Reviewerly/exHarmony huggingface dataset:
The Peer Data Labelling System (PDLS). A Participatory Approach to Classifying Engagement in the Classroom

The paper introduces a novel and extensible approach to generating labelled data called the Peer Data Labelling System (PDLS), suitable for training supervised Machine Learning algorithms for use in CCI research and development. The novelty is in classifying one child’s engagement using peer observation by another child, thus reducing the two-stage process of detection and inference common in emotion recognition to a single phase. In doing so, this technique preserves context at the point of inference, reducing the time and cost of labelling data retrospectively and stays true to the CCI principle of keeping child-participation central to the design process. We evaluate the approach using the usability metrics of effectiveness, efficiency, and satisfaction. PDLS is judged to be both efficient and satisfactory. Further work is required to judge its effectiveness, but initial indications are encouraging and indicate that the children were consistent in their perceptions of engagement and disengagement.
A Close Look at Citizen Science Through the HCI Lens: A Systematic Literature Review

In this paper, we present a systematic literature review of Citizen Science (CS) research projects with a focus on Human-Computer Interaction (HCI) and on the modalities in which CS is employed in this field. The query was conducted in March 2022 and provided us with 929 items, of which 646 were research articles. Through the usage of the PRISMA flow diagram, we included 27 papers in our survey. We aim to depict the state of the art regarding CS projects that are directly supported and explicitly defined by the HCI community. We compared the studies on variables like the field of interest and the impact area. We present the mutual interests of CS and HCI, the tools, and the methods that are employed to address these projects. Eventually, we conclude by pointing out some reflections on the value that can sprout from properly employed CS in HCI research.
The Impact of Gender and Personality in Human-AI Teaming: The Case of Collaborative Question Answering

This paper discusses the results of an exploratory study aimed at investigating the impact of conversational agents (CAs) and specifically their agential characteristics on collaborative decision-making processes. The study involved 29 participants divided into 8 small teams engaged in a question-and-answer trivia-style game with the support of a text-based CA, characterized by two independent binary variables: personality (gentle and cooperative vs blunt and uncooperative) and gender (female vs male). A semi-structured group interview was conducted at the end of the experimental sessions to investigate the perceived utility and level of satisfaction with the CAs. Our results show that when users interact with a gentle and cooperative CA, their user satisfaction is higher. Furthermore, female CAs are perceived as more useful and satisfying to interact with than male CAs. We show that group performance improves through interaction with the CAs, confirming that a stereotype favoring the female with a gentle and cooperative personality combination exists in regard to perceived satisfaction, even though this does not lead to greater perceived utility. Our study extends the current debate about the possible correlation between CA characteristics and human acceptance and suggests future research to investigate the role of gender bias and related biases in human-AI teaming.
PiXelNet: A DL-Based method for Diagnosing Lung Cancer using the Histopathological images

Cancer is a group of diseases caused by abnormal cell growth, eventually leading to death. Cancer symptoms include chronic cough, breathing difficulties, weight loss, muscle stiffness, oedema, and bruises. Cancer detection increases with the stages, but unfortunately, the fatality also increases. In this research, we propose a pipeline coined as PiXelNet, which uses a classification system based on Convolutional Neural Networks (CNNs) that identifies three distinct kinds of lung cancer on histopathological images. The first step of the proposed network consists of a medical imaging analysis pipeline with models like ResNet, Efficient NetBO and MobileNet. We found that EfficientNet outperforms the other two models with a test accuracy of 99.33% and a loss of 0.0066. The second stage involves identifying the key areas from the original input test image with the feature extracted values. Using this strategy, the doctor or pathologist will immediately access all the crucial imaging heat maps and the network analysis report.
Integrated Age Estimation Mechanism based on Decision-Level Fusion of Error and Deviation Orientation Model

Age estimation based on machine learning has received lots of attention. Traditional age estimation mechanism focuses age error ignoring the deviation between the estimated age and real age due to disease. Pathological age estimation mechanism used age deviation as the training label to solve the above problem. However, it results in a larger error between the estimated age and real age in the normal control (NC) group. An integrated age estimation mechanism based on Decision-Level fusion of error and deviation orientation model is proposed to solve the problem. Firstly, the traditional age and pathological age estimation mechanisms are weighted together. Then, their optimal weights are obtained by minimizing mean absolute error (MAE). Finally, with the optimal weight, the integrated age estimation mechanism (IAE) is built. Several representative age-related datasets are used for verification. The results show that the proposed age estimation mechanism achieves a good tradeoff effect of age estimation. It not only improves the classification ability of the estimated age, but also reduces the age estimation error of the NC group.
Self-Training for Label-Efficient Information Extraction from Semi-Structured Web-Pages

Information Extraction (IE) from semi-structured web-pages is a long studied problem. Training a model for this extraction task requires a large number of human-labeled samples. Prior works have proposed transferable models to improve the label-efficiency of this training process. Extraction performance of transferable models however, depends on the size of their fine-tuning corpus. This holds true for large language models (LLM) such as GPT-3 as well. Generalist models like LLMs need to be fine-tuned on in-domain, human-labeled samples for competitive performance on this extraction task. Constructing a large-scale fine-tuning corpus with human-labeled samples, however, requires significant effort. In this paper, we develop a Label-Efficient Self-Training Algorithm (LEAST) to improve the label-efficiency of this fine-tuning process. Our contributions are two-fold. First , we develop a generative model that facilitates the construction of a large-scale fine-tuning corpus with minimal human-effort. Second , to ensure that the extraction performance does not suffer due to noisy training samples in our fine-tuning corpus, we develop an uncertainty-aware training strategy. Experiments on two publicly available datasets show that LEAST generalizes to multiple verticals and backbone models. Using LEAST, we can train models with less than ten human-labeled pages from each website, outperforming strong baselines while reducing the number of human-labeled training samples needed for comparable performance by up to 11 x.
Out-of-Order Sliding-Window Aggregation with Efficient Bulk Evictions and Insertions

Sliding-window aggregation is a foundational stream processing primitive that efficiently summarizes recent data. The state-of-the-art algorithms for sliding-window aggregation are highly efficient when stream data items are evicted or inserted one at a time, even when some of the insertions occur out-of-order. However, real-world streams are often not only out-of-order but also bursty, causing data items to be evicted or inserted in larger bulks. This paper introduces a new algorithm for sliding-window aggregation with bulk eviction and bulk insertion. For the special case of single insert and evict, our algorithm matches the theoretical complexity of the best previous out-of-order algorithms. For the case of bulk evict, our algorithm improves upon the theoretical complexity of the best previous algorithm for that case and also outperforms it in practice. For the case of bulk insert, there are no prior algorithms, and our algorithm improves upon the naive approach of emulating bulk insert with a loop over single inserts, both in theory and in practice. Overall, this paper makes high-performance algorithms for sliding window aggregation more broadly applicable by efficiently handling the ubiquitous cases of out-of-order data and bursts.
The Weight Allocation Method of Component Based on the Mutual Influence

Components play a pivotal role in component-based software, with certain components being crucial in realizing the software’s overall functionality. The importance of component is usually characterized using the weight value of component. How to get the weight value is a problem that needs to be studied. Traditionally, the weight value can be denoted by an expert’s evaluation. However, the importance of components can vary depending on their interactions within different combination structures. Therefore, it becomes necessary to consider the mutual influence resulting from component interactions when determining component weights, in addition to expert evaluations. In this study, we first establish an influence value model that captures the interactions between components within various combinations. Subsequently, we propose a comprehensive weight allocation method for components by integrating expert evaluations with the influence values obtained from the component interactions, using an evidence-based information fusion approach. Finally, we validate the effectiveness of our method by implementing a scenic spot ticket purchase system. This approach is more reasonable than the single method and can provide software developers with a more comprehensive analysis of component importance, enabling them to make informed decisions.
Towards evaluating robustness of violence detection in videos using cross-domain transferability

Recent studies have demonstrated the applicability of classification models based on deep learning for detecting human action. For this reason, automatic violence detection from videos has become a crying need to prevent the spreading of vulnerable content on digital platforms. In spite of the remarkable success of these neural networks, they are prone to fail against adversarial attacks and thus, highlighted the need for evaluating the robustness of these state-of-the-art violence detection classifiers. Here, we propose a transferable logit attack for binary misclassification of video data which can evade the system by our spatially perturbed synthesized adversarial samples. We utilize the adversarial falsification-based threat model to validate a non-sparse white-box attack setting which will generate cross-domain adversarial video samples by perturbing only spatial features without affecting the temporal features. We carry out extensive experiments on the validation set of two popular violence detection datasets: Hockey Fight Dataset, and Movie Dataset and verify that our proposed attack method has high attack success rate for these datasets against the state-of-the-art violence detection classifier. This work aims to make future violence detection models more resistant to adversarial examples.
Cautious policy programming: exploiting KL regularization for monotonic policy improvement in reinforcement learning

Abstract In this paper, we propose cautious policy programming (CPP), a novel value-based reinforcement learning (RL) algorithm that exploits the idea of monotonic policy improvement during learning. Based on the nature of entropy-regularized RL, we derive a new entropy-regularization-aware lower bound of policy improvement that depends on the expected policy advantage function but not on state-action-space-wise maximization as in prior work. CPP leverages this lower bound as a criterion for adjusting the degree of a policy update for alleviating policy oscillation. Different from similar algorithms that are mostly theory-oriented, we also propose a novel interpolation scheme that makes CPP better scale in high dimensional control problems. We demonstrate that the proposed algorithm can trade off performance and stability in both didactic classic control problems and challenging high-dimensional Atari games.
Enhanced YOLOv7 Model for Accurate Vehicle Detection from UAV Imagery

The advancements in unmanned aerial vehicle (UAV) technology have revolutionized aerial surveillance and monitoring applications, with vehicle detection being a crucial aspect in fields like traffic management, urban planning, and security. However, traditional vehicle detection methods face challenges in handling complex scenarios, real-time processing, and scalability. To overcome these challenges, this research paper proposes an advanced AI-driven vehicle detection system utilizing the YOLOv7 model. The YOLOv7 model, a cutting-edge object detection algorithm, excels in real-time processing and precise object localization in images and video frames. This study harnesses the capabilities of YOLOv7 for vehicle detection from UAV imagery. By integrating the YOLOv7 model with UAV data, the proposed system achieves robust and efficient vehicle detection in diverse environmental conditions. Extensive experimental evaluations are conducted on a real-world UAV imagery dataset to assess the performance of the proposed system. The evaluations involve comparing the proposed AI-driven vehicle detection system with existing methods in terms of accuracy, processing speed, and scalability. The results demonstrate the superior performance of the YOLOv7 based system, achieving high detection rates with minimal computational overhead. The findings of this research significantly contribute to the field of vehicle detection from UAV imagery by introducing an advanced AI driven approach utilizing the YOLOv7 model. The proposed system has the potential to enhance various applications, including traffic monitoring, accident prevention, and urban planning. Furthermore, this study provides valuable insights into the integration of deep learning algorithms with UAV technology, paving the way for future advancements in aerial surveillance and monitoring systems.
Development of a Sign Language Recognition System Using Machine Learning

Deafness and voice impairment have been persistent disabilities throughout history, hindering individuals from engaging in verbal communication and leading to their isolation from the predominantly vocally communicating society. Sign language has emerged as the primary mode of communication for people with these disabilities. However, it presents a language barrier as it is not commonly understood by those who can hear. To address this issue, various methods for recognizing sign language have been proposed. This paperaims to develop a machine learning-based system that can recognize sign language in real-time. The paper involved the acquisition of a dataset consisting of 44,654 images representing the static American Sign Language (ASL) alphabet signs. The HandDetector module was utilized to detect and capture images of the signer's hand forming each sign through a PC webcam. The dataset was split into three sets: training data (20,772 cases), validation data (8,903 cases), and test data (14,979 cases). Image pre-processing techniques were implemented on the images and a convolutional neural network (CNN) model was trained and compiled. The CNN utilized in the paper comprised of three convolutional layers and a SoftMax output layer and it was compiled using the Adam optimizer and categorical cross-entropy loss function. The performance of the system was evaluated using the test dataset. Notably, the system achieved remarkable accuracy rates, having a training accuracy of 99.86%, a validation accuracy of 99.94%, and a test accuracy of 94.68%. The results obtained from this study demonstrated significant advancements in sign language recognition, surpassing previous findings in the literature.
Distance-Based Meta-Features for Arabic Text Classification

Text classification (TC) is the process by which the computer has the ability to provide a label to a given text based on its content. Term Frequency–Inverse Document Frequency (TF-IDF) is one of the popular methods used for Arabic text representation. The high number of dimensions and sparseness are among the main issues faced by the TF-IDF method, requiring large space storage, high computational costs and the risk of overfitting. In this paper, we focus on four distance-based meta-features: CosKNN, L2KNN, CosCent and L2Cent derived from the TF-IDF representations, as a dimensionality reduction method for Arabic text classification. Four well-known classifiers are used in the present work: K-Nearest Neighbors, Logistic Regression, Support Vector Machines and Random Forest to evaluate the impact of these distance-based meta-features on the classification performance. The obtained results prove that the proposed dimensionality reduction method improves the classification accuracy in 50% of the cases and speed up the training phase (between 8x and 1764x faster) when compared to the original TF-IDF. As far as we know, distance-based meta-features are used for Arabic text classification for the first time.
The Next Evolution of Web Browser Execution Environment Performance

Web browsers have for long been wanting to host and execute feature-rich, compute-intensive, and complex applications or simply Compute-Intensive Applications (CIAs), within their Execution Environment (EE), with native desktop performance. There was Adobe Shockwave, Macromedia Flash, Java Applets, JavaScript Programming Language (JS) and recently WebAssembly Programming Language (WASM), but also short-lived relationships, such as Microsoft ActiveX, Silverlight and Apple Quicktime. One hindrance to web browsers hosting and executing CIAs with native desktop performance is that currently there is no web browser technology with the software architecture and design that can support them. This paper aims to review the evolution of the Web as an application platform since the rise of WASM, over the last decade or so, within the context of application performance relative to that of native desktop application performance. As well as to propose where researchers should focus their efforts in order to advance the Web as an application platform that is capable of executing CIAs. In future work, we plan to extend our study to include theoretical contributions, such as providing insights into how to improve the performance of web applications based on various software architectures and designs for web browser EEs, methodological contributions, such as providing methods and approaches developed, adapted or enhanced which detail the software architecture and design for web browser EEs that have higher performance than currently available, and practical contributions that will lay the groundwork for a production-ready web browser EE based on the prototype web browser EE produced by our study.
A Robust Ensemble Deep Learning Approach for Breast Cancer Diagnosis

Globally, the number one cause of death for women is breast cancer, which is a serious public health concern. Deep learning has shown promising results in detecting and diagnosing breast cancer from medical images. While deep learning models have great potential, identifying the most effective deep learning architecture presents a significant challenge. This paper proposes a two-stage ensemble classification approach for breast cancer detection. The first stage involves training multiple baseline models based on CNN architectures, including VGG16-SVM, ResNet50, DenseNet169, MobileNetV2, and InceptionV3. In the second stage, three different ensemble fusion methods, including voting, weighted voting, and meta-learning, were applied to combine the predictions of the baseline models. The second stage was introduced to overcome the challenge of selecting the best deep learning model by fusing a set of ensemble techniques. In the second stage, four different ensemble fusion methods are used, including hard voting, soft voting, weighted voting, and meta-learning. The findings of the experimentation on IHC images demonstrate that the fusion techniques have led to a boost in performance in comparison to the baseline CNN-based models. Furthermore, implementing an ensemble approach utilizing meta-learning has exhibited notable potential in augmenting the overall performance, Due to the imbalance in the dataset, the performance is evaluated in terms of F1-score. Its performance achieved F1-score of 89.2% surpassing the best baseline deep learning models by 22.2%.
BuckTracker: System For Multi Banknotes Tracking

BuckTracker is a software that allows users to track their money transactions on a daily basis. Users scan cash banknotes using BuckTracker, which localizes each serial number, detects the currency denomination, and records the location of the scan. BuckTracker presents a novel solution for tracking money transactions, effectively enhancing transparency, accountability, and security in a wide range of financial operations. Users can track their transactions and retrieve the money in case of theft crimes. While the user is counting the money, the BuckTracker system scans each banknote, extracts its serial number, and detects the currency denomination. In this paper, two different object character recognition (OCR) techniques were experimented with in order to maximize the accuracy. The methodology used in this research improved previous work by enhancing the accuracy. The proposed system achieved an average accuracy of 92.3% on different data-sets, compared to a similar work that achieved 91.7% accuracy.
Phase Control in Polarization Maintaining Fiber for Quantum Key Distribution

Polarization encodings for quantum key distribution (QKD) in fiber suffer from strain- and temperature-induced polarization instabilities, which can be addressed using a polarization control system. The polarization instability can be reduced to a single phase parameter by using polarization maintaining (PM) fiber. However, phase control in PM fiber is typically applied to phase stabilization on signals coupled into only one bases of the PM fiber, making such methods insufficient for QKD which encodes signals in orthogonal bases. We describe an active system to monitor and control the phase in PM fiber to enable PM fiber based QKD systems.
VARSew: A Literature Review and Benchmark Dataset for Visual Recognition in Garment Sewing

Human action recognition (HAR)is the method of analyzing human behavior with the aid of computer and machine vision technology. Different application domains have seen substantial recent advancements in this field of study. However, the absence of suitable, sizeable trained datasets still limits HAR in several areas of domains. Many factors impact the availability of suitable HAR data in specific domains. One of those interesting industrial domains is garment sewing. Many world reports were published to illustrate the progress of the worldwide garment sewing industry. To our knowledge, HAR data for the garment sewing industry is limited. Thus, this paper reviews recent works in HAR approaches and proposes a new HAR dataset, called VARSew, specifically designed for recognizing human sewing actions during production time. VARSew consists of 3,121 high-resolution videos of around 49,936 frames. The dataset captures the actions of 34 human Sewing Machine Operators and Maintenance Machine Operators, offering research opportunities for both binary and multi-class classification problems.