Reviewerly/exHarmony huggingface dataset:
The Meshwork Ledger, its Consensus and Reward Mechanisms

We propose a new blockchain ledger concept called “Meshwork ledger” and a corresponding consensus algorithm. Meshwork is a network of coequal client nodes that contribute to the endorsement of the transactions by providing digital signatures to a validator node that collects them in an aggregate signature scheme. The essential sustainability component of the ledger is the reward mechanism for the meshwork client nodes. The prime design objective is the coequality of all client nodes, meaning there is no advantage for getting rewards if the client is an early adopter, if the client has collected a significant stake of rewards or if the client just joined the meshwork. The consensus algorithm is based on aggregate multisignatures. A joint aggregate signature on a block of transactions is constructed with the signatures collected from the mesh client nodes in a multi-signature scheme. A signature provided on the block by a client node is acknowledged as approval. The core idea of the consensus is to race for the maximum number of signatures (approvals) on a block from the mesh clients, in order to append the block in the blockchain. The race in the consensus is among particular types of nodes called validator nodes that try to collect a maximum number of approvals (signatures) from the mesh clients. Clients that participated in the aggregate signature organized by the winner validator node get some reward as a small share of the total transaction fee. These reward transactions are performed in an off-chain manner, using a commit-chain. Compared with other blockchain consensus algorithms, the meshwork consensus algorithm is faster, significantly more energy-efficient and scalable.
Augmented Reality and Virtual Reality

Imagine a world in which the physical objects surrounding us could be constructed or destructed on a whim, tinkered and adjusted to fit the demands of the user. Such a world may never exist in the physical domain, but the worlds of augmented reality (AR) and virtual reality (VR) allow us to do just that. In this chapter, we examine this frontier of immersive multimedia and explore the enabling technologies and their impacts to the field, including graphics processing unit (GPU), Global Positioning System (GPS), and network support for multi-party interaction. We then closely examine the modern augmented reality systems and applications, in particular, Microsoft’s HoloLens. Finally, we discuss the challenges and restrictions of AR/VR, in terms of color and depth perception, localization, information presentation, and social acceptance.
Decision Support System Using Fuzzy Logic Method of Tahani Model for Student Selection

Abstract Tahani model of fuzzy database is one of the methods used for the making decision process. On this occasion, the researcher used the fuzzy database tahani model to build a DSS for the selection of new candidate students at the SMP Taman Siswa Kisaran. SMP is junior high school. This system was built to recommend to principals about new candidate students who are eligible to be accepted. Some of the main variables that form the basis of calculation, namely the average elementary school diploma, including Mathematic score, Indonesian Language score, and Pancasila Education score. The Other variables are Academic Test score and Religious Education Test score. The result of the system recommendations is based on the value of membership and fire strength from the calculation process in the system. To ensure the feasibility of the system, testing the result of the system calculations and comparing them with the results of calculations manually. If the result of the system calculation is the same as the result of the manual calculation, then the decision support system for the selection of new candidate students is feasible to be applied at the SMP Taman Siswa Kisaran.
Material Meets the City: Exploring Novel and Advanced Materials for the Smart Urban Built Environment

The urban realm is currently undergoing a transformation in which cities are being laced with sensors and networks of ever-connected devices. At the same time, more and more novel and advanced materials are finding their way into interaction design and Human-Computer Interaction (HCI) research, offering us new interaction possibilities. In this half-day online studio, attendees will have the opportunity to exchange and reflect on urban interaction designs for user engagement based on a set of novel, unconventional, or omnipresent materials in urban environments. We will further collect historical, public, and community locations of different social and societal meaning. Participants will be asked to develop hands-on concepts and use cases for the selected locations through a material-centered approach and think about new levels of user-environment engagement to extend and explore the current design space. The formulated concepts and use cases will be recorded in an online collaboration tool with the prospect to publish them as enhances for the new generation of media architecture.
Localizing Against Drawn Maps via Spline-Based Registration

We propose a method to facilitate robot navigation relative to sketched maps of human environments. Our main contribution centers around using thin plate splines for registering the robot's LIDAR observation with the hand-drawn maps. Thin plate splines are particularly effective for this task because they are able to handle many of the nonrigid deformations commonly seen in sketches of maps, which render traditional rigid transformations inappropriate. Our proposed approach uses a convolutional neural network to efficiently predict the control points which define the spline transform, from which we then compute the pose of the robot on the hand drawn map for navigation purposes. Our systematic evaluations in simulation using a synthetic dataset and real, hand-drawn sketches show that the proposed spline-based registration approach outperforms baseline methods.
Analysis and Research Based on Method of Questionnaire Survey and the Algorithm of Big Data Analysis

Abstract During the COVID-19 epidemic, the whole country suspended classes in schools without stopping studying. Under this circumstance, online teaching experienced a breakthrough development. This paper uses the method of questionnaire survey and the algorithm of big data analysis, and this paper explores the general situation of online teaching in local colleges and universities in southwest Guangxi which face challenges in online learning due to the relative shortage of educational resources and remote geographical location. This paper analyzes the supporting conditions, problems and shortcomings of online teaching in local colleges and universities in southwest Guangxi. Suggestions are also proposed on four aspects to improve online teaching and learning experiences, which are establishing the basis of online teaching conditions, strengthening the basic rules of online classroom management, improving teachers and students’ adaptation to online environment, and guiding students to improve independent learning ability.
A Brief Literature Review on Human Perceptions of Service Robots with a Focus on Healthcare

Service robots are becoming increasingly popular in the world where they interact with humans on a semi- or routine basis. It is essential to understand human perceptions of these robots, as they affect use, adoption, and interaction. The primary goal of this brief literature review was to learn about public perceptions of service robots, particularly in healthcare settings. A secondary goal was to understand the measures generally used to gather perception data. A restricted literature search was conducted from September-November 2019 using three databases, yielding 3,629 articles; a total of 22 were selected for this review published between 2011-2019. In general, a variety of largely untested surveys were used, and the studies surveyed illustrated the complexity of the perception space. Perceptions differ by gender, age, culture, and interact with trust, usability, work role, and perceptions of domains of life such as schools or healthcare. Limitations and the future need to better understand this area are discussed.
Automated Surveillance Security System Using Facial Recognition for Homes and Offices

Video cameras are the most popular way of security used by the majority of people in homes and offices. These systems require a human being for video surveillance so that if some unusual activity happens, appropriate action can be taken. Usually, cameras are installed to monitor the unusual activities which may be a threat to the workplace. In the case of home security, the major reason to install video cameras is to monitor that no unknown person should enter the home without permission. This demand for home security systems has been utilized to provide automated surveillance security system which can specifically be used in such places to monitor the presence of an unknown person in the premises so that the people using this system are alerted using notification. For identification of unknown face, our system has been trained on some known faces using LBPH face recognizer and then the threshold is applied to decide whether the face is known or unknown, finally an alarm is generated if the face is declared unknown. The above system works well on good quality videos and images.
Protection Profile Bricks for Secure IoT Devices

The Internet of Things (IoT) paradigm has been proposed in the last few years with the goal of addressing technical problems in fields such as home and industrial automation, smart lighting systems and traffic monitoring. However, due to the very nature of the IoT devices (generally low-powered and often lacking strong security functionalities), typical deployments pose a great risk in terms of security and privacy. In this respect, the utilization of both a Trusted Execution Environment (TEE) and a Trusted Platform Module (TPM) can serve as a countermeasure against typical attacks. Furthermore, these functional blocks can serve as safe key storage services and provide a robust secure boot implementation and a firmware update mechanism, thus ensuring run-time authentication and integrity. The Common Criteria for Information Technology Security Evaluation allows to determine the degree of attainment of precise security properties in a product. The main objective of this work is to identify, propose and compose bricks of protection profile (PP), as defined by Common Criteria, that are applicable to secure IoT architectures. Moreover, it aims at giving some guiding rules and facilitate future certifications of components and/or their composition. Finally, it also provides a structure for a future methodology of assessment for IoT devices.
Identifying the Development Trend of ARM-based Server Ecosystem Using Linux Kernels

In the last couple of years ARM-based servers have been gradually adopted by cloud service providers and utilized in the data centers. Such tendency may provide great business opportunities for various companies in the industry. Hence, the ability to timely track the development trend of the ARM-based server ecosystem (ASE) from technical perspective is of great importance. In this paper the level of development of the ASE is quantitatively assessed based on open-source data analysis. In particular, statistical data is extracted from 42 Linux kernels to analyze the development process of the ASE. Furthermore, an estimate of the development trend of the ASE in the next 10 years is made based on the statistical data. The estimated results provide insight on when the ASE may become as mature as today's x86-based server ecosystem.
A Novel Approach to Unsupervised Segmentation of Multitemporal VHR Images based on Deep Learning

Very-high-resolution (VHR) multi-temporal images are important in remote sensing to monitor the dynamics of the Earth surface. Image semantic segmentation classifies pixels and assigns them label from meaningful object groups. It has been extensively studied in context of single image analysis, however not explored for multi-temporal one. In this paper we propose to extend supervised semantic segmentation to the unsupervised joint segmentation of multi-temporal images. The proposed method processes multi-temporal images by separately feeding them to a deep network comprising of trainable convolutional layers. The training process does not involve any external label. Segmentation labels are obtained from argmax classification of the final layer. Multi-temporal segmentation labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on a VHR dataset from Trento, Italy. Both quantitative and qualitative results demonstrated the effectiveness of the proposed approach.
Perpetual Secret Sharing from Dynamic Data Structures

In this paper we propose a secret sharing scheme where the access structure changes over time following a dynamic data structure. The proposed scheme can accommodate an unbounded number of new parties and when the number of parties become very large, the scheme utilizes the dynamic data structure to search, add and delete parties which makes the scheme practical. Evolving Secret sharing was proposed in the literature to include unbounded number of parties into the scheme. We introduce a related idea called perpetual secret sharing which has the following advantages over the existing evolving schemes:- our construction reduces the memory usage of a dealer to a large extent and our scheme is implementable by constant-depth circuits <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(AC^{0})$</tex> . Moreover, the construction is flexible and with some modifications, the construction can handle dynamic and evolving versions of several other multipartite access structures such as hierarchical access structures, compartmental access structures etc.
TorMass: Tor for the Masses Domestic and Monetized Anonymous Communication

The increasing demand on privacy is driving a notable quest for privacy legal instruments and practical techniques. The Onion Router (Tor) is considered the most practical service for anonymous public communications; however, it is not yet mainstream despite more than one decade of practical use and research. The reason is referred to using message encryption layers, i.e., onions, via relays offered through unknown volunteers which does not fully protect legitimate users nor services. It can also be used for illegal purposes which hampers its admissibility in many countries. In this paper, we introduce a new ecosystem built on top of Tor to broaden its use by legitimate users and at the same time provide provenance when users violate the usage policy terms. We propose Anonymity Service Providers that provide paid relays as a service to users. A user buys this service from different providers to diversify the onion circuit and avoid collude and thus disclosing her identity. Anonymity is maintained as long as the user abides to the policy; otherwise, her identity is disclosed via a reporting system. This accountability reporting system can be implemented over a Smart Contract to make arbitration automatic. This work proposes new use cases and business opportunities that are worth consideration by both the research and business communities.
Performance of Information Technology Infrastructure Prediction using Machine Learning

Resource management is always an important issue related to good governance decision making. One of the common problem faced in managing IT Infrastructure is about allocating server resources to improve the performance. In this study we use a machine learning approach to make predictions about the performance of information technology infrastructure. The experiment took data from several servers in a company to be tested. The performance measure of resources used in this study are CPU Performance, Disk performance, Memory capacity, and Network performance. Several algorithms and machine learning methods are tested, such as Linear Regression, kNN, SVR, Decision Tree and Random Forest, to find the best model fit for the servers. The comparison result shows that Linear regression and kNN perform well in predicting the network performance in those three servers.
Adaptive Effect Evaluation Model of Composite Cloud Service in Cloud Manufacturing Environment

Composite cloud service adaptive adjustment is an important means to achieve the flexibility of manufacturing cloud service composite flexibility. In order to evaluate the adaptive adjustment effect of composite cloud services and ensure the smooth progress of manufacturing activities, an adaptive effect evaluation model of composite cloud services is proposed. The model adopts a post-evaluation method, taking the user satisfaction, product qualification rate, operational stability, cost, time, and the QoS distance as evaluation indicators. The comprehensive evaluation value is obtained through the weighted sum of each index, avoiding the evaluation distortion caused by a single indicator. The evaluation framework and evaluation algorithm of the adaptive effect evaluation model are given. Combined with examples, the method can reflect the real situation better, and has practical operability.
Highly scalable and load balanced web server on AWS cloud

Abstract Most of the industries maintaining their web server on their on-premise, which leads them to have high maintenance, more workload, increases cost. To overcome this we can use cloud technologies for building a web server which gives better performance. Thus we use AWS cloud technologies. In this research paper, a web server is built using the cloud formation technique CFT. Using this technique we can able to build our infrastructure using YAML code. To achieve these major cloud resources are used such as Ec2 Instance, Application load balancer for balancing the load given by users, auto-scaling group. We will configure these cloud resources as YAML and implement them using the cloud formation technique. So that the industries can have their Infrastructure as Code.
WITHDRAWN: Enterprise based data deployment inference methods in cloud infrastructure

Cloud computing is active innovation environment to represent and process the digital infrastructure through the cloud based servers and can be treated as synonym for the internet based technical-industry. Cloud computing can provides active services and various series of utilities based on the internet such as storage space and virtual servers based services; technical-operations-applications, privacy-secrecy-authorization for single plot form users and applications. Cloud computing will attain high reliability and best outcome scaling of economic financial systems by holding invention discovery and utility of cloud resource sharing service. Basically computing based cloud derives two primary models such as service based cloud computing model and deployment based cloud computing model. In present scenario, most of the industries already in full swing of using cloud resource services, many companies are taken it seriously are accepted wisdom of shifting to cloud computing and showing the interest to get more business and economic profits by taking the technical support of cloud services. Organisations already started the counting the financial asset benefits by deriving the cloud hostage and its lot of potentials. Companies are getting failure for choosing the very best amongst the rest, choice of the accurate type of cloud hosting is required to identify the business needs and to analyze the work demands. The companies operating their operations based on the cloud environment without knowing the basic information about which type of cloud environment can be suitable for their company desires the website for their operational implications, needs and also the companies are not measuring the usability and dependability of the cloud services for implications of their day to day technical and non-technical operations.
User Preference Aware Lossy Data Compression for Edge Caching

In order to handle users' heterogeneous quality of service requirements at the network edge, user preference aware lossy data compression is presented for edge caching. The symbols generated by an information source are compressed and then are transmitted to a base station. Before being transmitted to users, the symbols can be re-compressed at the network edge. User preferences are described by weights on the symbols. All possible values of the rates over the transmission links and the distortions suffered by the users form an achievable domain. The achievable domain is a convex set and an optimization problem is formulated in order to obtain its lower boundary. For discrete sources, the optimization problem is nonconvex and a DCA-based iterative algorithm is proposed to provide suboptimal code designs.
Intelligent masked-person detection system for epidemic prevention and control

In order to control the epidemic and complete the supervision of increasing population, we devised a kind of face detection system. This system detected face with MTCNN and then it detect whether the person wears the mask with MobileNet. Also we added non-standardized samples in the model training so that it can detect pedestrians who are not properly worn. The experimental results showed that the system can effectively identify the wearing of masks.
Face Validity of Various Display Methodologies in Conveying Spatial Awareness and Depth Perception

The information conveyed through visual cues varies greatly depending on the display method. Digital images are often used as trial demonstratives and are a vital resource in the presentation and transfer of information to a jury during litigation. In the situation where depth perception and spatial awareness are a key factor in the understanding of a subject incident, immersive technology, such as virtual reality (VR), may provide a valuable alternative to traditional media forms. The aim of this study was to evaluate the efficacy of different display methodologies in conveying depth perception and spatial awareness during an “optical illusion” misstep and fall case. This study provides a quantitative characterization of participants’ responses to various display methodologies ranging from 2D photographs to a VR environment. Results indicate that individuals were able to perceive a larger number of visual cues when immersed in a three-dimensional virtual environment, giving them a more accurate representation of the scene.
A cloud-based user recommender and e-governing system for public welfare

Abstract Several problems exist in traditional analysis and design for e-government. The modern Era requires finding few solutions to dynamically facilitate the activities involved in administrative sectors of democratic regions. Individuals of a state or a central can be given an opportunity to actively participate in making decisions by placing their individual views on the democratic process for the better welfare. This paper is presented with the aim of developing a interactive system that adapts to dynamically updating the current policy recommendations, role of every crow sourcing committees, existing policy operational areas, beneficiaries, up-coming proposal plans of state and central regions for the welfare of rural and urban sectors. The framework facilities demand analysis, bringing many benefits to e-government planning, such as grasping emphasis immediately, affording to maximal benefit of democratic policies and plans. The Interactive system benefits mutually the individual as a Citizen and the administrator as the local parties of government body with efficient utilization, hence aiming at better development and implementation of goal plans that put forwards the Nation to step in to next degree of development by accomplishing the sustainable growth.