Reviewerly/exHarmony huggingface dataset:
Relationships between people’s mental programs and trust in automation

Social and human factors are also important for the development of human-automation trust (HAT) besides technical factors of automation. The current article reviewed studies on the influences of social and human factors on HAT and provided a new perspective by combing Hofstede et al. (2010)’s mental programs model. Results showed that although few studies have discussed the relationships between HAT and human nature, oxytocin secretion can positively influence HAT. Then, it was found that different cultural characteristics tended to play different roles in HAT formation, however, the newest and direct evidence is still lacking. Lastly, some personality traits were verified to affect HAT, whereas the effects of other traits are still controversial across contexts. This literature review provided the need for future studies: validating how and why each level of mental programs affects HAT and clarifying the core factors and the synergies of each level of mental programs on HAT.
Tourism demand forecasting under conceptual drift during COVID-19: an ensemble deep learning model

To address the issue of tourism demand forecasting in the context of concept drift, a new ensemble deep learning model based on transformer is proposed, which includes three parts: data processing, base predictor pool construction, base predictor selection and combination. In the first part, the relevant data are collected and converted into the input form required by transformer. In the second part, a base predictor pool containing multiple predictors is constructed, where each predictor can capture a specific concept from historical data. In the final part, a predictor selection algorithm is proposed to select ‘effective predictors’ from the base predictor pool. These effective predictors are further integrated to generate the final forecasts. The proposed model is applied to the forecast of tourist volume of two attractions in China. The results show that the proposed model outperforms the benchmark models in the context of concept drift, benchmarked against eight models.
A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis

This study aimed to develop and validate machine learning models to predict intensities in children and adolescents with cystic fibrosis (CF) across different accelerometry brands and placements. Thirty-five children and adolescents with CF (11.6 ± 2.8 yrs; 15 girls) and 28 healthy youth (12.2 ± 2.7 yrs; 16 girls) performed six activities whilst wearing GENEActivs (both wrists) and ActiGraphs GT9X (both wrists and waist). Three supervised learning classifiers (K-Nearest Neighbour, Random Forest and eXtreme Gradient Boosted Decision Tree) were used to identify the input signal pattern for each PA type and intensity, with a 10-fold cross-validation utilized to assess the performance of the classifiers. ActiGraph GT9X on the dominant wrist and waist and GENEActiv on the dominant wrist failed to predict vigorous intensity PA activities. All other models, for activity type and intensities, exceeded 97% accuracy, with a sensitivity and specificity of greater than 95%, irrespective of accelerometer brand, placement or health condition.
DP-YOLO: Effective Improvement Based on YOLO Detector

YOLOv5 remains one of the most widely used real-time detection models due to its commendable performance in accuracy and generalization. However, compared to more recent detectors, it falls short in label assignment and leaves significant room for optimization. Particularly, recognizing targets with varying shapes and poses proves challenging, and training the detector to grasp such features requires expert verification or collective discussion during the dataset labeling process, especially in domain-specific contexts. While deformable convolutions offer a partial solution, their extensive usage can enhance detection capabilities but at the expense of increased computational effort. We introduce DP-YOLO, an enhanced target detector that efficiently integrates the YOLOv5s backbone network with deformable convolutions. Our approach optimizes the positive sample selection during label assignment, resulting in a more scientifically grounded process. Notably, experiments on the COCO benchmark validate the efficacy of DP-YOLO, which utilizes an image size of [640, 640], achieves a remarkable 41.2 AP, and runs at an impressive 69 fps on an RTX 3090. Comparatively, DP-YOLO outperforms YOLOv5s by 3.2 AP, with only a small increase in parameters and GFLOPSs. These results demonstrate the significant advancements made by our proposed method.
DeepVision: Enhanced Drone Detection and Recognition in Visible Imagery through Deep Learning Networks

Drones are increasingly capturing the world's attention, transcending mere hobbies to revolutionize areas such as engineering, disaster aid, logistics, and airport protection, among myriad other fascinating applications. However, there is growing concern about the risks that they pose to physical infrastructure, particularly at airports, due to potential misuse. In recent times, numerous incidents involving unauthorized drones at airports disrupting flights have been reported. To solve this issue, this article introduces an innovative deep learning method proposed to effectively distinguish between drones and birds. Evaluating the suggested approach with a carefully assembled image dataset demonstrates exceptional performance, surpassing established detection systems previously proposed in the literature. Since drones can appear extremely small compared to other aerial objects, we developed a robust image-tiling technique with overlaps, which showed improved performance in the presence of very small drones. Moreover, drones are frequently mistaken for birds due to their resemblances in appearance and movement patterns. Among the various models tested, including SqueezeNet, MobileNetV2, ResNet18, and ResNet50, the SqueezeNet model exhibited superior performance for medium area ratios, achieving higher average precision (AP) of 0.770. In addition, SqueezeNet's superior AP scores, faster detection times, and more stable precision-recall dynamics make it more suitable for real-time, accurate drone detection than the other existing CNN methods. The proposed approach has the ability to not only detect the presence or absence of drones in a particular area but also to accurately identify and differentiate between drones and birds. The dataset utilized in this research was obtained from a real-world dataset made available by a group of universities and research institutions as part of the 2020 Drone vs. Bird Detection Challenge. We have also tested the performance of the proposed model on an unseen dataset, further validating its better performance.
Enhancing AutoNLP with Fine-Tuned BERT Models: An Evaluation of Text Representation Methods for AutoPyTorch

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A Separate Conviction Study on Online Rumors and False Information Dissemination in China

With the rapid development of the Internet in the 21st century, more and more people choose to conduct social and other related activities in the online world, so the spread of online rumors and false information has become more frequent and threatening. In view of these practical factors, this paper mainly uses the method of legal normative analysis to study the separate criminalization of online rumors and false information dissemination in China. How to better face the rampant Internet chaos and the proliferation of criminal behavior. At present, Chinas criminal law norms have undergone two amendments and two judicial interpretations, but there are still many problems. The victims of personal and property rights that have not been properly protected are increasing with the development of the Internet, and it is all the more necessary to separately criminalize the spread of online rumors and false information. This article argues that it is necessary to add separate crimes to the criminal law to regulate it. This article focuses on the contradiction between Chinas current technological development and the old regulations. This has led to difficulties such as the criminal law cannot correspond to all online rumor-mongering behaviors, and the interpretation of the regulatory scope of false information is not clear enough. Attempts were made to change the status quo by separate legislation and additional interpretations. At the same time, the question of defining the boundaries of the offence, focusing on separate convictions, illustrates the necessity of such a move. In order to improve the green environment of the Internet, improve the stability of the Criminal Law, and improve laws and regulations.
Task-Based Visual Attention for Continually Improving the Performance of Autonomous Game Agents

Deep Reinforcement Learning (DRL) has been effectively performed in various complex environments, such as playing video games. In many game environments, DeepMind’s baseline Deep Q-Network (DQN) game agents performed at a level comparable to that of humans. However, these DRL models require many experience samples to learn and lack the adaptability to changes in the environment and handling complexity. In this study, we propose Attention-Augmented Deep Q-Network (AADQN) by incorporating a combined top-down and bottom-up attention mechanism into the DQN game agent to highlight task-relevant features of input. Our AADQN model uses a particle-filter -based top-down attention that dynamically teaches an agent how to play a game by focusing on the most task-related information. In the evaluation of our agent’s performance across eight games in the Atari 2600 domain, which vary in complexity, we demonstrate that our model surpasses the baseline DQN agent. Notably, our model can achieve greater flexibility and higher scores at a reduced number of time steps.Across eight game environments, AADQN achieved an average relative improvement of 134.93%. Pong and Breakout games both experienced improvements of 9.32% and 56.06%, respectively. Meanwhile, SpaceInvaders and Seaquest, which are more intricate games, demonstrated even higher percentage improvements, with 130.84% and 149.95%, respectively. This study reveals that AADQN is productive for complex environments and produces slightly better results in elementary contexts.
Beyond the hype—the <i>actual</i> use of blockchain in government

The last few years have seen considerable hype around blockchain technology, as well as promises about what it might offer to society, and its challenges to policymakers. Beyond this hype, what is known about the uses of blockchain in government? What can we learn from actual uses of blockchain for public policy? A recent review of the literature on blockchain concluded that the overwhelming majority of studies are still theoretical, and that empirical data on real applications of blockchain are only just starting to emerge. In this light, this Special Issue brings together papers that include insights into actual experiences of blockchain in government, focusing on questions such as blockchain's real benefits, costs and risks, and public policy responses. All the papers included go beyond a purely theoretical approach to blockchain and offer some insight into what we know - or do not know - about the reality of blockchain in government. Papers deal with blockchain technologies implemented in different countries, at distinct levels of government, and in various sectors. In all cases, an effort is made to extract findings and conclusions towards the challenge of thinking about how best policy can be designed and applied to make the best, and minimising the risks and costs, of this new, disruptive technology.
The Potentials and Pitfalls of Computer Visioning and Machine Learning Methods for Communication Researchers

This paper will discuss the potentials and pitfalls for incorporating computer visioning and machine learning in technical communication research. We share the results of a case study our research team engaged with in our attempt to demonstrate an affordable, easy-to-use machine learning method that can be formalized so that researchers who are interested in working with bigger datasets will have a road map for doing so. Using standard computing power and the insight of a graduate student with experience in computer visioning, we engaged in a process in which we tried to achieve a balance between accuracy and the time it would take for the system to process images. We achieved 99.2% test accuracy on correctly identifying visuals that were not graphs. Yet this optimization required a tradeoff in correct identification of what was a graph, with a success rate of under 4%. We reflect on the conclusions from our research, suggesting that successful implementation of computer visioning tools for technical communication research requires more technical and chronological planning than we had hoped, but it can still achieve things that are impossible to achieve through individual or even team-based human computation. We discuss what communication researchers should consider when processing massive datasets and what some of the limitations of these methods are.
Saliency detection of textured 3D models based on multi-view information and texel descriptor

Saliency-driven mesh simplification methods have shown promising results in maintaining visual detail, but effective simplification requires accurate 3D saliency maps. The conventional mesh saliency detection method may not capture salient regions in 3D models with texture. To address this issue, we propose a novel saliency detection method that fuses saliency maps from multi-view projections of textured models. Specifically, we introduce a texel descriptor that combines local convexity and chromatic aberration to capture texel saliency at multiple scales. Furthermore, we created a novel dataset that reflects human eye fixation patterns on textured models, which serves as an objective evaluation metric. Our experimental results demonstrate that our saliency-driven method outperforms existing approaches on several evaluation metrics. Our method source code can be accessed at https://github.com/bkballoon/mvsm-fusion and the dataset can be accessed at 10.5281/zenodo.8131602.
Ultrafast measurement of energy-time entanglement with an optical Kerr shutter

Correlations between the temporal and frequency properties of two photons may be exploited for optical quantum technologies, but they can be difficult to measure on ultrafast time scales. In this work, we measure the time-of-arrival correlations of two energy-time entangled photons using an optical Kerr shutter.
zkHealthChain - Blockchain Enabled Supply Chain in Healthcare Using Zero Knowledge

Globalization has led to complex, cloud-centric supply chains that require transparency and traceability in the manufacturing process. However, traditional supply chain models are vulnerable to single points of failure and lack a people-centric approach. To address these challenges, our proposed work presents an innovative healthcare supply chain model that utilizes blockchain technology combined with Zero Knowledge Proofs (zk-SNARKs) and role-based access control (RBAC) mechanisms. The addition of RBAC to the proposed model ensures that only authorized users can access certain data and functionalities within the system, while improving the security and access control. This approach guarantees secure storage of business-sensitive data while enabling real-time product tracking and traceability. The proposed model was tested using an Ethereum-based decentralized application (DApp), demonstrating the preservation of digital record integrity, availability, and scalability by eliminating single points of failure. The system also offers privacy and security for sensitive data through the use of zk-SNARKs. In case of product faults, the model enables error tracing without disclosing the entire data set through the use of document hashes. By incorporating RBAC access control mechanisms, our proposed solution offers an effective, secure, and privacy-preserving mechanism for handling sensitive data, also benefiting stakeholders in the supply chain ecosystem.
Scenario analysis of photovoltaic power output based on improved K-means algorithm

For proper grid operation, an accurate description of PV power output is essential. This research proposes a cuckoo-grey wolf-based improved k-means clustering technique. It is also applied to the analysis of photovoltaic power output scenarios. After improving the algorithm, it is applied to the calculation case for simulation, and the final results show that the cumulative distribution curve of the typical scene obtained by the improved k-means algorithm in this paper has the same trend as the cumulative distribution curve of the initial scene, and the difference between them is very small, and the difference degree is close to 0.The simulation results are analyzed to verify the effectiveness of the improved algorithm.
A Survey on Blockchain-Internet of Things-Based Solutions

The integration of Blockchain-IoT presents a promising platform for enhancing security, transparency, and efficiency in various applications. This work aims to provide a detailed overview of the state of the art in blockchain-IoT-based solutions. In this work, the authors begin by introducing the basic concepts of blockchain and IoT, highlighting their key features and their individual challenges. Next, this work explains benefits of combining these technologies, such as decentralized data management, immutable records, and enhanced trust and security. Further, this work explains the architecture and components of blockchain-IoT systems, including smart contracts, consensus mechanisms, and data integrity verification techniques.
Something Old, Something New: Extending the Life of CPUs in Datacenters

Datacenter operators are increasingly powering their operations with low-carbon energy sources such as wind and solar. As a result, the overall carbon footprint of datacenters increasingly comes from the manufacturing phase of server lifecycles. The most direct way to reduce the environmental impact of datacenters is to amortize the manufacturing emissions of hardware over longer timespans by keeping hardware in production beyond today's relatively short refresh cycles. In this paper we analyze the feasibility of server lifespan extension in datacenters using a series of microbenchmarks. We find that while newer processors outperform older ones, the difference is workload dependent, with some workloads showing promise for older CPUs. We also analyze the potential of incorporating overclocking to further extend server lifespans.
Privacy-Preserving Federated Learning for Healthcare Data

The evolution of technology has a significant impact on health data collection, transforming the way information is gathered, stored, and utilized in the healthcare industry. The big health record contains sensitive user information like contact details, health status, demographics, vaccination details, exposure history. It's worth noting that while the collection of big health records has been crucial for monitoring the patients' health history, it also raises important privacy and security considerations. Safeguarding the privacy of individuals' health data and ensuring compliance with relevant regulations is essential to maintain public trust and protect sensitive information. Therefore, healthcare data must adhere to privacy regulations and ethical considerations. This chapter elaborates on key challenges and solutions in privacy preservation within federated learning. The key challenges include data heterogeneity, information leakage, attacks, and regulatory compliances.
A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification

Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of unmanned aerial vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top–down features, and significant changes in vehicle scales. To address this, we propose a novel dual mixing attention network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play dual mixing attention module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of spatial mixing attention (SMA) and channel mixing attention (CMA). First, the original feature is divided according to the spatial and channel dimensions to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. In addition, DMAM can be easily plugged into any depth of the backbone network to improve vehicle recognition. The experimental results show that the proposed structure performs better than the representative method in the UAV-based vehicle ReID. Our code and models will be published publicly.
Towards the Design and Evaluation of More Sustainable Multimedia Experiences: Which Role Can QoE Research Play?

In this column, we reflect on the environmental impact and broader sustainability implications of resource-demanding digital applications and services such as video streaming, VR/AR/XR and videoconferencing. We put emphasis not only on the experiences and use cases they enable but also on the "cost" of always striving for high Quality of Experience (QoE) and better user experiences. Starting by sketching the broader context, our aim is to raise awareness about the role that QoE research can play in the context of various of the United Nations' Sustainable Development Goals (SDGs), either directly (e.g., SDG 13 "climate action") or more indirectly (e.g., SDG 3 "good health and well-being" and SDG 12 "responsible consumption and production").
Video Captioning Based on Sign Language Using YOLOV8 Model

One of the fastest-growing research areas is the recognition of sign language. In this field, many novel techniques have lately been created. People who are deaf-dumb primarily communicate using sign language. Real-time sign language is essential for people who cannot hear or speak (the dumb and the deaf). Hand gestures are one of the non-verbal communication methods used in sign language. People must be aware of these people's language because it is their only means of communication. In this work, we suggest creating and implementing a model to offer transcripts of the sign language that disabled individuals use during a live meeting or video conference. The dataset utilized in this study is downloaded from the Roboflow website and used for training and testing the data. Transfer Learning is a key idea in this situation since a trained model is utilized to identify the hand signals. The YOLOv8 model, created by Ultralytics, is employed for this purpose and instantly translates the letters of the alphabet (A-Z) into their corresponding texts. In our method, the 26 ASL signs are recognized by first extracting the essential components of each sign from the real-time input video, which is then fed into the Yolo-v8 deep learning model to identify the sign. The output will be matched to the signs contained in the neural network and classified into the appropriate signs based on a comparison between the features retrieved and the original signs present in the database.