Reviewerly/exHarmony huggingface dataset:
An authenticated and secure accounting system for international emissions trading

Expanding the international emissions trading system is crucial to filling the existing mitigation gap for the 2°C climate target. Trustworthy emissions accounting is the cornerstone of such a system encompassing different jurisdictions. However, traditional emissions measuring, reporting, and verification, as well as data disclosure practices that support data authenticity in a regional emissions trading system, might not be compatible with data transparency requirements in a future international system that covers countries with gaps in governance and social trust. This transparency requirement can cause severe data security concerns. In this study, we propose a cryptographic framework for an authenticated and secure emissions accounting system that can resolve this dilemma. We demonstrate that integrating a sequence of cryptographic protocols can preserve data authenticity and security for a stylized multi-country emissions trading system. We call for more research to promote joint work by policy researchers and computer scientists to strengthen collaboration in future international climate governance.Key policy insights Data authenticity and security concerns are key issues that have hindered international cooperation in mitigating climate change.Modern cryptography can help preserve both data authenticity and security in a multi-country emissions trading setting.More research is needed to promote joint work by policy researchers and computer scientists on international climate governance.
ReQTable: Square tabletop display that provides dual-sided mid-air images to each of four users

We propose "ReQTable", an optical system displaying dual-sided mid-air images to each of four users with less stray light. Dual Slit Mirror Arrays (dual SMAs) can produce a mid-air image that multiple people can view simultaneously without wearing special equipment. Dual SMAs are rectangular and can theoretically display mid-air images in the four surrounding directions. However, they also produce unwanted light, called stray light, which is especially noticeable when mid-air images are displayed in some directions. It is difficult for users to identify stray lights, and they cannot concentrate on viewing only the mid-air images. To improve a user experience, it is necessary to suppress stray light. ReQTable displays mid-air images in four directions with stray light reduction.
Dual Robot Avatar: Real-time Multispace Experience using Telepresence Robots and Walk Sensation Feedback including Viewpoint Sharing for Immersive Virtual Tours

Traveling to different places simultaneously is a dream for several people, but it is difficult to realize this aspiration because of our physical space limits. On one hand, virtual reality technologies can help alleviate such limits. According to the best of the authors’ knowledge, there is no study attempt to operate multiple telepresence robots in remote places simultaneously, with presenting walk sensation feedback to the operator for an immersive multispace experience. In this study, we used autonomous mobile robots; a dog and wheel type one, where their movements’ direction can be controlled by an operator (Fig. 1). The operator can alternatively choose/re-choose the space (or robot) to attend and can move the viewpoint using a head-mounted display (HMD) controller. A live video image with 4 K resolution is transmitted to the HMD via web real-time communication (WebRTC) network from a 360° camera placed to the top of each robot. The operator perceives viewpoint movement feedback as a visual cue and vestibular feeling via waist motion and proprioception on the legs. Our system also allows viewpoint sharing in which fifty users can enjoy omnidirectional viewing of the remote environments through the HMD without walk-like sensation feedback.
AeS‐GCN: Attention‐enhanced semantic‐guided graph convolutional networks for skeleton‐based action recognition

Abstract Skeleton‐based action recognition has been extensively studied in recent years and applied in virtual reality, detection systems and other cases with strong requirements for low cost as well as high accuracy, but most of the existing methods mainly focus on complex architecture of deep neural networks without considering computation efficiency. To balance accuracy and computation cost well, this paper proposes a simple and efficient attention‐enhanced semantic‐guided graph convolutional network (AeS‐GCN) for skeleton‐based action recognition. Firstly, we fuse semantics of joint type and frame index and dynamics together as representation of skeleton. Then, we use spatial attention block (SAB) to explore important features in spatial structure, in which adaptive GCN layer is adopted to adaptively model skeleton topology structure. Next, we use temporal attention block (TAB) to extract latent temporal information. The model proposed is a lightweight network and achieves the state‐of‐the‐art performance on mainstream datasets with less parameters and less computational complexity.
Differentially Private Algorithms for Statistical Verification of Cyber-Physical Systems

Statistical model checking is a class of sequential algorithms that can verify specifications of interest on an ensemble of cyber-physical systems (e.g., whether 99% of cars from a batch meet a requirement on their energy efficiency). These algorithms infer the probability that given specifications are satisfied by the systems with provable statistical guarantees by drawing sufficient numbers of independent and identically distributed samples. During the process of statistical model checking, the values of the samples (e.g., a user's car energy efficiency) may be inferred by intruders, causing privacy concerns in consumer-level applications (e.g., automobiles and medical devices). This paper addresses the privacy of statistical model checking algorithms from the point of view of differential privacy. These algorithms are sequential, drawing samples until a condition on their values is met. We show that revealing the number of samples drawn can violate privacy. We also show that the standard exponential mechanism that randomizes the output of an algorithm to achieve differential privacy fails to do so in the context of sequential algorithms. Instead, we relax the conservative requirement in differential privacy that the sensitivity of the output of the algorithm should be bounded to any perturbation for any data set. We propose a new notion of differential privacy which we call <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">expected differential privacy</i> (EDP). Then, we propose a novel expected sensitivity analysis for the sequential algorithm and propose a corresponding exponential mechanism that randomizes the termination time to achieve the EDP. We apply the proposed exponential mechanism to statistical model checking algorithms to preserve the privacy of the samples they draw. The utility of the proposed algorithm is demonstrated in a case study.
MB-BC: Drug Traceability System Based on Multibranched Blockchain Structure

The establishment of a complete drug traceability system is essentially important for public drug security and the business of pharmaceutical companies which is aimed at tracking where the drug has gone along the drug supply chain. Traditional centralized server-client technical solutions have been far from satisfaction for their bad performances in data authenticity, privacy, system resilience, and flexibility. In this paper, we propose a drug traceability scheme called MB-BC, which realizes the security and traceability of drug data through a novel multibranched blockchain scheme. Different from the characteristics of transparency of traditional blockchains, MB-BC realizes fine-grained access control of data between all levels in the system, which improves the security and privacy of data. MB-BC has further improved the existing consensus mechanism, strengthened the supervision of pharmaceutical companies, and further improved the safety and robustness of the system. Furthermore, the system combines data access strategies with smart contracts; each branch chain can also issue its smart contract to provide personalized services. Finally, security and performance evaluations show that the solution is advantageous in terms of data security, system robustness, supervisibility, and traceability, as well as efficient in terms of blockchain throughput, data query time, and blockchain consensus consumptions, compared with other typical approaches.
ProtoE: Enhancing Knowledge Graph Completion Models with Unsupervised Type Representation Learning

Knowledge graph completion (KGC) models are a feasible approach for manipulating facts in knowledge graphs. However, the lack of entity types in current KGC models results in inaccurate link prediction results. Most existing type-aware KGC models require entity type annotations, which are not always available and expensive to obtain. We propose ProtoE, an unsupervised method for learning implicit type and type constraint representations. ProtoE enhances type-agnostic KGC models by relation-specific prototype embeddings. Our method does not rely on entity type annotations to capture the type and type constraints of entities. Unlike existing unsupervised type representation learning methods, which have only a single representation for entity-type and relation-type constraints, our method can capture multiple type constraints in relations. Experimental results show that our method can improve the performance of both bilinear and translational KGC models in the link prediction task.
Advanced Monitoring &amp; Sensing of Mobility Impaired Individuals

Individuals suffering from mobility impairment suffer from reduced quality of life. Rehabilitation significantly improves mobility for many patients suffering from ailments, including stroke, physical injury, and neurological damage. Performing proper rehabilitation functionality is often accompanied by a medical professional. The medical professional can monitor the progress and mechanism to provide helpful feedback for a more robust recovery. However, access to a medical professional is often limited to appointment times, and, in more rural areas, access to medical facilities may require long-distance travel. As part of a larger rehabilitation project, this paper introduces a framework for using sensor fusion to monitor patient conditions passively to explore on-location care. The proposed work implements an event system to notify the users of proper implementation and the medical professionals with real-time data. Teleconferencing allows patients to access medical assistance when needed rather than during specified times.
Towards Easing Automation of Robotic Kitting Operations

This paper proposes a framework to make the robot programming faster, user-friendly, and flexible for kitting operations. This is achieved by innovating at three different levels; (i) at the motion stack level, it integrates the robot with a constraint-based robot programming paradigm in which a novel approach for obstacle avoidance is introduced, (ii) at the discrete planning level, it introduces adaptive planning to derive an optimal sequence of robot kitting operations, (iii) and at the knowledge representation level, the overall operation is aided by a world-model that formally represents and provides services from the knowledge of the task, the robot state, and the sensor data. The framework provides industrially relevant capabilities like, collision avoidance independent of the obstacle location, optimal discrete planning of the actions based on the costs and goals, and dealing with complex conditions such as object blocking and object graspability. The performance of the proposed framework is evaluated through a set of experiments.
A GA-Based Learning Strategy Applied to YOLOv5 for Human Object Detection in UAV Surveillance System

YOLO (You-Only-Look-Once) is a deep learning-based one-stage detection method that has been widely used and achieved great success in image classification and localization. As the state-of-the-art method, YOLO has been upgraded to version 5. This paper proposes a new approach to using a Genetic Algorithm (GA) within a YOLOv5 framework for human object detection applied in the Unmanned Aerial Vehicle (UAV) perspective image dataset. The dataset has challenges, such as a small target, the view of the object is from above, and there is an illumination and light effect. To comply with this challenge, we will utilize the dataset of visual images taken from a UAV (RGB-image) along with Thermal Infrared (TIR) information. GA is used for optimizing the Hyperparameter, which is one of the critical factors in determining the model’s performance. Based on our numerical experiments, we found that this YOLOv5-based transfer learning method using RGB-TIR dataset and optimized by GA can achieve higher accuracy compared with the original YOLOv5 for Human Detection on Unmanned Aerial Vehicle Perspective. The objective of this research is to create a surveillance system that will be used to monitor a wide area using autonomous UAVs that can exchange information with each other. In the end, the solution from this research can help related parties in tackling the problem of illegal activities with limited human resources.
Adaptive Video Streaming Based on Learning Intrinsic Reward

The adaptive bitrate (ABR) algorithm based on reinforcement learning (RL) can actively learn bitrate control policies and adapt to different network environments. However, the user quality of experience (QoE) as the optimization objective contains multiple indicators. In the case of complex task objective, whether the reward feedback from the environment can effectively guide the policy update is the challenge of RL-based ABR algorithm. We propose an ABR algorithm based on learning intrinsic reward, which encourages exploration by enhancing the agent's intrinsic motivation and enables the agent to understand transitions between states to make an effective policy. The results show that our approach improves the average QoE value.
Hiding Among Your Neighbors: Face Image Privacy Protection with Differential Private k-anonymity

The development of modern social media allows millions of private photos to be uploaded and shared, which provides a wide range of image acquisition but extremely threatens personal image privacy. Face de-identification is treated as an important privacy protection tool in multimedia data processing by modifying image identity information. Although there exist many traditional methods widely used to hide sensitive private information, they all fail to balance the trade-off between privacy and utility in qualitative and quantitative manners and cannot generate de-identified results with satisfactory visual perception. In this paper, we propose a novel face image privacy protection method with differential private k-anonymity, which can not only generate de-identified results with good image quality but also control the balance between privacy protection and image utility according to different application scenarios. The framework consists of the following three steps: facial attributes prediction, privacy-preserving attributes obfuscation, and naturally realistic de-identificated image generation. Our extensive experiments demonstrate the stability and effectiveness of the proposed model.
Subjective Quality Assessment of One-to-One Video-Telephony Services

Video-telephony applications have been widely used in people's daily life, such as online conferences, online education, and socialization. Especially during the COVID-19 pandemic, the business volume of video-telephony services has generally increased rapidly. This leads to a growing need for video-telephony service quality assessment and monitoring. This paper presents the subjective tests conducted in 'Computational model used as a QoE/QoS monitor to assess video-telephony services' (G.CMVTQS) project, which is under study in ITU-T SG12 Q.15. Two types of subjective tests are designed. Audiovisual material subjective test focuses on the quality of multimedia streams, while conversational subjective test focuses on the quality of interaction experience. These tests simulate real-life situations in video-telephony with different types of user terminals (PCs, TVs and mobile phones), environments (office, home, restaurant and outdoor), audio/video parameters (resolution, frame rate, bit rate and codec) and network impairments (packet loss, delay, jitter, bandwidth, synchronization). The final subjective database built collaboratively by five laboratories in different countries will be analyzed in the near future and provide more insights to develop new QoE/QoS monitor models for video-telephony.
Backhaul Traffic and QoE Joint Optimization Approach for Adaptive Video Streaming in MEC-Enabled Wireless Networks

In order to meet the Quality of Experience (QoE) requirements of mobile users and the Quality of Service (QoS) concerns for new high-performance, innovative services, Multi-access Edge Computing (MEC), Software Defined Mobile Networks (SDMN), and Cloud Radio Access Networks (C-RAN) are being introduced to the next generation of wireless networks in order to boost performance and to deliver Quality of Service (QoS). It is essential for mobile operators to allocate their available resources efficiently as telecom networks become increasingly complex, traffic continues to rise, and users demand faster bitrate speeds. In this paper, we investigate how to allocate resources appropriately across a wireless network enabled by MEC, SDMN, and C-RAN technology to deliver high quality adaptive video streams. We propose the Backhaul-Aware Cross-Layer Bitrate Allocation (BACLBA) algorithm, which utilizes information from higher layers regarding traffic patterns and desired video quality to maximize HTTP Video Adaptive Streaming (HAS) users' QoE and reduce the backhaul traffic by caching the popular videos on MEC servers. We solve a mixed-integer nonlinear programming problem that considers the limitations of radio resource availability, storage and transcoding capacities of MEC servers. BACLBA is designed to maximize users' QoE by minimizing the deviation between the achievable throughput at the MAC layer and the allocated bit rate for video frames at the application layer. Furthermore, it reduces the backhaul traffic by caching popular video content on MEC servers. Compared to a baseline scheme, our algorithm achieves a 20.50% higher system utilization rate, a 10.44% higher video quality, and a 50.33% reduction in backhaul traffic.
An Elastic DASH-based Bitrate Adaptation Scheme for Smooth On-Demand Video Streaming

The Video traffic has seen a surge in the last decade due to the widespread use of smartphones and the abundance of video streaming applications in the market. Considering the time varying characteristics of today's networks, ensuring high quality of experience (QoE) to all video traffic users has become a daunting challenge for most service providers. The dynamic adaptive streaming over HTTP (DASH) standard enables the adjustment of video bitrates to match the network conditions, therefore guaranteeing smooth video playback. Different DASH-based approaches have been proposed. Nonetheless, most of these schemes incur substantial bitrate oscillations due to their quick reactions to changes in bandwidth, which negatively impact the users' QoE. In this paper, we propose EDRA, a DASH-based bitrate adaption solution that aims at averting video playback interrupts while reducing the number of bitrate switches. EDRA dynamically adjusts the bounds of available video bitrates based on bandwidth estimations. It then selects the most suitable bitrate for each video segment taking into consideration the current and previous bandwidth measurements, the buffer level and the bitrate variation with respect to the previously downloaded segments. Simulation results show that EDRA outperforms existing commercial schemes as it incurs between 6% and 22% higher accumulated played utility and between 30% and 77% lower bitrate switches, ensuring a smooth video streaming experience at high throughput levels.
Deep Learning for Single-object Tracking: A Survey

With the rapid advancement of deep learning, this technique has also shown promise in single-object tracking, considerably enhancing the algorithm's performance. This paper will summarize and analyze the evaluation metrics, public datasets, state-of-the-art tracking methods, etc. It will classify the research status of the key technologies involved and reveal the mystery of single object tracking technology step by step. In addition, the significant challenges and problems of single object tracking technology are summarized.
Multi-label Feature Selection via Dual Graph and Non-convex Sparse Regression

Feature selection has been an important data preprocessing in multi-label learning and thus has been developed rapidly in recent years. Among these, the methods based on manifold regularization have received more attention. However, most existing methods only consider the geometric structure of the data manifold and ignore the local information in the feature space, which leads to the learned manifold information being incomplete. To tackle this problem, we propose a dual graph regularization for multi-label feature selection, which considers the geometric structure of the data manifold and feature manifold simultaneously. What is more, a new non-convex constraint consisting of the difference between <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2,1}$</tex> -norm and Frobenius norm, denoted as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2,1-2}$</tex> -norm, is introduced to obtain more row-sparse solution in order to delete a more redundant features efficiently. An alternating minimization strategy is designed to optimize the proposed objective function. Comprehensive experiments are conducted on eight multi-label data sets, and the results demonstrate that our method is superior to the compared methods.
Recaptured Image Forensics Based on Generalized Central Difference Convolution Network

With large advancements in image display technology, recapturing high-quality images from high-fidelity LCD screens becomes much easier. Such recaptured images can be used to hide image tampering traces and fool some intelligent identification systems. In order to prevent such a security loophole, we propose a recaptured image detection approach based on generalized central difference convolution (GCDC) network. Specifically, by using GCDC instead of vanilla convolution, more detailed features can be extracted from both intensity and gradient information from an image. Meanwhile, we concatenate the feature maps from multiple GCDC modules to fuse low-, mid-, and high-level features for higher performance. Extensive experiments on three public recaptured image databases demonstrate the superior of our proposed method when compared with the state-of-the-art approaches.
Research on Traffic Flow Forecasting Method Based on Graph Neural Network

Traffic flow data are typical spatial-temporal data. Most of the traditional traffic flow prediction methods only consider the temporal correlation of data, but ignore the spatial correlation, so that the accuracy of prediction reduce. We summarizes the traffic flow prediction model based on graph neural network (GNN), starting from the basic definition of graph convolution, covering frequency domain and spatial domain graph convolution, and introducing the chebyshev network, graph convolution neural network (GCN) and graph attention network (GAT). We consider the spatial correlation of traffic flow, building three forecasting model based on Graph Neural Network, compared and analyzed the prediction results, which proves the effectiveness of the application of graph neural network in traffic flow prediction.