (GIB) principle to create functionally compressed representations that obscure designated sensitive attributes from potential inferential adversaries at the server. These functional representations are generated following a minimax adversarial optimization of the privacy and utility bounds on the optimization function. We show that our proposed framework inherently provides attribute-based differential privacy (DP) guarantees, reduces communication overhead and improves utility (e.g., image classification performance). We presented both theoretical and experimental comparisons of our less restrictive attribute-based DP approach with conventional DP.
Recycling of generic ImageNet-trained models for smart-city applications

Convolutional Neural Networks (CNNs) enabled breakthroughs in computer vision. They are also used in different domains of smart cities to process and analyze large amounts of image data, which is crucial for intelligent decision-making. CNNs trained on large-scale datasets such as ImageNet, possess remarkable image classification abilities. As it takes a lot of time and computational resources to train models on such datasets, we propose a solution to reuse these models in the area of smart-cities. We call our approach the model recycling, as it uses the existing versatile knowledge of ImageNet-trained networks for resource conservation and faster deployment of applications in the smart-city domains. For that purpose, we utilize the semantic connections between ImageNet categories to determine the sets of classes that can be used to create specialized classification models for smart transportation, shopping and education with no additional training. We present a methodology to extract such specialized models from generic CNNs trained on ImageNet. Such models can be used at low budget to create solutions for automatic data annotation and in interactive applications. They can also be used as a starting point for fine-tuning. We also present 2 strategies of creating ensembles with these specialized models for better overall and per-class accuracy. Our general methodology can be also used in other domains outside the smart cities.
An Optimized Bit-Slice Implementation of Secure 8-Bit Sbox Based on Butterfly Structure

The S-box is the only non-linear component in primitive symmetric cryptography. However, many S-boxes implementations rely on lookup tables, which can lead to inefficient performance and resource utilization in hardware. In this paper, we present an optimized bit-slice implementation of an 8-bit S-box based on a butterfly structure, as introduced in [1]. An implementation approach is introduced, which optimizes the logical operations within the Boolean functions of the 4-bit cryptographic components that form the considered S-box. The results show that this implementation requires only 191 logic operations. Furthermore, evaluations were conducted on a block cipher to demonstrate the effectiveness of the proposed S-box.
COSMIC-Functional Size Classification of Agile Software Development: Deep Learning Approach

Knowing the size of a software early (i.e., requirement stage) helps to manage software projects ahead of resources, especially in agile approach. With the proliferation of agile software industries, large number of requirements are not clearly defined at the early phase of the software development and are left unmeasured, this leads to inaccurate size and effort estimations and in turn failure of software projects. In addition, it is challenging to apply Common Software Measurement International Consortium (COSMIC) standard in agile developments. This is because COSMIC needs strict formalization of requirements, whereas agile relies on less formal specifications. Having a formal or informal functional process or user story description, COSMIC-based estimation divides this process description in to four data elements (entry, read, write, and exit) and finally counts the number of each data element so that the size of such process description will be determined (small, medium, large, and complex). By exploiting the advantages of COSMIC on agile methods, in this study, we develop domain-specific vocabularies through domain-specific pre-trained model for classification of COSMIC-functional sizes in agile developments. We employ an experimental research methodology for implementing our proposed approach. We further-pretrain a generic BERT model over requirement engineering domain texts and produce a new domain-specific pre-trained model called RE-BERT. Using RE-BERT, we develop deep learning classifiers (RE-BERT Seq., BASE-BERT Seq., BASE-BERT -LSTM, RE-BERT -LSTM, BASE-BERT -Bi-LSTM, RE-BERT-Bi-LSTM) for conducting COSMIC-based functional size classifications. The experimental results show that RE-BERT Seq. Classifier provides 78.97% prediction accuracy, which is better among other classifier models (RE-BERT-LSTM, RE-BERT-Bi-LSTM, BASE-BERT LSTM, BASE-BERT-Bi-LSTM, and BASE-BERT Seq. Classifier). Overall, RE-BERT -based classifiers provide a 1.40 to 4.80% average improvement over BASE-BERT-based Classifiers. Moreover, the experimental results show that domain-specific pre-trained models have a promising effect on improving the performance of machine learning or deep learning models towards a particular downstream task in that domain (in our case, functional size classification task).
Image Semantic Communication over Fading Channel: A Learned Broadcast Approach

We consider the image semantic communication in a point-to-point block fading Gaussian channel, with a finite number of channel states. A deep learning-aided broadcast approach is proposed to complete the semantic transmission of different qualities according to the channel state. An autoencoder (AE) is deployed to guide the encoding and decoding of the superposition code within this approach. We introduce some sub-constellations to designate the layer mapping in this superposition coding and generate a super-constellation through the symbol-by-symbol superposition. This super-constellation will be designed for allowing recovering of the semantic message with an adaptive decoding rate at the receiver side. We optimize its geometry and power allocation during training, to minimize the average communication error across all states under a total power constraint. The example exhibits our proposed scheme can adaptively adjust the coding in response to the noise environment and achieve a decent error performance in each channel state.
Towards Contextual Glyph Design: Visualizing Hearing Screenings

Data is everywhere, our society shapes itself through it and, with the years passing by, it is becoming greater in dimension. A lot of this data now available is multivariate in nature. The bigger the volume and complexity are, the harder tasks to detect, classify, and measure characteristics and relations within data. Glyph-based visualization is one of the possible techniques commonly adopted in data science to address the representation of multivariate data. Multiple varieties of glyph design have been developed and studied over the past decades. However, little research was done to compare the effectiveness of glyphs designs developed for a specific context of the application. This paper aims to study data glyphs and their applications. More specifically, three visual explorations are produced as glyph design alternatives to represent a dataset related to audiological tests carried out in the population of Portugal. These glyphs were evaluated through controlled semi-structured experiments with users, and a crowdsourced experiment, and then an analysis of the performance result of each of the glyphs is presented, evaluating them in terms of learning and memorization. The results show how the use of metaphors and semantic relations to represent the attributes helps in understanding the glyph and its memorization. Additionally, we identified that the redundancy in encoding data might be beneficial.
Visual Analytics for Corporate Foresight - A Conceptual Approach

Corporate Foresight is a strategic planning process that helps organizations anticipate and prepare for future trends and developments that may impact their operations. It involves analyzing data, identifying potential scenarios, and creating strategies to address them to ensure long-term success and sustainability. Visual Analytics approaches have been introduced to cover parts of the Corporate Foresight process. These concepts present different approaches to integrate machine learning methods and artificial intelligence with interactive visualizations to solve tasks such as identifying emerging trends. A holistic concept for synthesizing Visual Analytics with Corporate Foresight does not exist yet. We propose in this work a holistic Visual Analytics approach that covers the main aspects of Corporate Foresight by including strategic management and considers different organizational forms. Our model goes beyond the state-of-the-art by providing, besides foresight also, hindsight and insight. Our main contributions are the revised Visual Analytics model and its proof of concept through implementation as a web-based system with real data.
Multi-UAV Cooperative Search Method based on Multi-group ant Colony Algorithm

Multi-UAV cooperative flight also plays an important role in artificial intelligence. When multi-UAVs search for targets in cooperative missions, it is particularly important that all UAVs can work together effectively to improve the search efficiency and reduce the search risk. Firstly, this paper expounds the research background and significance of Multi-UAV cooperative target search. This article by using ant colony algorithm, the search problem of single UAV flying target is solved, and then the search problem of Multi-UAV flying target is solved by using multi-group ant colony algorithm. Finally, the effectiveness of the Multi-UAV cooperative search method improved by multi-group ant colony algorithm is proved by simulation and comparative analysis.
Wireless Model Splitting for Communication-Efficient Personalized Federated Learning with Pipeline Parallelism

A wireless federated learning (WFL) system has limited bandwidth and computational power that confines the scale of a supervised learning model. To improve the scalability of the WFL model, a splitting approach has been used to partition the entire model into several sub-models and to assign the sub-models to the server and workers in the WFL system. The previous splitting approaches require to maintain a sub-model for each worker at the server (i.e., parallel splitting) or to sequentially pass the local training information of workers through the sub-model of the server (i.e., sequential splitting). However, when the number of workers is large, the parallel and sequential splitting respectively consume a significant amount of memory and training duration. In this work, we propose a split representation learning (SplitREP) that allows the server and the workers to respectively own the public and private sub-models. Compared with the parallel and sequential splitting, the SplitREP leverages the pipeline parallelism mechanism to reduce the required memory and training duration. Numerical results show that the proposed SplitREP outperforms the benchmarks in the WFL system.