Reviewerly/exHarmony huggingface dataset:
Rewarding and Efficient Data Sharing in EHR System with Coalition Blockchain Assistance

Internet of things is and will revolutionize our society and life tremendously. And data, generated by tons of IoT devices, is the foundation of all that. It is equatable to say that sharing enables data to play its due value and even makes it more valuable. While it is impractical for data owners to share for free, especially in terms of electronic health records (EHR) contain private information. Many data sharing schemes were proposed and very few of them can fulfil efficient sharing and fair payment simultaneously. One vital issue to achieve fair payment is how to verify the effectiveness of participants’ behaviours, such as the integrity of data stored in the third-party server. Furthermore, in most existed data sharing systems, each access is authorized by the data owner, which requires the data owner to be online in real time and leads to too many interactions and high overlheads. In this paper, we design a blockchain-assisted data sharing scheme by combining the attribute-based encryption (ABE) primitive. Our scheme achieves efficient one-to-many data sharing, fine-grained access control, efficient decryption, user-level revocation, fairness payment and reliable data integrity verification.
A Lightweight Heatmap-based Eye Tracking System

Eye tracking is playing an important role in many applications including human-computer interaction and behavior study. However, the existing approaches have at least one of the following limitations: (i) dedicated devices such as infrared camera and eye-tracker are required; (ii) complex calibration process is involved; (iii) substantial computing resources are consumed; (iv) users suffer from the risk of privacy leakage. To address the above limitations, we propose a H eatmap-based E ye T racking (HETrack) system. One of the key challenges in our system is to design a lightweight model for fine-grained tracking when the computing resources of device is limited. Also, it is necessary to protect user privacy in such a system. To address the above challenging issues, the proposed system consists of the following processes. First, when users randomly look at the screen of the device, HETrack obtains the raw image containing facial information. Then, we design a neural network model and train it with federated learning. The model can map the image to heatmap that implies the possibility of the user’s gaze position on the screen. Finally, HETrack can intercept the real-time video stream into frames, and employ the trained model to generate the heatmap of current frame for gaze estimation. We implement HETrack based on a Commercial-Off-The-Shelf (COTS) camera and conduct extensive experiments to evaluate its performance. Our HETrack system only requires once calibration; whereas, the state-of-the-art work proposed by Google requires 3~5 times calibration on average. Unlike previous approaches that transmit raw image data to a central server, in our HETrack system, only parameters are transmitted, thereby well protecting the user’s privacy. Experimental results demonstrate that the average distance error of estimated gaze point is 3cm, which is compatible with the state-of-the-art methods.
NASTER: Non-local Attentional Scene Text Recognizer

Scene text recognition has been widely investigated in computer vision. In the literature, the encoder-decoder based framework, which first encodes image into feature map and then decodes them into corresponding text sequences, have achieved great success. However, this solution fails in low-quality images, as the local visual features extracted from curved or blurred images are difficult to decode into corresponding text. To address this issue, we propose a new framework for Scene Text Recognition (STR), named Non-Local Attentional Scene Text Recognizer (NASTER). We use ResNet with Global Context Block (GC block) to extract global visual features. The global context information is then captured in parallel using the self-attention module and finally decoded by a multi-layer attention decoder with an intermediate supervision module. The proposed method achieves the state-of-the-art performances on seven benchmark datasets, demonstrating the effectiveness of our approach.
From Input/Output Logics to Conditional Logics via Sequents – with Provers

We consider cut-free sequent calculi for a number of deontic logics from the family of Input/Output logics. These sequent calculi provide a correspondence to the flat fragment of certain conditional logics. Two of the introduced calculi are non-standard in that they include non-derivability statements, and hence are interesting also from a purely technical perspective. We further modularise the calculi in an extended sequent framework. Proof search in the extended calculi is implemented in Prolog, providing seemingly the first automated reasoning systems for some of the considered logics.
DNEA: Dynamic Network Embedding Method for Anomaly Detection

Network embedding is a basic method for dynamic network analysis. Diverse dynamic network embedding methods have emerged in recent years, however, most existing works regard all observed information as true information, ignoring the anomalous edges in the dynamic networks. When the observed information is mixed with anomalous edges, the learned node embeddings cannot depict precise network properties effectively. Therefore, network embedding that can identify anomalous edges is a promising research topic. Inspired by this, we propose a novel end-to-end dynamic network embedding method called Dynamic Network Embedding for Anomaly Detection (DNEA), which can learn the robust node embeddings based on the neighborhood information and community structure in the dynamic networks. DNEA captures the dynamic characteristics of the network to reconstruct the network topology structure based on Stochastic Block Model (SBM), and detects anomalous edges from the perspective of reconstruction probability. In addition, DNEA utilizes negative sampling to handle the challenge of scarce anomaly labels. Experimental results on real-world datasets demonstrate DNEA can outperform the state-of-the-arts.
A Probability-Based Fault Tolerance Strategy For Service-Based Systems

In the cloud environment, software vendors have built a large number of multi-tenant service-based systems (SBSs) by integrating distributed services to provide customers with software-as-a-service. However, when operating in the volatile cloud environment, the system components of an SBS may experience quality degradation caused by various runtime anomalies, e.g., virtual machine faults, software exceptions, etc. Handling runtime anomalies of multi-tenant SBSs timely and cost-effectively is critical to ensuring the quality of the SBS. A number of fault tolerance approaches have been proposed in recent years to tackle this problem. However, existing approaches have not properly considered the fact that the quality of a distributed system component often varies over time. In this paper, we propose PFT4MTS (Probability-based Fault Tolerance for Multi-Tenant SBSs), a novel approach for formulating cost-effective fault tolerance strategies for multi- tenant SBSs by implementing redundancy for critical system components. First, PFT4MTS evaluates the criticality of each system component by its multi-dimensional quality over time and multiple tenants sharing the system components with differentiated quality preferences. Then, it employs integer programming to formulate the optimal fault tolerance strategy based on the criticalities of different system components. As shown by the experimental results, PFT4MTS outperforms four existing representative approaches in ensuring the quality of quality of multi-tenant SBSs upon runtime anomalies.
A Smart Contract Architecture to Enhance the Industrial Symbiosis Process Between the Pulp and Paper Companies - A Case Study

Pulp and Paper Companies collaborate to monitor and monetize waste and create value from their by-products. This process of Industrial Symbiosis requires the creation and maintenance of trusted and transparent relationships between all entities participating in these networks, which is a constant challenge. In this context, a blockchain-based system can help in establishing and maintaining these networks, serving as a ground truth between companies operating at a national or a global scale. This paper proposes a scalable and modular blockchain architecture design using smart contracts to enhance the industrial symbiosis process of the Pulp, Paper, and Cardboard Production Sector companies in Portugal. This design comprehends all entities participating in the network. The implementation of this design assumes the use of a permissioned ledger built using Hyperledger Fabric to provide the required trust and transparency between all entities.
Using temporal information for improved UAV type classification

The increased availability of unmanned aerial vehicles offers potential for numerous fields of application, but also can pose security and public safety threats. Thus, the demand for automated UAV detection systems to generate early warnings of possible threats is growing. Employing electro optical imagery as a main modality in such systems allows the direct interpretability by human operators and the straightforward applicability of deep learning based methods. Besides UAV detection, classifying the UAV type is an important task to categorize the potential threat. In this work, we propose a three-staged approach to address UAV type classification in video data. In the first stage, we apply recent deep learning based detection methods to locate UAVs in each frame. We assess the impact of best practices for object detection models, such as recent backbone architectures and data augmentation techniques, in order to improve the detection accuracy. Next, tracks are generated for each UAV. For this purpose, we evaluate different tracking approaches, i.e. Deep SORT and Intersection-over-Union tracker. Errors caused by the detection stage as well as misclassified detections due to similar appearances of different UAV types under specific perspectives decrease the classification accuracy. To address these issues, we determine a UAV type confidence score based on the entire track considering the confidence scores for single frames, the size of the corresponding detections and the maximum detection confidence score. We assess a number of different CNN based classification approaches by varying the backbone architecture and the input size to improve the classification accuracy on the single frames. Furthermore, ablation experiments are conducted to analyze the impact of the UAV size on the classification accuracy. We perform our experiments on publicly available and self-recorded data, including several UAV types.
Fully Discover the Balance of Lightning Network Payment Channels

The Lightning Network is a payment channel network that runs on top of Bitcoin. It is an open network, in which anyone with a computing device capable of connecting to the Internet can participate. Every participant is referred as a node. The direct link between two nodes is called a payment channel. Two nodes connected by the payment channel can conduct transactions, even in parallel. If two nodes without connecting directly a payment channel want to trade, the sender needs to start the route discovery algorithm to find a payment path connecting the sender and the receiver. The route discovery algorithm is based on the channel capacity, and the ip address of the node is public. To protect the privacy of users, the specific distribution of balances in the channel is confidential. The channel capacity is public and has been determined at the beginning of the channel establishment. The balance is private and has been dynamically changing, which brings challenges to the routing discovery algorithm. A channel balance disclosure attack was proposed to reveal the channel balance of most channels in the network. In this paper, we propose an enhanced channel balance disclosure attack that can reveal the balance of any channel in the network. We utilize a payment channel that can contain multiple outstanding parallel transactions at the same time to break the limit of the maximum value of a single transaction, thereby revealing the user’s precise balance. We explore countermeasures against enhanced balance disclosure attacks.
Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling

Federated learning has emerged as a popular technique for distributing model training across the network edge. Its learning architecture is conventionally a star topology be-tween the devices and a central server. In this paper, we propose two timescale hybrid federated learning (TT-Hf),which migrates to a more distributed topology via device-to-device (D2D) communications. In TT-HF, local model training occurs at devices via successive gradient iterations, and the synchronization process occurs at two timescales: (i) macro-scale, where global aggregations are carried out via device-server interactions, and (ii) micro-scale, where local aggregations are carried out via D2D cooperative consensus formation in different device clusters. Our theoretical analysis reveals how device, cluster, and network-level parameters affect the convergence of TT-HF, and leads to a set of conditions under which a convergence rate of O(1/t) is guaranteed. Experimental results demonstrate the improvements in convergence and utilization that can be obtained by TT-HF over state-of-the-art federated learning baselines.
A Blockchain-Enabled Framework for Requirements Traceability

Requirements traceability has been broadly recognized by researchers as an important quality of any software development process. However, among stakeholders, requirements traceability is often perceived as an extra task that disrupts their workflow. This perceived overhead demotivates stakeholders to participate in the creation, maintenance and use of traceability links. The challenges of implementing requirements traceability are amplified when complex and large-scale software systems are developed by cross-organizational and distributed teams. Different organizational backgrounds, conflicting objectives, and organizational boundaries lead to trust issues that complicate the implementation of traceability in such settings. In this paper, the authors propose a blockchain-enabled framework for requirements traceability. This framework aims to: (i) enable a holistic and reliable view of artifacts and traceability links, (ii) provide an incentive mechanism for creators of traceability links, (iii) ensure the authenticity and quality of traceability links by means of voting mechanisms, (iv) facilitate comprehension from traceability information through query services, and (v) enable interactive graphical visualization of traceability links.
Clustering and Classifying Users from the National Museums Liverpool Website

Museum websites have been designed to provide access for different types of users, such as museum staff, teachers and the general public. Therefore, understanding user needs and demographics is paramount to the provision of user-centred features, services and design. Various approaches exist for studying and grouping users, with a more recent emphasis on data-driven and automated methods. In this paper, we investigate user groups of a large national museum’s website using multivariate analysis and machine learning methods to cluster and categorise users based on an existing user survey. In particular, we apply the methods to the dominant group - general public - and show that sub-groups exist, although they share similarities with clusters for all users. We find that clusters provide better results for categorising users than the self-assigned groups from the survey, potentially helping museums develop new and improved services.
Attention Residual Network with 3D convolutional neural network for 3D Human Pose Estimation

3D human pose estimation is a very important task in human-computer interaction. Most of state-of-art methods are based 3D convolutional neural from voxelized grid or 2D convolutional neural network from depth images. The perspective distortions problems may badly effect the performance of the depth image-based methods. The degradation problem may badly effect the performance of 3D CNN-based methods. To overcome the perspective distortions problem, we use voxelized gird as input of proposed network. The output of proposed network is a heatmap for each joint, and then our method gets 3D coordinate of joint from the heatmap. To overcome the degradation problem and gradient vanishing problem, our network use residual blocks, which can make deeper network be easier to be trained. The attention mechanism can help our method focus on the more important information. We evaluated our method on a public human pose dataset and a human pose dataset collected by ourself. The experimental results show the efficiency of our method.
Comparative Study of the Virtual Machine Scheduling Approaches in Cloud Environment

In the current computing and technology scenario, computing, storage, and network resources are provided as a service to the end-users. The users put the demand from different time zone using scalable simulation. The requests are handled using service-oriented computing. The requests map on virtual machines. Our objective is the detailed comparative study of the static, dynamic, metaheuristic, and bio-inspired neural methodologies for the assignment of user requests on virtual machines. The optimal scheduling of requests on virtual machines depends on the resource assignment policy defined at task placement on a virtual machine across the data center network. The comparative study covers the dependent and independent virtual machine level scheduling using a scalable simulation with user-defined policies. The comparative results show that when shifting from a static approach (FCFS) to metaheuristic scheduling methodology (BB–BC-COST), the performance metrics improve by 26.97% when the maximum number of cloudlets are mapped on virtual machines.
Refresher Training through Quiz App for capacity building of Community Healthcare Workers or Anganwadi Workers in India

High and persistent child malnutrition levels with tardy reduction, seen in successive health surveys, continue to be a matter of concern in India, drawing attention to the need to revamp the four-decade-old Government program, Integrated Child Development Scheme (ICDS). ICDS field functionaries or Anganwadi Workers’ (AWWs) capacity deficit was identified as a significant factor affecting ICDS’s effectiveness. Considering rising numbers, over 1.4 million AWWs, and continuously advancing knowledge of community healthcare, conventional training pedagogy is ineffective in building and updating AWWs and their supervisors’ capacity, which calls for rethinking, using the ICT approach. Over 6 lakh AWWs in India were smartphone equipped by 2020. An android based quiz app was designed, following AWWs training modules’ content and need assessment results. The study investigates the quiz app’s effectiveness and compares it with conventional classroom instruction, with a group of AWWs, and discusses ways to make it an adequate substitute.
Secure Audio Transmission Over Wireless Uncorrelated Rayleigh Fading Channel

Audio communications and computer networking play essential roles in our daily lives, including many domains with different scopes. Developments in these technologies are quick. In consequence, there is a dire need to secure these technologies up to date. This paper presents an efficient model for secure audio signal transmission over the wireless noisy uncorrelated Rayleigh fading channel. Also, the performance of the utilized multiple secret keys-based audio cryptosystem is analyzed in different transformation domains. The discrete cosine transform (DCT), the discrete sine transform (DST), and the discrete wavelet transform (DWT) are investigated in the utilized multiple secret key-based audio cryptosystem. Simulation results show consistent results with the wireless noisy channel. The performance of the proposed multiple secret keys-based audio cryptosystem can be ranked concerning the employed domain as DWT, DCT, and DST transform techniques. The simulation experiments proved that the presented multiple secret keys-based audio cryptosystem for audio signals transmitted over the wireless noisy uncorrelated Rayleigh fading channel achieves reliable and secure wireless link utilizing combined multi security layers.
Secure Distributed Linearly Separable Computation

Distributed linearly separable computation, where a user asks some distributed servers to compute a linearly separable function, was recently formulated by the same authors and aims to alleviate the bottlenecks of stragglers and communication cost in distributed computation. For this purpose, the data center assigns a subset of input datasets to each server, and each server computes some coded packets on the assigned datasets, which are then sent to the user. The user should recover the task function from the answers of a subset of servers, such that the effect of stragglers could be tolerated. In this paper, we formulate a novel secure framework for this distributed linearly separable computation, where we aim to let the user only retrieve the desired function without obtaining any other information about the input datasets, even if it receives the answers of all servers. In order to preserve the security of the input datasets, some common randomness variable independent of the datasets should be introduced into the transmission. We show that any non-secure linear-coding based computing scheme for the original distributed linearly separable computation problem, can be made secure without increasing the communication cost (number of symbols the user should receive). Then we focus on the case where the computation cost of each server (number of datasets assigned to each server) is minimum and aim to minimize the size of the randomness variable (i.e., randomness size) introduced in the system while achieving the optimal communication cost. Novel information theoretic converse bound on the randomness size and some achievable schemes are proposed, where they coincide in some cases.
Conflictogenity and University Academic Culture

Aggression is an integral part of human nature, direct or indirect cause of intrapersonal, interpersonal and intergroup conflicts. The paper aims to show that conflict can be positive in nature and can serve as a dialectical foundation of organizational and social development. Scientific originality of the study lies in the fact that the author justifies conflict-generating nature of academic environment, reveals severe contradictions between components of this culture. The research findings are as follows: the author analyzes prerequisites for conflictogenity in higher education, introduces a definition of university academic culture, describes its systemic components.