Reviewerly/exHarmony huggingface dataset:
Community Spam Detection Methodologies for Recommending Nodes

The most popular and leading social network service online now days is Facebook, twitter and Linked In. When socializing becomes usual, the probability of threats and unwanted posts (Spams) comes naturally. To identify and block such Spams, there are a few techniques available recently. However, the efficiency of such tools to combat with spammers seem tedious due to the public unavailability of critical pieces of Facebook Information like Profile, Network Information, Posts and more. Literature shows that there are many researches been carried out to find and combat malicious accounts and spammers over last two decades. In this paper, a review of similar methods that works with detection of spammers in a community on Social Networking Website with the help of mindmap that is given. The work is comprehended in how data is collected, types of spammers, classifiers, machine learning, review on spammers and community detection and whether it is graph based or non graph based dataset. A survey of research publications on Spammers and Malicious account based on malicious categories for the detected communities with the help of various categories discussed in the mindmap
Multiplication Technique in Residue Number System

This paper presents the modulo multiplication technique in residue number system (RNS) using Vedic mathematics. Residue number system supports fast mathematsical computation. In this paper, the use of the combination of RNS and Vedic mathematics has improved the computation time for modulo multiplication operation. The proposed modulo multiplier is implemented in VHDL and synthesized using Xilinx ISE 14.1.The performance comparison analysis in terms of area, power and delay is done between the proposed technique and direct computation. The performance of the multiplier circuit has been compared using the 32 nm standard cells available in Synopsys Design Compiler. The presented Vedic modulo multiplier is efficient in terms of speed for large input data sizes
An Improved Hybrid Stacked Classifier for Multi Label Text Categorization

Nowadays, the applications of multi label classification are increasing very rapidly with the growth of information technology. One among it is, multi label text categorization which deals with the automatic categorization of documents or comments posted in a social site. Because of the exponential growth of digitization of unstructured categorical data, there is an emerging need for text categorization in particular with multiple labels. Conventionally, it has been solved by either transforming the problem into single class or extending the existing classifiers. An improved hybrid stacked classifier has been proposed to address the challenges in multiple label assignment for text document. The model has been built with three classifiers stacked together with Label Power set by taking class probabilities and a Meta classifier. The experimental results show that the proposed method outperforms well than the existing methods.
Hybrid Method of Local Binary Pattern and Classiﬁcation Tree for Early Breast Cancer Detection by Mammogram Classiﬁcation

- Breast cancer is an alarming disease due to a mutation in breast cells and it is one type of cancer among women which highly leads to their death. One of the most effective tools for early detection of breast cancer is mammography, which is a screening tool used to examine the human breast by using low-dose amplitude X-rays. Computer-Aided Diagnosis (CAD) is used as an important tool to help the medical professionals for classifying breast tissues into a different class. Computer-Aided Diagnosis (CAD) can be used to reduce human error in reading the mammograms and it shows effective results in the classification of benign and malignant abnormalities. The proposed method presents a new classification approach to detect the abnormalities in mammograms using Local Binary Pattern and Decision Tree Classification. A Uniform Local Binary Pattern(uLBP) is an extension of the original Local Binary Pattern in which only patterns that contain at most two transitions from 0 to 1 (or vice versa) are considered. In uniform Local Binary Pattern (LBP) mapping, there is a separate output label for each uniform pattern and all then on uniform patterns are assigned to a single label. These patterns are utilized to detect breast cancer by classification employing the Decision Tree Classification. Specificity and sensitivity are the two statistical measures used in this proposed method to verify and measure the significance of the test related to abnormalities in the breast tissues. Thus, it can be a measurement of performance tests for classifying the patients who do and do not suffer from cancer. The mini-MIAS mammography database is employed for testing the accuracy of the proposed method and the results are promising.
An Efficient Correlation Power Analysis Attack Using Variational Mode Decomposition 

Side channel attacks (SCAs) are now a real threat to cryptographic devices and correlation power analysis (CPA) is the most powerful attack. So far, a CPA attack usually exploits the leakage information from raw power consumption traces that collected from the attack device. In real attack scenarios, these traces collected from measurement equipment are usually contaminated by noise resulting in a decrease in attack efficiency. In this paper, we propose a variant CPA attack that exploits the leakage information from intrinsic mode functions (IMFs) of the power traces. These IMFs are the results of the variational mode decomposition (VMD) process on the raw power traces. This attack technique decreases the number of power traces for correctly recovering the secret key by approximately 13% in normal conditions and 60% in noisy conditions compared to a traditional CPA attack. Experiments were performed on power traces of AES-128 implemented in both microcontroller and FPGA by Sakura-G/W side channel evaluation board to verify the effectiveness of our method.
State of the Art Analysis of Low Light Video Noise and Various Environmental Conditions for Enhancement

Noise present in the digital videos sequence during acquisition, coding, transmission and processing can be effectively removed only with a previous knowledge of noise origin. Hence we have to review the origin of noise in videos/images is a pre requisite to denoise the same. Videos have been captured under controlled or uncontrolled environment. Many researchers who are fervently engaged in enhancing of video images quality taken under different situations like low light ambience, sunny, rainy, snow, camera shake etc have come up with various technical ideas to improve the image quality. The scope of this paper is to make an analysis of the proposals to overcome video noise. An attempt is made to study the presently available algorithms widely used to enhance video images taken under different light condition.
E-OWL: A FIRST USABILITY STUDY OF A NEW E-LEARNING PLATFORM FOR DIGITAL NATIVE STUDENTS

In today's society, whose higher education students are digital natives, the availability of information and communication technologies is a reality.It is pertinent the development of virtual learning environments, which are extremely useful in periods such as of those of a pandemic situation, which force the confinement of people.This article highlights the importance of the use of ICT in educational contexts, in particular, the use of e-learning platforms, in higher education, as support to the teaching/learning process.A literature review was carried out, in order to demonstrate some advantages for the teaching/learning process, both for students and teachers.Thus, the general structure of an e-learning platform, designated E-OWL, to support an area of mathematics at higher education level is described.Also, the opinion of students after having used the platform in an educational context is presented, through an usability study carried out using the SUS method.The results obtained show great satisfaction among students who used the platform, which is naturally a contribution to the improvement of learning and a useful tool in an educational context.This study encourages the use of technologies as a support to the teaching/learning process, promoting more interactive learning environments closer to the students.
The Role of Cyber Physical Systems to improve Smart Learning Environments

The growth of technologies lead to a lot of improvements in almost all the fields. A huge man power is used in all the fields especially in manufacturing sectors. We can reduce man power mainly for the work which can be done by machines in an efficient manner. Industry 4.0 is focusing mainly for this purpose. Cyber physical systems are used to build a system with better communication and data collection in a shared and collaborative manner from all components of that system in safe and reliable manner. As though CPS is starting its implementation in all the fields this work deals mainly with role of cyber physical systems for smart learning environments. This work forms a basic study of those systems.
Multimodal news authentication as a service: The «True News» Extension

The current work focuses on the problem of misinformation.Filtering and blocking every unreliable source is impossible even to consider.Thus, discovering the dedicated steps to indicate fake content, according to the fact-checking procedures, and utilize them in automated/semi-automated mechanisms, is the key to defend the truth.Despite the availability of various authentication applications/services, there is a lack of integrated systems supporting media veracity in real-world scenarios.There are useful tools and practices for detecting processed/altered content, usually investigating a single manipulation or relying on the credibility of one source, which turns to be inadequate.Based on the above, a browser extension is presented, aiming at evaluating news authenticity in a multimodal, integrating and collaborative way.Operating unobtrusively in the background until needed, the extension is a solution transparent to the user.Without having to open a new browsing tab or to switch navigation /media environment, the user can identify relevant information regarding the five (5) clues, which frame a news story (Title, Date, Creator, Source, Containing Images).In essence, with the aid of the True News Plugin, a user reveals information from trusted sources, classifying them by the accuracy of their domain names, while also spotting possible misinformation through doctored images.The main novelty of the proposed module is that it will help users determine whether they can trust an article or not.By going through five (5) essential steps and by answering ten (10) questions, the proposed methodology attempts to introduce a valuable free tool in the field of Digital Forensics.
Segmentation and Classification of Mammogram into Normal and Abnormal using Texture Features

Breast cancer is known to be a fatal disease since decades in women worldwide. Mammography is an effective tool used for the detection of breast cancer in the early stage. Computer aided tools helps medical field by ruling out the false identification of cancer cells in mammograms. Breast region extraction and classification of the extracted region into normal and abnormal is a crucial step in mammographic based diagnosis of breast cancer. Hence, in the proposed paper a method for segmentation of breast region and classification of breast region is presented. Breast region extraction is performed using Otsu’s thresholding method and intensity adjustments, enhancement is performed by Contrast Limited Adaptive Histogram Equalization (CLAHE). Gray Level Co-Occurrence Matrix (GLCM), Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) features are extracted to classify the breast region using K-Nearest Neighbors (KNN) classifier. The proposed algorithm is tested on Mammographic Image Analysis Society (MIAS) dataset, obtained minimum Root Mean Square Error (RMSE) and maximum Peak Signal-to-Noise Ratio (PSNR). For classification, 80.12% of accuracy is obtained with TPR and FPR of about 0.8317 and 0.3412 respectively.
Anomaly detection for elderly home care

In this paper, we propose a model for detecting anomalies in elderly home care. Two scenarios are investigated in detecting anomalies: 1) the elderly person's vital signs and their surrounding environment; 2) the mobility patterns of the elderly. We evaluated our proposed model by employing the isolation forest which detects anomalies using an isolation approach on a random forest of decision trees. We compare isolation forest on unlabeled data with statistical methods on labelled data. Subsequently, to show the reliability of the isolation concept, we compare it with a distance measure concept. The experiment shows that isolation forest has higher detection accuracy and lower error prediction for two attributes in the first scenario: skin temperature and heart rate, whereas, in the second scenario, multi-covariance determinant has a slightly better accuracy compared to isolation forest (3.9% difference in accuracy) and has a small number of prediction errors compared to isolation forest.
Mechanism for Ensuring Teacher’s Presence in Classroom Using Deep Learning

Education is one of the main components for the growth of a state, and there is not much research done in order to improve its quality especially in Pakistan.To improve Education quality the starting point is to boost Teaching Mechanism especially Government Schools where teacher are generally not present in class.Biometrics is not a valid system to use, as Teachers have found ways to tamper this type of attendance system.Using camera raises privacy issues as well as significant man or machine power is required to solve this problem.This paper presents a speaker recognition system applied to the problem of teacher identification in "ghost schools".The system uses a neural speaker embedding system that maps the audio lectures to a hyperspace where teacher similarity is measured by a cosine distance.We also present a corpus of audio lectures collected from 5 different teachers of 5 different courses.The dataset can be used for the evaluation of teacher identification system and contains audio lectures both for enrollment and testing.Our proposed system achieves an accuracy of 67% on the test set of the above mentioned corpus.We have made our code and corpus publicly available for reproducible research.
Evaluating Attitude and Behavior: An Info-Operational Procedure Related/Supported by the Cognitive Centers of Mind

In order to highlight the cognitive properties of the mind related with attitude and behavior and their evaluation by a suitable and realistic procedure, in this paper it is analyzed the relation of mind with the body from the informational perspective, showing that the functionality of mind is closely related to the body operability and the mind/body system works on the basis of specialized components and functions, allowing in this way a better operability with higher performances. It is shown that the mind is actually an informational system, working with thought as an informational operator on the accumulated life info-experience field, able to search a specific information in the mind “library” (memory), to order to the motor elements to execute various operations, or to connect the sensorial detectors (senses) to the external or internal world. On this field of information including the judgment criteria, there are received new signals from the internal and external sources, so an adaptive response defined as attitude is returned, expressed as informational output, or retained in memory for further use. Such signals are characteristic of specialized informational sources, determining/managing the affective/emotional info-reactive effects, expressing the body status and health or info-genetic impulses, which are selectively considered by order of priority and/or compared with the existing stable/validated criteria. The evaluation of the final/global attitude is therefore possible by evaluation on a positive/negative (YES/NO) scale of each pro-active partial contributing components resulted from the analysis of the reactive response of each cognitive center. Distinct formulas for the calculation of the momentary attitude and behavior with respect to an object/objective are presented. The obtained results can be applied on a large field of activities, from commercial, financial, educational or health public or private services to political tests of opinion, or on own selfevaluation of personal objectives development. The presented procedure permits also to follow the evolution of attitude on a particular objective during the time.
Phonotactic and Morphological Effects in the Acceptability of Pseudowords

We develop a large set of pseudowords that systematically varies length and phonotactic probability and obtain acceptability ratings using an online interface. We find that phonotactic likelihood and the presence of an apparent morphological parse both significantly predict acceptability; pseudowords containing known morphemes are more acceptable than otherwise comparable pseudowords that do not. We find support for the conjecture that novel words with apparent morphology are advantaged as additions to the lexicon. The resulting lexicon, as observed, is one in which long words are not a random sampling of phonotactically acceptable wordforms, but instead tend to be completely or partially decomposable into morphemes.
Investment in Cryptocurrencies

Technology has created a significant difference in the lives of the people due to paradigm shift from offline activities to online activities. Cryptocurrency is a digital coin money based on the concept of cryptography encryption and electronic connectivity to function. Cryptocurrency is one of the best inventions in the context of financial sector. Being a decentralised currency, it also opposes the intervention of central banks and digital currencies by them. It transforms the virtual trade market by introducing a free rein trading mechanism that operates without the involvement and regulation of a third party. Digital currencies in today’s scenario become need of the hour thus this paper compares the most prevalent cryptocurrencies of India on the basis of market capitalization rate. The paper also aims to study the key characteristics of the digital currencies.
Influential Incremental Learning-Based Privacy Preservation for Social Network Information

Social network influence dissemination focuses on employing a small number of seed sets to generate the most significant possible influence in social networks and considers forwarding to be the only technique of information transmission, ignoring all other ways. Users, for example, can post a message via this mode of distribution (called para), which is difficult to trace, posing a danger of privacy leakage. This research tries to address the aforementioned issues by developing a social network information transmission model that supports the paranormal relationship. It suggests a way of disseminating information called Local Greedy, which aids in the protection of user privacy. Its effect helps to reconcile the conflict between privacy protection and information distribution. Aiming at the enumeration problem of seed set selection, an incremental strategy that supports privacy protection is proposed to construct seed sets to reduce time overhead; a local influence subgraph method of computing nodes is given to estimate the influence of seed set propagation quickly; the group satisfies the constraints of privacy protection, and a plan is proposed to deduce the upper limit of the probability of node leakage state, avoiding the time cost of using the Monte Carlo method using the crawled Sina Weibo dataset. Experimental verification and example analysis are carried out, and the results show the effectiveness of the proposed method.
Using hybrid-CBR for intelligence monitoring and decision-making systems on SMART grid

Smart grid systems are being actively developed and implemented all over the world. However, along with developed systems for monitoring and data analysis, decision support functions are not fully implemented. Wherein decision support is necessary due to the complexity of possible emergencies. In this work, we offer the concept of an intelligent decision support system (IDSS) for the SMART grid, which is based on the hybrid Case-Based Reasoning (CBR) method. This method combines models of knowledge-based systems and models of neural networks and machine learning, which simplifies realization on complex changing objects of the SMART grid. In the first part of the research, we describe the concept of the proposed hybrid-CBR method, the principle of formalizing the situation at the objects of the SMART grid systems and present the involved neural network architecture Comparator-Adder. The second parts of the research reveal the concept of applied IDSS and also show the results of an experiment of retrieving precedent from a knowledge base with using a neural network. Experimental results show that our architecture successfully copes with the task of selecting the most similar situation. We believe that the MAPE error in this incident does not play a key role; the efficiency of the neural network is confirmed primarily by the coherence with the results of the expert choice and the absence of collisions.
A Lemmatizer for Low-resource Languages: WSD and Its Role in the Assamese Language

The morphological variations of highly inflected languages that appear in a text impede the progress of computer processing and root word determination tasks while extracting an abstract. As a remedy to this difficulty, a lemmatization algorithm is developed, and its effectiveness is evaluated for Word Sense Disambiguation (WSD). Having observed its usefulness, lemmatizer is considered for developing Natural Language Processing tools for languages rich in morphological variations. Among various Indian highly inflected languages, Assamese, spoken by over 14 million people in the North-Eastern region of India, is also one of them. In this present work, after a detailed study on the possible transformations through which surface words are created from lemmas, we have designed an Assamese lemmatizer in such a manner that suitable reverse transformations can be employed on a surface word to derive the co-relative (similar) lemma back. And it has been observed that the lemmatizer is competent to deal with inflectional and derivational morphology in Assamese, and the same was evaluated on various Assamese articles extracted from the Assamese Corpus consisting of 50,000 surface words (excluding proper nouns), and the result that it yielded with 82% accuracy was quite encouraging and satisfying, as Assamese is a low-level language and no research work has been done in the Assamese language regarding the lemmatization of words. Considering the result obtained, the lemmatizer is then evaluated for Assamese WSD. For this purpose, 10 highly polysemous Assamese words are taken into account for sense disambiguation. We have also regarded varied WSD systems and observed that such systems enhance the effectiveness of all the WSD systems, which is statistically significant.