Reviewerly/exHarmony huggingface dataset:
Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.
Formulation of Standards for Assessing the Impact of Physical Education on Hue University Students

Assessing the influence of physical education on students is crucial for enhancing both the physical education training and its associated components. To ensure precise assessment outcomes, it's vital to establish a set of evaluation standards that align with the scientific rigor and the unique aspects of physical education. We've undertaken research using conventional sports science research techniques to establish a comprehensive set of standards for evaluating the effects of physical education on university students, encompassing three standards, fifteen criteria, and forty-two indicators.
Connected-C$$\textrm{F}^2$$: Learning the Explainability of Graph Neural Network on Counterfactual and Factual Reasoning via Connected Component

Structural data, such as social networks, molecules, citation networks, etc., exists everywhere in various fields. The complex topology makes it difficult to process and fully utilize such informative data. In recent years, Graph Neural Networks (GNNs) have achieved great success on learning representations for structural data. However, most of them are still considered as black boxes and the non-transparency of the models makes the explanation and interpretation of the predictions by GNNs non-trivial. This research seeks to solve the explainability problem of GNNs considering Counterfactual and Factual reasoning from casual inference theory on connected substructures of graphs, which are more human-intelligible and intuitive while ignored by most existing methods. In this paper, we propose an original method, Connected-C $$\mathbf {\textrm{F}^2}$$ , to explain GNNs by formulating an optimization problem based on the counterfactual and factual reasoning condition and the connectivity condition of explanations. Two kinds of explanation strengths are given for the condition on reasoning, and the connectivity condition is a constraint on the number of connected components in graphs. This distinguishes Connected-C $$\mathbf {\textrm{F}^2}$$ from previous explainability methods. Experiments show that Connected-C $$\mathbf {\textrm{F}^2}$$ generates significantly improved explanations than the existing state-of-the-art methods.
A Deep Fake Detection System Using Diffusion Model Based on Graph Based Image Segmentation

With the development of technology and the increase in the need to use the internet and the transmission of personal data and save it on the cloud and personal computers and with the increase in security risks represented by penetrations and cyber threats, and among these threats are those represented by the use of deep learning techniques to produce deep fake images that are mostly used to violate cyber security through threats such as the ransom threat or publishing fake news and other cyber risks, so there was an urgent need to establish systems to detect deep forgery in images or videos, in this paper a system was proposed to detect deep forgery based on the principle of using the diffusion model based on the graph based on image segmentation where the system transforms the image to graph and then segment it into three areas which are fake, real and background. And do purification of the adversarial content in these areas using diffusion technology, and then identify the fake areas in the image and use GCN in order to distinguish between real and fake images. In order to evaluate the proposed method, it was compared with the other methods on different data sets (CELEB-DF (V2) data set, Face Forensics++ data set, and wild deep fake). The result shows that the proposed system achieved better results than the approaches in the literature.
Introduction to Software Tool Set

This chapter presents tool sets for deep learning applications and primarily focuses to illustrate the instructions to configure the environment step by step with data, operating system, application, hardware, and other auxiliary services. The novelty of this section is describing the detailed practical configuration techniques for setting up of virtual environments with TensorFlow and PyTorch open-source tool in governance with IBM Watson and Keras Support. The book further presents the eye-opening techniques to install, configure, and run methods for machine learning coding editors such as Jupyter Notebook in various environments. More importantly, the practical training of tool engagement for deep learning is inevitable for students, professionals, domain experts, and data scientists which is highly recommended to gain the best learning experience.
Privacy amplification for wireless federated learning with Rényi differential privacy and subsampling

&lt;abstract&gt;&lt;p&gt;A key issue in current federated learning research is how to improve the performance of federated learning algorithms by reducing communication overhead and computing costs while ensuring data privacy. This paper proposed an efficient wireless transmission scheme termed the subsampling privacy-enabled RDP wireless transmission system (SS-RDP-WTS), which can reduce the communication and computing overhead in the process of learning but also enhance the privacy protection ability of federated learning. We proved our scheme's convergence and analyzed its privacy guarantee, as well as demoonstrated the performance of our scheme on the Modified National Institute of Standards and Technology database (MNIST) and Canadian Institute for Advanced Research, 10 classes datasets (CIFAR10).&lt;/p&gt;&lt;/abstract&gt;
Bargmann transform and statistical properties for nonlinear coherent states of the isotonic oscillator

Abstract We construct a new class of nonlinear coherent states for the isotonic oscillator by replacing the factorial of the coefficients <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <m:msup> <m:mrow> <m:mi>z</m:mi> </m:mrow> <m:mrow> <m:mi>n</m:mi> </m:mrow> </m:msup> <m:mo>/</m:mo> <m:msqrt> <m:mrow> <m:mi>n</m:mi> <m:mo>!</m:mo> </m:mrow> </m:msqrt> </m:math> ${z}^{n}/\sqrt{n!}$ of the canonical coherent states by the factorial <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <m:msubsup> <m:mrow> <m:mi>x</m:mi> </m:mrow> <m:mrow> <m:mi>n</m:mi> </m:mrow> <m:mrow> <m:mi>γ</m:mi> </m:mrow> </m:msubsup> <m:mo>!</m:mo> <m:mo>=</m:mo> <m:msubsup> <m:mrow> <m:mi>x</m:mi> </m:mrow> <m:mrow> <m:mn>1</m:mn> </m:mrow> <m:mrow> <m:mi>γ</m:mi> </m:mrow> </m:msubsup> <m:mo>.</m:mo> <m:msubsup> <m:mrow> <m:mi>x</m:mi> </m:mrow> <m:mrow> <m:mn>2</m:mn> </m:mrow> <m:mrow> <m:mi>γ</m:mi> </m:mrow> </m:msubsup> <m:mo>…</m:mo> <m:msubsup> <m:mrow> <m:mi>x</m:mi> </m:mrow> <m:mrow> <m:mi>n</m:mi> </m:mrow> <m:mrow> <m:mi>γ</m:mi> </m:mrow> </m:msubsup> </m:math> ${x}_{n}^{\gamma }!={x}_{1}^{\gamma }.{x}_{2}^{\gamma }\dots {x}_{n}^{\gamma }$ with <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <m:msubsup> <m:mrow> <m:mi>x</m:mi> </m:mrow> <m:mrow> <m:mn>0</m:mn> </m:mrow> <m:mrow> <m:mi>γ</m:mi> </m:mrow> </m:msubsup> <m:mo>=</m:mo> <m:mn>0</m:mn> </m:math> ${x}_{0}^{\gamma }=0$ , where <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <m:msubsup> <m:mrow> <m:mi>x</m:mi> </m:mrow> <m:mrow> <m:mi>n</m:mi> </m:mrow> <m:mrow> <m:mi>γ</m:mi> </m:mrow> </m:msubsup> </m:math> ${x}_{n}^{\gamma }$ is a sequence of positive numbers and γ is a positive real parameter. This also leads to the construction of a Bargmann-type integral transform which will allow us to find some integral transforms for orthogonal polynomials. The statistics of our coherent states will also be considered by the calculus of one called Mandel parameter. The squeezing phenomenon was also discussed.
The affordances of extreme speech

New media studies invested in online political conflict, radical and antagonistic subcultures have taken an interest in the affordances that shape memes, vernaculars and online political communication. One often overlooked affordance is the ensemble of social, communication, platform and legal frameworks stipulating what users can and cannot say, which I call “speech affordances.” To explore this concept, I look at the strategic communication of 4chan, Twitter and YouTube subcultures tied to a historical meme, “Kekistan,” often perceived as a key example of the ideological cacophony of the 2015–2017 online “culture wars.” I focus on how 4chan's policy of user anonymity, YouTube's unmoderated comment sections and Twitter's more proactive moderation practices brought some influencers to alter the original connotations of the meme into “overt” messages tolerable to Twitter and YouTube out-groups and platform moderation policies. Speech affordances bear methodological implications for historical studies of speech moderation and the overall mechanisms in which problematic language adapts to spaces with distinct speech norms.
A Qualitative Study on the Factors Influencing Students' Chinese Culture Aphasia in College English Teaching

This study aims to investigate the factors influencing students' Chinese Culture Aphasia (CCA) in China's college English teaching. Employing a qualitative research approach, this study gathers rich data through literature review and interviews. Then, the research identifies five key factors influencing students' CCA in college English teaching, including differences of language and culture, textbook deficiency, the role of teachers, course evaluation system, and English language education system. Regarding these influence factors, this study offers several recommendations to promote a more comprehensive delivery of Chinese culture in college English teaching, which encompass encouraging students to actively engage in learning Chinese culture, updating textbooks, strengthening teachers' culture training, improving course evaluation system to better integrate Chinese culture education, and perfecting China's English language education system.
Investigating the Impact of Blockchain Technology on Social Sustainability and the Mediating Role of Ethics and CSR

The main aim of this study was to investigate the impact of blockchain technology, business ethics, and corporate social responsibility (CSR) on social sustainability. Additionally, this study sought to explore how CSR and business ethics serve as mediators in shaping these impacts. This study collected data from employees in the banking sector in the United Arab Emirates (UAE), using a purposive sampling technique. A cross-sectional research design was employed, and a questionnaire was developed to gather responses from 416 participants. The usable response rate was 62.67%. This study utilized structural equation modeling (SEM) with SmartPLS as a tool to analyze the data. The results of this study indicate that blockchain technology has a positive influence on CSR, business ethics, and social sustainability. Additionally, CSR and business ethics have a positive effect on social sustainability. This study confirms the mediating role of business ethics and CSR. These findings can be useful for bank officials and academic decisionmakers in developing strategies.
Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding

Knowledge graph embedding (KGE) aims to map entities and relations of a knowledge graph (KG) into a low-dimensional and dense vector space via contrasting the positive and negative triples. In the training process of KGEs, negative sampling is essential to find high-quality negative triples since KGs only contain positive triples. Most existing negative sampling methods assume that non-existent triples with high scores are high-quality negative triples. However, negative triples sampled by these methods are likely to contain noise. Specifically, they ignore that non-existent triples with high scores might also be true facts due to the incompleteness of KGs, which are usually called false-negative triples. To alleviate the above issue, we propose an easily pluggable denoising mixup method called DeMix, which generates high-quality triples by refining sampled negative triples in a self-supervised manner. Given a sampled unlabeled triple, DeMix firstly classifies it into a marginal pseudo-negative triple or a negative triple based on the judgment of the KGE model itself. Secondly, it selects an appropriate mixup partner for the current triple to synthesize a partially positive or a harder negative triple. Experimental results on the knowledge graph completion task show that the proposed DeMix is superior to other negative sampling techniques, ensuring corresponding KGEs a faster convergence and better link prediction results.
Forensic analysis for multi-platform Cisco Webex

As contactless work has become more popular, the use of video conferencing and collaboration applications has increased. These applications provide versions for each platform in order to enable communications using various OS and devices. In order to provide a continuous workflow when switching between devices, data is stored on the cloud and then synchronized. Therefore, methods for extracting and analyzing data from various platforms and collecting data stored in the cloud must be preceded for digital forensic investigation. We present the data analysis results of Cisco's Webex, a popular video conferencing and collaboration application, in Windows, macOS, iOS, and Android environments. Webex uses the data protection API provided by each OS to encrypt user data. We propose a method to unprotect data protected by the data protection API as well as a method to decrypt encrypted Webex user data. The decrypted data contained most of the user's data, and we analyze it to propose a method to recover deleted messages. We also propose a method to acquire cloud data by utilizing the decrypted data to migrate credential data stored on a device. The proposed method decrypts encrypted data on any platform and allows login via credentials.
How is Your Knowledge Graph Used: Content-Centric Analysis of SPARQL Query Logs

Knowledge graphs (KGs) are used to integrate and persist information useful to organisations, communities, or the general public. It is essential to understand how KGs are used so as to evaluate the strengths and shortcomings of semantic web standards, data modelling choices formalised in ontologies, deployment settings of triple stores etc. One source of information on the usage of the KGs is the query logs, but making sense of hundreds of thousands of log entries is not trivial. Previous works that studied available logs from public SPARQL endpoints mainly focused on the general syntactic properties of the queries disregarding the semantics and their intent. We introduce a novel, content-centric, approach that we call query log summarisation, in which we group the queries that can be derived from some common pattern. The type of patterns considered in this work is query templates, i.e. common blueprints from which multiple queries can be generated by the replacement of parameters with constants. Moreover, we present an algorithm able to summarise a query log as a list of templates whose time and space complexity is linear with respect to the size of the input (number and dimension of queries). We experimented with the algorithm on the query logs of the Linked SPARQL Queries dataset showing promising results.
UPAYA GURU DALAM MENINGKATKAN MINAT BACA PADA PESERTA DIDIK DI SD NEGERI KUTA JAYA II

This study aims to describe the efforts of teachers in increasing students' interest in reading at SD Negeri Kuta Jaya II, Tangerang Regency-Banten. This research uses qualitative/descriptive methods. This study involved 5th grade homeroom teachers and students, as research participants. Data were obtained through interviews and observations. The results showed that the interest in reading at SDN Kuta Jaya II is still low, which is caused by internal and external factors. The efforts made by teachers to increase students' interest in reading are by conducting literacy activities 15 minutes before learning, providing a reading corner to support students' reading activities, and acting as a motivator and facilitator for students to increase their desire to read. If the low interest in reading of students is left unchecked, it will affect the sustainable learning outcomes of students. Therefore, increasing students' interest in reading in elementary schools is very important.&#x0D; Keywords: Reading interest, students, elementary school
Assessing the Generalization Capabilities of Neural Machine Translation Models for SPARQL Query Generation

Recent studies in the field of Neural Machine Translation for SPARQL query generation have shown rapidly rising performance. State-of-the-art models have reached almost perfect query generation for simple datasets. However, such progress raises the question of the ability of these models to generalize and deal with unseen question-query structures and entities. In this work, we propose copy-enhanced pre-trained models with question annotation and test the ability of several models to handle unknown question-query structures and URIs. To do so, we split two popular datasets based on unknown URIs and question-query structures. Our results show that the copy mechanism effectively allows non-pre-trained models to deal with unknown URIs, and that it also improves the results of some pre-trained models. Our results also show that, when exposed to unknown question-query structures on a simple dataset, pre-trained models significantly outperform non-pre-trained models, but both non-pre-trained and pre-trained models have a considerable drop in performance on a harder dataset. However, the copy mechanism significantly boosts the results of non-pre-trained models on all settings and of the BART pre-trained model, except for the template split on LC-QuAD 2.0 dataset.
Forensics Analysis of TOR Browser

The Onion Router is a web browser that uses the Tor network to anonymize web traffic by making it simple to conceal one’s identity on social media. It uses the onion routing technology to access the multiple-level encrypted, Internet-impossible private mode. These features are being abused to engage in a variety of criminal activities, including cyber terrorism and the black market. The TOR erases all browsing history and other network traces, making it impossible for investigators to gather evidence. This study extracts and examines any potential artifacts that the TOR browser may have produced in local system files and memory dumps.
Memory Forensics for Artefacts Recovery from Ether Transactions

Use of cryptocurrencies in crimes like money laundering, ransomware, narcotics trade and terror funding has been on increase. There is a disturbing trend in the use of the cryptocurrencies even in conventional crimes like cheating, scams and financial frauds. The existing research in digital forensics of cryptocurrencies is dominated by Bitcoin and very less work has been done on digital artefacts identification in Ether transactions. There has been use of Ethereum in many criminal activities hence the knowledge of Ethereum forensics is very important for law enforcement agencies. According to Bankless Times, Ethereum is now used more than Bitcoin for illegal activities. The proportion of illicit transactions on the overall flow of Ethereum has risen to 0.33% versus 0.04% for bitcoin. In market capitalization, Ethereum is the second largest cryptocurrency. This paper is an endeavour to locate the digital artefacts related with Ether transactions in volatile memory. Cryptocurrency wallet “Exodus” has been used as it does not ask for KYC and such wallets are preferred by criminals. In all, 12 important digital artefacts were found in the volatile memory. As use of different cryptocurrencies other than Bitcoin is on rise in criminal activities, such research of digital artefacts identification with a given cryptocurrency will prove helpful for law enforcement agencies.
Re-examining Laws Pertaining to Admissibility of Digital Evidence in Investigations

Multiple laws exist today in India, which deal with the admissibility of electronic evidence. While the Indian Evidence Act, 1872 is the primary law in India dealing with all aspects of evidence, there are a few other specific laws which have also attempted to deal with the issue of electronic evidence. The primary law dealing with electronic evidence, namely, Section 65B of the Indian Evidence Act, 1872 has been the subject of multiple judicial battles and has run its course. There is an urgent need to relook at laws pertaining to electronic evidence and frame uniform laws on this issue which are easy to understand, simple to follow, and, most importantly, relevant to the times. This paper attempts to examine the historic background behind electronic evidence legislations and proposes a few solutions to issues being faced by investigating agencies.
CERT-In New Directives for VPN: A Growing Focus on Mass Surveillance and Data Privacy

Digitalization efforts are rewarding as Information Technology is bringing changes in almost every sector. Virtual Private Network (VPN) was expected to be a safeguard for sensitive and personal information for individuals. The focus of India’s cybersecurity watchdog, Indian Computer Emergency Response Team (CERT-In), focuses on safeguarding or prevention with feasible effort. It is difficult to maintain data privacy without hampering user identity. CERT-In directives try to enhance cybersecurity by bridging the gap in cyberincidence analysis. VPN is ever growing with Bring Your Own Device (BYOD), Work From Home (WFH) in place. A VPN allows users to browse the Internet while masking their device’s IP address, encrypting data, and routing through secure networks in other states or countries with no logs. The new CERT-In directives emphasize obligatory data collection, retention, and integration for Virtual Private Server (VPS) providers, VPN services, and Cloud providers for a minimum of 5 years. There is an urgent need to increase the security of the country’s digital infrastructure in the best feasible ways, but some new directives may not be privacy-friendly hampering user identity and data protection framework. It has major market implications and an increase in operational costs. Thus, making an Un-CERT-In time for VPN providers in India. This directive does not only defeat the purpose of VPNs but is also possibly aimed at state-sponsored surveillance. We have proposed a few solutions to go through this new rule for the end users.