Reviewerly/exHarmony huggingface dataset:
An Improved Content Based Image Retrieval Technique by Exploiting Bi-layer Concept

Applications for retrieving similar images from a large collection of images have increased significantly in various fields with the rapid advancement of digital communication technologies and exponential evolution in the usage of the Internet. Content-based image retrieval (CBIR) is a technique to find similar images on the basis of extracting the visual features such as color, texture, and/or shape from the images themselves. During the retrieval process, features and descriptors of the query image are compared to those of the images in the database to rank each indexed image accordingly to its distance to the query image. This paper has developed a new CBIR technique which entails two layers, called bi-layers. In the first layer, all images in the database are compared to the query image based on the bag of features (BoF) technique, and hence, the M most similar images to the query image are retrieved. In the second layer, the M images obtained from the first layer are compared to the query image based on the color, texture, and shape features to retrieve the N number of the most similar images to the query image. The proposed technique has been evaluated using a well-known dataset of images called Corel-1K. The obtained results revealed the impact of exploring the idea of bi-layers in improving the precision rate in comparison to the current state-of-the-art techniques in which achieved precision rate of 82.27% and 76.13% for top-10 and top-20, respectively.
Structural Model of the Healthcare Information Security Behavior of Nurses Applying Protection Motivation Theory

Background: Healthcare information includes sensitive data and, as such, must be secure; however, the risk of healthcare information leakage is increasing. Nurses manage healthcare information in hospitals; however, previous studies have either been conducted on medical workers from various other occupations or have not synthesized various factors. The purpose of this study was to create and prove a model of nurses’ healthcare information security (HIS). The hypothetical model used in this study was constructed on the basis of the protection motivation theory (PMT) proposed by Rogers. Methods: A total of 252 questionnaires scored using a five-point Likert scale were analyzed, incorporating data from nurses who had been working for more than one month in general hospitals with more than 300 beds in South Korea. The survey was conducted over a total of 30 days, from 1 to 30 September 2019. Results: The results showed that coping appraisal significantly influence HIS intentions (estimate = −1.477, p &lt; 0.01), whereas HIS intentions significantly influence HIS behavior (estimate = 0.515, p &lt; 0.001). A moderating effect on the association between coping appraisal and HIS intentions was found in the group of nurses who had been working for &lt;5 years (estimate = −1.820, p &lt; 0.05). Moreover, a moderating effect on the association between HIS intentions and HIS behavior was found in the group of nurses who had been working for &lt;5 years (estimate = 0.600, p &lt; 0.001). Conclusion: The results of this study can be used to develop a management plan to strengthen nurses’ HIS behavior and can be used by nursing managers as a basis for developing education programs.
Fingerprint Adversarial Presentation Attack in the Physical Domain

With the advent of the deep learning era, Fingerprint-based Authentication Systems (FAS) equipped with Fingerprint Presentation Attack Detection (FPAD) modules managed to avoid attacks on the sensor through artificial replicas of fingerprints. Previous works highlighted the vulnerability of FPADs to digital adversarial attacks. However, in a realistic scenario, the attackers may not have the possibility to directly feed a digitally perturbed image to the deep learning based FPAD, since the channel between the sensor and the FPAD is usually protected. In this paper we thus investigate the threat level associated with adversarial attacks against FPADs in the physical domain. By materially realising fakes from the adversarial images we were able to insert them into the system directly from the “exposed” part, the sensor. To the best of our knowledge, this represents the first proof-of-concept of a fingerprint adversarial presentation attack. We evaluated how much liveness score changed by feeding the system with the attacks using digital and printed adversarial images. To measure what portion of this increase is due to the printing itself, we also re-printed the original spoof images, without injecting any perturbation. Experiments conducted on the LivDet 2015 dataset demonstrate that the printed adversarial images achieve \(\sim \)100% attack success rate against an FPAD if the attacker has the ability to make multiple attacks on the sensor (10) and a fairly good result (\(\sim \)28%) in a one-shot scenario. Despite this work must be considered as a proof-of-concept, it constitutes a promising pioneering attempt confirming that an adversarial presentation attack is feasible and dangerous.
Adaptive sensor fusion labeling framework for hand pose recognition in robot teleoperation

Purpose The purpose of this paper is to mainly center on the touchless interaction between humans and robots in the real world. The accuracy of hand pose identification and stable operation in a non-stationary environment is the main challenge, especially in multiple sensors conditions. To guarantee the human-machine interaction system’s performance with a high recognition rate and lower computational time, an adaptive sensor fusion labeling framework should be considered in surgery robot teleoperation. Design/methodology/approach In this paper, a hand pose estimation model is proposed consisting of automatic labeling and classified based on a deep convolutional neural networks (DCNN) structure. Subsequently, an adaptive sensor fusion methodology is proposed for hand pose estimation with two leap motions. The sensor fusion system is implemented to process depth data and electromyography signals capturing from Myo Armband and leap motion, respectively. The developed adaptive methodology can perform stable and continuous hand position estimation even when a single sensor is unable to detect a hand. Findings The proposed adaptive sensor fusion method is verified with various experiments in six degrees of freedom in space. The results showed that the clustering model acquires the highest clustering accuracy (96.31%) than other methods, which can be regarded as real gestures. Moreover, the DCNN classifier gets the highest performance (88.47% accuracy and lowest computational time) than other methods. Originality/value This study can provide theoretical and engineering guidance for hand pose recognition in surgery robot teleoperation and design a new deep learning model for accuracy enhancement.
The effectiveness of interactive virtual reality for furniture, fixture and equipment design communication: an empirical study

Purpose This study aims to propose a novel approach to developing an interactive and immersive virtual environment for design communication in the furniture, fixture and equipment (FFE) sector. The study further investigates its effectiveness in enhancing the design communication and coordination between the stakeholder. Design/methodology/approach Quasi-experimental research was adopted involving 12 FFE professionals, designers and end-users in single-group pre-test-post-test design. The tests were performed primarily to ascertain the impact of the application of interactive virtual reality on delivering furniture design selection and coordination tasks. Further interviews were used to elicit participants' views on the functionality and usefulness of the proposed approach. Findings The findings indicate that an interactive immersive virtual FFE environment: enhances the productivity of the design team through a collaborative virtual workspace offering a synchronised networked design testing and review platform; reduces the time required for the stakeholders to comprehend the design options and test those; enhances the design communication and quality of the design and encourages the collaborative culture in the industry; improves the design satisfaction of the stakeholders; and finally, requires significantly less time for design decision-making when compared to traditional methods. Research limitations/implications Future studies should incorporate space planning concepts and explore non-experimental methodologies in a real-life FFE project setup. Practical implications The proposed approach provides opportunities for enhanced interpretation of design intent in FFE as well as efficiency in design selection and coordination tasks when compared with conventional two-dimensional methods of communication. Originality/value This study proposes a step change in the way furniture design is communicated and coordinated through an immersive virtual experience. Previous studies have not addressed the issue of impact on design coordination instead focussed on marketing and sales.
Critical analysis of the use of virtual technologies in clinical psychology in Europe (based on the content of the journal "Cyberpsychology, Behavior, and Social Networking"

The work is aimed at examining the use of virtual reality (VR) technologies in clinical psychology in Europe. More than 37 issues of the journal "Cyberpsychology, Behavior, and Social Networking" with impact factor 2.3 were analyzed, and the articles reflect both cyber and clinical and psychological orientation. The "keyword method" was used to analyze the text, which is understood as a search for typical terms that designate a criterion or replace it. Then the keyword "virtual reality" was associated primarily with the types of disorders in clinical psychology of the classification of group F (according to ICD-10). The topics "virtual reality" and "clinical psychology" are the most frequently discussed topics in the field of the problem area "cyberpsychology". According to the study, the possible applications of virtual reality in the field of clinical psychology are essentially limited to three disorders: anxiety disorders, depression, and autism. According to publications of specialists in general psychology, the term "virtual reality" is not mentioned, which shows that this topic is of secondary importance.
The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map

A novel design method for time series modeling and prediction with fuzzy cognitive maps (FCM) is proposed in this paper. The developed model exploits the least square method to learn the weight matrix of FCM derived from the given historical data of time series. A fuzzy c-means clustering algorithm is used to construct the concepts of the FCM. Compared with the traditional FCM, the least square fuzzy cognitive map (LSFCM) is a direct solution procedure without iterative calculations. LSFCM model is a straightforward, robust and rapid learning method, owing to its reliable and efficient. In addition, the structure of the LSFCM can be further optimized with refinements the position of the concepts for the higher prediction precision, in which the evolutionary optimization algorithm is used to find the optimal concepts. Withal, we discussed in detail the number of concepts and the parameters of activation function on the impact of FCM models. The publicly available time series data sets with different statistical characteristics coming from different areas are applied to evaluate the proposed modeling approach. The obtained results clearly show the effectiveness of the approach.
Detection of Generalized Tonic Clonic Seizures and Falls in Unconstraint Environment Using Smartphone Accelerometer

The detection of Generalized Tonic Clonic Seizures (GTCS) and Falls is of utmost importance due to the increase in prevalence of epilepsy and Sudden Death in Epileptic Patients during CoVID-19 pandemic, and prevention of serious injuries in Fall risk groups such as elderly requiring continuous monitoring for disease management and assisted living etc. Monitoring of Activities of Daily Living (ADLs) can assist in the detection of symptoms and onset of neurological disorders such as Alzheimer’s, stroke, and epileptic seizures. With a host of embedded sensors, improved memory, enhanced processing capabilities and availability to masses, smartphones can be used for Human Activity Recognition (HAR) through continuous monitoring of ADLs. This paper presents a tri-axial accelerometer-based approach to detect and classify activities performed by individuals by applying machine learning algorithms including RF, J48, NB, LMT and SVM to movement data. Movement data is collected in real-time from the embedded accelerometer of a smartphone worn by individual on upper-left arm in unconstraint environment. It is pre-processed using time and frequency domain analysis and spatial domain features are computed. Supervised machine learning techniques are applied to classify ADLs into five classes based on the intensity of movements: Stationary, Light Ambulatory, Intense Ambulatory, GTCS and Falls. We also used training data from MyNeuroHealth dataset collected from 23 individuals including epilepsy patients. Based on gathered results, Random Forest outperforms other classifiers with classification accuracy of 99.6% for stationary, 81.5% for light ambulatory, 99.8% for intense ambulatory and GTCS, and 97.2% for Falls corresponding to training data of 14000 samples. To date, activity classification in our system has been implemented on cloud instead of mobile phone application as subjects are using smartphones with dissimilar software and hardware specifications for assisted living applications.
Comparative analysis between the mental health of responsible for people with ASD and for children without ASD in the COVID-19 pandemic

OBJECTIVES: To compare the quality of mental health of those responsible for people with autism spectrum disorder (ASD) and those responsible for people up to 12 years old without autism during the COVID-19 pandemic, with regard to symptoms of different levels of depression, anxiety, and stress and find associations between these levels with psychosocial and economic variables. METHODS: It was a cross-sectional, observational, and quantitative study, which analyzed an online form with the DASS-21 scale and with questions regarding the psychosocial and economic characterization of residents of the municipalities in the south of the state of Rio de Janeiro during the COVID-19 pandemic. The disclosure took place through social networks and the data obtained were tabulated using Microsoft Excel 2016 software. RESULTS: 77 people participated in the study, divided into 2 groups: group A, of those responsible for people with ASD, totaled 30 people and group B, of those responsible for children without ASD up to 12 years old, 47 people. From group A, 60% had symptoms of depression, 76.67% with symptoms of anxiety, and 80% with symptoms of stress. In group B, 44.68% had symptoms of depression, 46.81% with symptoms of anxiety, and 70.21% with symptoms of stress. CONCLUSIONS: The results of this research address the need for interventions to prevent these disorders and the psychological support of this population, with the inclusion of the family nucleus in the therapeutic planning of people with autism and assistance in the development of crisis coping strategies, as in the current scenario.
Input-to-State Representation in Linear Reservoirs Dynamics

Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is not trained (e.g., via gradient descent), making them appealing for analytical studies by a large community of researchers with backgrounds spanning from dynamical systems to neuroscience. However, even in the simple linear case, the working principle of these networks is not fully understood and their design is usually driven by heuristics. A novel analysis of the dynamics of such networks is proposed, which allows the investigator to express the state evolution using the controllability matrix. Such a matrix encodes salient characteristics of the network dynamics; in particular, its rank represents an input-independent measure of the memory capacity of the network. Using the proposed approach, it is possible to compare different reservoir architectures and explain why a cyclic topology achieves favorable results as verified by practitioners.
Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model

Developing an agent in reinforcement learning (RL) that is capable of performing complex control tasks directly from high-dimensional observation such as raw pixels is a challenge as efforts still need to be made towards improving sample efficiency and generalization of RL algorithm. This paper considers a learning framework for a Curiosity Contrastive Forward Dynamics Model (CCFDM) to achieve a more sample-efficient RL based directly on raw pixels. CCFDM incorporates a forward dynamics model (FDM) and performs contrastive learning to train its deep convolutional neural network-based image encoder (IE) to extract conducive spatial and temporal information to achieve a more sample efficiency for RL. In addition, during training, CCFDM provides intrinsic rewards, produced based on FDM prediction error, and encourages the curiosity of the RL agent to improve exploration. The diverge and less-repetitive observations provided by both our exploration strategy and data augmentation available in contrastive learning improve not only the sample efficiency but also the generalization . Performance of existing model-free RL methods such as Soft Actor-Critic built on top of CCFDM outperforms prior state-of-the-art pixel-based RL methods on the DeepMind Control Suite benchmark.
SerialDetector: Principled and Practical Exploration of Object Injection Vulnerabilities for the Web

The last decade has seen a proliferation of codereuse attacks in the context of web applications.These attacks stem from Object Injection Vulnerabilities (OIV) enabling attacker-controlled data to abuse legitimate code fragments within a web application's codebase to execute a code chain (gadget) that performs malicious computations, like remote code execution, on attacker's behalf.OIVs occur when untrusted data is used to instantiate an object of attacker-controlled type with attacker-chosen properties, thus triggering the execution of code available but not necessarily used by the application.In the web application domain, OIVs may arise during the process of deserialization of client-side data, e.g., HTTP requests, when reconstructing the object graph that is subsequently processed by the backend applications on the server side.This paper presents the first systematic approach for detecting and exploiting OIVs in .NET applications including the framework and libraries.Our key insight is: The root cause of OIVs is the untrusted information flow from an application's public entry points (e.g., HTTP request handlers) to sensitive methods that create objects of arbitrary types (e.g., reflection APIs) to invoke methods (e.g., native/virtual methods) that trigger the execution of a gadget.Drawing on this insight, we develop and implement SerialDetector, a taint-based dataflow analysis that discovers OIV patterns in .NET assemblies automatically.We then use these patterns to match publicly available gadgets and to automatically validate the feasibility of OIV attacks.We demonstrate the effectiveness of our approach by an indepth evaluation of a complex production software such as the Azure DevOps Server.We describe the key threat models and report on several remote code execution vulnerabilities found by SerialDetector, including three CVEs on Azure DevOps Server.We also perform an in-breadth security analysis of recent publicly available CVEs.Our results show that SerialDetector can detect OIVs effectively and efficiently.We release our tool publicly to support open science and encourage researchers and practitioners explore the topic further.
Discrete-Time Circadian Rhythms Neural Network for Perturbed Redundant Robot Manipulators Tracking Problem With Periodic Noises

Via the Euler forward-difference rule, an Euler-type discrete-time circadian rhythms neural network model (E-DTCRNN) is proposed, developed, and investigated for motion planning of the redundant robot manipulator affected by periodic noises. In this article, an Euler-type discrete-time zeroing neural network model (E-DTZNN) is presented as comparison. The E-DTCRNN model is 0-stable, consistent, and convergent. In addition, through a hybrid torque and velocity optimization scheme synthesized by the proposed E-DTCRNN and the traditional E-DTZNN, a tracking trajectory is designed and applied to the motion planning of the redundant robot manipulator. Finally, groups of simulations and physical experiments verify the efficacy and noise suppression ability of the proposed E-DTCRNN model for motion planning of the manipulator.
Ants-Review: A Privacy-Oriented Protocol for Incentivized Open Peer Reviews on Ethereum

Peer-review is a necessary and essential quality control step for scientific publications but lacks proper incentives. Indeed, the process, which is very costly in terms of time and intellectual investment, not only is not remunerated by the journals but is also not openly recognized by the academic community as a relevant scientific output for a researcher. Therefore, scientific dissemination is affected in timeliness, quality, and fairness. Here, to solve this issue, we propose a blockchain-based incentive system that rewards scientists for peer-reviewing other scientists' work and that builds up trust and reputation. We designed a privacy-oriented protocol of smart contracts called Ants-Review that allows authors to issue a bounty for open anonymous peer-reviews on Ethereum. If requirements are met, peer-reviews will be accepted and paid by the approver proportionally to their assessed quality. To promote ethical behavior and inclusiveness the system implements a gamified mechanism that allows the whole community to evaluate the peer-reviews and vote for the best ones.
Improving source code suggestion with code embedding and enhanced convolutional long short‐term memory

Source code suggestion is the utmost helpful feature in the integrated development environments that helps to quicken software development by suggesting the next possible source code tokens. The source code contains useful semantic information but is ignored or not utilised to its full potential by existing approaches. To improve the performance of source code suggestion, the authors propose a deep semantic net (DeepSN) that makes use of semantic information of the source code. First, DeepSN uses an enhanced hierarchical convolutional neural network combined with code-embedding to automatically extract the top-notch features of the source code and to learn useful semantic information. Next, the source code's long and short-term context dependencies are captured by using long short-term memory. We extensively evaluated the proposed approach with three baselines on ten real-world projects and the results are suggesting that the proposed approach surpasses state-of-the-art approaches. On average, DeepSN achieves 7.6% higher accuracy than the best baseline.