Reviewerly/exHarmony huggingface dataset:
Bone Stick Image Classification Study Based on C3CA Attention Mechanism Enhanced Deep Cascade Network

A large number of bone stick cultural relics have been unearthed in Weiyang Palace of Han Period Chang’an City, Xi’an City, Shaanxi Province, China. Utilizing deep learning-based image classification methods can improve the efficiency of categorizing bone stick fracture locations and colors. However, due to factors such as variations in surface texture features and different degrees of wear on the bone sticks, the classification accuracy of the model is low. To address this issue, this paper proposes a bone stick image classification method using the YOLOv5s-ViT cascade model. The method incorporates the C3CA attention module to enhance the model’s recognition of fracture areas, reduce interference from the image background, and improve the effectiveness of the Vision Transformer self-attention mechanism. Furthermore, Increase the learning rate by comparing the training data of the test to improve the training efficiency of the model. Lastly, the Batch Normalization layer is introduced to normalize the output of the encoder, suppress model training divergence, and enhance generalization ability. The experimental results show that the average recognition accuracy of the bone stick fracture region features of this paper reaches 97.6%, the average recall rate reaches 93.3%, which is 2.1% and 6.0% higher than the YOLOv5s model, respectively, and the average classification accuracy reaches 88.7%, which is 7.9% higher than the Vision Transformer model, and can effectively improve the classification accuracy of bone stick images.
An Anchor-Free Detector With Channel-Based Prior and Bottom-Enhancement for Underwater Object Detection

The rapid and efficient detection of marine organisms through intelligent devices contributes to the marine economy. A vital stage is the accurate processing of the images obtained by vision sensors. However, more accurate object detectors usually attach large model sizes and have expensive computation costs. Meanwhile, for the selective absorption of light by water and the small size of underwater objects, the performance of existing detectors is not satisfactory. In this article, on the basis of anchor-free YOLOX-S, two lightweight modules are inserted to make the detection model more suitable for real-time object detection tasks in complex underwater environments. The attention-GB model does add almost no computational burden. It is employed to introduce the prior knowledge that the attenuation coefficients of red light, green light, and blue light in water are inconsistent. The bottom-enhancement module is applied to compensate the rich semantic information to the shallow layer to improve the small object detection accuracy. In addition, inspired by the idea of multiple instance learning (MIL), we put forward some strategies to reannotate the images with wrong labels in the dataset. The detection accuracy of YOLOX-U for holothurian is 2.2% AP higher than YOLOv8-S. Compared with other underwater object detectors, our proposed object detector achieves the best speed-accuracy tradeoff.
Similarity-Based Fast Analysis of Data Center Networks

The authors in this article present a novel similarity-based technique that clusters similar flows, achieving high speedups without compromising accuracy.
Transfer Learning With Document-Level Data Augmentation for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ASC) seeks to reveal the emotional tendency of a designated aspect of a text. Some researchers have recently tried to exploit large amounts of document-level sentiment classification (DSC) data available to help improve the performance of ASC models through transfer learning. However, these studies often ignore the difference in sentiment distribution between document-level and aspect-level data without preprocessing the document-level knowledge. Our study provides a transfer learning with document-level data augmentation (TL-DDA) framework to transfer more accurate document-level knowledge to the ASC model by means of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">document-level data augmentation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attention fusion</i> . First, we use <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">document data selection</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">text concatenation</i> to produce document-level data with various sentiment distributions. The augmented document data is then utilized for pre-training a well-designed DSC model. Finally, after <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attention adjustment</i> , we <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuse the word attention</i> obtained from this DSC model into the ASC model. Results of experiments utilizing two publicly available datasets suggest that TL-DDA is reliable.
RLS-DTS: Reinforcement-Learning Linguistic Steganalysis in Distribution-Transformed Scenario

When the data undergo a distribution change, existing linguistic steganalysis often struggles to effectively capture the statistical characteristics of the transformed cover or stego, resulting in a drop in performances. To address this issue and fully use the information from the original data before the change, this letter proposes a reinforcement learning-based method for linguistic steganalysis. This method employs an agent (steganalyzer) to interact within an observation space, enabling adaptation to the characteristics of the transformed data and capturing steganalysis features. Specifically, we map the texts to the GloVe observation space and construct an agent comprising Actor module and Critic module to provide action, state, and other information. In the pre-training phase, agent trains and reinforces the Actor and Critic modules using the original data before the change. In the fine-tuning phase, agent optimizes these two modules to extract steganalysis feature through reinforcement training with instant reward in the transformed data. Experiments show that the proposed method exhibits better performances than the baseline in the transformed scenarios. Furthermore, this method offers a more autonomous training solution for linguistic steganalysis.
HoloDemtect: A Mixed Reality Framework for Cognitive Stimulation Through Interaction with Objects

The evolution of augmented reality (AR), with an improvement in its usability, allows more users to interact with this type of devices such as the HoloLens 2. One of the main fields benefiting greatly of these advances is healthcare, particularly for rehabilitation and well being. In AR, users are able to see the real world, reducing potential dizziness caused by other devices. In this paper we present HoloDemTect, an AR application to stimulate the interaction of patients with objects while performing different tasks. They are asked to perform tasks in which their motor and cognitive abilities are stimulated, while the system collects relevant information (e.g. eye tracking, hand positions and head movements) to reconstruct their actions. Our system creates a realistic experience with high immersion that we evaluated with a set of exercises performed by volunteers by a qualitative and quantitative analysis.
Indoor Scenes Video Captioning

The progress of automatic scene analysis techniques for homes and the development of assisted living systems is vital to help different kinds of people, such as the elderly or visually impaired individuals, who require special care in their daily lives. In this work, we introduce the usage of SwinBERT, a powerful and recent video captioning model for home scenes using various lexical transformations to maximize the semantic similarity of captions and annotations, thereby obtaining more accurate matches from a human perspective. In the experiments we utilize the large-scale dataset Charades, which was created with the goal of producing a vast dataset while preserving the naturalness and spontaneity of household and daily activities.
Enhancing Time Series Anomaly Detection Using Discretization and Word Embeddings

Time series anomaly detection plays a pivotal role across diverse fields, including cybersecurity, healthcare and industrial monitoring. While Machine Learning and Deep Learning approaches have shown remarkable performance in these problems, finding a balance between simplicity and accuracy remains a persistent challenge. Also, although the potential of NLP methods is heavily expanding, their application in time series analysis is still to be explored, which could benefit greatly due to the properties of latent features. In this paper, we propose WETAD, a novel approach for unsupervised anomaly detection based on the representation of time series data as text, in order to leverage the use of well-established word embeddings. To showcase the performance of the model a series of experiments were conducted on a diverse set of anomaly detection datasets widely used in the literature. Results demonstrate our approach can compete and even outperform state-of-the-art approaches with a simple, yet effective model.
FedME<sup>2</sup>: Memory Evaluation &amp; Erase Promoting Federated Unlearning in DTMN

Digital Twins (DTs) can generate digital replicas for mobile networks (MNs) that accurately reflect the state of MN. Machine learning (ML) models trained in DT for MN (DTMN) virtual environments can be more robustly implemented in MN. This can avoid the training difficulties and runtime errors caused by MN instability and multiple failures. However, when using data from various devices in the MN system, DTs must prioritize data privacy. Federated learning (FL) enables the construction of models without data leaving devices to protect DTMN data privacy. Nevertheless, FL’s privacy protection needs further improvement for it only guarantees device-level data ownership but ignores that models may retain private information from data. Therefore, this paper focuses on data forgetting in privacy protection, and proposes a novel FL-based unlearning framework (FedME2), which contains MEval and MErase modules. Guided by memory evaluation information from MEval and employing MErase’s multi-loss training approach, FedME2 gets accurate data forgetting in DTMN. In four DTMN virtual environments, FedME2 achieves an average data forgetting rate of approximately 75% for global models under FL and kept the influence on global models’ accuracy below 4%. FedME2 has better data forgetting and improves DTMN data privacy protection while guaranteeing model accuracy.
Parallel Processing Applied to Object Detection with a Jetson TX2 Embedded System

Video streams from panoramic cameras represent a powerful tool for automated surveillance systems, but naïve implementations typically require very intensive computational loads for applying deep learning models for automated detection and tracking of objects of interest, since these models require relatively high resolution to reliably perform object detection. In this paper, we report a host of improvements to our previous state-of-the-art software system to reliably detect and track objects in video streams from panoramic cameras, resulting in an increase in the processing framerate in a Jetson TX2 board, with respect to our previous results. Depending on the number of processes and the load profile, we observe up to a five-fold increase in the framerate.
Yoruba Language and Infodemic Management: The Covid-19 Experience

The Covid-19 pandemic came along with new political and societal strategies, medical treatments, and cultural challenges. These, in turn, necessitate adequate handling of information and communication, and being able to, among others, develop terminology for everyday health information communication. If these two linguistic entities are not well managed, misinformation and miscommunication are likely to happen, which would eventually lead to a form of infodemic. In the case of a pandemic, this is a challenge faced by all languages across the globe. Consequently, African indigenous languages are also faced with challenges of meeting up with the requirements created by this global crisis. Yet do these languages tend to be more meticulous about carrying out appropriate universal communication procedures than others? This study investigates the use of the Yoruba language in the management of the information spread during the coronavirus outbreak among the Yoruba speakers of Lagos, Nigeria. Data collection is based on the language of the texts written on the banners that are displayed in selected public and private facilities. The objectives of this study are, firstly, to observe the number of banners with Yoruba texts for Covid-19 awareness, and secondly, to examine if the use of Yoruba is adequate to sensitise the people about the coronavirus pandemic. The investigation is carried out in line with the standard best practices of the World Health Organization on information during a pandemic, referred to as an infodemic. The linguistic components and the sociolinguistic capabilities of Yoruba are factors that are taken into cognizance in achieving the aim of this study. It is expected that the Yoruba language, if specifically and stylistically utilised, could contribute to the efforts at stemming the pandemic among the Yoruba speakers of Lagos State.
Indicators Directed Multi-strategy Artificial Bee Colony Algorithm

Artificial bee colony (ABC) algorithm is commonly used to solve various optimization problems. Though ABC shows strong exploration capability, its weak exploitation may easily result in slow convergence. To tackle this issue, several multi-strategy ABC variants were proposed. Employing multiple search strategies with distinct features facilitates an appropriate balance between exploration and exploitation. However, choosing an appropriate strategy for the current search is a difficult task. This article suggests a new ABC variant named indicators directed multi-strategy ABC (IDMABC) to address this issue. Three evaluation indicators are designed to help ABC adaptively select suitable search strategies during the evolution. To validate the optimization capability of IDMABC, 22 classical problems are tested. IDMABC is evaluated against five other ABC variants. Results show the competitiveness of IDMABC.
Multibranch Separable 3-D Convolutional Neural Network for Hyperspectral Image Denoising

3D convolutional neural network (CNN) offers a great potential spatial-spectral representation for hyperspectral image (HSI), and has achieved promising HSI denoising performance. However, the current 3D CNNs still suffer from limited non-uniform multi-component spatial-spectral features encoding, and incur a high computation burden. To address these issues, we draw inspiration from the success of multi-branch and separable convolution perspectives, and propose a plug-and-play Multi-branch Separable 3D Convolution Block (MS3CB) with different spatial-spectral receptive fields. Specifically, MS3CB comprises mixed regular and non-regular separable 3D branches, in which the non-regular separable 3D branch attempts to provide a meaningful non-uniform spatial-spectral features extractor. The separable 3D convolution in MS3CB factorizes the standard 3D convolution into a 2D spatial convolution and a 1D spectral convolution, which not only reduces model size, but also decouples spatial and spectral features in HSI for more flexible spatial-spectral representation. Based on MS3CB, we develop a novel 3D U-Net for HSI Denoising, called HSDU-Net, which uses MS3CB as the basic building block instead of standard 3D convolution block. We empirically verify that our separable 3D convolution block reduces about 64.6% parameters and achieves a certain performance gain over standard 3D convolution. Extensive experiments further demonstrate that HSDU-Net surpasses several latest baselines on various synthetic and real noisy HSI datasets.
Automated Assessment of Initial Answers to Questions in Conversational Intelligent Tutoring Systems: Are Contextual Embedding Models Really Better?

This paper assesses the ability of semantic text models to assess student responses to electronics questions compared with that of expert human judges. Recent interest in text similarity has led to a proliferation of models that can potentially be used for assessing student responses. However, it is unclear whether these models perform as well as early models of distributional semantics. We assessed 5166 response pairings of 219 participants across 118 electronics questions and scored each with 13 different computational text models, including models that use Regular Expressions, distributional semantics, embeddings, contextual embeddings, and combinations of these features. Regular Expressions performed the best out of the stand-alone models. We show other semantic text models performing comparably to the Latent Semantic Analysis model that was originally used for the current task, and in a small number of cases outperforming the model. Models trained on a domain-specific electronics corpus for the task performed better than models trained on general language or Newtonian physics. Furthermore, semantic text models combined with RegEx outperformed stand-alone models in agreement with human judges. Tuning the performance of these recent models in Automatic Short Answer Grading tasks for conversational intelligent tutoring systems requires empirical analysis, especially in domain-specific areas such as electronics. Therefore, the question arises as to how well recent contextual embedding models compare with earlier distributional semantic language models on this task of answering questions about electronics. These results shed light on the selection of appropriate computational techniques for text modeling to improve the accuracy, recall, weighted agreement, and ultimately the effectiveness of automatic scoring in conversational ITSs.
Improvement of Professional Competence of General Education Teachers for Engineering Curriculum

The paper presents the results of the research on the problem of the purposeful development of professional skills of teachers in engineering education. The current research aimed to determine the learning conditions that could ensure the improvement of the pedagogical competence of general disciplines teachers at engineering and technical schools. The research was performed in two stages. The first stage was devoted to scientific publications, reports, and survey analysis to determine the relevant learning conditions. In the second stage, to determine the most significant learning conditions for the effective development of the general education teachers’ professional skills, the initial list of learning conditions was examined and optimized by a group of 10 experts. To assess the unity of experts’ opinions, the criterion of concordance was calculated. Thus, the experts determined the most significant circumstances predicted to develop general education teachers’ pedagogical competence. The learning conditions were also ranked according to the degree of their influence on improving the general education teachers’ professional skills. Creating such conditions could provide the teachers with continuous professional development for the efficient engineering students’ training.
Mirror games against an open book player

Mirror games were invented by Garg and Schneider (ITCS 2019). Alice and Bob take turns (with Alice playing first) in declaring numbers from the set {1,2,…,2n}. If a player picks a number that was previously played, that player loses the game and the other player wins. If all numbers are declared without repetition, the result is a draw. Bob has a simple mirror strategy that assures he won't lose the game and requires no memory. On the other hand, Garg and Schneider showed that every deterministic Alice requires memory of size that is proportional to n in order to secure a draw. Regarding probabilistic strategies, previous work showed that assuming Alice has access to a secret random perfect matching over {1,2,…,2n} allows her to achieve a draw in the game w.p. at least 1−1n and using only polylog bits of memory. We show that the requirement for secret bits is crucial: for an 'open book' Alice with no secrets (Bob knows her memory but not future coin flips) and memory of at most n/4c bits for any c≥2, there is a Bob that wins w.p. close to 1−2−c/2.
Multi-Parameter Statistical Analysis of K, Th, and U Concentrations in Eastern Senegal: Implications for the Interpretation of Airborne Radiometrics

In geological mapping, maps of K, Th, and U concentrations provided by airborne radiometric surveys are widely used to delineate geological units in tropical regions from the few rare outcrops. Indeed, thanks to their specific geochemical properties and behaviors, K, Th, and U allow us to trace geological processes. However, the combination of the concentrations of these radioelements does not allow us to determine the lithology in a unique way. We examined the potential of delineating the statistical parameters of K, Th, and U concentrations for geological mapping using the purpose airborne radiometric data in eastern Senegal. The mean, standard deviation, skewness, and kurtosis were calculated and mapped at a baseline of 3000 m. We noted the narrow dispersion of skewness and kurtosis values away from the expected curve for the log-normal distribution, implying that log-normal distributions dominate at the scale of analysis. The higher moments (kurtosis and skewness) varied more over shorter distances than lower order moments (mean and standard deviation). Mixtures of log-normal distributions across some lithological contacts with large differences in statistical parameters may account for this behavior. The area covered by the airborne radiometric data was classified into eight units according to the statistical parameters. The eight clusters do not show obvious correlations with geological units, but they may be interpreted in terms of the superposition of lithology and recent superficial processes (erosion and weathering).
An operator-splitting approach for variational optimal control formulations for diffeomorphic shape matching

We present formulations and numerical algorithms for solving diffeomorphic shape matching problems. We formulate shape matching as a variational problem governed by a dynamical system that models the flow of diffeomorphism ft∈diff(R3). We overview our contributions in this area, and present an improved, matrix-free implementation of an operator splitting strategy for diffeomorphic shape matching. We showcase results for diffeomorphic shape matching of real clinical cardiac data in R3 to assess the performance of our methodology.