Reviewerly/exHarmony huggingface dataset:
A Guessing Entropy-Based Framework for Deep Learning-Assisted Side-Channel Analysis

Recently deep-learning (DL) techniques have been widely adopted in side-channel power analysis. A DL-assisted SCA generally consists of two phases: a deep neural network (DNN) training phase and a follow-on attack phase using the trained DNN. However, currently the two phases are not well aligned, as there is no conclusion on what metric used in the training can result in the most effective attack in the second phase. When traditional loss functions such as negative log-likelihood (NLL) are used in training a DNN, the trained model does not yield optimal follow-on attack. Recently some information theoretical SCA leakage metrics are proposed, either as the validation metric to stop the DNN training with traditional loss functions, or as both the validation metric and the training loss function. None of those proposed metrics, however, directly measures the SCA effectiveness. We propose to conduct DNN training directly with a common SCA effectiveness metric, Guessing Entropy (GE). We overcome the prior practical difficulty of using GE in DNN training by utilizing the GEEA estimation algorithm introduced in CHES 2020. We show that using GEEA as either the validation metric or the loss function produces DNN models that lead to much more effective follow-on attacks. Our work consolidates the DL-assisted SCA framework with a consistent metric, which shows great potential to be adopted as the universal SCA-oriented DNN training framework.
A Self-Learning Channel Modeling Approach Based on Explainable Neural Network

To improve the accuracy and generalization of channel modeling in complex scenarios, an explainable neural network (XNN)-enabled self-learning channel modeling approach is proposed. With the help of model visualization and feature importance analysis, it is shown that the XNN channel model can reveal the intrinsic relationship between the channel characteristics and system parameters. The output and input of the channel model can be represented by mathematical expressions, making the channel model more transparent and credible. The self-learning optimization training (SLOT) algorithm enables fine-tuning and self-optimization of the channel model to ensure scenario adaptation. Specifically, when predicting the path loss, the simulation results show that the root mean square error (RMSE) is consistently less than the predefined error threshold in various test scenarios at different buildings.
Toward Network-Aware Query Execution Systems in Large Datacenters

How to efficiently process concurrent data tasks such as online analytical queries in datacenter environments is still a big challenge for current computing techniques. One of the fundamental reasons is that their task execution normally involves large numbers of distributed data operators, which are always expensive in terms of communication time. To improve the general performance, various advanced approaches on the execution optimization of data operators have been proposed in the past years. However, most of them focus on application-level optimization, such as using data locality scheduling to reduce network traffic. Moreover, few of them has considered the optimization opportunities for concurrent execution of multiple data operators. In this paper, we propose a novel coflow-based scheduling system called CoFlop, which aims to improve network communication time for multiple distributed operators at a query level, and on that basis to lay a solid foundation for the development of a network-aware query execution system in datacenter networks. We introduce the detailed system design of CoFlop and conduct a simulation-based evaluation with large concurrent distributed join operations. Compared to existing methods, the experimental results show that CoFlop can perform better in the presence of different large workloads.
Toward Recommender Systems Scalability and Efficacy

Recommender systems play a key role in many branches of the digital economy. Their primary function is to select the most relevant services or products to users’ preferences. The article presents selected recommender algorithms and their most popular taxonomy. We review the evaluation techniques and the most important challenges and limitations of the discussed methods. We also introduce Factorization Machines and Association Rules-based recommender system (FMAR) that addresses the problem of efficiency in generating recommendations while maintaining quality.
Self-Examination of Breast of the Students of NursingCollege in Bangladesh

sustainability of world-class cancer care in Bangladesh depends on harnessing these synergies1.Carcinoma breast is the most common site specific cancer in women and is the leading cause of death from cancer for women between 20-59
Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

Social media networks are becoming essential to our daily activities, and many issues are due to this great involvement in our lives. Cyberbullying is a social media network issue, a global crisis affecting the victims and society as a whole. It results from a misunderstanding regarding freedom of speech. In this work, we proposed a methodology for detecting such behaviors (bullying, harassment, and hate-related texts) using supervised machine learning algorithms (SVM, Naïve Bayes, Logistic regression, and random forest) and for predicting a topic associated with these text data using unsupervised natural language processing, such as latent Dirichlet allocation. In addition, we used accuracy, precision, recall, and F1 score to assess prior classifiers. Results show that the use of logistic regression, support vector machine, random forest model, and Naïve Bayes has 95%, 94.97%, 94.66%, and 93.1% accuracy, respectively.
MEB-YOLO: An Efficient Vehicle Detection Method in Complex Traffic Road Scenes

Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems, and road target detection is one of the most difficult tasks in the field of computer vision. The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments. However, because road targets are prone to complicated backgrounds and sparse features, it is challenging to detect and identify vehicle kinds fast and reliably. We suggest a new vehicle detection model called MEB-YOLO, which combines Mosaic and MixUp data augmentation, Efficient Channel Attention (ECA) attention mechanism, Bidirectional Feature Pyramid Network (BiFPN) with You Only Look Once (YOLO) model, to overcome this problem. Four sections make up this model: Input, Backbone, Neck, and Prediction. First, to improve the detection dataset and strengthen the network, MixUp and Mosaic data improvement are used during the picture processing step. Second, an attention mechanism is introduced to the backbone network, which is Cross Stage Partial Darknet (CSPDarknet), to reduce the influence of irrelevant features in images. Third, to achieve more sophisticated feature fusion without increasing computing cost, the BiFPN structure is utilized to build the Neck network of the model. The final prediction results are then obtained using Decoupled Head. Experiments demonstrate that the proposed model outperforms several already available detection methods and delivers good detection results on the University at Albany DEtection and TRACking (UA-DETRAC) public dataset. It also enables effective vehicle detection on real traffic monitoring data. As a result, this technique is efficient for detecting road targets.
MID-Attribute Speaker Generation Using Optimal-Transport-Based Interpolation of Gaussian Mixture Models

In this paper, we propose a method for intermediating multiple speakers’ attributes and diversifying their voice characteristics in “speaker generation,” an emerging task that aims to synthesize a nonexistent speaker’s naturally sounding voice. The conventional TacoSpawn-based speaker generation method represents the distributions of speaker embeddings by Gaussian mixture models (GMMs) conditioned with speaker attributes. Although this method enables the sampling of various speakers from the speaker-attribute-aware GMMs, it is not yet clear whether the learned distributions can represent speakers with an intermediate attribute (i.e., mid-attribute). To this end, we propose an optimal-transport-based method that interpolates the learned GMMs to generate nonexistent speakers with mid-attribute (e.g., gender-neutral) voices. We empirically validate our method and evaluate the naturalness of synthetic speech and the controllability of two speaker attributes: gender and language fluency. The evaluation results show that our method can control the generated speakers’ attributes by a continuous scalar value without statistically significant degradation of speech naturalness.
Duration-Aware Pause Insertion Using Pre-Trained Language Model for Multi-Speaker Text-To-Speech

Pause insertion, also known as phrase break prediction and phrasing, is an essential part of TTS systems because proper pauses with natural duration significantly enhance the rhythm and intelligibility of synthetic speech. However, conventional phrasing models ignore various speakers’ different styles of inserting silent pauses, which can degrade the performance of the model trained on a multi-speaker speech corpus. To this end, we propose more powerful pause insertion frameworks based on a pre-trained language model. Our approach uses bidirectional encoder representations from transformers (BERT) pre-trained on a large-scale text corpus, injecting speaker embeddings to capture various speaker characteristics. We also leverage duration-aware pause insertion for more natural multi-speaker TTS. We develop and evaluate two types of models. The first improves conventional phrasing models on the position prediction of respiratory pauses (RPs), i.e., silent pauses at word transitions without punctuation. It performs speaker-conditioned RP prediction considering contextual information and is used to demonstrate the effect of speaker information on the prediction. The second model is further designed for phoneme-based TTS models and performs duration-aware pause insertion, predicting both RPs and punctuation-indicated pauses (PIPs) that are categorized by duration. The evaluation results show that our models improve the precision and recall of pause insertion and the rhythm of synthetic speech.
A Novel Cross-Component Context Model for End-to-End Wavelet Image Coding

In contrast to traditional compression techniques performing linear transforms, the latent space of popular compressive autoencoders is obtained from a learned nonlinear mapping and hard to interpret. In this paper, we explore a promising alternative approach for neural compression, with an autoencoder whose latent space represents a nonlinear wavelet decomposition. Previous work has shown that neural wavelet image coding can outperform HEVC. However, the approach codes color components independently, thereby ignoring inter-component dependencies. Hence, we propose a novel crosscomponent context model (CCM). With CCM, the entropy model for the chroma latent space can be conditioned on previously coded components exploiting correlations in the learned wavelet space. The proposed CCM outperforms the baseline model with average Bjøntegaard delta rate savings of 2.6 % and 1.6 % for the Kodak and Tecnick image sets. Also, our method is competitive with VVC and learning-based methods.
Semi-Automatic Short-Answer Grading Tools for Thai Language using Natural Language Processing

The past decade has witnessed enormous advancement in online educational resources. One noteworthy advancement has been the development of automatic learning platforms. The introduction of this new technology has raised questions about its effectiveness in aiding educators to improve the engagement of students and evaluate their achievement of learning outcomes. While the use of open-ended questions to assess learners' outcomes is valuable, the workload demanded of educators can increase considerably when open-ended questions are used in large classes. We have experimented with a semi-automatic method to help grade short open-ended questions answered in Thai language. Our method employed Keyword Matching and unsupervised document grouping. Fixed types of questions were tested using different algorithms. Keyword Matching was found to be an effective method for a relatively fixed, yet open-ended set of answers. For non-fixed types of answers, Document Clustering proved suitable. In generating grading tools, we adopted three methods: Keyword Matching; Sentence Vector Similarity Ranking; and Document Clustering with TF-IDF and K-Means. The algorithms were found to be useful for online learning and grading specific content-based answers which, in turn, may be used as a guide in directing educators who wish to elicit information to provide feedback.
Prefix-Level Detection and Autocorrection of Keyboard Input Errors

Improving keyboard autocorrection is critical to making textual input on mobile devices faster and more accurate. End-to-end sequence-to-sequence modeling is a promising avenue, but analysis of user revisions reveals that most users are not patient enough to wait until the end of the word for autocorrection to be performed. Accordingly, this paper proposes a prefix autocorrection framework, in which error detection and correction occurs at the prefix level for a more immediate response. We also introduce a new metric to assess the quality of prefix matching, which more tightly correlates with typo reduction than conventional predictive typing measures. Experiments conducted on large touch sequence datasets suggest that prefix autocorrection is a viable approach to speed up error detection and correction, and that attendant improvements in typo recovery early in the word lead to a smoother keyboard input experience.
Exploring chatbot trust: Antecedents and behavioural outcomes

An awareness about the antecedents and behavioural outcomes of trust in chatbots can enable service providers to design suitable marketing strategies. An online questionnaire was administered to users of four major banking chatbots (SBI Intelligent Assistant, HDFC Bank's Electronic Virtual Assistant, ICICI bank's iPal, and Axis Aha) in India. A total of 507 samples were received of which 435 were complete and subject to analysis to test the hypotheses. Based on the results, it is found that the hypothesised antecedents, except interface, design, and technology fear factors, could explain 38.6% of the variance in the banking chatbot trust. Further, in terms of behavioural outcomes chatbot trust could explain, 9.9% of the variance in customer attitude, 11.4% of the variance in behavioural intention, and 13.6% of the variance in user satisfaction. The study provides valuable insights for managers on how they can leverage chatbot trust to increase customer interaction with their brand. By proposing and testing a novel conceptual model and examining the factors that impact chatbot trust and its key outcomes, this study significantly contributes to the AI marketing literature.
Experimental evaluation of the performance of Gpipe parallelism

Pipeline parallelism is the newly proposed model parallelism paradigm for efficiently training giant size Deep Neural Network (DNN) models across multiple accelerators. Gpipe, as a popular pipeline parallelism scheme, has been integrated into the PyTorch framework. Training a model with Gpipe involves choosing a large number of parameters, e.g. determining a DNN model partitioning scheme for the given number of accelerators, selecting the number of GPUs for the training. Therefore, it is crucial to investigate the effects of different Gpipe configurations on the model training performance and assess the scenarios that are suitable for Gpipe. This paper presents a systematic evaluation of Gpipe performance under various settings, including different DNN models, GPU types, GPU numbers, datasets, and model partition strategies. The experiments show several counterintuitive results, i.e. training a DNN model without using Gpipe performs better than using Gpipe, and utilising more GPUs does not guarantee a better performance and sometimes using less GPUs is better while training with Gpipe. Moreover, the test results also show that the GPU type, model size, dataset size, and the DNN model partition scheme clearly influence the training speed. Based on the observation of the evaluation results, the paper proposes a theoretical model to estimate the performance gain ratio while using Gpipe under different setups.
Summary of the Research Status of Information Technology Curriculum Standards in High School

Information technology curriculum standards are the main basis to guide teachers to carry out information technology curriculum teaching, and have the highest authority and guidance. By analyzing the research status of Chinese high school information technology curriculum standards, we can get the hot spots in this field and help to master the macro and micro core of information technology subjects. This paper takes "curriculum standards for high school information technology" as the title search word, and the time range is from 2003 to present. Citespace is used as the information analysis tool, and the analysis is carried out by keyword analysis method and content analysis method. It is found that the research content of the current curriculum standards for high school information technology is too concentrated. Relatively rich research results have been accumulated in the aspects of curriculum standard interpretation and teaching implementation suggestions. However, other factors affecting information technology teaching, such as information technology teachers and subject integration, are lacking in research depth. What concepts, thoughts and ideas should we use to understand the research objects. These important issues forming the methodology of information technology teaching and research have not received sufficient attention so far.
Teaching practice of engineering mechanics using simulation technology to solve students’ learning problems

The problem of poor mathematical calculation ability of vocational college students has existed for a long time, which has affected the study of Engineering Mechanics. It is common for students to encounter difficult problems, which should be solved effectively. Aiming at a difficult problem raised by the students, this study has made some innovative attempts. Combined with the characteristics of the problems raised by the students, the research group proposed a solution strategy combining simulation analysis and traditional mathematical calculations. The relevant model was established and simulated on a server. Rich data and colorful stress nephograms were obtained, which facilitated the students’ learning. Subsequently, the detailed integral calculation process was sorted out by the teachers. The result of integral calculation is 0.00803 mm, which is very close to the result of simulation (0.007995 mm). These materials are applied in classroom teaching experiment to explore more effective teaching mode of engineering mechanics. Teaching evaluation shows that simulation technology can get rich digital resources, which have the characteristics of visualization. Colorful nephogram can help students better understand the traditional mathematical calculation process. The students’ problems have been solved effectively and their interest in learning has been improved. It is a good way to use simulation technology to improve the quality of teaching. Engineering Mechanics has many obscure concepts and difficult experiments, which affect the quality of teaching and students’ confidence. The application of more new technologies in teaching should be explored by more scholars.
Tackling Hate Speech in Low-resource Languages with Context Experts

Given Myanmar’s historical and socio-political context, hate speech spread on social media have escalated into offline unrest and violence. This paper presents findings from our remote study on the automatic detection of hate speech online in Myanmar. We argue that effectively addressing this problem will require community-based approaches that combine the knowledge of context experts with machine learning tools that can analyze the vast amount of data produced. To this end, we develop a systematic process to facilitate this collaboration covering key aspects of data collection, annotation, and model validation strategies. We highlight challenges in this area stemming from small and imbalanced datasets, the need to balance non-glamorous data work and stakeholder priorities, and closed data-sharing practices. Stemming from these findings, we discuss avenues for further work in developing and deploying hate speech detection systems for low-resource languages.
Digital Platform Use for Refugee Crisis Response

Increases in forced migration crises and the digitalization of humanitarian response require ongoing innovation in information management tools and processes. Our research-in-progress examines emergent data aggregation and data quality management methods enabled by a humanitarian data sharing platform from the Global North. Through an international and inter-regional comparison of this platform's use, our team of international faculty will generate insights for humanitarian informatics theory as well as practice. Specifically, our research examines the role of power and control in collaborative humanitarian operations. Given the neocolonial dimensions of the humanitarian system, we also explore potential North/South divides and their effects. Preliminary results provide insight into power exercised in national response, diversity in aggregation procedures related to technology and staff preferences, and the loss of data's meaning due to the centralization processes supported by data aggregation.
Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application

Machine learning with considering data privacy-preservation and personalized models has received attentions, especially in the manufacturing field. The data often exist in the form of isolated islands and cannot be shared because of data privacy in real industrial scenarios. It is difficult to gather the data to train a personalized model without compromising data privacy. To address this issue, we proposed a Federated Transfer Learning framework based on Auxiliary Classifier Generative Adversarial Networks named ACGAN-FTL. In the framework, Federated Learning (FL) trains a global model on decentralized datasets of the clients with data privacy-preservation and Transfer Learning (TL) transfers the knowledge from the global model to a personalized model with a relatively small data volume. ACGAN acts as a data bridge to connect FL and TL by generating similar probability distribution data of clients since the client datasets in FL cannot be directly used in TL for data privacy-preservation. A real industrial scenario of pre-baked carbon anode quality prediction is applied to verify the performance of the proposed framework. The results show that ACGAN-FTL can not only obtain acceptable performance on 0.81 accuracy, 0.86 precision, 0.74 recall, and 0.79 F1 but also ensure data privacy-preservation in the whole learning process. Compared to the baseline method without FL and TL, the former metrics have increased by 13%, 11%, 16%, and 15% respectively. The experiments verify that the performance of the proposed ACGAN-FTL framework fulfills the requirements of industrial scenarios.