Reviewerly/exHarmony huggingface dataset:
Securing of Cloud Storage Data Using Hybrid AES-ECC Cryptographic Approach

Internet has revolutionized the world in a way no one could have ever imagined. It paved the way for various different technologies, that have transformed the world exceptionally. Internet enabled cloud technology which provides cost-effective, scalable, on-demand computing resources with little to no downtime. Cloud storage allows its users to store and access private data from anywhere in the world without needing any high-end computing system. Cloud storage isn’t always secure, but that doesn’t imply it isn’t. The security of a data depends on the security policies followed by the provider along with the security of the communication channel via which the data is being sent. Encryption is used to obfuscate the data so that it can only be viewed when correct credentials, known as encryption keys, are provided. Following study proposes an encryption technique using ECC (Elliptic Curve Cryptography) along with AES (Advance Encryption Standard) to provide data confidentiality in an efficient way for securing data on cloud and hence, protect the personal information of user from any adversary. This new method is more effective, and the results are superior as a consequence.
New method for the mechanical characteristics of self-weight collapsibility testing of loess foundation based on centrifugal model test

The mechanical characteristics of self-weight collapsibility of loess is mainly studied through laboratory tests, numerical simulations, and field immersion tests. However, a laboratory test under the lateral restraint condition cannot reflect the 3D stress state of soil and the influence of lateral extrusion deformation. The numerical simulation method is complex, and the calculation results depend on the accuracy of the constitutive model of loess deformation. Although the field immersion test produces results consistent with the stress and deformation conditions of actual foundations, the test cycle is long, and the cost is high. To overcome these issues, based on the strong self-weight collapsible loess in Ili, Xinjiang, and the variation laws of its physical and mechanical properties along the buried depth, a site is generalized and layered, and typical soil layers are selected to prepare a centrifugal model. A centrifugal model test is then conducted on the self-weight collapsible loess foundation. Taking the regional correction coefficient as the index, the rationality of the new method is verified by comparing the results of the centrifugal model test with those of the laboratory test and field immersion test.
The State of the Art of Digital Archiving in Romania

This article aims to summarise the current situation regarding digital archaeological archives in Romania.
Improving Learning Activities and Writing Skills in Indonesian Language Content the Environmental Theme of Our Friends Using the Proton Model at Sdn Hatungun 1 Tapin

Learning activities have a big influence on the success of a lesson, this also has a big influence on students' writing skills. But in fact, the method used when learning is still one-way, learning is less meaningful so that students are unable to find ideas/ideas into written form, students' writing interest is low because they use multiple choice questions, many students are still not correct in determining the choice. words (diction) in a sentence, the placement of punctuation marks is not right and they don't understand what a nonfiction story is. This study aims to determine the increase in learning activities and writing skills of elementary school students using the PROTON model. This study uses a qualitative research approach with the type of research in the form of Classroom Action Research which consists of four meetings, data analysis uses two methods, namely qualitative and quantitative. The results showed that by using the PROTON model on the Indonesian content of the Friends of Our Environment Theme there was an increase in student learning activities at the fourth meeting, namely 87.5% in the very active category and in students' writing skills at the fourth meeting as much as 93.75% in the very category. good. The results of this study are expected to be used as an alternative in improving the learning activities and writing skills of elementary school students, especially in the Indonesian content of the Environmental Theme of Our Friends in grade 5.
Efficient mining of intra-periodic frequent sequences

Frequent Sequence Mining (FSM) is a fundamental task in data mining. Although FSM algorithms extract frequent patterns, they cannot discover patterns that periodically appear in the data. However, periodic trends are found in many areas such as market basket analysis, where discovering itemsets periodically purchased by customers can help understand periodic customer behavior. This is the task of Periodic Frequent Pattern Mining (PFPM). A major limitation common to traditional PFPM algorithms is that they reduce the periodicity between non-disjoint itemsets. They do not take into account the periods between disjoint itemsets. Thus, they find itemsets that appear periodically, but would fail to find a periodic appearance of distinct itemsets. To address this limitation, this paper extends the traditional problem of FSM with intra-periodicity and provides a theoretical background to extract intra-periodic frequent sequences. This leads to a new mining algorithm called Intra-Periodic Frequent Sequence Miner. Experimental results confirm its efficiency.
From a network model to a model network: strategies for network development to narrow the LIS research–practice gap

Purpose The purpose of the empirical study was to examine whether strategies shown to work well in one model of network development for library and information science (LIS) practitioners and researchers could be applied successfully in the development of a new network and contribute to the narrowing of the research–practice gap in LIS. Design/methodology/approach Overall, 32 members of a new professional network were surveyed by a questionnaire following the completion of a programme of four network events held between 2019 and 2021. Findings The analysis demonstrates the transferability of the existing model of network development to a new network and that it can be successfully adapted for online delivery of network events and activities. Practical implications The criteria deployed for the evaluation of the new network could be used in other similar settings. Funding bodies can also use these findings as demonstration of the value of their investment in network grants. Originality/value This contribution on means of growing collaborative networks to narrow the LIS research–practice gap stands out in contrast with prior research that tends to focus the support of research productivity of academic librarians in North American universities for the purposes of career development. Here wider aspects of research engagement are considered of value for LIS practitioners from a range of sectors and institutions, beyond North America, for purposes that are broader than personal advancement.
An Enhanced Object Detection Model for Scene Graph Generation

With computer vision improving, a higher level of understanding is needed to solve more complex problems such as semantic image retrieval, image captioning, and scene understanding. Scene understanding has been a long-studied problem due to its complexity and lack of proper data representation. A scene Graph is one of the most powerful data representations that can better understand the scene context. The task of a Scene Graph is to encode the objects presented in the scene, their attributes, as long as the relationships between these objects. With the scene Graph proving its capabilities in complicated tasks, the automation of scene graph generation became a must. Great research has been made to obtain accurate Scene Graphs using different deep learning architectures. The common module among those different architectures is the object detection module, where objects are firstly located in the input image. In this work, we propose using the most recent object detectors from the YOLOv5 family for the scene graph generation task. The proposed YOLOv5x6 achieved a State-Of-The-Art result of 32.7 mean average precision compared to previous works. Furthermore, the paper reviews the different object detectors used in literature for the scene graph generation.
A chatbot system to support visually impaired students: a pilot experiment

Students with visual impairment need for different methods to assist their studies such as read tools and conversation bots (chatbots). Assistive technologies can be incorporated as a training or support tool and specifically in the educational field as complementary material. This paper proposes a chatbot as a study that supports an intelligent system for visually impaired students. Finally, the paper reports a pilot experiment regarding the effectiveness, time of response, and speech output of the proposed system in two scenarios of the software engineering area. Overall, the system proved to be tolerant of typing errors and returned quick responses by 0,9 seconds on average. Our results also demonstrate the efficiency in output with speech employing a library called gTTS.
Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver

Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS-BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS-BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS-BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample.
AI restoration brings ‘dying’ masterpieces back to life, but tricks copyright?

Abstract Recently, artificial intelligence (AI) has shed light on the restoration of fragmentary masterpieces. It is said that AI-restored content can reproduce the missing part in fragmentary works, reveal authors’ initial intention, and give the younger generation a chance to appreciate the essence of cultural heritage. AI restoration leads to a discussion on whether copyright law should apply to AI-restored works. The existing literature about the nexus between the restoration of works and copyright is scarce, and none is related to AI-restored works. Therefore, this paper focuses on investigating the potential copyright issues regarding AI-restored works and exploring how the traditional copyright regime fails to solve those issues. Besides, this paper suggests recalibrating the copyright regime to enhance the authenticity of AI-restored output. Lastly, this cutting-edge article provides theoretical evidence for IP scholars interested in AI-related issues and advocates future research on the relation between AI models and copyright law.
Domain Adaptation for Stance Detection towards Unseen Target on Social Media

Stance detection aims at identifying people's stand-point towards a given target. New targets are constantly appearing on social media, making previous annotated data unusable by stance detection models relying on classical supervised machine learning. Thus, cross-target stance detection which uses labeled data from source targets to learn a model that can be adapted to the destination new target, has become a prevailing research direction. However, previous methods rely on manually chosen similar source-destination target pairs and lack generalization to unseen targets with no explicit relation to known ones. To this end, we investigate the problem from a domain adaptation perspective and further propose a novel Unified Target-aware Domain Adaptation method (UTDA) that leverages knowledge transfer capability of transformer-based language model. The proposed method can effectively extract critical target-shared features for detecting stance by feature disentanglement and automatically learn to identify target relations. UTDA can easily be applied to a new unseen target since it does not rely on any pre-defined target pairs. Experimental results on two benchmark stance datasets demonstrate that our method achieves better performance than strong baselines.
Breast MRI Registration Using Gorilla Troops Optimization

Metaheuristics assume a significant part in enhancing issues, and most of them are enlivened by the aggregate knowledge of living peculiarities in nature. In excess of 10% of ladies are experiencing bosom disease in their lives in the entire world. Registration of breast magnetic resonance imaging is a method to align pre- and post-contrast images to analysis and classifying of cancer category. In this research, breast magnetic resonance imaging registration using gorilla troops algorithm is proposed. GTO is a metaheuristics-based optimizations algorithms that tested to registering the image of breast magnetic resonance. After that, images are successfully registered using GTO algorithm. The result of GTO-based registration method is compared with registration based on PSO method. The results implicates, in case of registration of breast magnetic resonance images, the GTO-based registration methods beat the PSO-based registration method.
Augmented-syllabification of n-gram tagger for Indonesian words and named-entities

As one of the statistical-based models, an n-gram syllabification commonly gives a high syllable error rate (SER) for Bahasa Indonesia, one of the low-resource languages, since it fails for a high out-of-vocabulary (OOV) rate. Two previous models: bigram-syllabification with flipping onsets (BFO) and a combination of bigram with backoff smoothing based on phonological similarity (CBSPS), which use augmentation methods, can reduce the OOV rate. However, there are two problems in both BFO and CBSPS. First, they use an n-gram that is applied syllable-level, instead of grapheme-level, so that they suffer on the sparsity of n-grams. Second, they rely on a procedure to detect the positions of both vowels and diphthongs. Both problems make them not capable of distinguishing diphthongs from derivative words as well as syllabifying named-entities, which have many ambiguities related to vowels and semi-vowels. In this paper, a syllabification based on an n-gram tagger, which is applied on grapheme-level and does not rely on both vowel and diphthong detections, is developed to solve both problems. Besides, three data augmentation methods are exploited to enrich the dataset. The 5-fold cross-validations (5-FCV) using both datasets of 50 k words and 15 k named-entities show that the proposed augmented-syllabification of n-gram tagger (ASnGT) model is significantly better than both BFO and CBSPS. It is also significantly better than the fuzzy k-nearest neighbor in every class (FkNNC)-based model for formal words and named-entities. However, it suffers from derivative words, where it cannot easily distinguish them from both absorption words and terms of foreign languages. Besides, it also undergoes some foreign named-entities.
Female students’ perception of m-learning in the higher education institutions of Palestine during the COVID-19 pandemic

To understand the viability of utilizing m-learning as the principal facilitation channel of education, the present study aims at identifying the Palestinian female students’ perceptions of using mobile phones in higher education during the COVID-19 pandemic in the Gaza Strip to investigate the influence of using mobile phones on the female students’ academic achievement. For the purpose of the study, a questionnaire was designed by the researchers to collect the data from a sample of (156) second-level diploma female students who were selected from the Departments of Child Education and Midwifery at the University College of Applied Sciences (UCAS), and the quantitative descriptive analytical approach was followed. The results indicated that the use of mobile phones improved the UCAS female students’ skills, enhanced their academic achievement, made the learning experience more enjoyable, and improved their use of mobile devices. Additionally, there is no statistically significant relationship between the students’ perception of using mobile phones in the learning process and their academic specialization and their academic achievement. Upon the results of the study, the researchers recommend implementing m-learning in the educational process effectively and conducting future studies to measure the impact of courses’ content design and educational programs using smartphone applications.
Security provisions in smart edge computing devices using blockchain and machine learning algorithms: a novel approach

It is difficult to manage massive amounts of data in an overlying environment with a single server. Therefore, it is necessary to comprehend the security provisions for erratic data in a dynamic environment. The authors are concerned about the security risk of vulnerable data in a Mobile Edge based distributive environment. As a result, edge computing appears to be an excellent perspective in which training can be done in an Edge-based environment. The combination of Edge computing and consensus approach of Blockchain in conjunction with machine learning techniques can further improve data security, mitigate the possibility of exposed data, and it reduces the risk of a data breach. As a result, the concept of federated learning provides a path for training the shared data. A dataset was collected that contained several vulnerable, exposed, recovered, and secured data and data security was precepted under the surveillance of two-factor authentication. This paper discusses the evolution of data and security flaws and their corresponding solutions in smart edge computing devices. The proposed model incorporates data security using consensus approach of Blockchain and machine learning techniques that include several classifiers and optimization techniques. Further, the authors applied the proposed algorithms in an edge computing environment by distributing several batches of data to different clients. As a result, the client privacy was maintained by using Blockchain servers. Furthermore, the authors segregated the client data into batches that were trained using the federated learning technique. The results obtained in this paper demonstrate the implementation of a Blockchain-based training model in an edge-based computing environment.
Evaluation of Grasp Posture Detection Method using Corneal Reflection Images through a Crowdsourced Experiment

To achieve adaptive user interfaces (UI) for smartphones, researchers have been developing sensing methods to detect how a user is holding a smartphone. A variety of promising adaptive UIs have been demonstrated, such as those that automatically switch the displayed content and the position of interactive components in accordance with how the phone is being held. In this paper, we present a follow-up study on ReflecTouch, a state-of-the-art grasping posture detection method proposed by Zhang et al. that uses corneal reflection images captured by the front camera of a smartphone. We extend the previous work by investigating the performance of this method towards actual use and its potential challenges through a crowdsourced experiment with a large number of participants.
Let’s Compete! The Influence of Human-Agent Competition and Collaboration on Agent Learning and Human Perception

In interactive agent learning, the human may teach in a collaborative or adversarial manner. Past research has been focusing on collaborative teaching styles as these are common in human education settings, while overlooking adversarial ones despite promising results in recent research. Moreover, agent performance has been the main focal point while neglecting the perspective of the human teacher, who is crucial to the instructional process. In this work, we examine the impact of competitive and collaborative teaching styles on agent learning and human perception. We conducted a study (N=40) for participants to demonstrate a task in different interaction modes for teaching a computer agent: collaboratively, competitively, or without interacting with the agent. Most participants reported that they preferred competing against the computer agent to the other two modes. Despite smaller numbers of demonstrations given from the user, the agent performance from the interactive modes (collaborative and competitive) was comparable to the non-interactive mode (solo). The agent was perceived as being more competent in the competitive mode than the collaborative mode despite the marginally worse in-task performance. These preliminary findings suggest that competitive types of interaction, when agents or robots learn from humans, lead to better human perception of the agent’s learning when compared to collaborative, and better user engagement when compared to non-interactive learning from demonstrations.
Research on domain text classification method based on BERT

Domain texts usually have significant domain features and long text lengths. Text classification models suitable for general fields cannot well meet the task of text classification in specific domains. Therefore, this paper proposes a domain text classification method based on BERT. First, segment the long domain text to obtain the sequence combination, and input it into the BERT pre-training model to obtain the word vector. Then the vector is compressed and encoded to obtain the pooled sequence feature vector. Finally, it is sequentially input to the Encoder layer for domain feature extraction, and the text is divided into various categories in the output layer. The experimental results show that the proposed BERT_VCA model has an average improvement of 1.12% in F1 value compared with the BERT_BASE model in the domain text classification task.
Literature Review of Smart Contracts Using Blockchain Technology

With a spike in usage of Blockchain in various fields like cryptocurrency, smart contracts, etc. due to its decentralized and digitally distributed peer-to-peer network featuring elevated speed, efficiency and security. It provides the contract management solutions by consensus mechanism, scalability and reliability on ‘off-chain’ resources. This work focuses on rectifying bugs and cybersecurity attacks e.g. Re-entrancy attacks with utilization of vaults, GHOST protocol, Bitcoin-NG, botnet C&C command, ERLAY protocol, bug prevention tools like Oyente and SolidiFi, and fuzzing tools like ReGuard and Contract-Fuzzer.