Reviewerly/exHarmony huggingface dataset:
Detecting Toxic Comments Using FastText, CNN, and LSTM Models

The use of social media has become a necessary daily activity. It provides a platform to share news, information, and social interaction. However, many people now take social media platforms for granted, using them to harass and threaten others, which results in cyberbullying. Toxic comments are online remarks that are insulting, abusive, or inappropriate, and frequently cause other users to quit a debate. People are unable to openly express their thoughts owing to cyberbullying and harassment. Identifying and classifying such remarks by hand is a time-consuming, inefficient, and unreliable operation. To solve this issue, this research article focuses on developing a deep learning system to analyze toxicity and produce efficient results in order to restrict its negative consequences, which will aid institutions to put the necessary measures into practice. Our proposed model uses Long Short-Term Memory (LSTM) along with FastText word embedding, resulting in a model with high performance. To make the social networking experience better, this model tries to improve the detection of different sorts of toxicity. Toxic, Severe Toxic, Obscene, Threat, Insult, and Identity-hate are the six categories that our methodology divides such comments into. Multi-Label Classification aids us in providing an automatic answer to the problem of poisonous remarks that were faced.
Code Mixed Information Retrieval for Gujarati Script News Articles

Large amount of digitized content is being created and made available online in many Indian languages. Searching from such content is difficult because of lack of regional language input tools and interface. Further documents may also contain commonly used English words written using Indian script which we refer as code mixed content. One of the easier methods of query input could be typing query’s words using Roman script mixing words of Indian language and English in single script which we refer as code mixed queries. For various Indian languages work is done on single language (code) transliterated search but code mixed information retrieval is very less explored area. Gujarati is an Indian regional language spoken by 55 million people in the world, but very less work is done for code mixed information retrieval and it lacks transliteration pair corpus. To search code mixed contents we need to identifying language of a word and we have to back transliterate it from Roman to Indian language script. Moreover certain words belong to both the languages. All these issues are major challenges in code mixed IR especially for Indian languages. A novel hybrid approach is proposed in this paper for Gujarati words transliteration which combines the use of Roman script Gujarati Soundex based match with alternative use of machine learning based improved CRF with post processing. A huge transliteration pair corpus of Gujarati script is created for words of both languages. Analysis of approaches for language identification and transliteration is presented in this paper along with compared retrieval result for different transliteration approaches and obtained an overall increase of 17% in MAP as compared to other approaches.
Selective Encryption for Owners’ Control in Digital Data Markets

A recent solution for permitting data owners to make their data selectively available on digital market platforms combines the adoption of selective owner-side encryption and smart contracts deployed on blockchains. Selective encryption, coupled with key derivation techniques to reduce key management burden, guarantees that only subjects who are authorized for a resource can read its content. Smart contracts permit to regulate the interactions among the interested subjects, the possible economic incentives to be paid to the owners as expected in markets, and the information needed to decrypt resource (i.e., the updates to the structure used for managing key derivation) upon payment. In this paper, we investigate different approaches for managing and updating the key derivation structure to enable selective data sharing on digital data markets, while limiting access times to resources and the cost of operations on the blockchain.
Trusted Implementation and Enforcement of Application Security Policies

Although system-level security policies are enforced directly in many modern operating systems, they do not provide adequate support for application-level security policies. Application software uses objects of higher abstraction requiring individual security policies with application-specific semantics. While frameworks assist in application policy implementation, developers are still responsible for their application’s security architecture which often leads to large and heterogeneous application trusted computing bases rendering protection from unauthorized manipulation hard to achieve. This work contributes to improving this situation. We present AppSPEAR – an application-level security policy enforcement architecture tailorable to application requirements. To foster streamlined and tool-supported security engineering workflows, we moreover present a policy specification language (DynaMo), a corresponding Rust source code generation approach, and a developer framework leveraging Rust and Intel SGX for trusted and memory-safe AppSPEAR implementation and policy integration.
An Efficient (MFFPA-2) Multiple Fuzzy Frequent Patterns Mining with Adjacency Matrix and Type-2 Member Function

Fuzzy technique is used to handle quantitative datasets and to generate meaningful representations of the dataset. The study proposes efficient mining of numerous fuzzy frequent itemsets from quantitative datasets using a type-2 membership function. The adjacency matrix and the Tid-fuzzy-list structure are built to mine (MFFI) several fuzzy frequent itemsets by accessing or reading the dataset only once. An effective pruning mechanism has been developed to condense the search space and minimize candidate itemsets, thus reducing execution time. Extensive experiments are conducted to verify efficiency regarding runtime, memory use, and join counts with different minsupport thresholds. Experimental results demonstrate that the designed approach MFFPA-2 achieved superior performance compared to cutting-edge techniques.
TinyPillarNet: Tiny Pillar-based Network for 3D Point Cloud Object Detection at Edge

Limited by huge computational cost, high inference latency and large memory consumption, existing 3D point cloud object detection methods are hard to be deployed on Internet of Things (IoT) edge devices. To handle this challenge, we present an extremely tiny framework termed TinyPillarNet. This framework leverages innovative pillar encoder to represent point cloud as immensely tiny pseudo-maps for extremely shrinking the input 3D sensing data. Moreover, a compact dual-stream feature extraction network is put forward to respectively extract intrinsic feature and distributional saliency map, which jointly boosts the detection precision with the lowest hardware cost. Extended experiments on KITTI benchmark demonstrated that our TinyPillarNet yields applicable precision with a record tiny weight size of 1.69MB at a high inference speed of 1.67 times faster than the current record. Furthermore, the specially designed prototype verification system achieves a superior energy efficiency, which outperforms the similar deep learning based point cloud processing solutions on FPGA with a big margin.
MDA-FLH: Multidimensional Data Aggregation Scheme with Fine-grained Linear Homomorphism for Smart Grid

Privacy-preserving multidimensional data aggregation aggregates the data of all different users into a single value, preventing the leakage of personal data while ensuring its availability. However, most current multidimensional data aggregation schemes only consider sum operations, i.e., the message of each dimension in the aggregation result is the sum of the corresponding dimensional messages of all individual message vectors. We propose a multidimensional data aggregation scheme with fine-grained linear homomorphism, called MDA-FLH. Firstly, we construct a fine-grained linear homomorphic encryption scheme which can assign different weights to each dimension of user’s data and maintain the linear homomorphic property in each dimension. We combine the Chinese remainder theorem and Paillier encryption to encode the multidimensional data with CRT and assign different weights to each dimension of user’s data in the Paillier ciphertext. Secondly, our scheme has the property of fault tolerance. In conjunction with extended Shamir’s threshold secret-sharing scheme, a security-enhanced and fault-tolerant data aggregation method has been designed so that it is resistant to internal attacks such as the control center (CC) access to individual private data if given the corresponding ciphertext. Finally, two practical schemes are designed based on MDA-FLH: fine-grained electricity price statistics scheme and multistep electricity price statistics scheme. Security analysis shows that our scheme can achieve privacy, confidentiality, integrity, and source authentication. Performance analysis shows that our scheme is efficient, especially that SMs are computationally economic, which makes our scheme more suitable for resource-constrained SM. Also, our scheme can provide linear homomorphism operations on each dimension, which further expands its applications.
Cloud Based Remote File Access from PC to Mobile Using File Directory

People can share their digital data or information with others through networks with the aid of file sharing techniques. Peer-to-peer, cloud storage, and removable storage are the three ways that file sharing can be accomplished, according to the examination of file sharing methods. The most popular option for remote file sharing today is using a cloud storage services. Therefore, by utilizing cloud storage services, a file access method is established, in which the user can access a copy of a file that is located on their PC device using a mobile phone with additional security services offered. It is possible because JSON is utilized to render the file structure in the mobile application and NodeJS is used to index the local machine’s available files. The user can choose the necessary file from the rendered file system using their mobile device, and the URL will download the file to their device. Security is provided in this file access method by a key issued by the NodeJS application, which uses the JSON Web Token (JWT) technique for confirmation of the user. Result of the developed work is compared with file sharing services provided by Google drive, Gmail and others, and the proposed work is performing better in terms of all metrics.
Enhanced Image Caption Generator Using Deep Learning

Image captioning has evolved into one of the most extensively used technologies in the modern period. Image captioning is the course of creating a description for an image. Recognizing the significant objects, their characteristics, and connections between objects in the image is required. It generates sentences that are both syntactically and semantically correct. In this project, we thoroughly examine an image caption creation technique based on deep neural networks. The approach may produce an English sentence explaining the content of an image when given an input of an image. CNN, RNN, and sentence generation are the three parts of the method that is examined. A numerous deep neural network-based models are described for creating image captions, concentrating on the various RNN approaches and examining their impact on phrase synthesis.
Improving Time Complexity and Utility of k-anonymous Microaggregation

For research in medicine, economics and social sciences specific data of individuals is needed. Thus it should be publicly available, but this should not offend the privacy of each individual. Microaggregation applied to databases is a standard technique to protect privacy. It clusters similar people in larger groups to achieve so called k-anonymity – every individual is hidden in a cluster of size at least k. Then the data can be made public for all kinds of analysis, whereas other concepts like differential privacy keep the database secret and allow only specific questions about the data to be asked by outsiders. The modification of a database to achieve anonymity should be as small as possible to keep its utility – that means the loss of information should be minimized. In this respect microaggregation typically performs much better than other anonymization techniques like generalization or suppression. However, minimizing the information loss by k-anonymous microaggregation is an NP-hard optimization problem for $$k \ge 3$$ . Not only computing optimal solutions efficiently is unlikely, nontrivial approximations are lacking, too. Therefore, a bunch of heuristics all with at least quadratic time complexity have been developed. This paper improves microaggregation significantly and provides a tradeoff between computational effort and utility. First, we make a detailed analysis and tuning of the maximum distance methodology – the common approach to generate a clustering that provides k-anonymity. We review the methods proposed so far and design a new algorithm $$\texttt{MDAV}^{*}_\gamma $$ that gives better utility on standard benchmarks. A different approach of quadratic time complexity based on Lloyd’s algorithm has been proposed and named ONA, but not completely analysed. This paper fills this gap and improves several steps resulting in a new algorithm $$\texttt{ONA}^{*}$$ with better utility. Mondrian is a another approach for clustering data that can be adopted for microaggregation. It is quite fast, but typically achieves very pure utility. We improve on this and design an almost linear time algorithm that gives acceptable utility, however worse than the quadratic time algorithms. Finally, we combine both techniques, ONA and Mondrian, to construct a new class of parameterized algorithms called $$\texttt{MONA}$$ . They are quite fast with time complexity between almost linear and quadratic, and deliver competitive utility compared to the MDAV approach.
Chunk Grouping Method to Estimate Available Bandwidth for Adaptive Bitrate Live Streaming

The Common Media Application Format (CMAF) is a standard for adaptive bitrate live streaming. The CMAF adapts chunk encoding and enables low-latency live streaming. However, conventional bandwidth estimation for adaptive bitrate streaming underestimates bandwidth because download time is affected not only by network bandwidth but also by the idle times between chunks in the same segment. Inaccurate bandwidth estimation decreases the quality of experience of the streaming client. In this paper, we propose a chunk-grouping method to estimate the available bandwidth for adaptive bitrate live streaming. In the proposed method, by delaying HTTP request transmission and bandwidth estimation using grouped chunks, the client estimates the available bandwidth accurately due to there being no idle times in the grouped chunks. In addition, we extend the proposed method to dynamically change the number of grouping chunks according to buffer length during downloading of the previous segment. We evaluate the proposed methods under various network conditions in order to confirm the effectiveness of the proposed methods.
New Drug Rehabilitation Using Virtual Reality : The Future Perspectives of Addiction Interventions

본 연구의 주요 목적은 가상현실(VR)을 사용한 물질사용장애(Substance Use Disorders, SUD)로 인한 심리적·생리적·사회환경적인 갈망(psychological·physiological·socioenviron mental craving)을 치료하는 새로운 마약재활(drug rehabilitation)과 중독개입(addiction intervention)에 관련한 문헌을 중심으로 소개하고 리뷰하는 데 있다. 물질 관련 장애 (substance-related disorders)는 해로운 결과(harmful consequences)에도 불구하고 지속해 서 소비하는 것이 특징인 다면적이고 복잡한 정신질환(multifaceted·complex psychiatric disorders)이다. 중독(addiction)은 학습(learning), 보상(reward), 동기(motivation), 기억 (memory), 인지억제조절(cognitive inhibitory control) 등과 관련된 다양한 뇌 네트워크에 영향 을 미친다. 현재 미국중독센터(American Addiction Centers, AAC)에서는 가상현실(VR)을 사용 하여 중독(addiction)과 공존정신건강장애(co-occurring mental health disorders)를 치료한 다. 즉, 미국중독센터(AAC)는 가상현실지원치료(VR-assisted therapy)로서 외상 후 스트레스 장애(PTSD), 우울(depression), 불안(anxiety) 등과 같은 공존정신건강장애와 중독으로 어려움 을 겪고 있는 환자들을 위해 증거 기반 긍정기술(evidence-based positive techniques)인 가상 현실치료(Virtual Reality Therapy)를 진행하고 있다. 가상현실치료(Virtual Reality Therapy) 의 주요한 이점은 뇌가 정보를 처리하는 방식에 영향을 줌으로써 작동한다. 중독은 뇌의 화학적 성질을 변화시켜, 중독에 도움이 되는 방식으로 행동하도록 뇌를 점진적으로 재배선(rewiring)한 다. 따라서 본 연구에서는 물질사용장애(SUD)의 발달 및 유지와 관련된 조절장애가 있는 신경인 지과정(dysregulated neurocognitive processes)을 목표로 하는 치료전략과 더불어 물질 관련 장애에 대한 효과적인 치료법의 가능성을 제시했다. 끝으로 물질사용장애(SUD)의 치료와 개입에 대한 최근의 연구개발과 새로운 관점을 제기했다.
Software Development Life Cycle (SDLC) in Software Engineering – A Brief Review

The software development life cycle process is a crucial methodology that software developers use to create high-quality software products. In the requirements-gathering phase, the software development team works with stakeholders to define the project's objectives, scope, and requirements. The design phase involves creating the software architecture, which outlines the software's structure, components, and interactions. In the testing phase, the software is tested to ensure that it meets the requirements and functions correctly. The SDLC process plays a critical role in creating software that meets user requirements, is of high quality, and can be maintained and updated over time. By following a systematic approach, software development teams can ensure that the software they create is reliable, secure, and efficient. Overall, the SDLC process is a comprehensive methodology that software developers use to create high-quality software products. Software Development Life Cycle plays a major role in the life of a software engineer. Design thinking can be used to make the software development life cycle more powerful. This paper involves a discussion on how software development life cycle can be made powerful and used to satisfy the client's requirements.
Fast selective encryption algorithms based on moments and chaos theory

In this work, we introduce a novel selective encryption scheme based on chaos theory and momentstransforms, two moments families were considered namely Tchebichef and Hahn. The goal is to propose afast and secure encryption scheme that can be deployed in real world scenarios. The proposed algorithmsoperate in the transform domains of Tchebichef and Hahn moments. We encrypt only the most significantcoefficients of the moments transforms. First, we down sample the computed moments’ matrices coefficients,then we use two logistic maps for confusion and diffusion of the down-sampled Tchebichef’s and Hahn’scoefficients, the resulting matrix is the encrypted image. This approach improves drastically the timeperformance of the encryption algorithm while keeping a “good” security level. In order to prove theperformance of our algorithms, we run different experiments and test the algorithms on different criteria:MSE, correlation coefficient, differential analysis, entropy and time performance. The presented results provethat the encryption algorithms proposed are secure and outperform state-of-the-art algorithms.
The design of team formation system - a coordination with the perspectives of Delone and Mclean IS success model

Instructors must constantly think of more innovative teaching and learning techniques to ensure that the graduates they produce are in line with the changing needs of the industry. One such technique is project- based learning; the learning technique in which students are divided into smaller teams. From previous researches, team formation using personality traits was seen as more appropriate to form an ideal and balanced team. To do so, students must answer questions that will be analysed and based on the analysis, their personality traits are identified and incorporated into teams of various deal personality traits. Problem occurs as this process is seen as a daunting task for the instructors.
Passion and Quest for Quality in Education

The paper presents the passion and quest for quality in education. The purpose of this paper is to explore quality benchmark in education. It defines quality and demonstrates different dimensions of quality in education. It also presents why quality education is important to the learners. It is a theoretical article. The available relevant literature has been reviewed, analyzed and linked with life, learning, social justice and quality education benchmark. Therefore, quality education is the evaluation of educational level. It definitely comes from the quality of person who are educated. It also implies to the developing of basic qualities on child and providing training in a relevant field. Hence, it is said that, quality education refers to the relevancy of knowledge; it is the character of the set of elements in the input, process and output of education system that provides services satisfy in both internal and external strategic consistencies in quality education by meeting their explicit and implicit expectations. Stakeholders of education such as teachers, students, parents, curriculum developers, syllabus designers and textbook writers are responsible to strive, struggle, survive and thrive with quality in education.
Modern symmetric searchable encryption technique for string classification in cloud technology

In this paper, we give an effective and easy-to-use symmetric Searchable symmetric encryption (SSE) for string search. Unlike previous plans, we use hash affixing instead of a chain of encryption activities for record age, which makes it suitable for lightweight applications. Here is the first time that probabilistic trapdoors for string search in SSE are suggested. We give solid proof that our plan isn't secure against a real but curious server, depending on the definitions. Here, we also show another idea for search design protection, which protects some of the spillage from the trapdoor. I've shown that our plan is safe under the inquiry design in the definition of noticeability. I explain why SSE's scheme for string search can't meet the flexibility criteria mentioned, and I also suggest changes to our plan so that it can be used against moving enemies at the cost of more changes to communications and more memory space.
A Neural Network Method for Systematic Evaluation of Informatization Development Level in Smart Court Construction

&lt;p&gt;The systematic evaluation of informatization development level (IDL) is an evaluation that follows the development trend of digitization, networking and intelligence, and focuses on the formation of business capabilities of informatization. Research on informatization evaluation methods has been extensively studied by both domestic and international academics over the years. However, traditional evaluation methods suffer from flaws like complex mechanism design, unreliable metric conversion, difficulty obtaining the relative importance of indexes, complex evaluation process, and high computational volume. This paper attempts to introduce the neural network method into the information system evaluation, and uses the Extreme Learning Machine (ELM) algorithm to establish the evaluation model. The evaluation of the smart court system is used as an example to simulate and test the model, and the results show that the neural network-based evaluation model of informatization system is more applicable to large-scale evaluation indexes, and by continuously increasing the learning samples, it objectively improves the accuracy of evaluation, effectively avoids human subjective factors, and has the advantageous features of advanced, accurate and convenient.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;