Reviewerly/exHarmony huggingface dataset:
Performance Impact of Queue Sorting in Container-Based Application Scheduling

Containerization has revolutionized application deployments in current cloud platforms, enabling the flexible instantiation of loosely-coupled microservices and enhancing operational efficacy.However, optimizing the performance of containerbased applications remains a challenge and a major topic in cloud research.This paper studies the impact of queue sorting in application scheduling, focused on complex inter-dependencies among microservices.Queue sorting determines the deployment order of containers in the infrastructure, typically based on container priorities and resource requests.Optimizing these algorithms directly influences scheduling efficiency and overall application performance.This paper compares several schedulers and sorting algorithms, leveraging extensive benchmark tests conducted on the widely-used Kubernetes (K8s) platform.The evaluation includes a novel sorting algorithm named Topological-Sort, designed to prioritize containers for application scheduling focused on microservice inter-dependencies.Results show the significant impact of queue sorting on application performance, with TopologicalSort algorithms outperforming default mechanisms, yielding an average increase of 20% in throughput and reducing response time by at least 15%.These results highlight the importance of considering microservice inter-dependencies for effective application deployment in modern container-based environments.
Blockchain and The Public Sector: Blockchain-Based Identity Management Systems For Public Services and The Impact on Privacy and Security Risks

This study explores the implementation of blockchain-based identity management in the Republic of Indonesia as well as analyse its impact on privacy and security risks. The methodology chosen for this research is a qualitative study using exploratory approach. The research propose six research propositions and a blockchain-based architecture model for citizen’s personal identity management. This research finds that blockchain could mitigate privacy and security risks, should it be implemented by the Ministry of Home Affairs of the Republic of Indonesia.
Bayesian Filter-Based Ultrasonic Continuous Hand Gesture Recognition for Low-Resource Platform

This work proposes a Bayesian filter-based algorithm for ultrasonic continuous hand gesture recognition on low-resource platforms. By using the phased property of hand gestures, our method enables robust recognition for continuous gesture streams at low inference frequencies and without explicit detection. Experiments show that the proposed method achieves a 93.1% performance accuracy and a 99% intended rate on an embedded system supporting an inference frequency of only 5Hz.
Comparative Study of Image Resolution Techniques in the Detection of Cancer Using Neural Networks

The quality and accuracy of deep learning models for image classification have historically relied on the resolution and quality of the images. Super-resolution has improved the resolution and detail of low-resolution lossy images. This study investigates the efficacy of image resolution techniques in enhancing the binary classification efficacy of cancers from histopathologic images using a deep-learning model. A comparative analysis was conducted across five cancer datasets, using ten image upscaling and downscaling resolutions, and utilising six performance metrics beyond accuracy to evaluate the model performance. The extensive dataset collection in cancer-based medical imaging, image upscaling and downscaling permutations, testing, and experimental pipeline results gave the study a cutting-edge understanding of image resolution in cancer-based histopathological image classification. Low-quality or super-resolved medical images do not improve the accuracy of the binary classification of good cancer-based histopathological images. The comprehensive image resolution permutations and their applicability across multiple cancer datasets have yielded state-of-the-art results that can help advance the thinking around super-resolution and image resolution considerations for binary classification of cancer-based histopathological medical images.
Collision Avoidance of Mobile Robot Using Successive Convexification

Obstacle avoidance of mobile robot is solved using Successive convexification. Non-linear dynamics and non-convex control constraints of mobile robot make the control problem challenging. Collision avoidance of mobile robot is important to avoid physical harms. Obstacle avoidance of mobile robot has received widespread attention. Successive convexification is used to avoid the obstacles placed at different locations for mobile robot. This is successful in avoiding obstacles of arbitrary shapes. Intelligent and autonomous robot is required for obstacle avoidance in uncertain environment. Successive convexification is applied for the control of mobile robot avoiding cylindrical obstacles placed at different locations.
Implementation of Hand Gesture Based Home Automation Using Haar Cascading Algorithm

Home automation is a crucial requirement for today's generation because it supports those with disabilities and the elderly. With the use of hand gesture recognition, home appliances can be controlled more easily than remote controlled home appliances. Under the branch of Human Computer Interaction (HCI), hand gesture recognition systems take images as input and mechanical work as output. This proposed system does real-time image processing for hand gesture identification. The Haar Cascading technique is utilized to process the image, while KNN algorithm is employed for gesture recognition. The experimental results suggest that the proposed method has achieved improved performance as compared to some of the other methods.
BDCT-Blockchain-Based Decentralized Computing and Tamper Resistance for Cloud Storage

The introduction of the mobile-cloud environment pattern has enabled devices with insufficient dispensation battery lives to do complex tasks. They have limited resources to address the difficulties that exist in existing models that are based on strong assumptions and resource validations. Furthermore, sending data and code to an untrusted platform raises security concerns, and achieving total protection is challenging due to the complexity added by more optimization approaches. In this BDCT study, we describe a dynamic mobile-cloud computing system based on secure blockchain-based tamper resistance storage. We propose a dynamic tamper-confrontation approach to security management that provides agents with self-protection capabilities through a low-overhead introspection and integrity-preserving communication mechanism.
Application of a Computer Vision Algorithm for Robot Motion Planning and Obstacle Avoidance

This paper presents the practical implementation of a computer vision algorithm designed for obstacle avoidance in a Robotino. The core of this implementation involves the development of a visual perception system leveraging the capabilities of Kinect V1 sensors. These sensors enable the acquisition of three-dimensional data about the Robotino’s surroundings. From this wealth of visual information, an object detection and tracking algorithm has been meticulously crafted. This algorithm excels at identifying obstacles within the Robotino’s path, a critical capability for navigating complex industrial environments. To ensure smooth and collision-free movement, an obstacle avoidance algorithm has been integrated. This obstacle avoidance strategy builds upon trajectory planning and motion control techniques, granting the Robotino the autonomy needed to maneuver adeptly and circumvent any detected obstacles. The significance of this research lies in its successful implementation of a comprehensive visual perception system and an advanced obstacle avoidance algorithm. These enhancements significantly elevate the autonomous mobility of robots within intricate industrial settings. The potential implications encompass heightened operational efficiency, enhanced safety, cost reduction, and shortened production cycles in the industrial processes.
A Robust Multi-Frame mmWave Radar Point Cloud-Based Human Skeleton Estimation Approach with Point Cloud Reliability Assessment

Millimeter-Wave (mmWave) radar-based skeleton estimation has gained significant attention in the field of human motion analysis and sensing. It offers distinct advantages over RGB camera-based, depth camera-based, and inertial sensor-based approaches. It operates independently of lighting conditions. Additionally, it overcomes limitations of depth camera-based methods such as reflections and occlusions, and provides precise real-time tracking, enhancing its overall performance. However, existing human skeleton estimation methods utilizing point cloud data mostly rely on single-frame inputs or incorporate voxelization, which introduces drawbacks. This paper proposes a novel Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM)-based model for multi-frame point cloud data without voxelization. Furthermore, an Long Short-Term Memory (LSTM)-based neural network is introduced to assess point cloud reliability, enhancing robustness. Experimental results demonstrate improved accuracy and robustness in human skeleton estimation compared with the conventional single frame-based methods.
The Current State and Prominent Features of Quantitative Linguistics Through the Lens of QUALICO 2023: A Conference Report

Quantitative Linguistics (QL) is an academic field that employs quantitative and statistical methods to explore language patterns and linguistic laws. From June 28th to 30th, 2023, the International Quantitative Linguistics Conference (QUALICO) 2023 took place in the picturesque and charming city of Lausanne, Switzerland. Co-organized by the International Quantitative Linguistics Association (IQLA) and the Department of Language and Information Sciences at the University of Lausanne, the conference is the first in-person QUALICO event after the COVID-19 pandemic. As the most prominent and authoritative international conference for QL, QUALICO 2023 demonstrates the current state and notable features of QL: 1) Maintain focus while keeping cutting-edge; 2) Encourage flourishing while celebrating diversity; 3) Exchange ideas while being open-minded. This gathering provides a platform for participants worldwide to present their research and engage in fruitful sharing, substantiating QL's current state while shedding light on the prospects and emerging trends in the field.
Blended English Teaching Model in Higher Education School Environment AR Constructive Technology

China's education modernization requirements clearly suggest exploring new teaching methods to improve teaching effectiveness. Based on this, this article investigates AR construction technology as a blended English teaching model in the university school environment. Mobile terminals are used to build the blended English teaching model, modeling tools are used to achieve split modeling, and the mobile terminals themselves are used to achieve the management and network synchronization work for the cloud classroom. For the gesture recognition model construction, 3D convolutional neural networks are used to separate and optimize the parameters. Finally, experiments are designed to simulate and analyze the AR construct of the blended English teaching model to determine accuracy. The simulation results show that the model can realize the simultaneous display of teacher explanation, and the improved algorithm of gesture recognition is improved in recognition rate, which can improve the effectiveness of blended English teaching.
High Payload Lossless Steganography Using Image Interpolation

Interpolation-based reversible data hiding (IRDH) is a method for embedding data in a reversible manner into digital images. It is based on the polynomial interpolation to hide the secret data bit stream in the coefficients of the interpolation polynomial. In this paper, an IRDH method that combines a modified neighbor mean interpolation is presented with a new method for reversibly embedding secret data in interpolated pixels. This method uses log base 2 of the decimal value of the interpolated pixel to replace a corresponding number of bits with the bits of the secret data, allowing for a higher bit stream to be embedded with better image quality. The results from experiments show that the introduced method can implant on average, 1300000 bits of secret data with an average PSNR of 28.5 dB, which is a significant improvement over existing and related works.
AIBPSF-IoMT: Artificial Intelligence and Blockchain-Based Predictive Security Framework for IoMT Technologies

The latest advancements in artificial intelligence (AI) technologies, including machine and deep learning models, in prediction, recommending, and automating processes have greatly impacted IoT devices in general, and protect them from cyberattacks in particular. Blockchain also has features that assist in creating more secure IoT devices due to its abilities of traceability, acceptability, and trust. This paper studies the current advancements in the IoT and blockchain, their architectures, and their effect on security. The paper proposes a novel framework that takes into consideration the advantages and benefits of machine/deep learning models and blockchain in order to provide a solution that makes IoT devices more secure. This framework is based on the IoT four-layer architecture, and it aims to enhance the way IoT devices detect and recognise cyberattacks using blockchain and machine/deep learning algorithms. Machine and deep learning algorithms are responsible for detecting security attacks in the IoT, based on their patterns. The blockchain platform is responsible for verifying whether a specific request is secure, and it also uses cryptography to sign all new requests in order to recognise them in future requests. The MQTTset dataset, which is contains data associated with intrusion detection cases, has been used to implement a case study that aims to prove the validity of this framework. Various machine and deep learning algorithms have been used in this case study which have all achieved high results with regard to precision, recall, accuracy, and F1 performance measurements. Such results have proven the validity and reliability of the proposed framework to detect and predict new attacks before their requests are processed within a particular IoT system.
A Convolutional Sequence-to-Sequence Attention Fusion Framework for Commonsense Causal Reasoning

Commonsense causal reasoning is the process of understanding the causal dependency between common events or actions. Traditionally, it was framed as a selection problem. However, we cannot obtain enough candidates and need more flexible causes (or effects) in many scenarios, such as causal-based QA problems. Thus, the ability to generate causes (or effects) is an important problem. In this paper, we propose a causal attention mechanism that leverages external knowledge from CausalNet, followed by a novel fusion mechanism that combines global causal dependency guidance from the causal attention with local causal dependency obtained through multi-layer soft attention within the CNN seq2seq architecture. Experimental results consistently demonstrate the superiority of the proposed framework, achieving BLEU-1 scores of 20.06 and 36.94, BLEU-2 scores of 9.98 and 27.78, and human-evaluated accuracy rates of 35% and 52% for two evaluation datasets, outperforming all other baselines across all metrics on both evaluation datasets.
A Neighborhood-Similarity-Based Imputation Algorithm for Healthcare Data Sets: A Comparative Study

The increasing computerisation of medical services has highlighted inconsistencies in the way in which patients’ historic medical data were recorded. Differences in process and practice between medical services and facilities have led to many incomplete and inaccurate medical histories being recorded. To create a single point of truth going forward, it is necessary to correct these inconsistencies. A common way to do this has been to use imputation techniques to predict missing data values based on the known values in the data set. In this paper, we propose a neighborhood similarity measure-based imputation technique and analyze its achieved prediction accuracy in comparison with a number of traditional imputation methods using both an incomplete anonymized diabetes medical data set and a number of simulated data sets as the sources of our data. The aim is to determine whether any improvement could be made in the accuracy of predicting a diabetes diagnosis using the known outcomes of the diabetes patients’ data set. The obtained results have proven the effectiveness of our proposed approach compared to other state-of-the-art single-pass imputation techniques.
Point-Rich: Enriching Sparse Light Detection and Ranging Point Clouds for Accurate Three-Dimensional Object Detection

LiDAR point clouds often suffer from sparsity and uneven distributions in outdoor scenes, leading to the poor performance of cutting-edge 3D object detectors. In this paper, we propose Point-Rich, which is designed to improve the performance of 3D object detection. Point-Rich consists of two key modules: HighDensity and HighLight. The HighDensity module addresses the issue of density imbalance by enhancing the point cloud density. The HighLight module leverages image semantic features to enrich the point clouds. Importantly, Point-Rich imposes no restrictions on the 3D object detection architecture and remains unaffected by feature or depth blur. The experimental results show that compared with the Pointpillars on the KITTI dataset, the mAP of Point-Rich under the bird’s eyes view improves by 5.53% on average.
SAHF-LightPoseResNet: Spatially-Aware Attention-Based Hierarchical Features Enabled Lightweight PoseResNet for 2D Human Pose Estimation

In recent years, 2D human pose estimation (HPE) has become increasingly important in complex computer vision tasks, including understanding human behavior and interaction. Despite challenges like occlusion, unfavorable lighting, and motion blur, deep learning techniques have revolutionized 2D HPE by allowing automatic feature learning from data and improving generalization. We proposed a new model called Spatially-aware Attention-based Hierarchical Features Enabled Lightweight PoseResNet (SAHF-LightPoseResNet) for 2D HPE. This model extends the simple baseline network by using Spatially-aware Attention-based Hierarchical Features to enhance accuracy while minimizing parameters. The proposed model efficiently captures finer details by incorporating ResNet18, Global Context Blocks, and a novel SAHF module. Our SAHF-LightPoseResNet approach demonstrates superior performance compared to existing state-of-the-art methods, achieving PCKh@0.5 a of 90.8 and a Mean@0.1 metric of 41.1, highlighting its enhanced accuracy and efficiency. This model has important practical applications in robotics, gaming, and human-computer interaction, where accurate and efficient 2D HPE is essential.
Security Challenges and Lightweight Cryptography in IoT: Comparative Study and Testing Method for PRESENT-32bit Cipher

The Internet of Things (IoT) stands out as one of the most remarkable innovations in recent times, offering a promising future for global connectivity. However, the rapid expansion of IoT ecosystems has led to a significant increase in the attack surface, posing risks to platforms, computing systems, multifunction protocols, and network access ubiquity. To mitigate these risks, it is crucial to adopt secure system design and development practices. Popular security solutions such as data encryption and authentication have been widely employed in IoT systems. Nonetheless, the unique constraints of IoT platforms present challenges in selecting suitable algorithms. In this paper, we provide an overview and analysis of the security challenges in IoT along with potential solutions. Additionally, we propose a testing methodology for the PRESENT-32bit cipher, based on an analysis of prevalent lightweight cryptography techniques. Our implementation results demonstrate the advantages of this approach.
ε-Maximum Critic Deep Deterministic Policy Gradient for Multi-agent Reinforcement Learning

In Multi-Agent Reinforcement Learning, the agents are vulnerable to the other agents and the training environment, which can lead to agents’ policy achieving a local optima easily and poor convergence efficiency. To tackle the above challenges, we propose a novel algorithm, g-Maximum Critic Multi-Agent Deep Deterministic Policy Gradient algorithm (g-M2DDPG), which leverages a new critic technique called g-Maximum Critic to balance the exploitation and exploration in updating Q-value function. We empirically evaluate our algorithms in three kinds of mixed cooperative and communication environments. These experimental results demonstrate that our algorithms significantly accelerates the learning process and outperform existing baseline algorithm MADDPG.
Modeling multi-style portrait relief from a single photograph

This paper aims at extending the method of Zhang et al. (2023) to produce not only portrait bas-reliefs from single photographs, but also high-depth reliefs with reasonable depth ordering. We cast this task as a problem of style-aware photo-to-depth translation, where the input is a photograph conditioned by a style vector and the output is a portrait relief with desired depth style. To construct ground-truth data for network training, we first propose an optimization-based method to synthesize high-depth reliefs from 3D portraits. Then, we train a normal-to-depth network to learn the mapping from normal maps to relief depths. After that, we use the trained network to generate high-depth relief samples using the provided normal maps from Zhang et al. (2023). As each normal map has pixel-wise photograph, we are able to establish correspondences between photographs and high-depth reliefs. By taking the bas-reliefs of Zhang et al. (2023), the new high-depth reliefs and their mixtures as target ground-truths, we finally train a encoder-to-decoder network to achieve style-aware relief modeling. Specially, the network is based on a U-shaped architecture, consisting of Swin Transformer blocks to process hierarchical deep features. Extensive experiments have demonstrated the effectiveness of the proposed method. Comparisons with previous works have verified its flexibility and state-of-the-art performance.
Design of Query Based Gallery Selector and Mask-Aware Loss for Person Search

Person search is a challenging computer vision task that aims to simultaneously locate and identify a query person from panoramic images. To address the issue of scene similarity and its impact on search accuracy and efficiency, we propose a query based gallery selector module that employs cosine similarity to calculate the similarity between candidate images in the gallery and the query persons feature embedding, then selects and reorders images in the gallery based on their similarity to the query person, thus improving the accuracy and efficiency of searching. Furthermore, we introduce a mask-aware mechanism that improves the localization loss function for predicted bounding boxes. During training, the network is guided to increase its robustness in occluded scenarios. Experimental results on public person search datasets PRW and CUHK-SYSU demonstrate the effectiveness of our proposed method.