Reviewerly/exHarmony huggingface dataset:
Imperceptible Steganography Scheme with High Payload Capacity using Genetic Algorithm and Particle Swarm Optimization

Security is the most significant parameter in all type of confidential data transfers. Steganography is used to enhance the security of such confidential communications. Steganography is a method of covert communication in which the existence of secrecy is concealed. In image steganography, achieving high data embedding capacity and simultaneously retaining good visual quality is a very tricky and difficult objective. In this paper, a reversible, secure, extremely imperceptible and high payload capacity steganography technique in the spatial domain is proposed. The proposed method employs evolutionary computation techniques to identify the most optimum locations and arrangements for secret data embedding. The proposed technique uses Particle Swarm Optimization to find the best possible order of data hiding whereas Genetic algorithm is used to identify the best possible arrangements to modify secret data to produce least amount of change in cover-image. The result of the proposed scheme is compared with many steganography techniques and the proposed scheme outperforms the existing schemes in terms of imperceptibility. The proposed technique produces an average PSNR value of 46.40 dB at 2 bit per pixel data embedding rate.
Human activity recognition by using MHIs of frame sequences

A motion history image (MHI) is a temporal template that collapses temporal motion information into a single image in which intensity is a function of recency of motion. In recent years, the popularity of deep learning architectures for human activity recognition has encouraged us to explore the effectiveness of combining them and MHIs. Based on this, two new methods are introduced in this paper. In the first method, which is called the basic method, each video splits into N groups of consecutive frames, and the MHI is calculated for each group. Transfer learning with the fine-tuning technique is used for classifying these temporal templates. The experimental results show that some misclassification errors are created because of the similarities between these temporal templates; these errors can be corrected by detecting specific objects in the scenes. Thus, spatial information consisting of a single frame is also added to the second method, called the proposed method. By converting video classification problems into image classification problems in the proposed method, less memory is needed and the time complexity is greatly reduced. They are implemented and compared with state-of-the-art approaches on two data sets. The results show that the proposed method significantly outperforms the others. It achieves recognition accuracies of 92% and 92.4% for the UCF Sport and UCF-11 action data sets, respectively.
Unconstrained online handwritten Uyghur word recognition based on recurrent neural networks and connectionist temporal classification

This paper conducts the first experiments applying recurrent neural networks-RNN accompanied with connectionist temporal classification (CTC) to build end-to-end online Uyghur handwriting word recognition system. The traced pen-tip trajectory is fed to network without conducting segmentation and feature extraction. The network is trained to transcribe handwritten word trajectory to a string of characters in alphabet which has total 128 character forms. In order to avoid overfitting during training and improve generalisation of the model, dropout technique is implemented. An online handwritten word dataset has been established and used for model training and evaluation in writer independent manner. Recognition results are evaluated by calculating the Levenshtein-edit distance and 14.73% character error rate CER on test set of 3,600 samples for 900 word classes has been observed without help of any lexicon search and language model.
Quasi Attribute Utility Enhancement (QAUE)- A Hybrid Method for PPDP

The data analytics has become prominent for today’s world because it is defined as the methodology of investigating data sets in order to draw conclusion about the information it contain. The Data Mining is a key part of Data Analytics because it has techniques and tools which help to explore knowledge which is hidden in data. The outcome of data analytics is very crucial to Business organizations because it helps in decision making process. In Data Analytics there are two roles which are very prominent and they are Data publisher and Data Analyzer. Data Publisher is the one who provides data for analytics which is collected from heterogeneous sources. Data Analyzer receives data from Data publisher and uses for data analytics. The main issue involves here is data privacy, which is concerned with the proper treatment of data i.e. approval, discern and regulations. A separate field called PPDP- Privacy Preserving Data Publishing mainly concentrates on how data is shared, used by data analysts and it may be implicit or explicit to organizations (third party) such that it should be safer from untrusted people and attacks. The PPDP offers several approaches to publish data in safe manner and supports data utility, but there is a need of domain specific privacy concern because privacy needs are different based on the domain and in mean time how data is utilized. In the paper a hybrid approach is proposed to preserve data privacy in concern with data publisher which supports domain specific data privacy and utility
Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

Twitter sentiment analysis is a vital concept in determining the public opinions about products, services, events or personality. Analyzing the medical tweets on a specific topic can provide immense benefits in medical industry. However, the medical tweets require efficient feature selection approach to produce significantly accurate results. Penguin search optimization algorithm (PeSOA) has the ability to resolve NP-hard problems. This paper aims at developing an automated opinion mining framework by modeling the feature selection problem as NP-hard optimization problem and using PeSOA based feature selection approach to solve it. Initially, the medical tweets based on cancer and drugs keywords are extracted and pre-processed to filter the relevant informative tweets. Then the features are extracted based on the Natural Language Processing (NLP) concepts and the optimal features are selected using PeSOA whose results are fed as input to three baseline classifiers to achieve optimal and accurate sentiment classification. The experimental results obtained through MATLAB simulations on cancer and drug tweets using k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) indicate that the proposed PeSOA feature selection based tweet opinion mining has improved the classification performance significantly. It shows that the PeSOA feature selection with the SVM classifier provides superior sentiment classification than the other classifiers.
Contextualising Hate Speech: A Study of India And Malaysia

Hate speech is a verbal expression, which is designed to promote hatred based on race, religion, ethnicity and violence between different communities .Hate speech are systematically used to lash hatred towards minorities and for inflaming regional, religious and ethnic passion.It has been repeatedly observed that inflaming and deleterious words have been used by people of influence to further their vested interests and incite violence.Indian laws as well as Malaysian laws forbid anyone from making hate speeches that disturb the communal harmony and creates discord among the people.When the issue of suppression of hate speech comes to purview, the notion of reasonable restriction comes into picture.Consequent debate narrows down to two sharply contrasting opinions ie: restraints v. freedom of speech and expression.It is difficult to balance both divergent opinions.Article 19(1) of the Indian Constitution, provides for the freedom of speech and expression but also places reasonable restrictions under Article 19(2) on the grounds of public order, decency and morality.Similarly, Article 10 of the Malaysian Constitution empowers the Malaysian citizens with the freedom of speech and expression subject to restrictions imposed by the Parliament.Whenever these contradictory opinions are presented in front of the court, even the Apex court of the respective countries has shown its reluctance towards curbing such communally charged statements.Even if the judgment restraining hate speech is pronounced, it often comes too late, sometimes after the damage has already been inflicted, thus failing to exert any deterrent effect.By using doctrinal research methods, this paper a) discusses the terminology of hate speech; b) analyzes different legal provisions that deals with hate speech in India and Malaysia; and c) discusses non-legal solutions to the problem.This paper argues that although several legal provisions are available to curb hate speech, Alternative Dispute Resolution and counter speech may be used as alternative ways to address hate speech.
Geoinformatics and systems analysis in geophysics and geodynamics

The paper is devoted to the application of geoinformatics and systems analysis methods for processing and interpretation of geospatial data in geophysics and geodynamics. The modern uses of observations with Global Navigational Satellite Systems as a main source of geospatial data are discussed. The advances in the interpretation of geomagnetic data are described and basic points of systems analysis are presented in this context. The systems analysis in geophysics and geodynamics is illustrated by the approaches to estimating and forecasting the stability of structural-tectonic blocks of the Earth’s crust aimed at geoecologically safe burial of high-level radioactive waste in the Nizhne-Kanskii granitoids massif (Krasnoyarsk krai).
Navigating the Post-COVID-19 Era of ‘Next Normal’ in the Context of Philippine Higher Education

This year holds an important promise to humanity as codified treatments to control the effects of the COVID-19 are already in the final phase of the clinical test.With that positive note, there is also a new glimmer of hope in higher education as it will navigate another challenging but interesting era.This essay article explores the concept of a post-COVID-19 era called 'next normal' in the context of Philippine higher education.Drawing from the experiences, observations, and reflections, the researcher conceptually introduces the idea of a next normal and then discusses it in the context of Philippine higher education.This paper contends that the next normal is not the 'what' but the 'how' as higher education navigates beyond the COVID-19 crisis.It is characterized by an active reshaping process that involves the following actions: bolstering teaching and learning continuity, developing educational recovery plans, keeping the machine running as an obligation, and attuning the curriculum to the next demands.A conclusion answering the metaphorical cure to COVID-19 in higher education to pave the way to the next normal is presented.Finally, a next normal framework for higher education which authorities and teachers may consider in educational planning is offered at the end of the paper
Geopolitical meridians of world-class universities

This article discusses results from two waves (2017, 2019) of world-class universities identification, thus allowing provision of a geopolitical snapshot of advanced universities global market. Results show that United Europe is becoming a leader, while Asia and the United States positions have worsened. Economic and cultural factors are based on different countries foundations of success in formation of global universities. The quality of the economic prerequisite involves the existence of global high-tech companies in the country; their number and strength determine the number and strength of world-class universities. The quality of the cultural prerequisite involves wide dissemination of the philosophy of cooperation, that is, intensive national and international exchange experience among universities through creation of numerous collaborations, for example, international leagues and unions, regional consortia and groups, and professional associations and alliances.
SQL Injection Detection and Prevention Techniques in ASP.NET Web Application

Injection in SQL (structure query language) is one of the threats to web-based apps, mobile apps and even desktop applications associated to the database. An effective SQL Injection Attacks (SQLIA) could have severe implications for the victimized organization including economic loss, loss of reputation, enforcement and infringement of regulations. Systems which do not validate the input of the user correctly make them susceptible to SQL injection. SQLIA happens once an attacker can incorporate a sequence of harmful SQL commands into a request by changing back-end database through user information. To use this sort of attacks may readily hack applications and grab the private information by the attacker. In this article we introduce deferential sort of process to safeguard against current SQLIA method and instruments that are used in ASP.NET apps to detect or stop these attacks.
Detection of Colorectal Cancer by Deep Learning: An Extensive Review

Colorectal cancer, which is frequent, recognized tumours in both genders around the globe.As per the report generated by WHO in 2018, colon cancer placed in the third position, whereas 1.80 million individuals are affected.Precisely, it is the succeeding leading cancer, which is the second most common cause of cancer in females, and the third for males.The loss of control over the integrity of epidermal cells in bowel or malignancy can be the cause of colorectal cancer.An effective way to recognize colon cancer at an early stage and substantial treatment can reduce the ensuing death rates to a great extent.To perform Screening of Morphology of Malignant Tumor Cells in the colon, a Gastroenterologist may refer to cancer diagnosis tests for pathological images.In any Histology method, the process takes a significant duration of time due to infinite numbers of glands in the gastrointestinal system, which may lead to irreconcilable outcomes.By diagnosing through computer algorithms, can give practical and contributory results.Hence, accurate gland segmentation is one crucial prerequisite stage to get reliable and informative morphological image data.In recent times, the scholars applied deep learning algorithms to pathological image analysis for the diagnosis of cancer disease.We propose that features extracted from the diagnostic tests, given as input to a deep learning architecture used along with semantic segmentation algorithm, provide results that are accurate than the existing image segmentation algorithms.This work is the extensive review of deep learning architectures used for semantic segmentation on the histological images of the colon.
International geoscience collaboration to support critical mineral discovery

First posted July 8, 2020 For additional information, contact: Director, Geology, Geophysics, and Geochemistry Science CenterU.S. Geological SurveyBox 25046, MS-964Denver, CO 80225-0046 The importance of critical minerals and the need to expand and diversify critical mineral supply chains has been endorsed by the Federal governments of Australia, Canada, and the United States. The geoscience organizations of Geoscience Australia, the Geological Survey of Canada and the U.S. Geological Survey have created the Critical Minerals Mapping Initiative to build a diversified critical minerals industry in Australia, Canada, and the United States by developing a better understanding of known critical mineral resources, determining geologic controls on critical mineral distribution for deposits currently producing byproducts, identifying new sources of supply through critical mineral potential mapping and quantitative mineral assessments, and promoting critical mineral discovery in all three countries.
C-QWERTY: a text entry method for circular smartwatches

We present a study aimed at comparing different circular layouts for entering text on smartwatches.In particular, we measured the extent through which the use of a QWERTY layout increases user performance in the earliest sessions of use.To this aim, we designed C-QWERTY, a soft keyboard designed for circular smartwatches in which the keys are arranged along the edge of the screen in a circular layout.In order to make the keyboard more familiar to users, the order of the keys is similar to the one of traditional rectangular QWERTY keyboards.The method supports two interaction modes: tapping and gesture, in which a whole word can be written with a single gesture.As an evaluation, we compared the C-QWERTY layout with Cirrin, another circular layout in witch the order of the characters has been optimized to minimize the distance between successive keys during writing.The experimental results showed that the C-QWERTY layout outperformed the Cirrin one with respect to the text entry speed with an average increase of 27 percent.
Possible effects of emoticon and emoji on sentiment analysis web services of work organisations

Work organisations are increasingly interested in using sentiment analysis algorithms to get rapid feedback from microblogging platforms such as Twitter. However, real-life posts can differ from the training data. The subject domain may vary or and emojis and emoticons used to clarify, enhance or even reverse the sentiment of a post. This paper studies the effect of emojis, emoticons and subject on polarity classification using nine tweet-related sentiment analysis web services. A web application was developed to extract from the live Twitter stream, and twelve specific research test sets were created. These were labelled by volunteers, uploaded back into the application and then compared against nine different sentiment analysis web services using two- and three-class accuracy measures. Distinct differences were found in the performance of the sentiment analysis web services of organisations. Sentiment analysis web services can vary significantly in classification performance depending and the effect of emoticons and emojis.
Pose and occlusion invariant face recognition system for video surveillance using extensive feature set

Face recognition presents a challenging problem in the field of image analysis and computer vision. Different video sequences of the same subject may contain variations in resolution, illumination, pose, and facial expressions. These variations contribute to the challenges in designing an effective video-based face-recognition algorithm. In this proposed method, we are presenting a face recognition method from video sequence with various pose and occlusion. Initially, shot segmentation process is done to separate the video sequence into frames. Then, face part is detected from each frame for further processing. Face detection is the first stage of a face recognition system. After detecting the face exactly the facial features are extracted. Here SURF features, appearance features, and holo-entropy is used to find out the uniqueness of the face image. The active appearance model (AAM) can be used to find out the appearance-based features in the face image. These features are used to select the optimal key frame in the video sequence which is based on the supervised learning method, modified artificial neural network (MANN) using bat algorithm. Here bat algorithm is used for optimising the weights of neurons. Finally, based on the feature library, the face image can be recognised.
Performance Analysis of Data Encryption Algorithm

In this paper, author have performed experimental analysis of Jumbling-Salting (JS) algorithm for larger text size. In the earlier research work, JS Algorithm was symmetric-password encryption algorithm. JS algorithm consists of two prominent cryptographic processes namely Jumbling and Salting. Jumbling consists of three major randomized processes viz. Addition, Selection and Reverse. Jumbling process jumbles the random characters into password string. Salting process adds a random string based on some timestamp value. The output of Jumbling and Salting process is given to predefined AES block to perform 128-bit key encryption to maintain the cipher text size uniform. In this research, the capability of JS algorithm is enhanced. The paper therefore shows the performance of JS algorithm regarding length of cipher text size with respect to AES and DES algorithms. This extended research ensures that JS algorithm is not only suitable for smaller text like password, pin, passcode etc. but it is also favorable for larger texts.
A Novel Human Activity Recognition Scheme for Smart Health Using Multilayer Extreme Learning Machine

In recent years, wearable sensors have increasingly been employed in smart health applications. Wearable sensors can not only be used to collect users’ valuable health-related data, they can be used in conjunction with other infrastructure-bound sensors, such as Microsoft Kinect sensors, to facilitate privacy-aware fine-grained activity tracking. This fusion of multi-modal data promises a new type of smart health application that coaches a user to live a healthier lifestyle by monitoring the user in real time and reminding him or her when he or she engages in an unhealthy activity. In this article, we investigate how to achieve fine-grained activity recognition in the context of such an application. In our scheme, identification accuracy is improved by incorporating a nonlinear local similarity measure, namely kernel risk-sensitive loss (KRSL), into a novel multilayer neural network (NN) learning algorithm, a stacked extreme learning machine (S-ELM). Furthermore, to achieve good generalization performance with minimal human intervention, the popular optimization algorithm Jaya is also used to adjust key parameters in our proposed approach. Experiments were conducted to verify the effectiveness of the proposed scheme.
A Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank

We show that the choice of pretraining languages affects downstream cross-lingual transfer for BERT-based models.We inspect zeroshot performance in balanced data conditions to mitigate data size confounds, classifying pretraining languages that improve downstream performance as donors, and languages that are improved in zero-shot performance as recipients.We develop a method of quadratic time complexity in the number of languages to estimate these relations, instead of an exponential exhaustive computation of all possible combinations.We find that our method is effective on a diverse set of languages spanning different linguistic features and two downstream tasks.Our findings can inform developers of largescale multilingual language models in choosing better pretraining configurations.
Coastal Marine Geochemical Provinces and Background Values in Sediments: A Methodological Approach

The Italian approach to determining background values (BGVs) of metals and trace elements in marine sediments according to the national legislation transposing the water framework directive (WFD) has been illustrated. This study may be helpful for all countries that need to establish local BGVs for the correct assessment of contamination in areas with wide geological and geochemical variability and, particularly, for those that need assessing the good chemical status according to the WFD. The first step was clearly defining the BGV as a concentration value that allows for discriminating between natural and anthropogenic contributions and establishing the concept of the marine geochemical province (MGP). Successively, criteria for delimiting the MGPs were provided, similar to those adopted for the geochemical mapping of the territory. Finally, an integrated method based on the constitution of a dataset from unpolluted sediments obtained from pre-industrial levels was chosen for determining the BGV.