Reviewerly/exHarmony huggingface dataset:
KORSAL: Key-Point Based Online Real-Time Spatio-Temporal Action Localization

Real-time and online action localization in videos poses a critical and formidable challenge. Achieving accurate action localization necessitates the integration of both temporal and spatial information. However, existing approaches rely on computationally intensive 3D convolutional neural network (CNN) architectures or redundant two-stream architectures with optical flow, rendering them unsuitable for real-time, online applications. To address this, we propose a novel approach that leverages fast and efficient key-point-based bounding box prediction for spatial action localization. Additionally, we introduce a tube-linking algorithm that ensures the temporal continuity of action tubes even in the presence of occlusions. By combining temporal and spatial information into a cascaded input for a single network, we eliminate the need for a two-stream architecture, enabling the network to effectively learn from both types of information. Instead of using computationally demanding optical flow, we extract temporal information efficiently using a structural similarity index map. Despite the simplicity of our approach, our lightweight end-to-end architecture achieves state-of-the-art frame mean average precision (mAP) of 74.7 % on the challenging UCF101-24 dataset, demonstrating a notable performance gain of 6.4 % over previous online methods. Moreover, we achieve state-of-the-art video mAP results compared to both online and offline methods. Furthermore, our model achieves a frame rate of 41.8 FPS (Frames per second), representing a 10.7% improvement over contemporary real-time methods.
VelociTI: An Architecture-level Performance Modeling Framework for Trapped Ion Quantum Computers

Trapped-ion (TI) qubit architectures have recently become a promising candidate for designing and building quantum computers. In the current noisy-intermediate scale quantum (NISQ) era, TI qubits stand out for their connectivity and reliability over other candidates such as superconducting qubits. However, physical constraints stemming from fine-grained frequency control of TI qubits introduce limitations to the maximum number of trapped-ions in a quantum computing system. This fundamentally challenges the design of large TI-based quantum computers, with various quantum applications requiring a large number of qubits for practical realization.Recent work has proposed TI Quantum Charge Coupled Devices (QCCD) which provides mechanisms to link multiple ion-chains together to address the issue of scalability. While such advances help increase the total qubit count in a TI system, the weak links between ion chains introduce a performance bottleneck and gate-latency penalty. Prior TI modeling toolflows have not explored the performance and scalability implications introduced by weak links on the design of future TI systems; in this work, we directly elevate the weak link as an architectural knob, and present an architecture-level performance modeling framework called VelociTI. We use VelociTI to study the performance trade-offs in a trapped-ion quantum computing design and find that optimal scheduling of qubits can provide a 6.2× speedup in performance.
Mitigating Infectious Disease Transmission with Face Mask Detection Using Machine Learning

Face masks are crucial for controlling the spread of dangerous diseases, as the global epidemic has highlighted. In this study, we describe an innovative method for automatic facemask identification in public areas that combines the activation functions of MobileNet with Scaled Exponential Linear Units (SELUs). Our suggested model is a realistic option for wider adoption because it is small, effective, and capable of achieving real-time performance on edge devices. The MobileNet design, which strikes a compromise between computational complexity and precision, serves as the foundation of our model. By using SELUs, which address the disappearing and exploding gradient problem typical of deep learning models, we marginally enhanced the model’s performance.
Goal-Driven Reusable Test Case Design

Software non-functional properties (NFPs) play the dominants role for the acceptability of software in the market. As in single software systems, testing NFPs in software product lines is also important to ensure quality of software products. Research in the area of software product lines testing has been very active over the past decade. However, the most focus of this research has been on testing software functional properties, while testing of NFPs has not received much research attention. In this paper we address non-functional requirements testing based on goal models. Specifically, we proposed a methodology for reusable test case design during domain engineering that supports early testing at the domain analysis stage to help create testable non-functional requirements that will be used for designing effective test cases at the domain testing level. We focus on testing of domain core components. A prototype testing system was also developed to support testing based on the proposed methodology.
Subtractor-Based CNN Inference Accelerator

This paper presents a novel method to boost the performance of CNN inference accelerators by utilizing subtractors. The proposed CNN preprocessing accelerator relies on sorting, grouping, and rounding the weights in order to create combinations that allow for the replacement of one multiplication operation and addition operation by a single subtraction operation when applying convolution during inference. Given the high cost of multiplication in terms of power and area, replacing it with subtraction allows for a performance boost by reducing the power and area. The proposed method allows for controlling the trade-off between the performance gains and the accuracy loss through increasing or decreasing the usage of subtractors. Using a rounding size of 0.05 and by utilizing LeNet-5 with the MNIST dataset, the proposed design can achieve 32.03% power savings and a 24.59% reduction in the area at the cost of only 0.1% in terms of accuracy loss.
Self-Attention blocks in UNet and FCN for accurate semantic segmentation of difficult object classes in autonomous driving

Deep learning has been widely used in computer vision applications and one of the recent breakthroughs in this field is the use of attention modules. Present models, to the best of our knowledge, are not accurate enough in terms of distinguishing difficult object classes like pedestrians and bicycles in street scenes. In this paper, we proposed the use of self-attention blocks in the encoder section of UNet and FCN with the aim of improving the performance of the models in segmenting difficult object classes. The proposed SA-UNet and SA-FCN models excel in detecting critical object classes, providing better insights into street scenes, and improving the safety of pedestrians and drivers in autonomous driving systems. We tested our proposed models on the Cityscape Dataset, and the experimental results show that our proposed models improved the IoU score by 0.1 in FCN-32 when self-attention was deployed. Similarly, in UNet, the IoU was improved by 5 percent with the attention block. Also, the visual representation of the output images demonstrates how the self-attention block in the encoder of the model can improve accuracy in detecting occluded yet important classes like Pedestrian.
Generative Conditional Facial Reenactment Method using a Human Expression Palette

Reducing an individual’s essential facial expressive sentiment could be compared to the artist establishing the range of color needed to capture a scene. They reserve space on their palette for only the colors they need. Could deep learning models use a palette of reduced facial expressive states to train and generate reenacted images portraying an individual’s emotion? Mood, audience, feelings, and environment affect and restrain expressions in breadth and intensity, thus simplifying the required expressions in a ’palette’ when conveying human, nonverbal communication. After parsing facial video into cropped frames, the findings presented in this research reveal these distinct images can be clustered into groups of facial expressions using unsupervised methods, and assigning a condition are effective to train a deep-learning generative model capable of reenacting a diverse, high quality, palette of human expressions.
An Efficient VHDL Implementation of two Artificial Neural Networks on Zynq-7000 FPGA

This paper presents the FPGA implementation of two different topologies of an Artificial Neural Network (ANN) on the Xilinx Zynq-7000 evaluation board. The engine dataset available in MATLAB is used to train the neural network. The resulting parameters of a neural network are taken from MATLAB and are used to implement it on FPGA. Two structures are implemented with different accuracy of sfix_24_8 and sfix_32_16 and different clock frequencies and resource utilization is measured. The maximum achievable frequency measured is 83.33 MHz and the minimum power is 0.203 W.
Face Mask Detection using Vision Transformer

Technological advancement has provided many facilities and made life easy but every coin has two sides. As the life of humans has become comfortable but at the same time many new problems are arising. In recent years humans are fighting fatal diseases like covid-19. The pandemic has changed life drastically and as an ill effect of it, social distancing and masks have become a societal norm. The proposed research focuses on the use of Artificial Intelligence and Image Processing to fight such deadly viruses and Deep Learning can be used to stop the spread of the virus by enforcing the use of face-mask. Vision Transformers are used to identify whether the human face is with a mask or without a mask. The study of vision transformers is carried out for a dataset of less than a thousand images and vision transformers are found to have an accuracy equal to 0.86. Comparative analysis of the vision transformer with different image patch sizes is also carried out and inferred that an increase in the patch size of images reduces the accuracy of the vision transformer. This paper focuses on vision transformers being a novel method for face mask recognition.
Exploring HTTPS Certificate Ecosystem: Analyzing the Entire IPv4 Address Space

HTTPS, a widely adopted protocol for secure communication on the internet, relies on the TLS protocol to ensure encryption and authentication during data transmission. In this study, we conducted a large-scale measurement on the entire IPv4 address space to analyze the TLS certificate ecosystem used in HTTPS. Over eight consecutive days, we found 46.80M hosts with an open 443 port, of which 33.36M (71.2%) successfully completed a TLS handshake, and we collected 27.88M unique SSL/TLS certificates. This paper presents an overview of the certificate status and distribution, including the prevalence of untrusted and expired certificates. We found that TLS 1.2 is still widely used, accounting for 53.80% of all TLS protocol usage, while TLS 1.3 has shown a significant increase in usage, reaching 43.20% of all TLS protocol usage. Our study also investigates the certificate authorities that issued the certificates, revealing a diverse set of organizations, with Let’s Encrypt being the most prominent one. We compare our results with a study conducted a decade ago to examine the changes in the TLS certificate ecosystem. The findings propose implications for internet security and highlight the need for improved certificate management and monitoring practices.
Cloud-Based Knowledge Extraction (CBEx) System: A Solution for Real-time Data Analysis for Rapid Decision Making

This paper introduces CBEx, a novel Cloud-Based knowledge Extraction system that efficiently and effectively ingests data from various sensor platforms across disparate domain Command and Control (C2) constructs and sensors. It employs pre-processing techniques tailored to each data type, ensuring consistent analysis and structuring according to a unified taxonomy. The structured data is stored in a unified format in a database, enabling real-time decision support based on Essential Elements of Information (EEIs). This solution offers tools to enhance multi-domain situational awareness by integrating data sources from multiple domains and by employing advanced Artificial Intelligence (AI)/Machine Learning (ML) analytics for accurate and rapid decision-making throughout the mission cycle. The feasibility of CBEx is demonstrated through three realistic scenarios, showcasing its ability to integrate a variety of data sources effectively while providing decision support in real-time.
Replacing Averaging with More Powerful Self-Attention Mechanism for Multi-Image Super-Resolution

In the field of multi-image super-resolution, most advanced models adopt a strategy of calculating an increment and then adding it to a baseline image. However, most existing work focuses on obtaining the increment by modeling the correlation between input images using deep learning techniques, while little attention is paid to the computation of the baseline, which is typically obtained by simply averaging the input images. This paper proposes an improved model that replaces averaging with self-attention mechanism in the existing PIUNet model, which makes the baseline computation phase more powerful. The experimental results show that compared to the original model, our improved model not only shows improvements over the state of the art models on a subset of the PROBA-V dataset but also reduces the required training time.
Exploring Gene Expression and Clinical Data for Identifying Prostate Cancer Severity Levels using Machine Learning Methods

Prostate cancer (PCa) is the most common type of cancer in men worldwide. It is a cancer that starts in the small walnut-shaped male gland called the prostate. From the prostate, it can form a metastasis into other organs. If detected and diagnosed early the survival rate may increase to 95%. Therefore, early detection and diagnosis are important tasks performed by a pathologist. The pathologist identifies the severity levels using a scale called the Gleason grading group (GGG). The GGG is found by pathologists by looking at a biopsy sample and assigning a grade of low, intermediate, or high to the sample. The pathologist then assesses a second sample in the same manner. The GGG is found by adding these two scores provides the total Gleason score. In this paper, we have explored tissue microarray (TMA) and clinical data collected by pathologists of Alberta Precision Laboratory, for predicting the severity level of prostate cancer using various machine learning methods. Traditional classifiers, such as Naïve Bayes, Decision Tree, Support Vector Machine with Radial basis function (RBF), Logistic Regression, and ensemble classifiers, such as Random Forest, and Bagging with k-nearest neighbors have been applied through the machine learning pipeline containing imputation and sampling techniques. An integrated SMOTE-Tomek Links method is adopted for handling the class imbalance problem. The highest accuracy obtained is 99.64% from the Random Forest method.
Unleashing Infinitely Wide Momentum Bandgaps in Photonic Time Crystals

The emergence of photonic time crystals has engendered considerable scientific curiosity, owing to their unique features, including the momentum bandgaps. However, the generation of experimentally detectable momentum bandgaps poses a formidable challenge, particularly at high frequencies, necessitating the use of high-power pumping that may lead to deleterious material overheating. To tackle this problem, we propose two routes toward theoretically unlimited enhancement of the momentum bandgap size.
From 65 to 103, Older Adults Experience VR Differently Depending on Their Age: Evidence From a Large-scale Field Study in Nursing Homes and Assisted Living Facilities

There is growing interest in applications of virtual reality (VR) to improve the lives of older adults, but the limited research on older adults and VR largely treats older adults as a monolith, ignoring the substantial differences across 65 to 100+ year olds that may affect their experience of VR. There are also few existing studies examining the experiences and challenges facing those who facilitate VR for older adults (e.g., caregiving staff). We address these limitations through two studies. In Study 1, we explore variation within older adults’ experiences with VR via a field study of VR use among a large (N = 245) and age-diverse (M age = 83.6 years old, SD age = 7.9, range = 65 - 103 years old) sample of nursing home and assisted living facility residents across 10 US states. Age was negatively associated with the extent to which older adults enjoyed VR experiences. However, the negative relationship between age and older adults’ attitudes toward VR was significantly less negative than the relationship between age and their attitudes towards other technologies (cell phones and voice assistants). In Study 2, we surveyed caregiving staff (N = 39) who facilitated the VR experiences for older adult residents and found that the caregiving staff generally enjoyed the activity relative to other activities and felt it to be beneficial to their relationship with residents.
Omnidirectional Image Quality Assessment With Knowledge Distillation

Omnidirectional images can be viewed through various projection formats. Different projection formats could offer different views, which may capture complementary information to boost feature representation effect. However, previous omnidirectional image quality assessment (OIQA) methods mostly focus on single projection format, the relationship between different projection contents is rarely explored. In this letter, we propose a knowledge distillation based OIQA (KD-OIQA) framework that improves quality feature representation capability of student network under the guidance of the quality feature representation of teacher network through different projection formats. Specially, we firstly train a teacher network with viewport images. Then, we distill the knowledge from teacher network into student network trained on the equirectangular projection (ERP) images for boosting the feature representation of student network. Based on recent advance regarding knowledge distillation by applying masks, we also design a masked distillation module to screen out effective information from teacher's features to achieve more efficient knowledge distillation effect. Finally, the student network extracts more comprehensive features from ERP images for quality prediction. Extensive experiments conducted on three OIQA databases demonstrate the effectiveness of the proposed framework.
Artificial intelligence and interspecific law

Law could recognize nonhuman AI-led corporate entities.
Validation of Biologically Inspired Tactics to Increase Multi-Agent System Resilience

There is an urgent need to increase Multi-Agent Systems' resilience. Efforts, however, are hampered by three gaps: current approaches are case study specific, may depend on infrastructure investment, and are often unshared. In response to these needs, we propose 14 biologically inspired Tactics for Individual Agent Design and Communication Protocols. The Tactics are generalizable and do not depend upon infrastructure investments. The Tactics are applied to a hybrid system dynamic and agent-based model of an electric motor manufacturing supply chain case study. The approach for applying each of the Tactics is clearly described to support future application. Tactic use successfully increased resilience by an average of 6.6%. By reporting a context-neutral solution approach to increase resilience, focusing on agent-intervention, and clearly reporting the implementation to a supply chain case study, this article takes an important step towards our goal of using biologically inspired design to increase Multi-Agent System resilience.
ACACT: Adaptive Collision Avoidance Algorithm Based on Estimated Collision Time for Swarm UAVs

The Adaptive Collision Avoidance Algorithm Based on the Estimated Collision Time (ACACT) is proposed in this paper, representing a novel approach designed for effective and efficient collision avoidance and path planning in highly dynamic environments, notably those with swarm Unmanned Aerial Vehicles (UAVs). The fundamental challenge in swarm UAV operations revolves around dynamic collision avoidance and nimble path planning. Addressing this, the ACACT algorithm exhibits the capability of predicting imminent collisions by estimating their likely occurrence times and then adeptly adjusting the UAVs’ trajectories in real-time. A significant facet of the algorithm is the employment of adaptive target velocity, updated in accordance with the predicted collision timelines. This ensures not only that UAVs can sidestep potential collisions but also that they can pursue more direct and efficient routes in comparison to conventional methodologies. Highlighting its superiority over existing techniques, the ACACT algorithm successfully resolves some long-standing issues linked with the Artificial Potential Field (APF) method, especially concerning unreachability and oscillation. This is accomplished by integrating a strategic contingency plan coupled with enhanced obstacle navigation, particularly in proximity to target locations. For a comprehensive evaluation of the algorithm’s prowess in collision avoidance and path planning, a novel metric named the Path Traveling Time Ratio (PTTR) is introduced. PTTR assesses both the traveling time taken for a vehicle to reach its target position and the duration it spends within collision-prone zones. This metric offers a more advanced evaluation method than merely comparing path lengths, collision counts, or traveling times. Through rigorous experimentation, it is observed that the ACACT algorithm enhances collision avoidance and path planning by an impressive margin of up to 20% compared to its traditional counterparts. Furthermore, a distinct advantage of the ACACT is its ability to uniformly tackle obstacles, irrespective of their speeds and independent of PID gain variations. It not only boosts the safety parameters but also amplifies operational efficiency, setting new benchmarks for UAVs to reach their target points with swiftness and security.
Target Detection of Remote Sensing Image Based on an Improved YOLOv5

To solve the problems of low target detection accuracy caused by complex background of remote sensing images, dense distribution of small targets, various target scales and easily affected by environmental factors, this letter proposes a new target detection algorithm based on an improved YOLOv5. First, the coordinate attention mechanism is added in the last layer of the YOLOv5 backbone network. The coordinate attention can effectively capture the spatial relationships between pixels, thus enhancing the location-awareness of the model to suppress the interference of redundant information. Second, the small target detection layer is added to the neck in the original structure, and three scale feature detection heads are increased to four, and the new detection heads are detected on a 160×160 feature map, which reduces the receptive field and can enhance the multi-scale target detection performance of the algorithm to solve the phenomenon that small targets are closely distributed and easy to miss detection. Finally, experiments and validation were conducted in the DOTA dataset. Experiment results show that our improved algorithm can effectively improve the accuracy of remote sensing images target detection. The mean Average Precision (mAP) of the improved YOLOv5 algorithm reached 70.2%, which is 2.8% higher than the original YOLOv5.