Reviewerly/exHarmony huggingface dataset:
VEIN: High Scalability Routing Algorithm for Blockchain-based Payment Channel Networks

The payment channel networks (PCNs), as the main method of blockchain off-chain expansion, have received extensive attention in recent years. Using the PCNs, two unconnected parties in a transaction can forward payments through existing payment channels of other nodes, which dramatically reduces interactions with the blockchain. But in large-scale dynamic PCNs, the routing mechanism is a challenge. Existing PCNs routing algorithms have some limitations. The landmark routing destroys the decentralization of blockchain. The static routing requires nodes having a global view and ultra-high computing power, which can not be applied to light nodes. We propose VEIN, a dynamic multi-path source routing algorithm, which is suitable for not only full nodes but also light nodes. We present an ingenious routing protocol, a modified max-flow algorithm to find edge-disjoint paths, and a path selection algorithm to deal with the NP-hard multi-path selection problem. Extensive experiments show that VEIN increases the transaction success ratio by 34% with the state-of-art algorithm, and realizes multiple orders of magnitude reduction in storage. In addition, we implement a prototype of VEIN on the Ethereum testnet to verify its feasibility.
The Rise of Artificial Intelligence

Our inventions defined the work we engaged in for centuries; created new industries and employment opportunities around them. They, however, had often unforeseen consequences that affected the way we lived, interacted with each other, and redefined our societal rules. The established narration portrays the impact of major technological leaps in civilization on employment as temporary disruptions: Many finds themselves without employment taken away from them by efficient, laborsaving inventions, but, in the long run, through gradual adaptations, improved education and gaining higher qualifications, everyone benefits. In this chapter, the authors explore the impact of the rapid expansion of artificial intelligence (AI) in relations to the labor market. The authors argue that this rather optimistic, even naïve scenario, collapses while confronted with the exponential growth of AI; in particular, with the potential arrival of syneoids – robotic forms of “strong AI” possessing, or even exceeding, the full range of human cognitive abilities.
Keystroke Dynamics

Online learning has entirely transformed the way of learning by the students. Online tests and quizzes play an important role in online learning, which provides accurate results to the instructor. But, the learners use different methods to cheat during online exams such as opening a browser to search for the answer or a document in the local drive, etc. They are not authenticated once they login and progress to attend the online exams. Different techniques are used in authenticating the students taking up the online exams such as audio or video surveillance systems, fingerprint, or iris recognition, etc. Keystroke dynamics-based authentication (KDA) method, a behavioral biometric-based authentication model has gained focus in authenticating the users. This chapter proposes the usage of KDA as a solution to user authentication in online exams and presents a detailed review on the processes of KDA, the factors that affect the performance of KDA, their applications in different domains, and a few keystroke dynamics-based datasets to authenticate the users during online exams.
Imagining Human-Machine Futures: Blockchain-based “Decentralized Autonomous Organizations”

Blockchain-based “Decentralized Autonomous Organizations” (DAOs) communities risk perpetuating the 1990s Californian Ideology of techno-elitism in their imaginary of “autonomy” via technological determinism, free-market economics, and “engineering” approach to social and political challenges (Barbrook & Cameron, 1996). Imaginations include algorithmic governance via Artificial General Intelligence agents running on decentralized blockchains, hiring labor in DAOs, and bartering payment in cryptocurrency. The role of humans in this imaginary is limited. This piece explores autonomy in blockchain-based DAOs to investigate visions of algorithmic assemblages. Do DAOs imagine a different future where the role of humans is one of symbiosis and augmentation with machines? I draw on cybernetic interpretations of DAOs as “autopoietic” organisms to imagine a co-constitutive relationship between people and algorithms in the information age. By understanding the promises and practices of blockchain technology and decentralized governance, we can better engage with these emergent objects of social and policy inquiry.
Anti-occlusion and Scale Adaptive Target Tracking Algorithm Based on Kernel Correlation Filter

The Kernel Correlation Filter Tracking Algorithm(KCF)is a lightweight tracking algorithm with the advantages of fast tracking speed and good effect. However, when the target is occluded and the scale changes, the algorithm will have tracking drift and tracking loss. Aiming at the kernel-related filter tracking algorithm that cannot solve the tracking failure caused by occlusion, target scale changes and other factors in the tracking process, an anti-occlusion and scale-adaptive kernel-related filtering algorithm is proposed. We build a scale pool and use the scale pool to train a one-dimensional fast scale filter to solve the problem of target scale changes. This paper uses the average occlusion distance metric and the size of the context occlusion factor to determine the occlusion state of the target, and dynamically select the learning update rate of the target model according to the target occlusion state. When it is judged that the target is severely occluded, the target position is predicted according to the previous motion state of the target, and the small-range re-detection positioning mechanism proposed in this paper is used to re-detect the target within a certain range of the predicted position. At the same time, the re-detected target is occluded again Judgment to determine whether the target is out of the occlusion. If it is determined that the target is still severely occluded, it means that the target is not out of the occlusion area, the re-detection of the target position is inaccurate, and the predicted position is output. Experimental results show that the accuracy and success rate of the algorithm in this paper are 0.819 and 0.669, which are 8.33% and 7.04% higher than the KCF algorithm. The tracking effect of this algorithm is better than that of KCF algorithm.
Pairwise Preferences-Based Optimization of a Path-Based Velocity Planner in Robotic Sealing Tasks

Production plants are being re-designed to implement human-centered solutions. Especially considering high added-value operations, robots are required to optimize their behavior to achieve a task quality at least comparable to the one obtained by the skilled operators. A manual programming and tuning of the manipulator is not an efficient solution, requiring to adopt towards automated strategies. Adding external sensors (e.g., cameras) increases the robotic cell complexity and it doesn’t solve the issue since it is usually difficult to build explicit reward functions measuring the robot performance, while it is easier for the user to define a qualitative comparison between two experiments. According to these needs, in this paper, the recently-developed preferences-based optimization approach GLISp is employed and adapted to tune the novel developed path-based velocity planner. The implemented solution defines an intuitive human-centered procedure, capable of transferring (through pairwise preferences between experiments) the task knowledge from the operator to the manipulator. A Franka EMIKA panda robot has been employed as a test platform to perform a robotic sealing task (i.e., material deposition task), validating the proposed methodology. The proposed approach has been compared with a programming by demonstration approach, and with the manual tuning of the path-based velocity planner. Achieved results demonstrate the improved deposition quality obtained with the proposed optimized path-based velocity planner methodology in a limited number of experimental trials (20).
Building Intelligent Transportation Cloud Data Center Based on SOA

This paper is targeted at issues including traditional stovepipe data center, low utilization of IT equipment and data resources as a result of rigid IT structure, high maintenance costs and high energy consumption in system operation. By taking Beijing Municipal Committee of Transport (BMCT)'s data center as an example, a way to establish distributed traffic cloud data center based on SOA (Service-Oriented Architecture) fused with cloud computing is introduced in this paper; in addition, network-aware energy conservation scheduling DENS (Data- center Energy-efficient Network-aware Scheduling) algorithm applied in cloud data center is put forward to realize the full utilization of all kinds of resources in the cloud data center. Experimental results also show the effectiveness of the proposed algorithm by comparing with traditional DENS algorithms.
E-Banking Security

The online banking industry has overgrown in recent years and will continue to grow as economic organizations remain to encourage customers to handle online banking transactions such as money transfers, access to account information, or payment of monthly bills. During this period, internet criminals and fraudsters attempting to steal personal customer information hijacked online banking. This article proposes reviewing the ways by which fraudulent activities are performed and what banks are doing to prevent such activities, as well as the new security measures that banks are using to increase customer confidence. Therefore, the authors present the threats, challenges to address such threats, some trends, and future landscapes regarding online banking security.
Tilt correction method of pointer instrument

In the process of robot inspection of pointer instrument in substation, the image acquired by camera will bring reading error due to the tilt of dial. This paper proposes an instrument correction method based on feature registration. Firstly, the contour feature of instrument image is extracted by edge detection, and then corners are drawn on the contour feature map of instrument, and the image of instrument to be corrected is compared with the template instrument image At the same time, by using random sampling consensus algorithm (RANSAC) to remove the mismatched points, the corners are accurately matched, and the projection transformation matrix between the instrument image block to be registered and the template instrument image is solved. The projection transformation matrix is applied to the instrument image to be corrected for tilt correction. The experimental results show that the proposed method can better realize the tilt correction of pointer instrument in the process of patrol inspection.
Artificial Neural Network

This is studied and implemented in MATLAB Simulink on the ability of think about problems and decisions in a reasonable way without exaggerating of electric vehicles specifically for hybrid energy storage system. It formally suggest something as a possible to plan for hybrid electric storage system consists of lithium ion battery and super capacitor to charge the electric vehicle. The control system is designed using Artificial Neural Network to improve something the results obtained using the PI controlled techniques. It reduces the calculation complexity of the system by reducing the values of Kp and Ki calculations. The neural network promotes self-learning capability of the system and also improves the system by reducing any fluctuations if any. In this paper, we study and implement techniques for design of Hybrid Energy Storage System for Electric Vehicles.
Enhanced Fixed-Interval Smoothing for Markovian Switching Systems

This paper considers the problem of fixed-interval smoothing for Markovian switching systems with multiple linear state-space models. An enhanced algorithm that is capable of accurately approximating the Bayesian optimal smoother is proposed. It utilizes the exact expression for the quotient of two Gaussian densities to help solve the backward-time recursive equations of Bayesian smoothing, and computes the joint posterior of the state vector and model index. The proposed algorithm only involves the approximation of each model-matched state posterior, which is a Gaussian mixture, with a single Gaussian density for maintaining computational tractability in retrodiction. The validity of the newly developed smoother is verified using a simulated maneuvering target tracking task.
Gender Classification Based on Body Images

The gender classification for human being is one of the interesting problems in computer vision and plays an important role in various daily applications. Especially during the pandemic, people are wearing masking blocking their faces, extracting body information becomes much more important. In this paper, we investigate the problem of predicting gender from 2D human body images using various deep neural network frameworks. Our proposed method does not acquire other biometrics feature extraction and calculation which is an end- to-end method. This work demonstrates estimating soft biometric characteristics such as gender with mere body images without faces can also achieve high accuracy. We also proposed a pipeline to clean human body images to leave only body information and created a labeled 2D image dataset containing only body information despites the face and background information. Several different modified deep neural networks are tested and compared on this dataset. The best model is modified from Resnet50 and it provides relative high accuracy for gender classification using pure human body images.
Report on the CHIIR 2019 Second Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2019)

The Second Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2019) was held in conjunction with the ACM SIGIR Conference on Human Information Interaction &amp; Retrieval (CHIIR 2019) in Glasgow, Scotland. WEPIR 2019 followed on from the first WEPIR held at CHIIR 2018. The purpose of the workshop was again to bring together researchers from different backgrounds, interested in advancing the evaluation of personalisation in information retrieval. The workshop focused on further development of a common understanding of the challenges, requirements and practical limitations of personalisation in information retrieval and its evaluation.
A Real-time Football Goalkeeper Robot System Based on Fuzzy Logic Control

This paper develops a football goalkeeper robot system that can entertain people or assist routine training in indoor or outdoor environments. The system uses an Intel RealSense D435 depth camera to provide image data and the yolov3-tiny Net to locate the football. In the process of coordinate transformation, the least square method was utilised to obtain the seven parameters, so that the error can be kept within an acceptable range (within the pre-specified range).Using the fuzzy logic control algorithm to predict the hitting points on the football baffle and PID algorithm to control motor rotation to prevent the football from entering crossbar. In the experiments, comparison between the fuzzy logic control algorithm and the Kalman filter algorithm was implemented. The results shows that the fuzzy logic control has a better performance.
Modelling of Cloud Computing Enablers Using MICMAC Analysis and TISM

This article describes how Cloud Computing is not just a buzzword but a shift from IT departments to the outsourcing vendors without impacting business efficiency. Some organizations are moving towards cloud computing but many have resistance to adopting cloud computing due to limitations in knowledge and awareness of the classifying elements, which effect decisions on the acceptance of cloud computing. Therefore, this article has focused on accumulating the elements, which can act as enablers, by reviewing existing literature and studies from both professional and academic viewpoints. All the identified enablers have been structurally modeled to develop the relationship matrix and establish the driving power and dependence power of every element. This is done by employing Total Interpretive Structural Modeling (TISM) and Cross Impact Matrix Multiplication Applied to Classification (MICMAC) analysis.
Service Layer Security Architecture for IOT Using Biometric Authentication and Cryptography Technique

Data security and authentication mechanism is a very challenging job for smart devices. And more ever, IOT is suffering with login and verification process. Here, in our paper, we have focused on human characteristics-based security system which cannot be pinched easily such as iris, thumb, palm, DNA and voice-based authentication system. Using biometric authentication theory, we have presented that how biometric systems are the boundless computational resources and prospective of flexibility, reliability and cost reduction along with high-security performance resources. To maintain the security of biometric traits over the Internet channel, end user can apply the cryptography algorithm such as ElGamal, MAC Omura, Cramer–Shoup, RSA. As a final point, this paper is contributed for evidencing the strength of integrating the biometric authentication system with cryptography techniques and its application on Internet-based applications. In order to develop strong security, we have proposed an integrated approach of three mechanisms using biometrics, OTP and cryptography. The work is validated for biometrics through AVISPA (SPAN) security tool which is worldwide acceptable for approving the security architecture.
Supporting Agile User Fusion Analytics through Human-Agent Knowledge Fusion

For many types of data and information fusion, input from human users is essential, both in terms of defining or adjusting the processing steps, as well as in interacting with, understanding, and communicating the results. In many cases, information fusion should increase understanding for the human user(s) working as part of a team of interacting agents, taking into account the needs of each user type, and the factors that might affect individual and team performance. This paper focuses on the decision support that could be provided to users, by presenting a candidate environment to support comprehensive information fusion and exchange in support of human-agent knowledge fusion (HAKF). The paper outlines two distinct HAKF use cases of (1) foraging data for open source intelligence analysis, and (2) sensemaking fusion from sensors and machine agents, using Cogni-sketch. In the first use case, a traditional open source intelligence gathering exercise demonstrates information gathered from multiple sources and maps it to a common model of sensemaking. The second use case shows machine-led activities including fusion of machine vision and object identification, and the utilization of human-led semantic definitions of events and situations in support of sensemaking.
Home UbiHealth

At the third computing era, users interact with many computing devices surrounding or implanted in them, in a natural way, anytime and anywhere, implementing the concept of ubiquitous computing. Moreover, the ubiquitous computing implementations provide the advantageous characteristics of awareness and personalization, which are precious in healthcare applications (i.e., the operating computing devices in the patient's environment can be aware about the evolving situations and actively participate in the provision of the medical treatment). In addition, the ubiquitously supported healthcare services can be provided anywhere and at any time allowing specific cases of the hospitalization model to be transferred to the home healthcare model. The adoption of the home healthcare model in a ubiquitous computing environment provides the prerequisites for the development of the home UbiHealth model. Extending the formal provision of medical services at home provides the capability to cover the medical needs of all the population categories.
Cloud-based approach in data centers

Data centers play an important role in modern information technology (IT) infrastructures. A data center is a home to the computational power, storage, and applications necessary to support an enterprise. Data centers process billions of Internet transactions every day. Nowadays, data centers are the beating hearts of the companies they serve. Large data centers with thousands of servers have been deployed by renowned information and communications technology organizations, like IBM, Microsoft, Amazon, Yahoo, eBay, and Google, to provide cloud computing services. The phenomenal increase in the size and number of data centers and the resultant increase in operational cost has stimulated research in various domains of data centers, such as energy efficiency, resource management, networking, and security.Presented are data centers from a cyber-physical system (CPS) perspective. Current methods for controlling IT and cooling technology (CT) in data centers are classified according to the degree to which they take into account both cyber and physical considerations. To evaluate the potential impact of coordinated CPS strategies at the data center level, we introduce a control-oriented model that represents the data center as two coupled networks: a computational network representing the cyber dynamics and a thermal network representing the physical dynamics. These networks are coupled through the influence of the IT on both networks: servers affect both the quality of service (QoS) delivered by the computational network and the generation of heat in the thermal network. Using this model, three control strategies are evaluated with respect to their energy efficiency and computational performance: a baseline strategy that ignores CPS considerations, an uncoordinated strategy that manages IT and CT independently, and a coordinated strategy that manages IT and CT together to achieve the optimal performance with respect to both QoS and energy efficiency. Simulation results show that the benefits that can be realized from coordinating the control of IT and CT depend on the distribution and heterogeneity of the computational and cooling resources throughout the data center. A new cyber-physical index (CPI) is introduced as a measure of this combined distribution of cyber and physical effects in a given data center. We illustrate how the CPI indicates the potential impact of using coordinated CPS control strategies.
Computer Fraud Challenges and Its Legal Implications

In this chapter, the authors provide a position-based discussion in reference to selective cyberthreats to devices and data breaches (which include malware, phishing, social engineering, data communication interception, malicious insider actions, just to name a few), and further provide information on applicable defenses and their legal implications. Although the authors do not assess the different threats and defenses in order to help prevent future vulnerability towards hacking for consumers, they do provide a conceptual understanding of the growing threats of such attacks and the inability of current legal safeguards to counter such threats. Particularly vulnerable are mobile systems, which are more prone to loss and theft once intercepted.