Reviewerly/exHarmony huggingface dataset:
Dynamic First Access Isolation Cache to Eliminate Reuse-Based Cache Side Channel Attacks

Cache side channel attacks have been used to extract users’ sensitive information such as cryptographic keys. In particular, the reuse-based cache side channel attacks exploit the shared code or data between the attacker and the victim, which can steal the secret with high speed and precision. It has threatened not only the host level but also the cloud level severely. Previous defensive measures are either not flexible enough, or cause a high performance or storage overhead. In this work, we present a dynamic first access isolation cache that eliminates reuse-based cache side channel attacks by providing fine grained first access isolation to overcome these problems. First of all, there are [Formula: see text] bits in each cache line to record the access information and prevent the first time cache hit state brought by the victim from being utilized by the attacker while keeping data shared. Second, we use hierarchy security levels and domains to achieve flexible one way isolation between different domains, and the domains can be a group of processes, a single process, or even a fraction of code. Finally, the monitoring-driven dynamic scheduling mechanism can change the level of a domain at run time, which improves the robustness of this new design. The solution works at all the cache levels and defends against attackers running both on local and cloud. Our implementation in the Zsim simulator demonstrates that the performance overhead for standard performance evaluation corporation 2017 is less than 0.1%, and 0.21% for the multi-thread benchmarks. It performs better than the original first time miss design because of the one way isolation in our design. The only hardware modification is the [Formula: see text] bits per cache line, and several security registers per hardware context, which only brings 3.71% storage overhead.
Automatic Jammer Signal Classification Using Deep Learning in the Spectrum of AI-Enabled CR-IoT

The emerging Internet of things (IoT) technology facilitates ubiquitous and seamless connectivity of various objects to provide different services. It is envisioned to incorporate self-awareness (SA) capabilities into the IoT devices to make the entire network autonomous and intelligent, giving the concept of cognitive radio (CR) CR-IoT network. Like other wireless networks, CR-IoT suffers from various kinds of abnormal attacks. However, due to the developments of deep learning models, it has become possible to efficiently recognize and classify malicious signals present in the signal transmission. In this work, we implemented deep learning models (AlexNet and GoogLeNet) to classify jammer signals present in a CR-IoT network using fast Fourier transform (FFT) and continuous wavelet transform (CWT) features extracted from the received orthogonal frequency division multiplexing (OFDM) signal spectrum. The CR-IoT network is considered in which users and a jammer are present. Both models are capable of classifying signals into the normal signal spectrum, jammer with high power, and jammer with low power. The performance of the proposed method is evaluated using receiver operating characteristic (ROC) curves.
Current and Future Trends In Mobile Device Forensics

The contemporary nature of Mobile Devices has brought about evolution process making computational algorithm and networking capabilities to keep up to pace with its constantly growing workload requirements in it usage. This has allowed devices such as smartphones, tablets, and Personal Digital Assistants (PDAs) to perform increasingly complex activities making it more efficient enough to replace the traditional options like desktop computers and notebooks. However, due to their portability and size, these devices are more prone to theft, to become compromised, or to be exploited for attacks and other malicious activity. The need for investigation of the aforementioned incidents resulted in the creation of the Mobile Forensics (MF) discipline. MF, a sub-domain of digital forensics, is specialized in extracting and processing evidence from mobile devices in such a way that attacking entities and actions are identified and traced. Apart from the MF being used for extraction and processing evidence from mobile devices, the MF has recently expanded its scope to encompass the organized and advanced evidence representation and analysis of future malicious entity behavior. The activity of mobile device in crime also falls under the classification of cybercrime as sophisticated devices such as smartphones can be used by hackers on the move. Nonetheless, data acquisition still remains its focus. While the field is under continuous research activity, new concepts such as the involvement of Cloud Computing in the MF ecosystem and the evolution of enterprise mobile solutions – particularly Mobile Device Management (MDM) and Bring Your Own Device (BYOD) – bring new opportunities and issues to the discipline. The current paper presents the research conducted with the MF ecosystem during the last 7 years (Konstantia Barmpatsalou, Tiago Cruz, Edmundo Monteiro, and Paulo Simoes. 2018. Current and Future Trends in Mobile Device Forensics), identifies the gaps, and highlights the impact or implication in different jurisdiction. There is a need to have well defined research programmes to constantly search into the behaviors and flaws of institution on cyber safety and challenges that behalf governance on decision making. Keywords: Mobile Forensics, Cybersecurity, Device Management, smartphones, Politics, Trust BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: (2022): Current and Future Trends In Mobile Device Forensics Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 215-220 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P35
Forensic Analysis on Streaming Multimedia

Since the advent of technology and digitalization of multimedia, there has been a massive increase in cybercrime. During streaming, with the availability of a network or internet source, multimedia; audio and visual can easily be accessed whiles being aired live. This technology dates as far back as 1990s. Similar to still videos and images, the user is able to download, pause, reverse or forward the show. The ability to stream multimedia has made it easier for users to partake or retrieve multimedia from the comfort of their homes, offices or personal spaces without necessarily being present. However, there are several challenges that affect the functionality of this technology, slow network connection and cybercrime. The issue of slow network may easily be handled by network providers, but cybercrimes has become rampant over the years. These attackers, also known as cyber criminals, use various activities to attack data. Some of their activities include phishing, data breach, identity theft and harassment. The paper has been written to assess forensic analysis of streaming multimedia. While exploring existing studies, it was realized that despite the rich availability of digital image forensics, video forensics hasn’t been explored much. This is because of the difficulty involved in analyzing the video data. Video data is always presented in a compressed form, unlike still images that are obtained in their original state. The compressed data often cancels or totally compromises the existing fingerprints, hence making it difficult to monitor or recover data. It was also revealed that, much has not been done so far as the research area is concerned. Keywords: Mobile Forensics, Cybersecurity, Streaming, Media, Video, Networks BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Emmanuel Tettey Okan (2022): Forensic Analysis On Streaming Multimedia Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 221-226 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P36
Smartphone RSS Traces For Forensic Analysis

This paper’s topic is on Smartphone RSS traces for Forensic analysis; RSS represents Really Simple Syndication whereas smartphone RSS traces refers to a small software program that collects and displays RSS feeds. It allows you to scan headlines from a number of news sources in a central location and has the ability to suggest information of interest to you based on the criteria of your search. RSS feed has been in existence for many years but has never been as widely used as it is today, the term syndication refers to publishing information on the Internet that can be used by other sites as well as by stand-alone reader applications. The introduction of blogs hyped content syndication before, the syndicated content, known as feeds, was typically found on news sites only. Once blogs began proliferating on the Internet, content started being shared left and right, and users began using readers and aggregators to track their favorite blogs. In addition, those who subscribe to podcasts are actually using technology built on RSS. Whether providing a feed for a blog, providing a feed of changes for some software you may be writing, or doing your own podcast, the applications for content syndication are almost endless, and its usage is growing at a phenomenal rate every day.” Robert Richards, January 2006- Article on “RSS feed and content Syndication”. Forensically, Smartphone RSS feed gives the forensic examiner a fair idea about the conception of the individual whose phone contains the analyzed feed. This is because the feed usually contains information of interest to the user which helps you think like the user in order to get the needed information you are looking for. This paper will also identify gaps with smartphone RSS feed users and suggest solutions/recommendations to mitigate them in order to be safe even after subscribing to certain RSS feeds. Keywords: RSS, Forensics, Syndication, Tracing, Cybersecurity, Feeds BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Roselyn Abena Terbie Hammond (2022): Smartphone RSS Traces For Forensic Analysis Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 227-232 www.isteams.net/ITlawbookchapter2022. dx.doi.org/
ELECTRONIC CRIMINAL PROCEEDINGS: INTERNATIONAL EXPERIENCE OF USING INFORMATION SYSTEMS FOR ALGORITHMIZATION OF CRIMINAL JUSTICE

This article is aimed at studying and comparative analysis of international experience of using software and innovative technologies in the criminal justice sphere in certain countries as well as determining of the key causes of the imbalance between the development tendencies and implementation of such systems in Ukraine; ways and methods of submitting and accessing the information and evidence in electronic criminal justice system.The significance of this article is evident from the necessity of the comparative analysis of a positive and effective experience of the acting systems of informatization of the criminal justice system of different countries in order to resolve the issue of reforming the criminal justice system of Ukraine by means of creating an information system of pre-trial investigationelectronic criminal justice system in Ukraine with the purpose of improving the modern concept of the electronic component of the criminal justice system in order to realize the fundamental principles of a democratic society such as access to justice as one of the key principles of ensuring legislative rights and freedoms of a person in a democratic state.This research paper contains the analysis of the experience of the innovative technology functions in the electronic systems of the official bodies of the criminal justice system in the context of reviewing the application of various information programs based on the model and methods of receiving and saving information; performance analysis of the program's strategic elements; comparison of the complex approach methods to implementing innovative processes of organization, application and control in the electronic criminal proceedings in different countries.Based on the research results, the main areas of focus have been established in order to implement the system of modern and effective electronic criminal proceedings in Ukraine.
Character 3-gram Mover's Distance: An Effective Method for Detecting  Near-duplicate Japanese-language Recipes

In user-generated recipe websites, users post their-original recipes. Some recipes, however, are very similar in major components such as the cooking instructions to other recipes. We refer to such recipes as "near-duplicate recipes". In this study, we propose a method that extends the "Word Mover's Distance", which calculates distances between texts based on word embedding, to character 3-gram embedding. Using a corpus of over 1.21 million recipes, we learned the word embedding and the character 3-gram embedding by using a Skip-Gram model with negative sampling and fastText to extract candidate pairs of near-duplicate recipes. We then annotated these candidates and evaluated the proposed method against a comparison method. Our results demonstrated that near-duplicate recipes that were not detected by the comparison method were successfully detected by the proposed method.
Electron-positron pair production in combined Sauter potential wells

Electron-positron pair production, in combined Sauter potential wells and an oscillating one is imposed on a static Sauter potential, is investigated by using the computational quantum field theory. We find that the gain number (the difference of pair number under combined potentials to the simple addition of pair number for each potential) of the created pairs depends strongly on the depth of static potential and the frequency of oscillating potential. In particular, it is more sensitive to the frequency compared with the depth. For the low-frequency multiphoton regime, the gaining is almost positive and exhibits interesting nonlinear characteristics on both depth and frequency. For the single-photon regime, however, the gaining is almost negative and decreases near linearly with depth while it exhibits an oscillation characteristic with frequency. Furthermore, the optimal frequency and depth of gain number are found and discussed.
Improving Distant Supervised Relation Extraction by Dynamic Neural  Network

Distant Supervised Relation Extraction (DSRE) is usually formulated as a problem of classifying a bag of sentences that contain two query entities, into the predefined relation classes. Most existing methods consider those relation classes as distinct semantic categories while ignoring their potential connection to query entities. In this paper, we propose to leverage this connection to improve the relation extraction accuracy. Our key ideas are twofold: (1) For sentences belonging to the same relation class, the expression style, i.e. words choice, can vary according to the query entities. To account for this style shift, the model should adjust its parameters in accordance with entity types. (2) Some relation classes are semantically similar, and the entity types appear in one relation may also appear in others. Therefore, it can be trained cross different relation classes and further enhance those classes with few samples, i.e., long-tail classes. To unify these two arguments, we developed a novel Dynamic Neural Network for Relation Extraction (DNNRE). The network adopts a novel dynamic parameter generator that dynamically generates the network parameters according to the query entity types and relation classes. By using this mechanism, the network can simultaneously handle the style shift problem and enhance the prediction accuracy for long-tail classes. Through our experimental study, we demonstrate the effectiveness of the proposed method and show that it can achieve superior performance over the state-of-the-art methods.
HUBUNGAN ADIKSI GAME ONLINE DENGAN KUALITAS TIDUR

This study aims to analyze the relationship between online game addiction and sleep quality in health science faculty students. The data collection technique is using total sampling where all members of the population who meet the inclusion and exclusion criteria can be used as samples in the study. This type of research is quantitative by using quantitative research design using correlation descriptive design, univariate analysis in this study using frequency distribution and bivariate analysis using Kendal tau. The results showed that the respondents whose gender in this study were 158 women (86.8%). Respondents who were male in this study were 24 (13.2%), the majority of online game addiction were in the moderate category as many as 163 respondents (89.6%), the majority of sleep quality was in the poor category as many as 123 (67.6 %), There is a significant relationship between online game addiction and sleep quality in students of the Faculty of Health Sciences, Musi Charitas Catholic University, for the p-value in the Kendall Tau statistical test, namely p = value = 0.000 (&lt;0.05) and for the r value (Correlation coefficient) of 0.344. , the strength of the relationship is weak with a positive direction of the relationship.
EXPERT RECOMMENDATION THROUGH TAG RELATIONSHIP IN COMMUNITY QUESTION ANSWERING

Community Question Answering (CQA) services are technical discussion forums websites on social media that serve as a platform for users to interact mainly via question and answer. However, users of this platform have posed dissatisfaction over the slow response and the preference for user domains due to the overwhelming information in CQA websites. Numerous past studies focusing on expert recommendation are solely based on the information available from websites where they rarely account for the preference of users’ domain knowledge. This condition prompts the need to identify experts for the questions posted on community-based websites. Thus, this study attempts to identify ranking experts’ derived from the tag relationship among users in the CQA websites to construct user profiles where their interests are realized in the form of tags. Experts are considered users who post high-quality answers and are often recommended by the system based on their previous posts and associated tags. These associations further describe tags that often co-occur in posts and the significant domains of user interest. The current study further explores this relationship by adopting the “Tag Relationship Expert Recommendation (TRER)” method where Questions Answer (QA) Space is utilized as a dataset to identify users with similar interests and subsequently rank experts based on the tag-tag relationship for user’s question. The results show that the TRER method outperforms existing baseline methods by effectively improving the performance of relevant domain experts in CQA, thereby facilitating the expert recommendation process in answering questions posted by technical and academic professionals.
A Survey Of Packet Analysis For Network Forensics

Network forensics is a branch of the network security paradigm (a collection of rules and configurations for protecting the integrity, confidentiality, and accessibility of computer networks and data using both software and hardware technologies) that focuses on network attack prevention and detection. It solves the present model's lack of specific investigation tools for probing harmful activities in networks. It also monitors the network for attacks and analyzes the attackers' characteristics. Packet analysis is the most common technique in network forensics, and it may replay the whole network traffic for a given period if the packet characteristics gathered are sufficiently detailed. The data collected can be utilized to track down traces of illegal internet activity, data breaches, unauthorized website access, malware infection, and so on across the network. This article provides a thorough packet analysis approach with extensive network traffic categorization and pattern detection capabilities, as well as a broad examination of the use of packet analysis in network forensics. Because not all network data can be used in court, the categories of digital evidence that may be acceptable are described in depth. The features of both hardware appliances and packet analyzer software are examined in light of their potential applications in network forensics. Keywords: Network Forensics, Computer &amp; Network Security, Digital Forensics, Local and Wide Area Network, Internet BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Tsatsu, K. Sabblah (2022): Analysis of Attack Intention Recognition Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 263-266 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P42
Mobile Device’s Digital Forensic Process Model

The primary goal is to compare various digital forensics process models, particularly mobile devices. One must conduct investigations forensically to prosecute digital offenders, with the resulting evidence acknowledged in a court of law. Digital forensic process models outline the necessary procedures that one must follow to ensure a successful enquiry. Keywords: Mobile Devices, Digital Devices, Digital Evidence and Smartphone BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free
Mobile/Telephone Contacts and Calls In Forensics Analysis

When tackling and preventing crime, the analysis of mobile or tele phone data, particularly contacts and calls among crime actors plays a significant role in both civil and criminal cases. Forensic analyst and investigators are able to reconstruct illegal and suspicious activities between specific individuals on the basis of the relationships connecting those individuals. This paper discusses mobile / tele phone contacts and calls in forensic analysis. By analysing the contacts and call log data among individuals related to a criminal activity, forensic analysts and investigators get a proper perspective about a particular criminal network. It is worth mentioning that not all mobile phones are smartphones. But they all have the same ability to store the names and phone numbers as well as call logs. When dealing with a digital device such as a mobile phone, the method used to extract data from it must have little impact on the memory for the evidence to be admissible in court. As such, the methods used to acquire data from a mobile phone will play an important part in an investigation which requires evidence to be extracted from a mobile phone. The investigator must be sure that the information acquired actually reflects that which is stored in the phone, and that the procedures used do not adversely affect the integrity of the information, or any other data stored in the device (McCarthy, 2005). Mobile or tele phone contacts and call data is important to law enforcement in tracking illegal activities and providing evidence for dismissals or acquittals. Keywords: Mobile, Telephone, Calls, Threats, Forensic Analysis, Cyber Security, BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Samuel Opoku Daniels (2022): Mobile/Telephone Contacts and Calls In Forensics Analysis Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 315-320 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P50