Reviewerly/exHarmony huggingface dataset:
How Insufficient Recognition Shapes the Rate and Scope of Contributions to the Commons

Scholars have long extolled the power of reputation for maintaining contributions toward collective resources. Incentivized to achieve reputations as good citizens of the collective, individuals are less inclined to freeride off the contributions of others. However, in examining the effects of reputation, researchers have largely relied on no-deception experimental designs wherein the granting of recognition for contributions to the collective is error-free and reputation is perfectly calculated. Reality is more complicated, with individuals often receiving insufficient recognition for their contributions, and we know little about how such insufficient recognition shapes the subsequent rate and scope of contributions to the collective. Leveraging a unique data feature from one of the largest online question-and-answer knowledge communities, we examine how occurrences of insufficient recognition shape subsequent contributions. Contrary to our expectations, we find that individuals contribute more after insufficient recognition and are less likely to venture away from their knowledge domain. Individuals with higher reputations display higher increases in contributions, but do not differ in their range of content. Results reveal a double-edged sword of insufficient recognition on cooperation—it increases future contributions, but narrows their conceptual scope relative to prior activity. We discuss the implications of these findings for maintaining diverse knowledge commons.
Optimization Approaches for Nonlinear State Space Models

The Local Model State Space Network (LMSSN) is a recently developed black box algorithm in nonlinear system identification. It has proven to be an appropriate tool on benchmark problems as well as for real-world processes. A severe shortcoming though is the long computation time that is necessary for model training. Therefore, a different optimization strategy, the adaptive moment estimation (ADAM) method with mini batches is used for the LMSSN and compared to the current Quasi-Newton (QN) optimization method. It is shown on a numerical Hammerstein example and on a well known Wiener-Hammerstein benchmark that the use of ADAM and mini batches does not limit the performance of the LMSSN algorithm and speeds up the nonlinear optimization per investigated split by more than 30 times. The price to be paid, however, is higher parameter variance (less interpretability) and more tedious hyperparameter tuning.
Smart Monitoring at the Surface of a Compact Blockchain Privacy Defence

Abstract As surveillance video tracking (VSS) is being more and more deployed, more and more people are struggling with concerns related to breaches of data protection. While monitoring for crime dissuasion is favourable for the majority of the population, citizens do not embrace intrusive private life oversight. However, there is currently no easy-to-use and secure video monitoring system privacy preservation solution. This difficult topic has been shed light on the new progress of blockchain (BC) technology besides their Internet of Things (IoT) implementations. In this article we suggest a Blockchain based Lightweight Confidentiality Framework for edge cameras surveillance. It helps the VSS to monitor the discretion of individuals caught in the videos without violating them. The Lib-Pri system converts the used VSS into a system, which acts as a amalgamated blockchain system that controls integrity, handles bubbly keys, shares functions and endorses video entry. The policy implementation of data protection controls on the edge of computers is done without intervening with the network for real-time video analytics.
On the p-th division polynomial

By using the relations between supersingular elliptic curves defined over a finite field of characteristic p>3 and the p-th division polynomial, we present a property about the reduction modulo a prime p>3 of the p-th division polynomial based on a heuristic argument and numerical evidence. We prove this property determining the p-th division polynomial of supersingular elliptic curves. As a consequence of this result, we present a criterion to discard supersingular elliptic curves.
Advertising activities of research libraries in the period of pandemic

The article examines the advertising activities of scientific libraries during the pandemic period, including the role and importance of advertising in the process of promoting library and information services and products. The author notes that advertising allows to reach a wider audience in a short time, to increase the efficiency and quality of the service process and to ensure the stability of the library position in the society. Advertising can create a stable information demand for definit library and information services and products. The article also touches questions of SMM (Social Media Marketing) usage in libraries. It is emphasized that SMM is a solution for marketing issues of the library through social networks. Its main purpose is to provide the profile audience with information about the library’s services and products in the form of feedback, communication with the user and exchange of their views, as well as to encourage them to use the library resources as a whole. The advantage of using SMM instruments is the possibility to attract more users. The author also studied the activities of one of the largest Azerbaijani libraries during the pandemic and came to the conclusion that in the current situation on the information market, the leader-ship of publishing and bookselling enterprises with advertizing services and products is obvious. In this connection such coordination of libraries with these enterprises can support and maintain the position of the library in the society.
Secure Event Ticket Booking Using Decentralized System

Over the past years, it has been noticed that there is a steady increase in the number of events being conducted across the globe, and the entry to these events is monitored through a system of tickets. One of the biggest problems faced by the event organizers regarding the current system of events is the duplication and reselling of the tickets at a cost higher than the original cost. It has become easier for the conmen to carry out this process as most of the tickets are purchased through various online platforms. Therefore, it is important to establish a reliable system to make sure that there won’t be any tampering of the ticket. The objective behind this chapter is to introduce a new system of ticketing wherein, the issues faced in the current system can be tackled with the help of blockchain technology. In our proposed model, an online platform is engineered where the digital tickets are linked to the purchasers with the help of their mobile numbers. All the events are stored in the form of the ledger based on handling variance and tracking of the system. Due to its characteristics of decentralization, transparency, integrity, and immutability, blockchain can be effectively used to eliminate the above problems and improve the proof of ownership, making the tickets tamper-proof. This system will introduce trust between the participants involved in this chain and allows the consumers to authenticate and verify the ownership of the tickets before purchasing them. However, the usage of blockchain technology brings some constraints to our model when it comes to a large population.
A Customized VR Rendering with Neural-Network Generated Frames for Reducing VR Dizziness

This research is to develop a NN system that generates smooth frames for VR experiences, in order to reduce the hardware requirement for VR therapy. VR is introduced as a therapy to many symptoms, such as Acrophobia, for a long, but not yet broadly used. One of the main reason is that a high and stable frame rate is a must for a comfortable VR experience. Low fresh rate in VR could cause some adverse reactions, such as disorientation or nausea. These adverse reactions are known as “VR dizziness”. We plan to develop a NN system to compress the frame data and automatically generate consecutive frames with extra information in real-time computation. Provided with the extra information, the goal of compressing NN is to conclude and abandon textures that are dispensable. Thereafter, it would compress left and right frames in VR into a data frame with all necessary information. Subsequently, we send it to clients, the client put the data frame into rebuilding GAN system and get three pairs of frames. After applying some CV methods such as filtering and alias, these frames are shown on users’ HMD. Furthermore, the compressed data can be transported easily, which allows medical facilities to build a VR computing center for multiple clients, making VR more broadly available for medical usages.
Exploring Perspective Switching in Immersive VR for Learning First Aid in Lower Secondary Education

There is an increased interest in how immersive learning can be used to support educational practices. Immersive virtual reality (IVR) with head-mounted displays (HMDs) is a technology that provides for immersive and smart learning environments where learners can interact and participate in an entirely virtual world. Based on literature and lessons learned from an ongoing research project, we identify potential benefits and challenges by incorporating perspective switching in an immersive VR application with. The VR application will be used to teach and stimulate learning within first aid in lower secondary education. The overall goal is to develop a user-centered VR application through Participatory Design (PD). The main contribution of this paper is a conceptual framework and guidelines for the process of developing IVR where users can switch between a first-person perspective and a third-person perspective.
Attribute grammars fly first-class... safer!

AspectAG is a domain specific language embedded in Haskell to represent modular attribute grammars. In AspectAG attribute grammar fragments can be defined independently (even in separate modules) and then combined in a safe way. This flexibility is achieved through the use of extensible records, which are implemented using type-level programming techniques.
Optimal Path Smoothing while Maintaining a Region of Safe Operation

Multi-vehicle control scenarios often require controlling vehicles to move together in coordinated formations. A virtual structure formation control approach is taken to generate collision-free trajectories. These trajectories are refined within constraints imposed by the structure to create collision-free, executable trajectories. Trajectory refinements are made by taking advantage of the differential flatness of wheeled vehicle trajectories. A decoupled, linear quadratic optimal control formulation is presented with constraints defined to respect the nonlinear dynamics of a wheeled vehicle. An example is provided to understand the computational trade-offs of the various proposed constraints.
Augmentation of SECaaS model with eCISO in cloud-based security services: A Comprehensive study

Abstract In today’s competitive world and the time of business discontinuity and falling economies due to many reasons, enterprises worldwide are trying to process business data securely and minimize IT systems’ operational cost with technologies in cloud computing. With the benefit of cost-efficiency in cloud services, there are some concerns regarding IT security, IT governance, and compliance, which plays a vital role in the business as usual. Security as a service model is a cloud-based security service model to provide secure environments, mediums, and systems to enterprises. In this paper, we are attempting to introduce a new service model to cloud-based security platform and perform a comprehensive study with augmenting security as a service (SECaaS) model with eCISO services for better governance of risks and decision making related to IT security to optimize the service model to help out CEOs and CIOs to ensure the security of their IT systems and data.
Intelligent Blockchain for Internet of Things Based Healthcare Management

Healthcare sector is transforming expeditiously to provide patient-centric treatments to recoup their health. Recently, COVID-19 has shaken the world with its spread and the world economy has seen the worst phase in life because of this pandemic. Monitoring a COVID-19 infected patient closely is not possible as significant distance should be maintained between the doctor and patient in order to avoid the spread of the virus. Internet of Things (IoT) has the potential to address this apprehension. This cutting edge technology has the potentialto play vital role in the pandemic management with its ability to remotely trace, track and monitor the virus infected patients. Artificial intelligence (AI)is capable of providing support in risk analysis, spread patterns and medical advancements. The advantages of integrating AI with IoT and methodologies used for pandemic management are prototyped in this paper. The applications of these technologies in different phases of COVID-19 pandemic management life cycle are discussed. In specific, emphasis is made on the effective utilization of technology to provide patient-oriented treatment for faster recovery of the patients. The use of technology in the pandemic management embellishes the frontline warriors and healthcare industries in combating with theCOVID-19 pandemic. The prototype also houses mechanisms required for surreptitious data transfer between different entities of the system.
Webcam-based categorization of task engagement of PC users at work

In this paper, we propose a support method for PC users to monitor their task engagement. Our approach is based on the number of keyboard strokes and pixel changes on the screen, and images from an ordinary webcam equipped with a PC. With our system, supervisors or teachers would improve their quality of guidance and instructions given at the right moment when workers or students require some support due to reasons such as slow progress and technical difficulties. In conventional methods, a special device such as an acceleration sensor for each individual is often required to acquire information on one’s working status and body movements, which is difficult to deploy in a real environment due to its cost for sensors. A face detection method based on Deep Neural Network, such as SSD, allows as to implement a cheaper system using an ordinary web camera. We calculate the average difference between two grabbed frames from the user’s screen to estimate the amount of screen changes between a given time interval. The number of key strokes typed by the user is another factor to estimate their task engagement. These factors are used to categorize the work mode of users. We use the K-Means method based on the Euclidean distance to cluster the recorded factors to determine thresholds for task categorization. We conducted experiments seven participants to evaluate the accuracy of our categorization method. Every participant is asked to categorize 15 scenes into four work modes. A scene includes a camera image with the PC user’s face, the screenshot path the moment, and the number of key strokes. The results from these participants are then compared with those of our system that categorized the same scenes with the thresholds on three factors. Approximately only 60% of these results matched each other, where we have enough room to improve our approach. Future work includes the selection of features that are far more effective for categorization, a better estimation of pixel changes on the PC screen, and evaluation experiments with more participants.
Berge–Fulkerson coloring for C(12)‐linked permutation graphs

Abstract It is conjectured by Berge and Fulkerson that every bridgeless cubic graph has six perfect matchings such that each edge is contained in exactly two of them . Let be a permutation graph with a 2‐factor . A circuit is ‐alternating if is a perfect matching of . A permutation graph with a 2‐factor is ‐linked if it contains an ‐alternating circuit of length at most 12. It is proved in this paper that every ‐ linked permutation graph is Berge–Fulkerson colorable . As an application, the conjecture is verified for some families of snarks constructed by Abreu et al., Brinkmann et al., and Hägglund et al.
Sampling estimating equation for logistic regression with multi-dimensional missing binary covariates: application to a divorce data

Abstract This paper focuses on analysing a divorce data collected in Sichuan Province, China. The responses of this divorce data are binary variables. Among 17 covariates, there are 12 binary variables with missing values. Thus, the vectors of covariates with missing values are multi-dimensional and have binary components. Existing approaches usually focus on one-dimensional binary covariates and therefore cannot deal with this divorce data. To figure out multi-dimensional binary covariates, a sampling estimating equation based on the logistic regression and sampling the missing binary covariates are proposed. Statistical inference based on this sampling estimating equation is presented. This paper has two main contributions. First, the proposed sampling estimating equation is able to obtain reasonable parameter estimators under multi-dimensional missing binary covariates setting, which cannot be handled by existing approaches. Second, for two-category problem, subjects with multi-dimensional missing binary covariates can be classified efficiently, which is intractable for existing approaches. Real data analysis of the divorce data reveals profound results for the divorce cases in Sichuan Province. The proposed sampling estimating equation performs well in terms of classification and prediction on this divorce data.
A New Data Collection Interface for Dynamic Sign Language Recognition with Leap Motion Sensor

This paper presents a new data collection tool for sign language recognition with Leap Motion Controller. The main motivation of this study is to develop assistive technology in order to support hearing-impaired people. This study is a part of RoboRehab project where we aim to develop an affectively aware robotic rehabilitation platform to assist hearing-impaired children in hospitals. The tool is cost efficient and has a simple and user-friendly interface design. The study involves a set of 12 dynamic two-handed signs (words) from Turkish Sign Language (TID).
Review of Improved Collaborative Filtering Recommendation Algorithms

In the Internet era with rapid data growth, it is difficult for users to quickly find the project information they need from the massive information on the Internet. As a technology based on data mining, the recommendation system effectively solves the problem of information overload. Collaborative filtering recommendation is a classic and effective recommendation algorithm. It focuses on the relationship between the user’s interest preferences and items. The purpose is to recommend items that meet the user’s interest preferences, such as product recommendation, audio recommendation, tourist attraction recommendation, etc. The field has a wide range of applications. Therefore, this article introduces the principle and related properties of collaborative filtering, as well as its improvement and application, and discusses future trends and directions for improvement.
S&amp;CNet: A lightweight network for fast and accurate depth completion

Dense depth completion is essential for autonomous driving and robotic navigation. Existing methods focused on attaining higher accuracy of the estimated depth, which comes at the price of increasing complexity and cannot be well applied in a real-time system. In this paper, a coarse-to-fine and lightweight network (S&CNet) is proposed for dense depth completion to reduce the computational complexity with negligible sacrifice on accuracy. A dual-stream attention module (S&C enhancer) is proposed according to a new finding of deep neural network-based depth completion, which can capture both the spatial-wise and channel-wise global-range information of extracted features efficiently. Then it is plugged between the encoder and decoder of the coarse estimation network so as to improve the performance. The experiments on KITTI dataset demonstrate that the proposed approach achieves competitive result with respect to state-of-the-art works but via an almost four times faster speed. The S&C enhancer can also be easily plugged into other existing works to boost their performances significantly with negligible additional computations.
Smart Contracts on Algorand

This Paper makes three main contributions. First, this Paper surveys Algorand Smart Contracts and the Algorand Network, including software systems and algorithmic architectures. Second, this Paper discusses various software mechanisms enabling developers to execute transfers on the Algorand Network. Third, this Paper advances Algorand Smart Contracts by introducing the Algogeneous Smart Contract. Algogeneous Smart Contracts are a new type of Algorand Smart Contract, which are simpler to develop and utilize artificial intelligence to ensure contracts are legally compliant and enforceable.
Supervised Learning Architecture-Based L&amp;T Using RSSI

As discussed in the previous chapters, being an interconnection of artificial neurons, the ANN is capable to mimic the behavior of biological neurons with the help of activation functions. The connections between neurons are through appropriate weights, which get automatically adjusted during the off-line ANN training step. This is called as supervised learning. In off-line training step, the training vector is necessary to train the ANN. This training vector includes a set of inputs and corresponding outputs. Once trained in off-line phase, the ANN becomes ready to estimate (predict) the system output for random input vector in the online phase. The GRNN is one of the important supervised learning architecture, which we discussed and applied to target L&T domain in the previous chapter. In this chapter, we are going to discuss the applications of other important supervised learning architectures such as feed-forward neural network (FFNT), radial basis function neural network (RBFNN), and multilayer perceptron (MLP). All of these architectures will be trained with RSSI measurements and the corresponding 2-D locations of the mobile target in off-line phase. Two cases are considered during experimentation. In Case I, the impact of various training functions on FFNT-based target L&T system is analyzed in the context of localization accuracy, whereas in Case II, the target localization performance comparison is made between traditional trilateration-, FFNT-, MLP-, GRNN-, and RBFN-based target L&T system. In order to accomplish fair comparison of all of the supervised learning architectures in both the cases, all of them are fed with four RSSI measurements, and they are supposed to estimate 2-D target location, corresponding to this input vector of four RSSI measurements. In both of the cases, locations of anchor nodes as well as target locations to be estimated are kept fixed.