Reviewerly/exHarmony huggingface dataset:
A Study of KEM Generalizations

The NIST, in its recent competition on quantum-resilient confidentiality primitives, requested the submission of exclusively KEMs. The task of KEMs is to establish secure session keys that can drive, amongst others, public key encryption and TLS-like secure channels. In this work we test the KEM abstraction in the context of constructing cryptographic schemes that are not subsumed in the PKE and secure channels categories. We find that, when used to construct a key transport scheme or when used within a secure combiner, the KEM abstraction imposes certain inconvenient limits, the settling of which requires the addition of auxiliary symmetric primitives. We hence investigate generalizations of the KEM abstraction that allow a considerably simplified construction of the above primitives. In particular, we study VKEMs and KDFEMs, which augment classic KEMs by label inputs, encapsulation handle outputs, and key derivation features, and we demonstrate that they can be transformed into KEM combiners and key transport schemes without requiring auxiliary components. We finally show that all four finalist KEMs of the NIST competition are effectively KDFEMs. Our conclusion is that only very mild adjustments are necessary to significantly increase their versatility.
An educational experience in ancient Rome to evaluate the impact of virtual reality on human learning processes

Immersive Virtual Reality technology has recently gained significant attention and is expanding its applications to various fields.It also has many advantages in education, as it allows to both simplify the explanation of complex topics through their visualization, and explore lost or unreachable environments.To evaluate the impact of immersive experiences on learning outcomes we developed an educational experience that lets users visit an ancient Roman Domus and provides information about daily life in Roman times.We designed a between-subjects data collection to investigate learning ratio, user experience, and cybersickness of participants through anonymous questionnaires.We collected 76 responses of participants (18-35 y.o.) divided into three conditions: a Immersive Virtual Reality experience, a slide-based lecture and a 2D desktop-based experience.Our results show that the virtual reality experience is considered more engaging and as effective as more traditional 2D and slidebased experiences in terms of learning.
Exploring The Effect of Memory Strategies On Students’ Grammar Skills: Indonesian Learner Context

One of the major problems faced by Indonesian young learners when learning English grammar is being difficult to memorize sentence pattern with present tense. Grouping with vertical and horizontal lines were believed as an effective memory strategy to memorize the sentence pattern. Therefore, this study targeted to explore whether or not the use of memory strategy was applicable for students when constructing sentences. The research used quasi-experimental design. Each of the classes consisted of 10 participants chosen through total purposive sampling technique. Statistical technique using SPSS program was applied to confirm the hypotheisis. Based on the test of normality, it was found that the probability value of 0.134 is greater than the significance level of 0.05. This indicated that the data were in a normal distribution. Therefore, test of significance was done through parametric test and it showed that the significance value of 0.025 was equal to the probability level of 0.025, which means that there is no significant difference between experimental and control groups’ ability. In conclusion, the use of vertical and horizontal lines were not more effective in improving the students’ grammar skills than the conventional strategy. However, the strategy can be further developed and applied in teaching students with intermediate level.
Unsupervising Denoising Model Based Generative Adversarial Networks

This paper addresses a novel method, based on generative adversarial network (GAN) for denoising images especially in an unsupervised setting from corrupted and unpaired datasets of images. The only source of information is offered by the observations and the measurement process statistics. The process aims to find the maximum a posteriori (MAP) estimate of the distribution given each measurement, optimizing the noise generating distribution represented analytically defined noise as the likelihood and the GAN as the prior. In which the generator takes a corrupted observation as input to generate realistic reconstructions, and then adds a penalty term tying the reconstruction to the related observation. We test our approach on a variety of common datasets with varied sizes and levels of corruption. The proposed method offers an alternative to unsupervised denoising and achieves results that are comparable to the state-of-the-art in generative models noise removal.
A Survey of Deep Learning Based Natural Language Processing in Smart Healthcare

Natural language processing (NLP) is the subfield of artificial intelligence that has the potential to make human language analyzable by computers. NLP is increasingly proving its importance in the medical field where a huge amount of data remains unstructured (free text) stored as electronic medical records (EMR); discharge summaries, lab reports, clinical notes, pa-thology reports, etc. Traditional Machine learning (ML) based approaches have been widely used for medical NLP tasks, but these methods require a set of manual work and still suffer in terms of accuracy. However, deep learning (DL) based methods have made significant improvement. The main goal of this study is to present the state-of-the-art DL based NLP tech-niques in healthcare. We started by presenting word embedding techniques and popular deep learning models used in this area, and then reviewed ap-plications of NLP tasks in medical domain such as classification, predic-tion, and information extraction. We concluded our study with analyzing cited architectures and showing the promising results of CNN and BiLSTM and BERT fine-tuning.
Arabic Sign Language Analysis and Recognition

For those who are deaf or hard of hearing, sign language continues to be the preferred form of communication. As technology has advanced, systems that can automatically distinguish between spoken language and vision-based sign language have been created. This paper examines and identifies Arabic alphabet sign language (ArSLR). The Convolutional Neural Network (CNN) model is used by the system to visually recognize motions from the input sequence of hand photographs. We employed two datasets: the isolated words dataset for dynamic gestures given across multiple frames and the alphabet dataset for static gestures presented throughout a single frame. The suggested systems combine model training, feature extraction, and prediction. To boost performance, we concentrated on hyperparameter validation. The system’s accuracy has exceeded 98% after testing and comparing various metrics, which is comparative to other works utilizing the similar dataset.
Use of Audiovisual Media to Improve Learning Outcomes in Writing Fable Stories for Class X Students of SMKN 1 Sengkang

This research is a class action research that aims to find out how the application of Audiovisual Media in Improving Learning Outcomes of Class X Students of SMK Negeri 1 Sengkang. This type of research is class action research (PTK). The research consists of three stages, namely precyclical, cycle I, and cycle II. Data collection techniques are carried out by tests and nontests. Test techniques are in the form of written tests of writing fables, while non-test techniques include observation techniques, questionnaires, interviews, field notes, and documentation. The instruments used in this study were test and non-test instruments. In data analysis, qualitative and quantitative techniques are used. The aspects observed are changes in student attitudes during the learning process using audiovisual media carried out quite well and changes in student attitudes and student learning outcomes have improved. This can be seen from the percentage of learning outcomes in the precyclical stage of only 58.00%. After applying audiovisual media, student learning outcomes increased to 70.56% in cycle I and increased again to 86.75% in cycle II. Thus, it can be concluded that there is an increase in learning outcomes in writing fable stories by applying the concept of audiovisual media to class X students of SMK Negeri 1 Sengkang.
Arabic Handwritten Characters Recognition by Combining PHOG Descriptor with Ensemble Methods

Handwritten character recognition is a system that is widely used in the modern world for a variety of applications in various fields, but it is still difficult in the case of arabic language. The arabic alphabet is distinguished by the fact that many characters have similar shapes but differ in where the dots are placed in relation to the main body of the character. Furthermore, some people handwrite these dots as dashes, complicating the task of a recognition system. Over the last three decades, this field of study has received a lot of attention. However, it still faces several challenges, such as the variation in human handwriting and its cursive nature. In this paper, we created a new system with a PHOG descriptor inspired by the pyramid representation and the Histogram of Orientation Gradients. The performance of most machine learning models depends on the quality, quantity, and relevance of the data. However, insufficient data is one of the most common challenges in machine learning implementations, as collecting this data can be expensive and time consuming in many cases, for this purpose we tested various parameters of this descriptor and used a data augmentation method with Gaussian Noise, which significantly increased accuracy. For the classification phase, we used five-fold cross-validation, which is a statistical method used to estimate the skill of machine learning models. K-nearest neighbors, decision trees, random forests and naive bayes are the four machine learning techniques we used to train and evaluate our system. We used ensemble methods mainly bagging and stacking to evaluate the impact of combining multiple models. We tested our system using the AlexU Isolated Alphabet (AIA9K) Dataset, which contains 8,737 characters. When compared to other systems using the same database, the results of the suggested system are promising. The experiments revealed that using classification with stacking algorithm outperforms classification with individual classifiers and bagging algorithm when naive bayes, k-nearest neighbors and decision tree are used as Meta classifiers. We were able to achieve a high classification rate of 97.22% with random forest.
A Parallel Execution Model for Permissioned Blockchain Systems

At present, academics have researched the directions that may become performance bottlenecks in blockchain systems, such as consensus protocols, encryption algorithms and contract execution. For example, in recent years, many improvements to consensus protocols have greatly improved the throughput of blockchain systems. However, the bottleneck of blockchain smart contract execution has not been well solved. Currently, the miner-validator architecture based on serial contract execution limits the throughput of smart contract execution to a great extent. This paper proposes a new smart contract parallel execution architecture, which migrates the concurrency control methods used in mature databases to the blockchain. The test of Ethereum data and a benchmark specifically for blockchain show that its throughput is greatly improved compared with the traditional serial execution mode.
Blockchain for Storing Shadow Data of IoT Devices

The demand for secure storage of shadow service data of Internet of things devices makes the traditional centralized platform storage method unable to ensure the security and credibility of key data. This paper analyzes that the main security risks faced by the shadow data storage of Internet of things devices are the lack of credibility guarantee mechanism and the lack of secure encryption and decryption combined with verifiable methods. A series of advantages of block chain technology can solve the above problems. By combining the key technologies such as blockchain structure, encryption algorithm, consensus mechanism and so on, the DeviceShadow-BlockChain mechanism of Internet of things is proposed. An adaptive RAFT consensus algorithm is designed, which is suitable for reaching consensus quickly. In addition, asymmetric encryption technology combined with hash algorithm is used to ensure the security and unusurability of data transmission. In the application layer of the DeviceShadow-BlockChain of the Internet of things, the data chain management platform is used to realize the functions of device shadow data winding, fast consensus, distributed certificate storage and so on.
Hate Speech Detection: Performance Based upon a Novel Feature Detection

Hate speech is abusive or stereotyping speech against a group of people, based on characteristics such as race, religion, sexual orientation, and gender. Internet and social media have made it possible to spread hatred easily, fast, and anonymously. The large scale of data produced through social media platforms requires the development of effective automatic methods to detect such content. Hate speech detection in short text on social media has become an active research topic in recent years, as it differs from the traditional information retrieval for documents. My research is to develop a method to effectively detect hate speech based on deep learning techniques. I have proposed a novel feature based on the lexicon of short text. Experiments have shown that proposed deep-neural-network-based models improve the performance when a novel feature combines with CNN and SVM.
A Deep Convolutional Neural Networks Approach for Word-Level Handwritten Script Identification Using a Large Dataset

In this work, we propose a convolutional neural network (CNN) architecture to identify six word-level handwritten scripts involving Arabic, Latin, Chinese, Bangla, Devanagari and Telugu. A large dataset of 14k word images per script was constructed based on several public handwritten datasets. Then, three architectures are proposed and compared based on standard metrics performance and time execution. Experiments conducted on both test and validation classification show high performances that outperform the state-of-art techniques. Indeed, the best result was provided by CNN model with three-convolutional-polling pairs layers that achieved an average script identification accuracy of 97.67% and ran in a sufficiently fast time of 2 ms per frame during the test phase.
Understanding the Use of Blockchain in Medical Data Security: A Systematic Literature Review

Data security is one of the technology trends in Indonesia that arose with the emergence of digital transformation. However, many industries in Indonesia, such as the healthcare industry, did not focus on applying the best security practices in securing their confidential data, although many have converted their records into Electronic Medical Records. This research focuses on capturing the importance of applying data security for enhancing medical records data security, mainly focusing on Blockchain as the recommended security method. A systematic literature review will be used to capture related information, which captures the result of previous research and produces a new finding to increase the current knowledge of blockchain in the health industry. The result of this research is to increase the insight into the blockchain security system, starting from its benefits for data storage, data sharing, and data security problems in Electronic Medical Records systems and the challenges to implementing the blockchain technology, such as interoperability, security and vulnerabilities, network attacks, and lack of initiative or knowledge.
Meta-heuristic Algorithms for Text Feature Selection Problems

The increasing unstructured amount of text information on the Internet has become a significant obstacle to user needs. The text clustering approach is used in text mining to divide a group of texts into a predetermined number of clusters. The clustering algorithms suffer from the increasing amount of non-informative words in the corpus. Non-informative text features are removed from each document to improve clustering approach performance and computation. Here, several particle swarm optimization variants, such as inertia weight and constriction factor, are compared to improve the particle exploration experience. The PSO method is compared with the other commonly used metaheuristics (i.e., the Genetic algorithm and the Harmony search algorithm). Also, various exploration and initialization characteristics are integrated with the PSO to improves its performance. The experiments were carried out on four standard datasets: Reuters-21578, 20Newsgroups, Classic4, and WebKB. The experimental results show that PSO outperforms the other competing approaches in terms of clustering Accuracy, Precision, Recall, and F-measure. Moreover, adjusting PSO parameters improves its performance.
Self-Sovereign Identity for Privacy-Preserving Shipping Verification System

Internet made it easier for organizations and individuals to collaborate with each other in different activities in order to provide new goods and services to their customers. As for example, e-commerce facilitates the discovery of new partners and allows companies to conduct business. However, in such open environment ensuring transparency of the supply chain activities and privacy of the customers is a challenging task. Indeed, individuals do not know each other and the centralized architecture of Internet services does not allow individuals to control their credentials. To overcome this challenge, this paper exploits the concept of Self Sovereign Identity (SSI) for providing digital identity, trust, and privacy in the context of a Shipping Verification System, a very important activity in supply chain management. In particular, the proposed system relies on decentralized identifiers, verifiable credentials, and blockchain to allow customers to monitor the shipment of items by a shipping deliverer, on behalf of a marketplace. We implemented an instance of the proposed system, and we show that it achieves good performance results in two popular deployment configurations: a Local Indy Pool (consisting of 4 nodes) and the Sovrin BuilderNet (a developer test network consisting of 17 node).
Data Mining of Compound DeFi Project

The set of services by Decentralized Finance (DeFi) and by traditional finance intersects. Loans are examples of the intersection. Data scientists have no access to the loan data of the traditional banking system due to trade secrecy and client privacy reasons. Also, banks' operation is well regulated. Modern DeFi loan data is openly available in public blockchains the corresponding projects operate in. But the problem is to represent DeFi's conveniently for analysis. At the same time, DeFis are unregulated. In the paper, we consider a decentralized Ethereum protocol to lend and borrow assets called Compound. We design a relational database, fill it with the project's data, and provide statistical details. The results help overcome the entering threshold for further data analysis.
NFT Scoring: An Analysis of the Considerable Features

As a cutting-edge technology, non-fungible tokens (NFT) have attracted a great deal of attention since 2021. Considering the numerous applications of these non-interchangeable digital assets in various industries and their tradability, NFTs have become an important element of many investors’ portfolios. Therefore, in order to evaluate NFTs and determine their main value, different tools must be used. The purpose of this study is to understand the dominant factors that influence the valuation of NFT assets. The purpose of this paper is to present a novel methodology for constructing a utility valuation model for NFTs as a whole. We will be able to analyze and diagnose the dynamics and performance of NFT markets using this model. We developed three models for scoring NFTs in this study, which can be used to speed up the evaluation process in three different dimensions.
LEChain:Linear Credit Evaluation System Based on Hyperledger Fabric Blockchain

Data sharing among different institutions can enhance the integrity of information to evaluate the credit level of financing enterprises more accurately, but it also faces the problems of privacy leakage and data fraud. To solve these problems, this paper proposes a complete linear credit evaluation system with secure sharing and multiparty collaborative computing based on Hyperledger Fabric Blockchain named LEChain. The system consists of four modules: distributed ledger, access control, security aggregation and model storage. With the help of 1-out-of-N oblivious transmission protocol, incorporating elementary matrix transformation data security aggregation realizes the privacy of data transmission and collaborative computation. Finally, a security analysis is given to show the privacy and compliance of the data aggregation.
Feature Selection for Text Classification Using Genetic Algorithm

Today’s large amount of text data makes feature classification a difficult text processing challenge. High dimensionality is the primary challenge in text processing, and feature selection is a common method for reducing dimensions. The most crucial factors in text classification are a strong text representation and a very accurate classifier. As a result, choosing the right features is essential for using machine learning algorithms effectively. Different optimization techniques, including the Genetic Algorithm (GA), have been effectively used for dimensionality reduction in the field of text classification. To evaluate the performance of GA for Feature Selection (FS), we compared the GA for FS with other filtering methods to prove the efficiency of the GA for FS, for that, we used the NB classifier and three benchmark document collections: SMS, BBC, and 20Newsgroups.
TSC-VEE: A TrustZone-Based Smart Contract Virtual Execution Environment

TrustZone as a trusted execution environment (TEE) has been proven to preserve the confidentiality of blockchain transactions supported by smart contracts. Despite some academic effort, TrustZone can only support limited languages for now. The lack of the corresponding execution environment for smart contracts seriously hinders blockchain applications from directly running on TrustZone. In this paper, we design the first virtual execution environment named TSC-VEE for performing Solidity smart contracts on TrustZone, to the best of our knowledge. TSC-VEE can be decomposed into fourfold: (1) an instruction set adapted to the isolation and world switching mechanism of TrustZone. (2) a runtime memory management mechanism that provides a pair of instructions with the corresponding processing mechanism to allocate and release the work memory. (3) a hybrid granularity resource analysis algorithm which computes and records the value of maximum stack height and static gas cost through bytecode pre-execution, avoiding runtime overflow and invalid computations. (4) a cross-isolation-environment prefetching approach that supports loading and storing the storage data from the normal world into the secure world on TrustZone before execution, thus avoiding switching the world state frequently at runtime. Extensive experimental results show that TSC-VEE can perform smart contracts correctly and efficiently on TrustZone. Compared with the most commonly used Ethereum client— <i>Geth</i> , TSC-VEE achieves execution performance improvements by <inline-formula><tex-math notation="LaTeX">$9.29\times$</tex-math></inline-formula> . We also implement the Ethereum virtual machine— <i>evmone</i> on TrustZone. TSC-VEE can reduce the latency by 12.63% with our optimization techniques, and decrease the work memory footprint by 22.95% on average when executing various scale contracts.