Reviewerly/exHarmony huggingface dataset:
Virtual Reality Self-help Treatment for Aviophobia: Protocol for a Randomized Controlled Trial

Background Aviophobia (the fear of flying) can greatly impact the daily life functioning of people with the condition. Traditional exposure-based treatment is hampered by the limited availability of airplane practice situations, which is a result of economical and practical concerns. Easily accessible and low-cost virtual reality exposure therapy may address these challenges. Objective The purpose of our study is to investigate the effectiveness of ZeroPhobia: Aviophobia (a self-help mobile app–based treatment) in reducing flight anxiety symptoms and depressive and anxiety symptoms. We will also investigate the effects of usage intensity, the sense of immersion, inherent absorption ability, and perceived user-friendliness on the treatment effect. Methods Participants (N=114) who are aged 18-64 years and experience at least mild symptoms of aviophobia will be recruited from the general Dutch population and randomized into a treatment group or waitlist control group. By using their own phones and rudimentary mobile virtual reality headsets, participants will receive six modules of psychoeducation and cognitive behavioral therapy, which will include six levels of virtual reality exposure therapy over a period of 6 weeks. Assessments will be conducted at baseline, posttest (ie, after 6 weeks), and 3- and 12-month follow-ups. The primary outcome measure of our study is the Flight Anxiety Situations Questionnaire. The secondary outcome measures include anxiety and depression measures and additional covariates (including usage intensity, the degree of immersion, etc). We will test treatment effectiveness by conducting an intention-to-treat analysis and estimating average treatment effects on the treated. The mechanisms of treatment effect will also be explored. Results The study was funded on September 25, 2018. Ethical approval was received on October 11, 2019. Recruitment closed on May 7, 2020. Conclusions Our study will further the scientific understanding and clinical implications of technology’s current ability to aid in providing effective, accessible treatment for the fear of flying. Trial Registration Netherlands Trial Registry NL70238.029.19; https://www.trialregister.nl/trial/8257. International Registered Report Identifier (IRRID) DERR1-10.2196/22008
Verifiable M+1st-Price Auction without Manager

A. M+1 st-price auction is an auction method in which all bidders submit their bids simultaneously, and the M highest bidders purchase M identical goods at the M+ 1st bidding price. Previous researches use trusted third party (TTP), trusted mix server, and honest managers. We propose an efficient and secure auction protocol that guarantees financial fairness, robustness and correctness without any manager or TTP and is secure under malicious model for the first time. We also proposed a new mechanism to verify bids. This new mechanism can be used in all auction protocols which is based on encrypted bits.
PDoT

Security and privacy of the Internet Domain Name System (DNS) have been longstanding concerns. Recently, there is a trend to protect DNS traffic using Transport Layer Security (TLS). However, at least two major issues remain: (1) How do clients authenticate DNS-over-TLS endpoints in a scalable and extensible manner? and (2) How can clients trust endpoints to behave as expected? In this article, we propose a novel Private DNS-over-TLS (PDoT) architecture. PDoT includes a DNS Recursive Resolver (RecRes) that operates within a Trusted Execution Environment. Using Remote Attestation , DNS clients can authenticate and receive strong assurance of trustworthiness of PDoT RecRes. We provide an open source proof-of-concept implementation of PDoT and experimentally demonstrate that its latency and throughput match that of the popular Unbound DNS-over-TLS resolver.
Partially Blind ECDSA Scheme and Its Application to Bitcoin

Blind signatures allow a user to obtain a signature without revealing message information to the signer. However, in many cases, the signer must record additional information relevant to the signature. Therefore, the concept of partially blind signature is introduced that enables the signer to obtain some information from the signed message. With the development of blockchain technology, users increasingly use Bitcoin for purchases and transactions with coin providers. Some studies have indicated that Elliptic Curve Digital Signature Algorithm (ECDSA)-based blind signatures are compatible with Bitcoin because they prevent the linking of sensitive information due to the untamability of Bitcoin. However, these approaches are not sufficiently flexible because blind signatures do not allow the signer to obtain any information. Here, we propose an ECDSA-based partially blind signature scheme. Security proofs are provided to demonstrate that the proposed scheme have satisfactory unforgeability and blindness. To the best of our knowledge, compared with other state-of-the-art schemes, our scheme is the first ECDSA-based partially blind signature scheme compatible with the current Bitcoin protocol.
A Robust Framework using Gamification to Increase Scientific Publication Productivity

This research focuses on the scientific publication productivity of higher learning institutions in Indonesia. The scientific publication productivity at Indonesia's higher learning institutions is still not ideal. Based on this issue, this study aims for a contribution in the form of a mechanism used as the main solution in optimizing scientific publication productivity. This mechanism is in the form of a framework based on gamification. This framework model consists of nine independent variables and 4 dependent variables. The independent variables used are networks, teamwork, competition, scores, leveling up, points, goals, inventory, and teammate. The dependent variable used is good sharing behavior, motivations, competence, and scientific publication enhancement. Researchers also make improvements to the Score variable, so that the calculation and ranking results become more accurate. Based on the preliminary test of the proposed framework, it shows that each construct of the framework has a positive impact on increasing motivations, except Leveling Up, while High Motivation had a positive impact of 0.807 on scientific publication enhancement. For reliability testing, done using AVE, CR, and CA shows all constructs have good reliability.
Privacy Protection of the Smart Grid System Based on Blockchain

Abstract With the development of new technologies for smart grid construction, the smart grid is gradually developing. Smart grid construction is an important field of next generation information technology, and its value and significance have been widely recognized. Smart grid construction not only provides development opportunities, but also brings great challenges. Security and privacy have become serious problems in the development of smart grid. Based on this, this paper discusses the system structure of the block chain, and puts forward the security diagnosis scheme of the smart grid according to the existing problems in the smart grid. Based on the theoretical basis of smart grid big data technology, this paper combines the grid data encryption process with the privacy protection process under the premise of protecting privacy and security, and develops the latest network security protocol. Then use block chain distributed storage to reduce the connection protection process in privacy, and use multi-level encryption. The experimental results show that the technology can improve the rate of sensitive data hiding and has good anti-cracking ability, which provides a theoretical reference for the construction of enterprise power grid.
Phishing Web Page Detection Methods: URL and HTML Features Detection

Phishing is a type of fraud on the Internet in the form of fake web pages that mimic the original web pages to trick users into sending sensitive information to phisher. The statistics presented by APWG and Phistank show that the number of phishing websites from 2015 to 2020 tends to increase continuously. To overcome this problem, several studies have been carried out including detecting phishing web pages using various features of web pages with various methods. Unfortunately, the use of several methods is not really effective because the design and evaluation are only too focused on the achievement of detection accuracy in research, but evaluation does not represent application in the real world. Whereas a security detection device should require effectiveness, good performance, and deployable. In this study the authors evaluated several methods and proposed rules-based applications that can detect phishing more efficiently.
Influence-aware graph neural networks

Network representation learning endeavors to learn low-dimensional dense representations for nodes in a network. With the rapid development of online social platforms, the analysis of social networks has become increasingly significant. Although network representation learning can facilitate the social network analysis, most existing algorithms merely exploit the explicit structure among nodes to obtain the node representations. Besides, traditional network representation learning techniques ignore the influence of nodes in a network when generating the representations of nodes. Motivated by this, we innovatively propose an influence-aware graph neural network (IAGNN) framework, which can learn the latent feature representations of nodes by incorporating both node influence and global structure information into the embedding process for encoding graph-structured data. The generated low-dimensional dense representations of the nodes in a network can be used for subsequent tasks such as user classification and user behavior prediction. Specifically, we assign different weights to each node according to different types of topology between their neighbors, and integrate with the basic influence of each node to generate an intermediate matrix with influence information. The intermediate matrix is encoded into low-dimensional and dense vector spaces by leveraging the attention mechanism and the graph convolution operation. Extensive experiments are conducted on five datasets, and IAGNN achieves an average accuracy of 3% higher than the comparison algorithms on the node classification and link prediction tasks. The experimental results demonstrate that our model can significantly outperform the state-of-the-art network embedding methods such as GCN, GAT, GraphSage, AGNN on node classification and link prediction tasks.
Resource Management for Energy-Efficient and Blockchain-Enabled Industrial IoT: A DRL Approach

Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks, 2) poor efficiency of consensus mechanism in blockchain, and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this paper, we integrate mobile edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of consensus process. Meanwhile, the weighted system cost including the energy consumption and the computation overhead are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes.
Embedded Radio Frequency Fingerprint Recognition Based on A Lightweight Network

The recent research on individual identification of Internet of Things (IoT) communication devices based on radio frequency feature have promoted autonomous deployments of wireless communication. But the identification of radio frequency signals is faced with the defect of high complexity, low degree of integration and high cost of calculation. We propose a lightweight model. Convolutional neural memory network (CNMN), which reduces the complexity of network model and accelerates calculation speed, has been installed on small computing devices to meet the identification needs of different scenarios. In the experiment, we will carry the universal software radio peripheral (USRP) on the low-power embedded platform Jetson Nano to form a miniaturized RF signal collection platform. The experimental results show that our RF identification model usesless computational costs. The proposed method can achieve better classification identification accuracy than traditional RF identification methods under the multi-classification target. The experiments of the signal collection are carried out in the real environment. Therefore, the networks we proposed are very reliable in real communication environment.
Full Stack Is Not What It Used to Be

The traditional definition of full stack development refers to a skill set that is required for writing software both for the frontend and backend of a web application or site. In recent years, the scope of full stack development has expanded significantly, though. Today, a full stack software developer is assumed to master various additional areas especially related to cloud infrastructure and deployment, message brokers and data analytics technologies. In addition, the emergence of Internet of Things (IoT) and the rapidly spreading use of AI/ML technologies are introducing additional skill set requirements. In this paper, we discuss the expectations for a modern full stack developer based on our industry observations, and argue that these expectations have significant implications for software and web engineering education.
Multilayered Defense-in-Depth Architecture for Cryptocurrency Wallet

A significant challenge in blockchain and cryptocurrencies is protecting private keys from potential hackers because nobody can rollback a transaction made with a stolen key once the blockchain network confirms the transaction. The technical solution to protect private keys is cryptocurrency wallets, a piece of software, hardware, or a combination of them to manage the keys. In this paper, we propose a multilayered architecture for cryptocurrency wallets based on a Defense-in-Depth strategy to protect private keys with a balance between convenience and security. The user protects the private keys in three restricted layers with different protection mechanisms. So, a single breach cannot threaten the entire fund, and it saves time for the user to respond. We implement a proof-of-concept of our proposed architecture on both a smart card hardware wallet and an Android smartphone wallet with no performance penalty. Furthermore, we analyze the security of our proposed architecture with two adversary models.
Hey ML, what can you do for me?

Machine learning (ML) algorithms are data-driven and given a goal task and a prior experience dataset relevant to the task, one can attempt to solve the task using ML seeking to achieve high accuracy. There is usually a big gap in the understanding between an ML experts and the dataset providers due to limited expertise in cross disciplines. Narrowing down a suitable set of problems to solve using ML is possibly the most ambiguous yet important agenda for data providers to consider before initiating collaborations with ML experts. We proposed an ML-fueled pipeline to identify potential problems (i.e., the tasks) so data providers can, with ease, explore potential problem areas to investigate with ML. The autonomous pipeline integrates information theory and graph-based unsupervised learning paradigms in order to generate a ranked retrieval of top-k problems for the given dataset for a successful ML based collaboration. We conducted experiments on diverse real-world and well-known datasets, and from a supervised learning standpoint, the proposed pipeline achieved 72% top-5 task retrieval accuracy on an average, which surpasses the retrieval performance for the same paradigm using the popular exploratory data analysis tools. Detailed experiment results with our source codes are available at: https://github.com/jpastorino/heyml.
Effects of postselected von Neumann measurement on nonclassicality of single-photon-added coherent state

The effects of von Neumann postselected measurement on nonclassicality of single-photon-added coherent state (SPACS) are studied. Explicit expressions and analytical results for various field properties of SPACS such as the photon number distribution, the Mandel Q_{m} factor and the squeezing parameter of field quadrature after postselected von Neumann measurement are investigated. The results showed that the nonclassicality of SPACS after measurement changed dramatically than initial state. The measurement let SPACS possess more strong sub-Poissonian photon statistics in some definite coupling strength regimes and large weak values which accompanied by low postselection probabilities.
The Design and Implementation of a Dashboard Web-Based Video Surveillance in OpenStack Swift

The purpose aims at deploying a fully functional OpenStack cluster with different settings and operational environments, followed by an in-depth analysis of its network performance and explanation of what happens behind the scene. Where OpenStack Virtual Machines perform these services for data storage files are stored in an authenticated cloud storage service center. The methodology used presents various scenarios using OpenStack Virtual machines and cloud-based to improve network performance video surveillance backend system architecture is developed with the Sensor web Enablement framework and cloud-based data storage. Data is obtained through simulators on camera/edge devices, file and metadata sharing, and fast cloud base storage. This study aims to compare the performance load resulting from large-scale video surveillance with small ones to show satisfactory performance, scalability, and good reliability from cloud-based storage systems for smart MCS. The most important feature is storage is adjusted dynamically, and there won't be any worry about space being inadequate or wasted. This paper presents a solution for deploying web applications for CCTV video surveillance, an object cloud storage service system based on the open-source cloud operating system OpenStack Swift. It's working in the OpenStack swift on IaaS and allocates resources for storage original data by multiple camera surveillance (MCS).
One-Shot Informed Robotic Visual Search in the Wild

We consider the task of underwater robot navigation for the purpose of collecting scientifically relevant video data for environmental monitoring. The majority of field robots that currently perform monitoring tasks in unstructured natural environments navigate via path-tracking a pre-specified sequence of waypoints. Although this navigation method is often necessary, it is limiting because the robot does not have a model of what the scientist deems to be relevant visual observations. Thus, the robot can neither visually search for particular types of objects, nor focus its attention on parts of the scene that might be more relevant than the pre-specified waypoints and viewpoints. In this paper we propose a method that enables informed visual navigation via a learned visual similarity operator that guides the robot's visual search towards parts of the scene that look like an exemplar image, which is given by the user as a high-level specification for data collection. We propose and evaluate a weakly supervised video representation learning method that outperforms ImageNet embeddings for similarity tasks in the underwater domain. We also demonstrate the deployment of this similarity operator during informed visual navigation in collaborative environmental monitoring scenarios, in large-scale field trials, where the robot and a human scientist collaboratively search for relevant visual content. Code: https://github.com/rvl-lab-utoronto/visual_search_in_the_wild.
Use of Auxiliary Classifier Generative Adversarial Network in Touchstroke Authentication

With the growing popularity of smartphones, continuous and implicit authentication of such devices via behavioral biometrics such as touch dynamics becomes an attractive option, especially when the physical biometrics are challenging to utilize, or their frequent and continuous usage annoys the user. However, touch dynamics is vulnerable to potential security attacks such as shoulder surfing, camera attack, and smudge attack. As a result, it is challenging to rule out genuine imposters while only relying on models that learn from real touchstrokes. In this paper, a touchstroke authentication model based on Auxiliary Classifier Generative Adversarial Network (AC-GAN) is presented. Given a small subset of a legitimate user's touchstrokes data during training, the presented AC-GAN model learns to generate a vast amount of synthetic touchstrokes that closely approximate the real touchstrokes, simulating imposter behavior, and then uses both generated and real touchstrokes in discriminating real user from the imposters. The presented network is trained on the Touchanalytics dataset and the discriminability is evaluated with popular performance metrics and loss functions. The evaluation results suggest that it is possible to achieve comparable authentication accuracies with Equal Error Rate ranging from 2% to 11% even when the generative model is challenged with a vast number of synthetic data that effectively simulates an imposter behavior. The use of AC-GAN also diversifies generated samples and stabilizes training.
IbisPaint X Apps in Creating Collaborative 3D Learning media of Pop-Up and Movable Books

Abstract The IbisPaint X Apps was an application with a concept of “share the fun of drawing” that had a function to draw many detailed designs. This study aimed to investigate how was the role of IbisPaint X Apps in supporting the process of creating collaborative 3D learning media of pop-up and movable book. This study was a library research. The procedures of study were used the theory of Mary W. George that implemented nine stages. The sources of data were taken from national and international journals, Indonesian and foreign textbooks. The results of study showed that IbisPaint X Apps could be used in creating collaborative 3D learning media of pop-up and movable books because it had complete features and could create good and detailed pictures. This collaboration created a 3D learning media that was more interesting, unique, and had two working mechanisms.