Reviewerly/exHarmony huggingface dataset:
Usability Study on Textual and Graphical Passwords

One of the most common methods used for authentication are passwords, in particular textual passwords. However, maintaining multiple textual passwords across various websites or information systems could be an overwhelming task especially when it comes to memorability. An alternative is Graphical passwords as pictures could be easier to recollect. Pictures on the other hand could be memory intensive. As a result, this research explores the use of graphical and textual passwords by integrating the strong points of both password scheme thereby providing stronger authentication platform that may be difficult for attackers to break. The use of a background image for memorability and security purpose was adopted in the implementation of the graphical scheme. A usability experiment was carried out with users from diverse background. The users were given the developed system that integrates the two password applications to use. A questionnaire was then administered to enable them share their user experience. Results from the evaluation of the system showed high level of acceptance of the use of a graphical password scheme from participants, even though it was a relatively new concept to them. In particular, 82% of the users preferred graphical passwords as a more secure scheme against the other 18% who preferred the use of a textual password. The textual password length and combination was simplified by avoiding excessive length since the graphical password augmented in terms of security. Overall, 77% of the participants were able to successfully carry out each task highlighted in the questionnaire.
A Pipeline for the Implementation of Immersive Experience in Cultural Heritage Sites in Sicily

Modern digital technologies allow potentially to explore Cultural Heritage sites in immersive virtual environments. This is surely an advantage for the users that can better experiment and understand a specific site, also before a real visit. This specific approach has gained increasing attention during the extreme conditions of the recent COVID-19 pandemic. In this work, we present the processes that lead to the implementation of an immersive app for different kinds of low and high-cost devices, which have been attained in the context of the 3dLab-Sicilia project. 3dLab-Sicilia’s main objective is to sponsor the creation, development, and validation of a sustainable infrastructure that interconnects three main Sicilian centres specialized in augmented and virtual reality. The project gives great importance to the cultural heritage, as well as to the tourism-related areas. Despite the presentation of the case study of the Santa Maria La Vetere church, the process of the final app implementation guided by the general pipeline here presented is general and can be applied to other cultural heritage sites.
Lightweight Digital Twin and Federated Learning With Distributed Incentive in Air-Ground 6G Networks

The sixth-generation (6G) wireless network is conceptualized to provide ubiquitous and reliable network access through effective inter-networking among space, air, and terrestrial networks, while posing considerable pressure on dynamic network orchestration. Digital twin (DT) provides an alternative approach to proactively make real-time resource allocation by mapping and learning the complex network topology. However, the dual challenges of limited energy capacity and insufficient computing power of unmanned aerial vehicles make it difficult to establish digital twin on aerial networks. In light of this, in this paper, we propose a lightweight DT empowered air-ground network architecture, where the DT modelling task is distributed to diverse ground devices based on federated learning. To improve the efficiency of DT modelling, we design a distributed incentive mechanism that incentivizes high-performance ground devices to take part in federated learning. Considering the computing burden and possible private disclosure caused by the incentive, we further solve the incentive scheme with a new distributed algorithm. The optimal solution, such as the local training rounds and required accuracy of terminal devices, could be calculated in parallel on ground devices. Simulation results show the effectiveness of the proposed lightweight DT scheme in terms of energy consumption and model accuracy.
Conversational Resilience: Quantifying and Predicting Conversational Outcomes Following Adverse Events

Online conversations, just like offline ones, are susceptible to influence by bad actors. These users have the capacity to derail neutral or even prosocial discussions through adverse behavior. Moderators and users alike would benefit from more resilient online conversations, i.e., those that can survive the influx of adverse behavior to which many conversations fall victim. In this paper, we examine the notion of conversational resilience: what makes a conversation more or less capable of withstanding an adverse interruption? Working with 11.5M comments from eight mainstream subreddits, we compiled more than 5.8M comment threads (i.e., conversations). Using 239K relevant conversations, we examine how well comment, user, and subreddit characteristics can predict conversational outcomes. More than half of all conversations proceed after the first adverse event. Six out of ten conversations that proceed result in future removals. Comments violating platform-wide norms and those written by authors with a history of norm violations lead to not only more norm violations, but also fewer prosocial outcomes. However, conversations in more populated subreddits and conversations where the first adverse event's author was initially a strong contributor are capable of minimizing future removals and promoting prosocial outcomes after an adverse event. By understanding factors that contribute to conversational resilience we shed light onto what types of behavior can be encouraged to promote prosocial outcomes even in the face of adversity.
The Effects of Acute Exercise and Virtual Reality Tasks on Children’s Memory Function and Exercise Preference

Background: It is well established that regular participation in exercise and virtual reality (VR) environments are important tools for improving or maintaining human health and cognitive function. Objectives: This study examined the effects of acute exercise and VR environments on children’s memory function and exercise preference (i.e. cycling with or without the use of VR technology). Methods: Α 2×3 factorial design was adopted with two measures (pre, post) and three study groups. Forty-five healthy children aged 9 to 13 years (Mage = 10.91±1.24 years) voluntarily participated in the study. Participants were randomly assigned into three groups receiving different treatment: (1) an exercise+VR group that cycled for 15 min in a VR environment (forest path) combined with basic mathematical problem-solving tasks, (2) an exercise group that cycled for 15 min, and (3) a no-exercise group that sat in a specific place in a lab for 15 min (the control group). Before and after the acute exercise, all the groups completed the Sternberg Memory Task. Upon completion of the intervention, the two exercise groups completed measures of enjoyment, intention and attitudes towards cycling. The exercise+VR group also completed a scale capturing the VR’s usability. Results: The results show that the exercise group scored higher on Sternberg’s Memory Task (p .05) after the acute exercise treatment (post) than before it (pre). Similarly, the exercise+VR participants scored higher on the post-intervention memory task than did the exercise and control groups (p .05). Moreover, the exercise+VR group reported higher scores on enjoyment, intention and attitude towards cycling than the exercise group, but this difference was not statistically significant (p .05). Finally, the exercise+VR participants recorded high scores on the usability of the VR system during the acute exercise programme. Conclusions: These findings are in line with those of previous studies, underlining the important roles of exercise and VR environments on youths’ cognitive function.
Proceduralist Readings, Procedurally

While generative approaches to game design offer great promise, systems can only reliably generate what they can “understand,” often limited to what can be handencoded by system authors. Proceduralist readings, a way of deriving meaning for games based on their underlying processes and interactions in conjunction with aesthetic and cultural cues, offer a novel, systematic approach to game understanding. We formalize proceduralist argumentation as a logic program that performs static reasoning over game specifications to derive higher-level meanings (e.g., deriving dynamics from mechanics), opening the door to broader and more culturally-situated game generation.
Emergency Management Investigation Using COPRAS Method

The purpose of this study, Joint Emergency Management Related intellectual Analysis of discussions and findings to do.Leadership, decision making, Inter governmental and corporate Relationships and technical applications in CEMM any such features have been explored.Their efforts are federal, state and important for local government in emergency management as a function for the current focus provided the foundation.The damage was unusual when widespread and severe only state and federal governments are great.Support can be expected.Therefore, local governments for the safety and security of the public Urgent to meet their responsibilities creating a management plan to be maintained.Community any natural or threatening and man-made danger Responsive emergency plans and local government to develop skills and with the private sector by working together Local Emergency Management Agency Supports this initiative.Local Responsibility for emergency management usually to the mayor or city manager at the reporting agency or Police, fire or public Existing as security Placed in the fields.The complexity of the problem And inherent uncertainty Considering the nature, This research was conducted by COPRAS An ambiguous multi-scale Accepts as a decision-making technique The risk of complex infrastructure Of the model for evaluation To illustrate performance and effectiveness Case analysis is implemented.To implement the goal, European Union economies, construction Departments, statistical economic data, Entropy system and Of the European construction sectors In terms of priority Valuable products are set.Also, emergency management of employees and their companies determine the needs, for them how to best serve and about what to do.It is important to engage in conversation.So, in this study, this designs for the domain whether the reviews are the best a research project to discover we outline.Of this paper the rest are organized as follows: Social networks and there in the emergency domain Current usage review is done.Literary following this from research and interviews Generated research questions.Next, the research method is described.In this paper we used COPRAS for ranking.The COPRAS method is the most ideal solution Short-distance and Alternative The solution with the longest distance from the solution Determines, but the comparison of these distances Does not consider importance.Reconstruction and staff comforting, workers and medical staff, Government unity of leadership, The involvement and support of army, Application of modern logistics technology, feedback of loss information, Financial ensuring measures, Evaluation on the efficiency Evaluation Parameters in Cause factors, Critical success factor (CSF), Well-planned emergency, Applicable emergency.Use Attention deficit from the result it is seen that feedback of loss information is got the first rank whereas is the Reconstruction and staff comforting is having the lowest rank.
Analisis Forensik Aplikasi Michat Menggunakan Metode Digital Forensics Research Workshop

The development of information technology has had various positive and negative effects. The positive impact that information technology can have is to make it easier for people to communicate with others in the world, and the negative impact is that society is out of control when using its applications. It is an attitude that causes various crimes in the world caused by cyberspace (cybercrime) One of the most widely used instant messaging applications is Michat. The MiChat app causes cybercrime, pornography, online gambling, fraud, cyberbullying and drug trafficking. Evidence research is conducted using one of the frameworks of the digital forensic research workshop. In this framework, forensic identification, preservation, collection, investigation, analysis, and presentation phases are designed to find evidence of digital crime. The forensic tools used to collect evidence are mobility forensic express pro, a DB browser for SQLite, and oxygen forensic detective. Results are presented in the form of reports and evidence, and text chat files, contacts, images, audio, video, and web caches are generated. Forensic tools have a 100% success rate in finding evidence. Comparing the functions of the three forensic tools, the mobility forensic express pro has a processing rate of 66.7%, DB browser for SQLite has 33.3%, and oxygen forensic detective has 83.3%. Digital evidence can be used as corroborating evidence in a trial
Improving Privacy Preservation Approach for Healthcare Data using Frequency Distribution of Delicate Information

In the modern world, everyone wishes that their personal information wouldn't be made public in any manner.In order to keep personal information hidden from prying eyes, privacy protection is essential.The data may be in the form of big data and minimization of risk and protection of sensitive data is important.In this research, a revolutionary customized privacy-preserving method is implemented that addresses the drawbacks of earlier personalized privacy as well as other anonymization methods.There are two main components that make up the proposed method's core.Delicate Information and Delicate Weight are two additional attributes which are used in the record table, are covered in the first section.The record holder's Delicate Information (DI) decides whether or not secrecy should be kept or if it should be shared.How delicate an attribute value is compared to the rest is indicated by its Delicate weight (DW).The second part covers a new representation used for anonymization termed the Frequency Distribution Block (FDB) and Quasi-Identifier Distribution Block (QIDB).According to experimental findings, the proposed system executes more quickly and with less data loss than current approaches.
DPGEN: Differentially Private Generative Energy-Guided Network for Natural Image Synthesis

Despite an increased demand for valuable data, the privacy concerns associated with sensitive datasets present a barrier to data sharing. One may use differentially private generative models to generate synthetic data. Unfortunately, generators are typically restricted to generating images of low-resolutions due to the limitation of noisy gradients. Here, we propose DPGEN, a network model designed to synthesize high-resolution natural images while satisfying differential privacy. In particular, we propose an energy-guided network trained on sanitized data to indicate the direction of the true data distribution via Langevin Markov chain Monte Carlo (MCMC) sampling method. In contrast to the state-of-the-art methods that can process only low-resolution images (e.g., MNIST and Fashion-MNIST), DPGEN can generate differentially private synthetic images with resolutions up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$128\times 128$</tex> with superior visual quality and data utility. Our code is available at https://github.com/chiamuyu/DPGEN
Is refactoring always a good egg?

Bug fixing and code refactoring are two distinct maintenance actions with different goals. While bug fixing is a corrective change that eliminates a defect from the program, refactoring targets improving the internal quality (i.e., maintainability) of a software system without changing its functionality. Best practices and common intuition suggest that these code actions should not be mixed in a single code change. Furthermore, as refactoring aims for improving quality without functional changes, we would expect that refactoring code changes will not be sources of bugs. Nonetheless, empirical studies show that none of the above hypotheses are necessarily true in practice. In this paper, we empirically investigate the interconnection between bug-related and refactoring code changes using the SmartSHARK dataset. Our goal is to explore how often bug fixes and refactorings co-occur in a single commit (tangled changes) and whether refactoring changes themselves might induce bugs into the system. We found that it is not uncommon to have tangled commits of bug fixes and refactorings; 21% of bug-fixing commits include at least one type of refactoring on average. What is even more shocking is that 54% of bug-inducing commits also contain code refactoring changes. For instance, 10% (652 occurrences) of the Change Variable Type refactorings in the dataset appear in bug-inducing commits that make up 7.9% of the total inducing commits.
Parameter Estimation in Sparse Linear-Gaussian State-Space Models via Reversible Jump Markov Chain Monte Carlo

State-space models are ubiquitous for modelling complex systems that evolve over time. In such models, key parameters are usually unknown and must be estimated. In particular, linear systems are parametrised by the transition matrix that encodes the dependencies among state dimensions. Due to physical and computational constraints, it is desirable to estimate this matrix by promoting sparsity in its components, in such a way that the interactions between elements of the state-space are reduced. In this work, we propose a novel Bayesian methodology to estimate model parameters and promote sparsity. The method, called SpaRJ, is based on reversible jump Markov chain Monte Carlo, and allows for the exploration of the space of sparse transition matrices in an efficient manner via the adaptation of the implicit model dimension. The methodology has strong theoretical guarantees and exhibits good performance in numerical examples.
Blockchain technology applications, concerns and recommendations for public sector

Blockchain technology is being hailed as the next significant phenomenon, with potential near-term uses that might profoundly impact society and the economy. While the previous studies emphasized cryptocurrency research, our article focused more on the blockchain-based applications applied in public sectors. This review was conducted by collecting articles from different libraries, and the search strategy was applied Nvivo software was utilized to code the literature and extract the themes used to build the body of this research. Finally, the study identified several applications and concerns regarding adopting and implementing blockchain technology within these sectors and the recommendations offered by scholars to manage this technology successfully.
Existing Vulnerability Information in Security Requirements Elicitation

In software engineering, the aspect of addressing security requirements is considered to be of paramount importance. In most cases, however, security requirements for a system are considered as non-functional requirements (NFRs) and are addressed at the very end of the software development life cycle. The increasing number of security incidents in software systems around the world has made researchers and developers rethink and consider this issue at an earlier stage. An important and essential step towards this process is the elicitation of relevant security requirements. In a recent work, Imtiaz et al. proposed a framework for creating a mapping between existing requirements and the vulnerabilities associated with them. The idea is that, this mapping can be used by developers to predict potential vulnerabilities associated with new functional requirements and capture security requirements to avoid these vulnerabilities. However, to what extent, such existing vulnerability information can be useful in security requirements elicitation is still an open question. In this paper, we design a human subject study to answer this question. We also present the results of a pilot study and discuss their implications. Preliminary results show that existing vulnerability information can be a useful resource in eliciting security requirements and lays ground work for a full scale study.
A Comparative Study and a New Industrial Platform for Decentralized Anomaly Detection Using Machine Learning Algorithms

The occurrence of anomalies and unexpected, process-related faults is a major problem for manufacturing systems, which has a significant impact on product quality.Early detection of anomalies is therefore of central importance in order to create sufficient room for maneuver to take countermeasures and ensure product quality.This paper investigates the performance of machine learning (ML) algorithms for anomaly detection in sensor data streams.For this purpose, the performance of six ML algorithms (K-means, DBSCAN, Isolation Forest, OCSVM, LSTM-Network, and DeepAnt) is evaluated based on defined performance metrics.These methods are benchmarked on publicly available datasets, own synthetic datasets, and novel industrial datasets.The latter include radar sensor datasets from a hot rolling mill.Research results show a high detection performance of K-means algorithm, DBSCAN algorithm and LSTM network for punctual, collective and contextual anomalies.A decentralized strategy for (real-time) anomaly detection using sensor data streams is proposed and an industrial (Cloud-Edge Computing) platform is developed and implemented for this purpose.
A Fast Real-time Face Gender Detector on CPU using Superficial Network with Attention Modules

A gender detector has become an essential part of digital signage to support the decision to provide relevant ads for each audience. Application installed in digital signage must be capable of running on low-cost or CPU devices to minimize system costs. This study proposed a fast face gender detector (Gender-CPU) that can sprint in real-time on CPU devices implemented on digital signage. The proposed architecture contains a superficial network with attention modules (SufiaNet). This architecture only consists of three convolution layers, making it super shallow and generating a small number of parameters. In order to redeem the lack of a super shallow network, the global attention module is assigned to improve the quality of the feature map resulting from the previous convolution layers. In the experiment, the training and validation process is conducted on the UTKFace, the Adience Benchmark, and the Labeled Faces in the Wild (LFW) datasets. The SufiaNet gains competitive accuracy compared to other common and light architectures on the three datasets. Moreover, the detector can run 84.97 frames per second on a CPU device, which is fast to run in real-time.