Reviewerly/exHarmony huggingface dataset:
BYOD Security: Using Dual Process Theory to Adapt Effective Security Habits in BYOD

Bring Your Own Device (BYOD) culture has evolved rapidly in the past years. However, with the increased mobile device usage at home and work environments, security and privacy risks have also increased. Malware, social engineering and phishing threats have been adapted to the new technology in order to infiltrate and take control over these personal devices. With human error being the root cause of most successful BYOD attacks, this study looks at applying a psychological perspective to shed light on this issue by applying Dual Process Theory (DPT) and investigating which System of Thinking is more effective in making healthy cyber security decisions. A quantitative study using a within-subjects group method completed a two-paradigm phishing email sorting task. The results showed that system-1 and system-2 thinking did not have significantly different results. This indicates that new security measures focusing on both system-1 and system-2 thinking should be developed to improve BYOD security habits. Individuals should be given more training and/or spend more time analysing security threats in order to maintain effective BYOD cyber-security practices. This study hopes to encourage future research in order to help develop psychologically backed methods to improve BYOD security.
Spatial Blockchain: Smart Contract Using Multiple Camera Censuses

With many data breaches and spoofing attacks on our networks, it becomes imperative to provide a reliable method for verifying the integrity of the source. Blockchain location-based proof-of-origin is explored for tracking trucks and vehicles. Blockchain applications that support quick authentication with these non-mutable ledger properties: consensus and implemented as smart contracts at the edge. This Blockchain application will now be known as the POWTracker platform, gathering data from multiple cameras. POWTracker is based on an existing GPS-based blockchain ledger and runs on an edge device that uses AI consensus and multiple cameras. By using GPS algorithms, we present a novel mining algorithm that rewards POW miners, providing a trustworthy, verifiable proof-of-location system.
Analyzing Secure Protocol for Majority Based Pairing in Attribute Chameleon Hash Consensus

In this paper, we are proposing the security analysis of our protocol Majority based Authority Pairing in Attribute based Chameleon Hash Function or MAP-ABCHC. Our MAP-ABCHC is an enhancement of the trap door key used to create hash function in Chameleon Hash CHF. Encryption is one of the key technologies for safeguarding confidentiality and/or privacy. Attribute based encryption (ABE) has become one of the encryption mechanisms ensuring privacy and anonymity. MAP-ABCHC, ABE and CHF to generate the trap door key. We encrypt the trap-door key using ABE and Proxy Re-Encryption (PRE) with attribute and access policy. The advantages of our proposed MAP-ABCHC protocol are to share the trap door key with majority of nodes in the system and increase the security of current state of art in CHF by adding one NP-hard problem to the public key generation in pairing based cryptography. This adds additional layer of security for our Mobile Health system mHealth application. We describe details of our novel contribution and analyzing the security of MAP-ABCHC protocol.
Quality Coin for Software Quality Improvement

Modern Software has growing complexity. It puts higher quality requirements on continuous software development. Software quality is beyond pass rate and can be reflected by more perspectives in the whole development cycle. The engineering team needs to embrace the challenges and we believe the blockchain (BC) technologies can help the transformation. This paper is about citing, ultra-large-scale platform software, Intel Media Driver as an example, which demonstrates the experience of leveraging one novel blockchain-based software quality cryptocurrency system, QCoin to drive the mindset change for software quality beyond pass rate. It is expected that the experiences shared can help more practitioners to apply the similar methodology in their continuous software development.
A Qualitative Investigation of Optical Flow Algorithms for Video Denoising

A good optical flow estimation is crucial in many video analysis and restoration algorithms employed in application fields like media industry, industrial inspection and automotive. In this work, we investigate how well optical flow algorithms perform qualitatively when integrated into a state of the art video denoising algorithm. Both classic optical flow algorithms (e.g. TV-L1) as well as recent deep learning based algorithm (like RAFT or BMBC) will be taken into account. For the qualitative investigation, we will employ realistic content with challenging characteristic (noisy content, large motion etc.) instead of the standard images used in most publications.
On the Empirical Validation of a Zero Trust Security Framework via Group Support System Research

Zero Trust (ZT) is a strategic approach to Cybersecurity. What is lacking in ZT is a holistic approach that addresses other departments and associated processes such as risk management, compliance, and finance. This paper elaborates on how a collaborative process via Group Support System sessions was applied to validate the ZT Framework. Next, we describe the design and engineering of a ZT artefact (dashboard) that addresses the problems at hand, according to Design Science Research (DSR). The last part of this paper outlines the empirical validation with GSS through practitioner-oriented research to better implement ZT strategies. It elaborates on how this validation was conducted during the COVID pandemic in 2020 with 73 security practitioners. The final result is a widely supported and validated framework with a strategic and holistic approach to better understand the required capabilities to operationalize a ZT security strategy successfully.
SHE-Based Video Retargeting with Multiple Ground Control Points

In this paper, we propose a fast algorithm to improve the performance of content aware video retargeting by matching with multiple ground control points (GCPs) in the saliency histogram domain. It is noted that prominent objects can be well-maintained in the retargeted video via multiple GCPs instead of a single one.
Is Free-Viewpoint Video the Future of Online TV?

The future of digital entertainment may lie in the development of free-viewpoint video and television, allowing users to navigate multiple video streams of an event to select new perspectives. In this paper, we describe a prototype free-viewpoint video system capable of computing in real-time video panorama from five HD cameras. The stitching process is pipelined to distribute the computations between numerous processing nodes organized as a GPU cluster to create visually appealing panoramas at 30 Hz without noticeable blurring or boundary artifacts. We also present how the system can be scaled-up to deal with larger number of cameras and how to deliver free-viewpoint TV using commercial IPTV technology.
Integrating Blockchain Technology into Cybersecurity Education

The rise of Blockchain technology has been rapid and is becoming more popular now than ever before. Blockchain technology is mostly known for being the framework for cryptocurrencies such as Bitcoin, but it can be applied to many areas and industries other than cryptocurrencies. Blockchain technology has managed to provide security, transparency, immutability, decentralization which help in keeping valuable data in place. Understanding blockchain is an essential part of cybersecurity professionals. However, with the ever-increasing demand for cybersecurity students to learn blockchain technologies, there are few hands-on labs/modules available for training current IT students, the future cybersecurity professionals. The objective is to develop a series of novel hands-on labs that would fit individual students’ needs for blockchain in various real-life applications. The goal of this study is to educate current IT/cybersecurity students on the application of Blockchain in supply chain, digital evidence and non-fungible tokens (NFTs) through hands-on labs, which is helpful to integrate blockchain technologies into the current cybersecurity curriculum. These hands-on labs explore fundamental knowledge, such as the cryptographical computations and hashing algorithms behind blockchain. Meanwhile, students will be exposed to learning how to use the Ethereum platform, Remix, to create a smart contract and write codes in Solidity. In addition, the hands-on labs focus on asset and shipment tracking and decentralized storage for digital evidence through blockchain techniques.
e-Voting: I Changed My Mind, Now What?

Voting is the foundation of a democratic society. In most countries, citizens have to go physically to elections, i.e., to a voting booth to make their decisions with pen and paper. In crisis, like pandemics, war times and other catastrophes, this may be impossible. But there are other examples, like concurrent events on the election day, that create problems. For example, in Berlin, on 2021 German election day, the city marathon took place on the same day, which lead to traffic jams that hindered timely delivery of voting paper. A solution to these challenges is electronic voting (E-Voting), where people can vote digitally from home on a specific device (or even on their smartphones). Actually, IT security mechanisms, like blind signatures, anonymity networks, homomorphic encryption and zero Knowledge Proofs exist, to name just a few, which have already been combined to create secure E-Voting infrastructures. We want to enhance these solutions with an additional feature we call ballot-changing. The three main benefits of our architecture are, firstly, limiting Distributed Denial of Service attacks (DDoS), secondly, the possibility of changing one’s previous vote (ballot-changing), and thirdly, individual auditability, i.e., every voter can make sure that the election result is correct and that their vote has been counted.
Look at, Look into and Look Around: A New Approach to Visualizing Network Data and Increasing the Quality of Decisions

Business decision-makers are increasingly exposed to using, processing, and analyzing very large amounts of data and such exposure has resulted in data interpretation problems as data are generated at higher rates and with finer accuracy. One such data type, a network data set, is the subject of the present work. Network data sets present some challenges to data visualization, which are unknown to the visualization of tabular data sets. The visualization feature complements purely analytical tools for longstanding and new problems in network data analysis. This paper describes an innovative solution to seeing data, interpreting network data, and using network data for improved decision-making in business. The core of our work is the design, build and test of a mixed-reality environment capable of retrieving data from any given data set and displaying them in a navigable, distributed, virtual environment that will provide an immersive visually sensory experience of the data. The environment allows the interaction of several decision-makers and uses a mix of virtual and augmented reality (XR) technologies.
A Compact Spectral Model for Convolutional Neural Network

The convolutional neural network (CNN) has gained widespread adoption in computer vision (CV) applications in recent years. However, the high computational complexity of spatial (conventional) CNNs makes real-time deployment in CV applications difficult. Spectral representation (frequency domain) is one of the most effective ways to reduce the large computational workload in CNN models, and thus beneficial for any processing platform. By reducing the size of feature maps, a compact spectral CNN model is proposed and developed in this paper by utilizing just the lower frequency components of the feature maps. When compared to similar models in the spatial domain, the proposed compact spectral CNN model achieves at least 24.11 $$\times $$ and 4.96 $$\times $$ faster classification speed on AT &T face recognition and MNIST digit/fashion classification datasets, respectively.
Diagnosis of Hepatitis C Patients via Machine Learning Approach: XGBoost and Isolation Forest

Although the transmission of Hepatitis C through blood transfusion is getting less and less prevalent with the use of anti-HCV tests for blood donors, availability and practice of screening remain low in developing countries. This results in Hepatitis C patients who are unaware of their condition until it worsens to become chronic liver diseases that are diagnosed through more costly or invasive methods–liver biopsy and radiology scans. Due to these limitations of the current methods of diagnosis, this study seeks to develop a machine learning model to diagnose patients with different stages of liver disease: hepatitis c, liver fibrosis, and cirrhosis. In this research, machine learning algorithms were applied to a dataset containing HCV patient information, and the algorithms were evaluated for their accuracy and performance in classifying the patients with the proper diagnosis. Findings from the study indicated that XGBoost can most accurately classify patients with an accuracy score of 95.48, but other algorithms used had high accuracy scores as well: the algorithm with the lowest accuracy score–Decision Tree–still had a score of 92.66. The second experiment also showed that the Isolation Forest algorithm could detect and isolate the suspect blood donors of the data with a relatively high accuracy of 93.22%. As both experiments of the study yielded a machine learning model of high accuracy, the algorithms used can be implemented into a diagnostic kit for liver disease to be used in developing countries where accessibility to current diagnosis tools is limited.
Anomaly Detection For Time Series Data Based on Multi-granularity Neighbor Residual Network

Anomaly detection of time series data is one of the important problems in industrial applications. Most of methods are difficult to obtain rich features of samples due to the lack of dimension. In this paper, we develop an anomaly detection method for time series data based on multi-granularity neighbor residual network (MGNRN). First, we construct a neighbor input vector with a sliding time window for each data sample, and define neighbor-based input matrix by considering multi-granularity neighborhood features. Second, we compute the linear and non-linear neighbor features of multi-granularity time windows for the sample. Finally, by combining the linear neighborhood residual with nonlinear residual, we predict the abnormal probability of the sample. Experiments verify the multi-granularity neighbor residual network improves the accuracy of abnormal detection, and show good performance on precision and F1 metrics.
Learning joints relation graphs for video action recognition

Previous video action recognition mainly focuses on extracting spatial and temporal features from videos or capturing physical dependencies among joints. The relation between joints is often ignored. Modeling the relation between joints is important for action recognition. Aiming at learning discriminative relation between joints, this paper proposes a joint spatial-temporal reasoning (JSTR) framework to recognize action from videos. For the spatial representation, a joints spatial relation graph is built to capture position relations between joints. For the temporal representation, temporal information of body joints is modeled by the intra-joint temporal relation graph. The spatial reasoning feature and the temporal reasoning feature are fused to recognize action from videos. The effectiveness of our method is demonstrated in three real-world video action recognition datasets. The experiment results display good performance across all of these datasets.
ASSESSING GROUNDWATER VULNERABILITY TO AGRICULTURE CONTAMINATION IN A SEMI-ARID ENVIRONMENT USING SI AND GOD MODELS, CASE OF BABAR BASIN, ALGERIA

The aquifer of the plain of Babar, with a very large area of around 355,000 ha, is of a great economic importance because it is used for irrigation and domestic consumption. The aquifer zone is mainly occupied by agricultural areas characterized by an increasing use of chemical fertilizers, threatening the quality of groundwater. The study of the vulnerability to pollution of this water table was carried out by applying the GOD and SI methods in a GIS environment. The comparison of the vulnerability maps obtained shows that the recovery rates are very different, but they have a certain degree of similarity in space, both methods show a low vulnerability to agriculture pollution. In terms of vulnerability characterization, the GOD method provides more representative information.
Motorsuz araçları tespiti için CNN tabanlı GUI geliştirilmesi

Today, various solutions are offered for traffic density. One of these suggestions is to popularize the use of bicycles in the category of non-motorized vehicles. For this, first of all, bicycle paths must be built. The use of bicycle lanes or the rate of bicycle use in normal traffic is an important data. Deep learning techniques, which have been popular in recent years, can be used to obtain this data. The aim of this study is to present a model that detects bicycles using various convolutional neural networks architectures. First of all, 962 open source bicycle images obtained from the internet are labeled. For this, trainings were conducted with YOLOv3, YOLOF, Faster R-CNN and Sparse R-CNN architectures. As a result of the trainings, a value of 0.92 mAP was reached with Faster R-CNN. At the end of the study, a software that detects bicycles in real time has been developed.
An attention-based deep convolutional neural network for classification and grading of interferents in serum specimens

Accurate diagnosis depends on the provision of high-quality and efficient laboratory testing in which the serum test is one of the most important tools. However, for various reasons, interferents in serum specimens exist, which affect the accuracy of biochemical tests. Hemolysis, icterus, and lipemia (HIL) are the three most frequent interferents in serum samples. Determining their existence and degree of interference in serum samples before testing is essential to improve test quality. In this study, a deep learning model for classifying and grading HIL interferences is designed under the assumption that the serum color images contain information on the category and degree of interference. Because the major features of the classification and grading should be obtained from the liquid region of the serum, an auxiliary segmentation task was designed to assist the main classification task. The segmentation result provided the position information of the serum region for the main classification task as a spatial attention mechanism, which helped the network to focus on learning the significant features selectively with a channel attention mechanism. The method unified the two-stage task of 3-class classification and 5-degree grading of interferents into one model. The accuracy of the 15-class classification was 98.62%, and the degree grading accuracy of the three serum interferents (HIL) reached 98.74, 98.52, and 98.54%, respectively. This research provides a new approach to realize the automatic classification and grading of HIL interferents in serum specimens.
Development Process for Information Security Concepts in IIoT-Based Manufacturing

Abstract Digital technologies are increasingly utilized by manufacturers to make processes more transparent, efficient and networked. Novel utilization elicits the challenge of preventing deployed information technology from compromising processual security. The digital enabling of formerly analog operation technology, the extensive use of information technology connectivity like MQTT, TCP/IP, Wi-Fi, and the deployment of IoT edge computing platforms create an application scenario for the Industrial Internet of Things (IIoT), which also introduces the associated vulnerabilities, which have been extensively exploited in the past. This paper introduces a development process for information security concepts designed for production scenarios based on the IIoT. This concept is then applied using an illustrative use case from aircraft production. The main contents of the development process include: Formulation of reasonable assumptions, system modelling, threat analysis including risk assessment, recommendation of countermeasures, reassessment after incorporating countermeasures. Specifically, a Data Flow Diagram as the model is developed, and a “risk first” variation of the STRIDE methodology is applied to identify threats and prioritize them. The aforementioned state-of-the-art methodologies are adjusted to our cyber-physical use case in the IIoT. The resulting concept aims to enable manufacturing processes to be digitized as sought. The adjustments to the methodologies are independent from our use case and may be suitable to a broad field of scenarios in the IIoT.
When Research Topic Trend Prediction Meets Fact-Based Annotations

Abstract The unprecedented growth of publications in many research domains brings the great convenience for tracing and analyzing the evolution and development of research topics. Despite the significant contributions made by existing studies, they usually extract topics from the titles of papers, instead of obtaining topics from the authoritative sessions provided by venues (e.g., AAAI, NeurIPS, and SIGMOD). To make up for the shortcoming of existing work, we develop a novel framework namely RTTP ( R esearch T opic T rend P rediction). Specifically, the framework contains the following two components: (1) a topic alignment strategy called TAS is designed to obtain the detailed contents of research topics in each year, (2) an enhanced prediction network called EPN is designed to capture the research trend of known years for prediction. In addition, we construct two real-world datasets of specific research domains in computer science, i.e., database and data mining, computer architecture and parallel programming. The experimental results demonstrate that the problem is well solved and our solution outperforms the state-of-the-art methods.
Characterization of learning profiles, according to perception channels, in systems engineering students.

Abstract This article refers to characterize the predominant cognitive preferences or also called predominant perception channels of first semester students of Systems Engineering, virtual modality, associated to a subject in the area of Computer Science and Technology. The study was carried out with the purpose of providing teachers with a valuable informative tool that serves as a basis for proposing methodological innovations that contribute to face the difficulties of teaching concepts related to computer architecture, taking into account the differences, of individuals, associated to sensory perception, according to the CHAEA model, which has been validated by several authors. The research was descriptive, showing that the predominant profiles in the groups of students who participated in the research are those corresponding to the Theoretical, Pragmatic profile, the former being the predominant one; with a lower tendency for the Reflective style. It is concluded that there is a tendency of diversity towards the 2 channels of perception in which a higher level of preference was observed on the part of the students; therefore, it is suggested that the teaching strategies required for these students be varied according to the predominance found.