Reviewerly/exHarmony huggingface dataset:
Cloud Computing Security using Cryptographic Algorithms

This paper aims to present the survey of cloud computing security. A cloud computing security is defined so that it can help to clarify cloud security. Cloud computing has evolved to become an essential component of both current and future information technologies. This technology was created to be used in conjunction with the internet, and it includes features such as data storage, remote access, and so on. Various cryptographic encryption algorithms are compared in terms of their various key features. The primary goal is to highlight such attacks and provide recommendations for resolving data breaching issues using cryptographic algorithms such as Symmetric Key Cryptographic Algorithms (DES, 3DES, AES), Asymmetric Key Cryptographic Algorithms (RSA, DSA, Diffie-Hellman Algorithm), and Hashing Algorithms (MD5, SHA). So, in the paper cloud security challenges and the cryptographic algorithms used to prevent them are discussed.
Free Fault Leakages for Deep Exploitation: Algebraic Persistent Fault Analysis on Lightweight Block Ciphers

Persistent Fault Analysis (PFA) is a new fault analysis method for block ciphers proposed in CHES 2018, which utilizes those faults that persist in encryptions. However, one fact that has not been raised enough attention is that: while the fault itself does persist in the entire encryption, the corresponding statistical analysis merely leverages fault leakages in the last one or two rounds, which ignores the valuable leakages in deeper rounds. In this paper, we propose Algebraic Persistent Fault Analysis (APFA), which introduces algebraic analysis to facilitate PFA. APFA tries to make full usage of the free fault leakages in the deeper rounds without incurring additional fault injections. The core idea of APFA is to build similar algebraic constraints for the output of substitution layers and apply the constraints to as many rounds as possible. APFA has many advantages compared to PFA. First, APFA can bypass the manual deductions of round key dependencies along the fault propagation path and transfer the burdens to the computing power of machine solvers such as Crypto-MiniSAT. Second, thanks to the free leakages in the deeper round, APFA requires a much less number of ciphertexts than previous PFA methods, especially for those lightweight block ciphers such as PRESENT, LED, SKINNY, etc. Only 10 faulty ciphertexts are required to recover the master key of SKINNY-64-64, which is about 155 times of reduction as compared to the state-of-the-art result. Third, APFA can be applied to the block ciphers that cannot be analyzed by PFA due to the key size, such as PRESENT-128. Most importantly, APFA replaces statistical analysis with algebraic analysis, which opens a new direction for persistent-fault related researches.
Centralized and Decentralized Data Backup Approaches

In this digital era, data are very insecure, and data are being compromised with several risks, potential attackers, several methodologies, and mechanisms have been evolved to ensure backup of critical data. The reliable data backup technology ensures the reliability and availability of data over the network. Demand of safety and security for information and storage is increasing over the world day by day. Data are generated from a wide range of domain such as information technology sector, health and education sector, defense, banking, e-commerce, and telecommunication. Therefore, the backup of data plays a vital role to maintain the confidentiality, integrity, and availability of data for the end users. Decentralized data backup using blockchain technology can provide data confidentiality, data integrity, authentication, and authorization with digital signatures. This paper presents a review of various data backup techniques using centralized backup systems and discusses the problems and resolution associated with them and finally, how to resolve those issues with the help of decentralized data backup mechanisms using blockchain technology.
Token Dropping for Efficient BERT Pretraining

Transformer-based models generally allocate the same amount of computation for each token in a given sequence.We develop a simple but effective "token dropping" method to accelerate the pretraining of transformer models, such as BERT, without degrading its performance on downstream tasks.In particular, we drop unimportant tokens starting from an intermediate layer in the model to make the model focus on important tokens more efficiently if with limited computational resource.The dropped tokens are later picked up by the last layer of the model so that the model still produces full-length sequences.We leverage the already built-in masked language modeling (MLM) loss to identify unimportant tokens with practically no computational overhead.In our experiments, this simple approach reduces the pretraining cost of BERT by 25% while achieving similar overall fine-tuning performance on standard downstream tasks.
Data-based variable universe adaptive fuzzy controller with self-tuning parameters

Data-based variable universe adaptive fuzzy controllers (VUAFCs) with self-tuning parameters are developed for complex systems with unknown universes in this paper. The main feature of the proposed VUAFC is the new defined contraction–expansion (C-E) factor on an infinite universe, which is more flexible and practical in real applications. Moreover, the data-based methods to tune the parameters including the peak points of output fuzzy subsets and the C-E factor parameters are proposed. The peak points of the output fuzzy subsets are mined by an improved Wang–Mendel method based on conflicting rules. The parameters of the VUAFC are optimized by solving an offline optimization problem using the chaotic particle swarm optimization (CPSO) algorithm. The simulation results on the strip temperature control of the radiant-tube indirect-fired furnace of annealing furnace show that our proposed method has strong practicability and good control performance. • A contraction–expansion (C-E) factor on the infinite universe is defined. • The new defined C-E factor makes the VUAFC more flexible and practical. • The parameters of the VUAFC are optimized offline based on data by chaotic PSO.
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution.Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar.To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning.Experimental results on two public datasets demonstrate the advanced effectiveness and robustness of HiURE on unsupervised relation extraction when compared with state-of-the-art models.
Underwater target tracking in three-dimensional environment using intelligent sensor technique

Purpose This paper aims to target tracking in the marine environment is typically obtained by considering the measurement parameters like frequency, elevation and bearing. Marine environmental surveillance provides critical information and assistance for the exploitation and maintenance of marine resources. Design/methodology/approach With the use of intelligent sensor techniques like Hull-mounted and towed array sensors, convenient, precise and dependable three-dimensional (3D) underwater target tracking is introduced. Findings This research investigates a method to develop a reliable Unscented Kalman Filter (UKF) algorithm for enhanced underwater target tracking in a 3D scenario by using bearing, frequency and elevation measurements. In applications for underwater target tracking, uncertainty and inaccuracies are typically described by using Gaussian additive noise. Originality/value The proposed UKF algorithm is tested and analyzed using 100 Monte Carlo simulations with the Gaussian generated noise.
Validation of the Questionnaire on Reading Preferences and Habits of Primary School Pupils at Risk of Social Exclusion

The acquisition of the habit of reading is a fundamental issue for primary school pupils, as it contributes to the integral education of the person and, in some cases, may be essential for pupils to remain in the educational system and to escape social exclusion. In order to carry out research on reading promotion in socially disadvantaged contexts, it is necessary to have an appropriate instrument for these profiles. The aim of this work was to adapt and validate a questionnaire on reading habits and preferences that is suitable for pupils in the first cycle of primary education who are at risk of social exclusion. To this end, a mixed qualitative and quantitative methodology was used to analyse the content validity and the construct validity, which obtained, as a result, a high correlation between the variables, an appropriate factor analysis, a high overall reliability of the instrument and, in general, an adequate model to correctly explain the data of the proposed structure. We concluded by obtaining a 22-item questionnaire that is divided into five factors and that will be appropriate for determining the reading habits and tastes of disadvantaged pupils, as well as the influence of families and schools in the task of encouraging reading.
Virtual Professional Learning for School Teachers to Support Them in Online Environment

In March 2020, the World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-2019) epidemic a pandemic and whole education system came to a standstill. An immediate transformation from an offline mode to online mode of teaching learning was needed. Majority of teachers in India were not prepared for digital pedagogy. Regardless of serious difficulties and deep-rooted traditional teaching and learning methods, they quickly involved themselves in their professional learning virtually and adopted the online teaching methods to keep the system going on. Thus, the present study is focused to find the answers of the questions emerged out of this situation such as: (i) how teachers prepared themselves to come forward for gigantic initiative? (ii) How did they learn the digital online techniques? (iii) How the deficiencies of electronic gadgets, such as mobile phones and tablets were arranged in short span of time and made available to all the stakeholders? This is a qualitative study using phenomenological enquiry as an approach, participants were selected with purposive sampling technique and data were collected through the in-depth (semi-structured) interviews of 15 school teachers, 5 school heads, and 5 block education mentors from Punjab state of India. The findings of the study indicated that with continuous motivation, school teachers took the initiative to come forward for digital teaching and learning. Some teachers out of firm professional commitment managed to pursue their professional learning mainly through their own efforts. In addition, the Education Department provided online crash courses to teachers. The clusters of teachers having sound knowledge of technology collaboratively trained the teachers at block levels. Majority of teachers have their own gadgets but underprivileged sections were provided mobiles and tablets by the government and non-government agencies. A quick shift to virtual professional learning resulted in significant improvements in the learning outcomes of students. Hence, the study will motivate the teachers of other states to pursue virtual professional learning to update themselves. Additionally, it suggests that teachers ought to be part of forums, interest groups, and online professional communities to interact with peers and know how the rest of the world is doing with digital education.
A hybrid approach to metamorphic cryptography using KIMLA and DNA concept

Increasing dependency on digital data in today's word required security in term of digital language. Some researcher advocates the use of cryptography to cope the required demand. On the other hand side some researcher advocate the use of steganography. Beauty of cryptography lies in the fact that o/p i.e., encrypted message is difficult to decode if key is unknown. On the other hand beauty of steganography lies in the fact that, secret message is hidden in such a way that it does not attract the attention of unauthorised user. In the proposed work, an effort has been made to fertilise the strength of both concept i.e., steganography and cryptography. DNA concept is used for encrypting the secured text. For hiding the message KIMLA algorithm is used. Implementation results shows that proposed algorithm is efficient in terms of providing security (robustness, payload capacity, etc.).
Effects of first- and third-person perspectives created using a head-mounted display on dart-throwing accuracy

The first-person perspective (1PP) and third-person perspective (3PP) have both been adopted in video games. The 1PP can induce a strong sense of immersion, and the 3PP allows players to perceive distances easily. Virtual reality technologies have also adopted both perspectives to facilitate skill acquisition. However, how 1PP and 3PP views affect motor skills in the real world, as opposed to in games and virtual environments, remains unclear. This study examined the effects of the 1PP and 3PP on real-world dart-throwing accuracy after head-mounted display (HMD)-based practice tasks involving either the 1PP or 3PP. The 1PP group showed poorer dart-throwing performance, whereas the 3PP task had no effect on performance. Furthermore, while the effect of the 1PP task persisted for some time, that of task 3PP disappeared immediately. Therefore, the effects of 1PP HMD-based practice tasks on motor control transfer more readily to the real world than do those of 3PP tasks.
LSA Based Text Summarization

In this study we propose an automatic single document text summarization technique using Latent Semantic Analysis (LSA) and diversity constraint in combination. The proposed technique uses the query based sentence ranking. Here we are not considering the concept of IR (Information Retrieval) so we generate the query by using the TF-IDF(Term Frequency-Inverse Document Frequency). For producing the query vector, we identify the terms having the high IDF. We know that LSA utilizes the vectorial semantics to analyze the relationships between documents in a corpus or between sentences within a document and key terms they carry by producing a list of ideas interconnected to the documents and terms. LSA helps to represent the latent structure of documents. For selecting the sentences from the document Latent Semantic Indexing (LSI) is used. LSI helps to arrange the sentences with its score. Traditionally the highest score sentences have been chosen for summary but here we calculate the diversity between chosen sentences and produce the final summary as a good summary should have maximum level of diversity. The proposed technique is evaluated on OpinosisDataset1.0.
Image tracking and matching algorithm of semi-dense optical flow method

The traditional optical flow method is based on the assumption of spatial consistency of the optical flow field. It is easy to reduce the tracking quality and even lead to the loss of target tracking in the areas of image feature missing, boundary and occlusion. A semi-dense optical flow method is proposed to realise stable tracking of image features. Firstly, the feature points are preserved by calculating the pixel points with large change of pixel gradient in the image; Secondly, according to the principle of grey level invariance, the grey level difference function between the corresponding feature points of adjacent frames is constructed; Finally, the gradient descent principle is used to optimise the grey difference function and realise the accurate matching of feature points of adjacent frames. The results show that compared with the traditional LK optical flow method, this algorithm can effectively improve the feature tracking capability, and at the same time can effectively retain the useful information in the image. Compared with the traditional feature point matching method, the algorithm presented in this paper has an efficient operation rate.
Optimization methods

Multiobjective optimization methods may be applied to get the best possible solution of a well-defined problem. Optimization methods are used in many areas of study to find solutions that maximize or minimize some study parameters, such as minimize costs in the production of a good or service, maximize profits, minimize raw material in the development of a good, or maximize production. In particular, they will be described to be used to maximize thermal energy use meanwhile the production cost will be minimized. Two methods are included: Genetic algorithm and Tabu search.
Use of NLP Based Combined Features for Sentiment Classification

Sentiment analysis is the technique of automatic detection of the belief or the mood of an author towards a certain subject in textual form. To extract the opinion present in text, the machine needs expertise in the area of natural language processing. In this paper, machine learning based document-level sentiment classification is performed on Amazon product reviews to classify them as positive and negative. Two NLP based feature extraction techniques (Word Relation and POS based) are used in this study to determine the features that are sentiment bearing. The features are extracted as basic features (unigrams, bigrams and trigrams) and their combinations (unigrams+bigrams, unigrams+trigrams, unigrams+bigrams+trigrams). In order to identify the features that are most informative and to bring down the computational time of the classification algorithms, feature selection techniques are used. Performance of independent and combined feature sets is assessed using accuracy, precision, recall and F-measure. From the experiments conducted, it is observed that combined features outperformed independent features using Boolean Multinomial Naive Bayes (BMNB) classifier.
Maxfax Bites

It has been a long-standing tradition for the President of BAOMS to introduce a novel idea, concept or experience to the membership of the association. The current President, Sat Parmar, has chosen his Presidential initiative to introduce a novel and modern method of learning and distributing educational material.
A novel approach to secret data concealment with high cover text capacity and security

Massive technological advancement and rapid internet growth has made the security of secret data more challenging for security researchers. Though, steganography techniques were also evolved but still require large cover text to conceal large message. Therefore, to deal capacity issue, present research articulates a novel approach to achieve high data concealment capacity of cover text along with security by using combination of steganography and cryptography. Current algorithm applies four-layer encryption on secret message and seven Unicode characters such as invisible plus (IP), invisible separator (IS), inhabit symmetric swapping (ISS), left to right override (LRO), (ZWNJ), zero width joiner (ZWJ), zero width non-joiner and zero width character (ZWC), are employed to embed secret information into English carrier text to generate stego text object. The result reflects tremendous increase of 300% in carrier text capacity and encryption significantly enhances the security of secret information. Moreover, the identical carrier and stego text reduces hacker's attention.
Autonomous PSO-DVSF&lt;SUP align="right"&gt;2&lt;/SUP&gt;: an optimised force field mobile robot motion planning approach for unknown dynamic environments

PSO-DVSF2 (Ziadi et al., 2016) is a PSO optimised mobile robot motion planning strategy based on the force field approach. PSO-DVSF2 has been previously developed to optimally guide the mobile robot in known dynamic environments. Autonomous PSO-DVSF2, the subject of this paper, is an improvement of PSO-DVSF2 to deal with unknown dynamic environments. In this new real-time PSO optimised mobile robot motion planning approach, the robot has to update F2 parameters all along the trajectory and not once in the beginning of the navigation as has been the case with the previous version. A comparison with the autonomous PSO-CF2 has been applied in various unknown environments (static and dynamic) using the 3D virtual Webots simulator. The robot localisation based on sensor readings with a local motion planning, and the variation of angular and linear speeds ensure the robot collision-free motion. Simulation results prove the efficiency of the autonomous PSO-DVSF2 to guide the robot along the shortest and safest path in complex unknown dynamic environments.
Automatic Face Recognition for Various Expressions and Facial Details

Face Recognition Technique (FRT) was a unique Biometric Technique which tries to spot the people from still images or video frames by using techniques of pattern recognition. Face recognition includes both face identification and face verification (authentication). The FR Design system follows two basic steps i.e. Feature extraction and classification of patterns. Automated FR finds many applications in real time environment ranging from Social Media, surveillance to biometric authentications. Many state-of-the-art face recognition techniques had been implemented, but the Automated Face Recognition (AFR) taken by digital cameras in unconstraint real‐world environment continues to be terribly difficult, since it involves vital variations in each acquisition conditions, yet as in facial expressions and in pose variations. Thus, this paper presents the theme of computer based automatic face recognition in lightweight of the most contests therein areas with developed solutions that supports applications of signal, image processing and computing strategies.
Mining Threat Intelligence from Billion-scale SSH Brute-Force Attacks

This paper presents a longitudinal study of 11 Billion SSH brute-force attacks targeting an operational system at the National Center for Supercomputing Applications.We report the nature of these distributed attacks in terms of i) persistence (i.e., consecutively attacking over an entire year), ii) targeted strategies (i.e., using stolen SSH keys), iii) large-scale evasion techniques (i.e., using randomized SSH client versions) to bypass signature detectors, and iv) behaviors of human-supervised botnet.The significance of our analyses for security operators include i) discerning cross-country attacks versus persistent attacks, ii) notifying cloud providers and IoT vendors regarding stolen SSH keys for them to verify the effectiveness of software patches, iii) deterring the above evasion techniques by using anomaly detectors/rate limiters, and iv) differentiating between fully automated attacks versus more sophisticated attacks driven by human.1.7 M new SSH client versions burst in Aug 2018 Evasion increased by 8,000x Coordination accelerated by 50x 70% of attacks were persistent Findings Human-supervised botnet used sophisticated credential-guessing techniques Attack