Reviewerly/exHarmony huggingface dataset:
An Approach for Securing QR code using Cryptography and Visual Cryptography

Quick Response code (QR code) is a 2-D matrix barcode which stores data in four different encoding modes (numeric, alphanumeric, kanji, binary). They are widely used nowadays and can be seen almost everywhere whether it is on cosmetics products, general stores, billboards and so on. It has become an important part of day to day activities. As an information sharing medium, it has become so user friendly and mobile-friendly that with just one scan through smart phones you get the information stored in it. The main intent of this work is to secure QR code from unauthorized access by allowing only those who have authorization to access it by using cryptography (by encrypting and decrypting the QR code using a key value). And further security was enhanced by applying ‘k’ out of ‘n’ visual cryptography scheme on the QR code. It creates ‘n’ no. of share of the QR code out of which ‘k’ no. of shares is required to restore it. This work will briefly explain how cryptography and visual cryptography were used to secure the QR code. The experimental results showed that there was no data loss during this process. Also, if at the time of decryption wrong key is entered then the QR code will not be generated. And also it is required to input minimum k number of generated shares of the QR code for the successful retrieval of QR code. Quality of the reconstructed QR code was also measured using PSNR and SSIM which showed that reconstructed QR code was of good quality as well as original QR code and reconstructed QR code were identical.
Hate Speech and Abusive Laungage Detection In Twitter and Challenges: Review

The web' content is increasing every day, especially on social media, where all users can express their opinions freely and without restrictions. Accordingly, many negative activities emerged, such as abusive language, racism, and hate speech. Hate speech or abusive language is a manifestation of negative social media that requires tools to detect it. This work provides a comprehensive view of the concepts of abusive language and hate speech. The methods used for detection will also be presented. The paper also reviews previous studies in this field. Finally, the paper recalls the challenges facing the detection of abusive language or hate speech.
User Story Clustering using K-Means Algorithm in Agile Requirement Engineering

In Agile software development user story plays an important role. User story is the concept of requirement engineering that manages requirements in the form of text as software requirement specification maintained in traditional development models. Proper format is used to write the user story which is easily understandable. The impact of poor user story design may lead to modifications in later phases of development. It also places negative impression on the customer, project and development team as well. Therefore the analysis of these user stories is necessary before they are assigned to the development team. In this paper we propose an approach of user story clustering using k-means algorithm to analyze these user stories. Through this approach similar user stories are clustered on the basis of similarity measures. Our experimental results show that as the value of k increases the quality of resulting cluster improves. To generate balanced clusters our approach uses tf-idf which is comparatively better than count vectorizer. This approach reduces the implementation time of requirements and hence the overall development life cycle of the software. To illustrate our approach we use a case study considering various user stories.
Explaining uncertainty and defectivity of inflectional paradigms

Abstract The current study investigates how native speakers of a morphologically complex language (Finnish) handle uncertainty related to linguistic forms that have gaps in their inflectional paradigms. We analyze their strategies of dealing with paradigmatic defectivity and how these strategies are motivated by subjective contemporaneousness, frequency, acceptability, and other lexical and structural characteristics of words. We administered a verb production (inflection) task with Finnish native speakers using verbs from a small non-productive inflectional type that has many paradigmatic gaps and asked participants to inflect the verbs in a given context. Inflectional uncertainty was measured by the number of different forms the participants produced for each verb. We classified produced forms that were not expected as either synonymous or novel and measured their optimal string alignment distance to expected forms. Our analyses revealed that a usage-based approach to paradigmatic defectivity fits better with the obtained results than a classical approach typically met in dictionaries and descriptive grammars. Thus, we argue, that paradigmatic defectivity can be better described as a dynamic rather than a static system, where gaps represent a continuum of possible inflectional choices rather than a lack of an inflectional variant.
Hierarchical and non-monotonic key-policy attribute-based encryption and its application

Hierarchical-authority attribute-based encryption (HABE) can resolve the performance bottleneck of the key generation center in the traditional attribute-based encryption (ABE) system. HABE has numerous applications, such as cloud computing and smart grid systems. Nevertheless, almost all of the existing HABE schemes are in the ciphertext-policy setting and do not support non-monotonic access structures. In this work, we propose the first key-policy hierarchical-authority ABE with non-monotonic access structures, dubbed KP-HABE-NMaCS, which supports the authority key delegation, constant size ciphertexts and the large universe simultaneously. We further define the security model of KP-HABE-NMaCS and prove the selective security of it in the standard model. Then we optimize our scheme to realize the outsourced decryption. Subsequently, we present the detailed theoretical analysis of our constructions and the existing HABE constructions in computation and storage costs, and meanwhile implement ours and conduct a series of experiments. Both results indicate that our KP-HABE-NMaCS construction makes improvements in expressiveness and features, and achieves efficiency comparable to the previous HABE constructions. Furthermore, they also show that our extension construction is suitable for resource-limited applications. Eventually, we explore how to apply our KP-HABE-NMaCS to build an ABE with equality test and time-based authorization.
Color Image Encryption Algorithm Based on Dynamic Block Zigzag Transformation and Six-Sided Star Model

As a result of the rise in network technology, information security has become particularly important. Digital images play an important role in network transmission. To improve their security and efficiency, a new color image encryption algorithm is proposed. The proposed algorithm adopts a classical scrambling–diffusion framework. In the scrambling stage, the dynamic block Zigzag transformation is designed by combining the chaotic sequence with the standard Zigzag transformation, which can dynamically select the transformation range and the number of times. It is used to scramble the pixel positions in the R, G, and B components. In the diffusion stage, the six-sided star model is established by combining the chaotic sequence and the six-sided star structure characteristics, which can store the 24 bits of the pixel in a defined order to realize bit-level diffusion operation. Experimental analyses indicate that our algorithm has the characteristics of high key sensitivity, large key space, high efficiency, and resistance to plaintext attacks, statistical attacks, etc.
DS-UNet: A dual streams UNet for refined image forgery localization

Nowadays, image manipulation has become very easy with the help of image editing applications. The abuse of tampered images would mislead people’s judgment, or even make the society suffer from instability if without any interference. To address this issue, a dual-stream UNet named DS-UNet is presented in this paper to detect image tampering and localize forgery areas. The DS-UNet employs an RGB stream to extract both high-level and low-level manipulated traces for a coarse localization, and adopts a Noise stream to expose the local noise inconsistency for a refined localization. The lightweight hierarchical fusion way provides the DS-UNet with the ability to perceive tampered objects at different scales for the reason that tampered objects always vary in shapes and sizes. After that, the DS-UNet applies a single decoder to receive the abundant low-level manipulated traces and spatial localization information in the skip connection path. Through the decoder, the object details and spatial dimensions are gradually recovered, and the high-resolution prediction maps are generated. In the comparative analysis, more evaluation indices are introduced than the existing works in order to obtain a more comprehensive assessment. Extensive experiments with satisfying results on five datasets demonstrate its superior performance over state-of-the-art methods.
Study on Quantum Radar Detection Probability Based on Flying-Wing Stealth Aircraft

The development of quantum radar technology presents a challenge to stealth targets, so it is necessary to study the quantum detection probability. In this study, an analytical expression of the quantum radar cross section (QRCS) for complex targets is presented. Based on this QRCS expression, a calculation method for the detection probability for quantum radar is creatively proposed. Moreover, a self-designed flying-wing stealth aircraft is adopted to obtain the detection probability distributions of the conventional radar and the quantum radar in different directions. As revealed by the result of this study, the detection probabilities of the quantum radar and the conventional radar are significantly different, and the detection probability of the quantum radar has obvious advantages in most regions with a certain distance.
Developing a conceptual framework for short text categorization using hybrid CNN- LSTM based Caledonian crow optimization

Due to the continuous progression of social media networking sites, people share their thoughts, viewpoints, videos, speech and images through short texts. But the short texts are manually understandable but hard for the machine to collect data for clarification. The limited terms present in the short texts seem difficult while categorizing, analyzing as well as evaluating. Since social media and Bibliographic repositories like Digital Bibliography and Library Project (DBLP) contains a substantial amount of information, it is necessary to mine only useful information from the existing short texts. To achieve effectiveness and efficiency in short text categorization, the content-related characteristics derived from various machine learning techniques are admired. The significant objective of the proposed approach involves the categorization of short text and enhancing its accuracy, which can improve further ﬁnding collaborative research communities.. In this work, a hybrid convolutional neural network-long short term memory (CNN-LSTM) based new Caledonian crow optimization (NC2LO) model is utilized to classify the short texts. The crows utilize both social and asocial learning for developing tool modelling skills. Getting attracted to the behaviour of this variety of crows, and motivated by the learning strategy of crows, a new Caledonian crow optimization model referred to as NC2LO model is established. The conceptual framework of the proposed methodology consists of four different phases namely the data collection phase, data preparation phase, data pre-processing phase and short text categorization phase to classify the short texts with a high accuracy rate. Then, the proposed CNN-LSTM based NC2LO for short text categorization is evaluated using four different types of datasets namely IMDb, AG news, Twitter, and Tagmy News. Finally, the comparative analysis is carried out to evaluate the accuracy rate for various approaches and the analysis demonstrated that the proposed approach achieves a high accuracy rate of about 97%.
Label-reconstruction-based pseudo-subscore learning for action quality assessment in sporting events

Most existing action quality assessment (AQA) methods provide only an overall quality score for the input video and lack an evaluation of each substage of the movement process; thus, these methods cannot provide detailed feedback for users. Moreover, the existing datasets do not provide labels for substage quality assessment. To address these problems, in this work, a new label-reconstruction-based pseudo-subscore learning (PSL) method is proposed for AQA in sporting events. In the proposed method, the overall score of an action is not only regarded as a quality label but also used as a feature of the training set. A label-reconstruction-based learning algorithm is built to generate pseudo-subscore labels for the training set. Moreover, based on the pseudo-subscore labels and overall score labels, a multi-substage AQA model is fine-tuned from the PSL model to predict the action quality score of each substage and the overall score for an athlete. Several ablation experiments are performed to verify the effectiveness of each module. The experimental results show that our approach achieves state-of-the-art performance.
The limitations for expression recognition in computer vision introduced by facial masks

Facial Expression recognition is a computer vision problem that took relevant benefit from the research in deep learning. Recent deep neural networks achieved superior results, demonstrating the feasibility of recognizing the expression of a user from a single picture or a video recording the face dynamics. Research studies reveal that the most discriminating portions of the face surfaces that contribute to the recognition of facial expressions are located on the mouth and the eyes. The restrictions for COVID pandemic reasons have also revealed that state-of-the-art solutions for the analysis of the face can severely fail due to the occlusions of using the facial masks. This study explores to what extend expression recognition can deal with occluded faces in presence of masks. To a fairer comparison, the analysis is performed in different occluded scenarios to effectively assess if the facial masks can really imply a decrease in the recognition accuracy. The experiments performed on two public datasets show that some famous top deep classifiers expose a significant reduction in accuracy in presence of masks up to half of the accuracy achieved in non-occluded conditions. Moreover, a relevant decrease in performance is also reported also in the case of occluded eyes but the overall drop in performance is not as severe as in presence of the facial masks, thus confirming that, like happens for face biometric recognition, occluded faces by facial mask still represent a challenging limitation for computer vision solutions.
Spectral mapping of zeolite bearing paleolake deposits at Lake Tecopa, California and its implications for mapping zeolites on Mars

While non-analcime zeolites are plausible mineral constituents of the Martian surface, their lack of prominent spectral characteristics in the visible to shortwave infrared wavelength region makes them difficult to identify using orbital data. This lack of detection has led to the assumption that they are limited or absent. Here we assess the capability of mapping non-analcime zeolites using different mapping techniques and present the possible reasons that could lead to limited detection of zeolites using an analog study from Paleolake Tecopa, California. We used spaceborne hyperspectral Hyperion and multispectral ASTER data to identify zeolites following X-ray Diffraction (XRD) and visible-near infrared and shortwave infrared (VNIR-SWIR) spectral analysis of collected samples containing zeolites and other associated minerals. We used four of the most common spectral mapping methods: (1) band ratios, (2) minimum noise fraction (MNF), (3) spectral angle mapper (SAM), and (4) linear spectral unmixing (LSU). We were able to map zeolite-rich tuff beds using carefully selected band indices, MNF band combinations, and the pixel spectra corresponding to a zeolite-rich abandoned quarry area. Hyperion results show less overall accuracy than the ASTER image products mostly due to the low signal-to-noise ratio of the Hyperion data. The spectral characteristics of zeolites in zeolite-rich pixels are masked by phyllosilicates (e.g., smectite) present in the ground resolution cell (GRC), making it difficult to identify zeolites in phyllosilicate-bearing deposits solely based on zeolite spectra. Therefore, the SAM product derived using the pixel spectrum of the well-exposed zeolite-rich quarry shows the highest accuracy in Hyperion image products. ASTER band ratio Band 2/Band 1, representing the ferric ion, was able to identify the zeolite-rich beds with the highest accuracies. However, most of the zeolite-rich paleolake beds were difficult to identify since they are buried by other beds, mixed with other beds due to physical weathering, or the areas of exposed beds are smaller than the GRC of the satellite. The results imply that the paucity of detected zeolites on Mars, as we see at Lake Tecopa, does not preclude their wider presence, either beneath other materials, obscured by surface dust, or mixed with more spectrally dominant phases.
“Better” Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI)

A recent surge of research has focused on counterfactual explanations as a promising solution to the eXplainable AI (XAI) problem. Over 100 counterfactual XAI methods have been proposed, many emphasising the key role of features that are "important" or "causal" or "actionable" in making explanations comprehensible to human users. However, these proposals rest on intuition rather than psychological evidence. Indeed, recent psychological evidence [22] shows that it is abstract feature-types that impact people's understanding of explanations; categorical features better support people's learning of an AI model's predictions than continuous features. This paper proposes a more psychologically-valid counterfactual method, one extending case-based techniques with additional functionality to transform feature-differences into categorical versions of themselves. This enhanced case-based counterfactual method, still generates good counterfactuals relative to baseline methods on coverage and distances metrics. This is the first counterfactual method specifically designed to meet identified psychological requirements of end-users, rather than merely reflecting the intuitions of algorithm designers.
PPR-RM: Performance-to-Power Ratio, Reliability and Makespan — aware scientific workflow scheduling based on a coalitional game in the cloud

A distributed scientific workflow scheduling scheme is proposed for maximized reliability and performance-to-power ratio (PPR) under an inevitable end-to-end delay (EED), i.e., makespan. The proposed algorithm is studied in a heterogeneous cloud computing infrastructure, where processing node and transmission link failures are unavoidable. Cloud nodes share the scheduling decision and the stored related data with their neighbors to maintain scalability and robustness, which are primarily significant for large-scale distributed schemes. This workflow scheduling optimization strategy (PPR-RM) considers the maximum reliability and PPR under EED constraints in a three-step procedure. In the first step, an optimal mapping algorithm is adopted to minimize the EED of the critical path's tasks using the iterative critical path search and Layer-based priority placing techniques (CPP). The second step samples the node's utilization levels with distinguished PPR, calculated as the number of transactions performed in a specific time divided by the average active power consumed. This step trains processing nodes at different utilization levels to guarantee those nodes operate at the most power-efficient utilization levels, i.e., the highest PPR levels. Further, this step tremendously decreases energy consumption without sacrificing EED and reliability. The final step reallocates the tasks on non-critical paths for maximum performance reliability. Our extensive results demonstrated that PPR-RM acquired considerably higher reliability values under the minimized EED and energy constraints than some competitor workflow mapping algorithms.
HDECO: A method for Decreasing energy and cost by using virtual machine migration by considering hybrid parameters

Energy and cost are important issues in cloud computing, which led to increased power supply consumption and more carbon dioxide production. In this paper, cost and energy have been studied with other methods and ECIB algorithm. By the study of the ECIB and previous methods, the HDECO method is presented. This paper provides a method to decrease energy and cost in instance-intensive cloud workflows, using virtual machine migration. In the proposed method, creating a classification of inputs, calculating real execution time, and distance parameters, as well as using an intelligent threshold detector (ITD) compared to the study of the previous methods, has improved cost and energy, which according to the specified parameters, an optimum solution is achieved. In the HDECO method, energy has been improved by creating an objective function and combining parameters into the fitness function. By observing the provided parameters, the actual execution time is improved, and also dynamic threshold is used in the proposed method to reduce energy and cost. In this method, the accuracy of prediction increased by classification of inputs to different levels, as well as applying the ITD to the previous tasks and the ECIB algorithm. In our method, the allocation of the specified parameters such as ITD, the actual execution time, and the number of migrations have changed compared to the previous methods and ECIB. Finally, by categorizing the inputs related to the processing power of the resources as well as the proper allocation of them to a resource that has less work and suitable load-balancing, the execution time is optimized and the amount of energy consumption and cost is improved.
A HEURISTIC COMPUTING APPROACH USING SEQUENTIAL QUADRATIC PROGRAMMING TO SOLVE THE FIFTH KIND OF INDUCTION MOTOR MODEL

The purpose of the current investigation is to solve the fifth kind of induction motor model using an advanced computational scheme by operating the artificial neural networks (ANNs), global scheme as genetic algorithm (GA) along with the rapid local search sequential quadratic programming technique (SQPT), i.e. ANN-GA-SQPT. ANNs are implemented to discretize the fifth kind of induction motor model to express the merit function based on the mean square error. The numerical presentation of the proposed ANN-GA-SQPT is pragmatic for three different problems based on the fifth kind of induction motor model to authenticate the efficacy, consistency and importance of the proposed ANN-GA-SQPT. Moreover, statistical representations are provided in order to check the precision, convergence and accuracy of the present ANN-GA-SQPT.