Reviewerly/exHarmony huggingface dataset:
A NEW NONLOCAL MODEL FOR THE RESTORATION OF TEXTURED IMAGES

In this paper, we focus on the mathematical and numerical study of a new nonlocal reaction-diffusion system for image denoising. This model is motivated by involving the decomposition approach of $H^{-1}$ norm suggested by Meyer [<span class="xref"><a href="#b25" ref-type="bibr">25</a></span>] which is more appropriate to represent the oscillatory patterns and small details in the textured image. Based on Schaeffer's fixed point theorem, we prove the existence and uniqueness of solution of the proposed model. To illustrate the efficiency and effectiveness of our model, we test the denoising experimental results as well we compare with some existing models in the literature.
Minimizing information loss in shared data: Hiding frequent patterns with multiple sensitive support thresholds

Abstract Privacy preserving data mining (PPDM) is the process of protecting sensitive knowledge from being discovered by data mining techniques in case of data sharing. Privacy preserving frequent itemset mining (PPFIM) is a subtask and NP‐hard problem of PPDM. Its objective is to modify a given database in such a way that none of the sensitive itemsets of the database owner can be obtained by any frequent itemset mining technique from the modified database. The main challenge of PPFIM is to minimize the distortion given to the data and nonsensitive knowledge while sanitizing all given sensitive itemsets. Distortion‐based sensitive itemset hiding algorithms decrease the support of each sensitive itemset under a predefined sensitive threshold through sanitization. Most of the distortion‐based itemset hiding algorithms allow database owner to define a single sensitive threshold for each sensitive itemset. However, this is a limitation to the database owner since the importance of each sensitive itemset varies. In this paper we propose a distortion‐based itemset hiding algorithm that allows database owner to assign multiple sensitive thresholds, namely itemset oriented pseudo graph based sanitization (IPGBS) algorithm. The purpose of IPGBS algorithm is to give minimum distortion to the nonsensitive knowledge and data while hiding all sensitive itemsets. For this reason, the IPGBS algorithm modifies least amount of transaction and transaction content. The performance evaluation of the IPGBS algorithm is conducted by using two different counterparts on four different databases. The results show that the IPGBS algorithm is more efficient in terms of nonsensitive frequent itemset loss on both dense and sparse databases. It has considerable good results in terms of number of transactions modified, number of items deleted, execution time and total memory allocation as well.
Secure Captchas via Object Segment Collages

CAPTCHA, a short form of ''Completely Automated Public Turing test to Tell Computers and Humans Apart'', is a computer program that can generate and grade tests that most humans can pass, but current computer programs cannot.This paper advances research on image-based CAPTCHA by incorporating the object segment collages.We first collect object segments from thousands of images by using our self-developed collection tool.Then, we create an image collage that compiles randomly selected object segments.To increase the difficulty for computer detection algorithms, we embed some features such as noisy background, object segment occlusion and object segment orientation update.We also develop a practical application using the newly proposed Object Segment Collage CAPTCHA.For the evaluation, we measure the accuracy performance of computer vs. human in solving CAPTCHA questions.In addition, we also conduct a user study over different CAPTCHA methods.
Prediction of Anemia Disease Using Classification Methods

Sickle cell is a hematological disorder (hematology is a study of blood in health and diseases) which may lead to organ damage, heart strokes, and serious complications. It may also reduce the human lifespan. Most of the sickle cells are observed in newborn babies. It was initially thought to be a particular feature of tribal peoples, but it has now been found in all populations. Sickle cell symptoms are observed in human beings as episodes of pain (crisis), painful swelling of hands, feet and vision problems. Detecting sickle cell as early as possible could help the patients to identify their symptoms and can support to take the medications using Antibiotics, Vitamins, Blood transfusion and pain-relieving medicines, etc. In general the counting of cells in the blood, classification, and assessment is done using a manual process takes a lot of time and may lead to wrong results as there are millions of RBCs in one smear. By using various classification algorithms, we can identify sickle cells in the human body effectively with high accuracy. The proposed method overcomes the drawbacks of manual assessment by introducing robust and effective classification algorithms to classify the Sickle Cells in blood using three-class labels: N (Normal), S (Sickle Cell), and T (Thalassemia Cell). In this paper we also present the accuracy levels of various classification algorithms on the dataset which is gathered from Thalassemia and Sickle Cell Society (TSCS) located at Rajendra Nagar, Hyderabad, India.
No Trade in a Blockchain

We show that there cannot be common knowledge trade in a truly decentralised environment, such as in a blockchain. A trade is an agreement to buy and sell a security that pays according to some state of nature; it is settled when the security’s value is verified by an oracle, an intermediary who knows the state of nature. However, since the identity of traders in a blockchain is hidden, an oracle can impersonate a trader, by participating in a trade only when he knows that the outcome will be favourable. If other traders know that this is possible, they might be unwilling to participate in any trade that is offered. In other words, anonymity of traders is incompatible with verifiability of trades. We find that the requirement on verifiability is relatively mild. In particular, it is enough that, among all values of the security that it is common knowledge that some trader thinks are possible, there exists an oracle that can verify either the maximum or the minimum.
Tagging Reading Comprehension Materials With Document Extraction Attention Networks

Reading comprehension tasks are commonly used for developing students' reading ability. In order to adaptively recommend reading comprehension materials to students engaged in computerized testing, the information in an item bank (a collection of test items stored in a dataset) must be effectively indexed. Familiarity with the topics present in the documents influences students' reading performance. As different question types require different skills, we tag documents with topics and questions with their corresponding types to measure the students' abilities and subsequently recommend relevant materials to them. However, automatic tagging has not been extensively studied in this field. In this article, we propose a document extraction attention network (DEAN) to accomplish the two aforementioned tasks. For topic tagging, DEAN utilizes questions to increase the sample size of documents implicitly through multitask learning. For type tagging, DEAN leverages the information gathered from documents, which aids in the task of prediction. Experiments demonstrate the effectiveness of our mutual use of information obtained from documents and questions. Results indicate that DEAN outperforms commonly used text classification methods when tested on a reading comprehension dataset.
M-Blocks (Medical Blocks): A blockchain based approach for patient record management using IBM Hyperledger

The purpose of this research is to highlight the usage of blockchain technology for the maintaining the health records of the patients across visiting hospitals in a highly secured environment. A blockchain is a distributed ledger technology (DLT) in which the blockchain is separated into blocks that contain a list of digital records that are secured by using encryption technology (cryptography). The blockchain is a data structure that show the way the data are stored and logically joined together in a peer to peer (P2P) network. Sharing health records between the various hospitals is a vital and life-saving during emergency. This will greatly reduce the time needed for the physicians to prescribe and take preventive actions required as quickly as possible. This helps to attend the patients on-time and save their life during the emergency situations. MBlocks stands for medical blocks with sequence of blocks having the patient's information shared and stored across various entities in a secured Hyperledger environment.
Femtocell Outage Detection in Multi-Tiered Networks using LSTM

Self Organizing Networks (SONs) are considered as one of the key features for automation of network management in new generation of mobile communications. The upcoming fifth generation (5G) mobile networks are likely to offer new advancements for SON solutions. In SON concept, self-healing is a prominent task which comes along with cell outage detection and cell outage compensation. 5G networks are supposed to have ultra-dense deployments which makes cell outage detection critical and harder for network maintenance. Therefore, by imitating the ultra-dense multi-tiered scenarios regarding 5G networks, this study investigates femtocell outage detection by means of the metrics generated in user equipments with the help of Long-Short Term Memory (LSTM). Based on the parameters such as signal to interference noise ratio and channel quality indicator, time series data of the user equipments in the femtocell site are trained and tested with LSTM network. On the average, in more than 77% of the cases the outage states of the femtocells are correctly predicted.
The Interplay Of Hand Gestures And Facial Expressions In Conveying Emotions A CNN–BASED APPROACH

Emotion recognition is the process of identifying human emotions. Identifying the emotion being conveyed by facial expressions is a well-studied area as is the processing of formal hand gestures (mudras, semaphores, sign language etc..) as a means of communication. Our study focuses on two observations:(1) hand gestures, which have no reference to any formal system, can convey meanings even without accompanying facial expressions and (2) facial expressions and `informal' hand gestures can nontrivially combine to convey messages with altogether new meanings. We present visuals to illustrate these observations. Experimentally, we present an image classification algorithm using Convolutional Neural Networks and TensorFlow library and OpenCV technology; with suitable datasets, we were able to train our system to recognize emotions conveyed by a limited set of hand gestures with no support from facial expressions (observation 1 above). We also indicate how the work ought to be extended to handle cases where hand gestures and facial expressions combine to convey interesting emotional signals.
Effective object proposals: size prediction for pedestrian detection in surveillance videos

Though detectors based on deep networks have witnessed a significant boost in pedestrian detection performance, small-scale pedestrian detection remains to be a challenging task. To this end, the authors propose a scale prediction method to eliminate the dependence of most existing detectors on pre-defined anchor boxes. Due to projective transformation, a pedestrian standing afar will appear smaller than one standing closeby. For a stationary surveillance camera, different blocks on the image correspond to the views of various depths leadings to diverse sizes of object proposals. A neural network structure is developed to empirically estimate the size of a pedestrian candidate in the pixel coordinates given its central location. Comprehensive sets of experiments on two real-world datasets demonstrate that the proposed method achieves superior performance with state-of-the-art methods. Besides, significant performance advantages on small pedestrian detection are observed.
Text multi-label sentiment analysis based on Bi-LSTM

The sentiment classification is still one of the most popular research directions in the past decade. Furthermore, the information contained in the Chinese text is definitely not limited to the positive or negative emotions. In view of the complexity of emotional texts, this paper combines various tags to increase the accuracy of sentiment analysis. Since the traditional methods are mostly based on single-direction propagation, which decreases semantic correlations. This paper applies the word2vec and bidirectional LSTM to train the model aiming at the semantic correlations between the forward and backward texts. The experiment results indicate that the accuracy of emotional prediction is significantly improved.
An Enhanced Digital Image Processing based Dehazing Techniques for Haze Removal

The proposed research paper explore Haze Removal a specific aspect of digital image processing. Visual images are often adversely affected by haze arising out of natural atmospheric phenomena such as moisture, aerosols, dust etc. or human action-smoke from fires, industrial emissions etc. Apart from these atmospheric factors, image quality could also be impacted by the positioning of the camera. Degradation of image quality can seriously affect critical decision making and lead even to serious accidents. Our focus is to examine, characterize and quantify the difference of a clear digital image from a haze-affected one and also to improve haze-affected images. We consider color images as well as grey scale ones. We apply the Dark Channel prior method in conjunction with approxdcp algorithm of Imreduce.
Improving SPV-Based Cryptocurrency Wallet

In order to maintain the cryptocurrency security, it becomes the foremost concern of the encrypted wallet actually storing your private keys governing your crypto wealth. Though hardware-based wallet is safe, people have to carry an additional load with them and protect them from physical damage/loss. The software wallet is more convenient but safety is at risk and cannot be guaranteed. The challenge is, the currently available smartphones have the inability to store all blocks. So, to get a more secure wallet one can use simplified payment verification. (SPV). This paper will introduce ways to make SPV more reliable and secure. As compared to hardware and software wallets, SPV wallets are more portable and secure. An attacker would not be able to steal the private key and the address of the wallet. Meanwhile, this method executed in a secure environment which protects the verification process and the header is not readable even by an encryption algorithm.
A Novel Document and Query Similarity Indexing using VSM for Unstructured Documents

Most of the Natural Language applications deal with the automatic detection of semantic text similarity between documents. In this paper, unstructured documents and queries are used for information retrieval. Hence to retrieve the most similar document for the given user query, the documents are retrieved in the order of similarity ranking. In the Vector Space Model, the text corresponding to documents and queries are converted into a numeric vector. Definition and number of dimensions are the critical aspects of VSM. The objective of this paper is to find out the most similar document from the set for the given user query. Different representations of the Vector Space Model is described in detail and the various similarity measures are calculated.
First Aid Mobile Application for University Clinic using Predictive String Search Algorithm

First aid skills are highly essential and a must-learn basic ability by everyone. Accidents can happen anytime in anywhere, that's why first aid is needed. This study aims to develop a game-based learning focusing and incorporating application and practices of first aid for information dissemination of proper emergency first aid specific to minor incidents. The application will educate and instruct the users on how to perform proper first aid where it has an interaction between the user and application through the application of game based learning approach with Intelligent Predictive String Search Algorithm which used for checking the step by step procedure in applying first aid. The system used Agile Model for the development of the system and used quasi-experimental design. Finding shows significantly in the pre-test result of is 16.63 while the post-test average is 31.60 therefore the system helped the student's to gained and learned first aid. Using the ISO 25010 to evaluate the system it gained 4.39 which mean Excellent while in the level of acceptability of respondents on the mobile game that has weighted mean of 4.47 which means Strongly Agree. This study can be possessing significant potential for increasing students learning in term of some accident occurrence and enhancing their learning effectiveness using game based learning. It can be developed and implementation especially in health related units or courses.
A Novel Method of Evidential Network Reasoning Based on the Logical Reasoning Rules and Conflict Measure

Evidential reasoning satisfies a weaker condition than probability theory and can deal with uncertain scenarios. However, there is still no complete and consistent theoretical system in the evidential reasoning now. In this paper, a novel method of evidential network reasoning based on the logical reasoning rules and conflict measure is proposed. The state of nodes is described through several basic probability assignments(BPAs). Two logical reasoning rules are defined to show that the occurrence of some antecedents in the parent nodes will lead to the sub-BPAs of the child node occurrence. Then the occurrence probability of sub-BPA with the child node is computed through antecedent probabilities in the parent nodes. Besides, the support degree of sub-BPAs is computed based on the evidential distance. The final BPA of the child node is obtained by combining weighted sub-BPAs. Two examples about fault diagnosis and threat assessment and prediction are given respectively to illustrate the effectiveness of the proposed method. Different from the previous work, this paper defines a new reasoning rule called OR rule, and the computation based on pure logic makes the reasoning process more intuitive. Besides, the weights of sub-BPAs are computed considering both the occurrence probability and conflict measure.
Application of SKATER and Ward’s methods in grouping Indonesian provinces based on monthly expenditure per capita of food commodity groups

Abstract Clustering is a fundamental data mining instrument that intends to find inherent cluster structure in data. Spatial clustering methods are usually used to assess the demographic data characterization. This study aims to classify provinces in Indonesia based on monthly expenditure per capita according to food commodity groups by using Ward’s and Spatial ‘K’luster analysis by tree edge removal (SKATER) methods and to identify a better classification between the two methods. The variables of this research constitute percentages of expenditure per capita for 14 groups of food commodities of 34 provinces in Indonesia during March 2018. The results of the first analysis (excluding outliers) revealed that SKATER method produced standard deviation rasio of 0.236, better than Ward’s method that produced standard deviation rasio of 0.370. However, from the second analysis (including outliers), the outcomes showed that the Ward’s method generated standard deviation rasio of 0.170, better than SKATER method that delivered standard deviation rasio of 0.199. Moreover, it can be concluded that the second analysis is better than the first analysis because it produced smaller standard deviation ratios based on the Ward’s and SKATER methods contrasted with the first one.
Performance Analysis of Collaborative Filtering based Recommendation System on Similarity Threshold

Recommendation systems (RS) are very popular for recommending items to its users on internet. Collaborative Filtering (CF) is a technique to recommend items to users on the basis of similarity between the users. Finding similarity between the users is very important challenge for recommendation systems. Two users are considering similar if the similarity between them is greater to a threshold value. The similarity threshold is minimum value of similarity between the users in CF based recommendation system to decide whether two users are considered to be similar or not. In this paper the performance of CF based recommendation system is analyzed on the basis of the similarity threshold. The performance is measured in terms of mean absolute error [MAE] which is calculated on a sample data set. MAE is calculated and compared at different levels of similarity threshold. The proposed work experimentally concludes the implications and benefits of deciding the similarity threshold and concludes that the high value of similarity threshold gives good results.
Foreground Background Separation and Tracking

Foreground background separation has become a significant factor in image processing for the past few years due to its many applications in the field of computer vision. In most researches, subtraction of images is used in the detection of foreground objects in an image. In this paper, the subtraction process will be substituted with a simple thresholding determined by genetic algorithm. Genetic algorithm solves for the ideal threshold that will be eventually used in classifying the pixels of an image is part of the foreground or the background. Erosion and dilation are other operations and processes used to come up with foreground objects. Tracking the foreground objects is another objective and will explore the capabilities of fuzzy logic most specifically the concept of fuzzy C-means (FCM) clustering.This study is part of a bigger research that aims to understand chicken's behavioral patterns in a poultry set-up. The whole system has three main processes namely: genetic algorithm for determining the threshold, classification of pixels as part of object detection, and tracking of foreground objects.
Target State Bias Estimation Using Co-Located Radar Measurements

The problem of radar bias estimation is very important for ballistic missile defence development. With increasing treats capability and it's impact ranges, a careful design of Interceptor kill Vehicle with suitable capabilities are very essential. Interception with larger radar biases requires more lateral force from kill Vehicle thus making them heavy, it also requires onboard seeker to have larger target tracking range. In this paper a novel approach is presented to estimates the radar measurement bias by using only one radar which is collocated to interceptor launch location. Here a radar is placed such that it can track both the target missile as well as interceptor missile simultaneously. The onboard interceptor navigation data is fused with the radar measurements to estimate the biases in radar measurements corresponding to target tracks. In this paper a target bias estimation using Extended Kalman Filter is formulated and presented for BMD system. The simulation result shows significant reduction in handing over error which leads to the better kill vehicle design against a high speed targets with technologically feasible Radio Frequency seeker.