Reviewerly/exHarmony huggingface dataset:
An Enhanced Low Rank Approximation SVD-Based Method for Image Denoising

Nonlocal self-similarity of images has attracted considerable interest in the field of image processing and has led to several state-of-the-art image denoising algorithms, such as block matching and 3-D, principal component analysis with local pixel grouping, patch-based locally optimal wiener, and spatially adaptive iterative singular-value thresholding. In this paper, we propose a computationally simple denoising algorithm using the nonlocal self-similarity and the low-rank approximation (LRA). The proposed method consists of three basic steps. First, our method classifies similar image patches by the block-matching technique to form the similar patch groups, which results in the similar patch groups to be low rank. Next, each group of similar patches is factorized by singular value decomposition (SVD) and estimated by taking only a few largest singular values and corresponding singular vectors. Finally, an initial de-noised image is generated by aggregating all processed patches. For low-rank matrices, SVD can provide the optimal energy compaction in the least square sense. The proposed method exploits the optimal energy compaction property of SVD to lead an LRA of similar patch groups. Unlike other SVD based methods, the LRA in SVD domain avoids learning the local basis for representing image patches, which usually is computationally expensive. The experimental results demonstrate that the proposed method can effectively reduce noise and be competitive with the current state-of-the-art denoising algorithms in terms of both quantitative metrics and subjective visual quality.
TLD Tracking Algorithm Based on Feature Fusion

Various places in life are related to target tracking. In order to ensure that the tracked objects can be correctly identified, the accuracy of the algorithm needs to be continuously improved. This article focuses on the problem that the recognition rate of TLD (Tracking-Learning-Detection) algorithm decreases and even tracking drift occurs when the target undergoes rapid movement, rotation, and deformation. These situations are not expected to happen. Therefore, this paper proposes to add HSV color space and local binary pattern (LBP) to the traditional TLD algorithm. Through this method, the recognition accuracy of the tracked target is improved when the target is moving, rotating, and deforming rapidly, thereby improving the robustness of the algorithm. After a large number of experiments and analysis in this paper, this method can achieve correct detection and accurate tracking of target objects in complex scenarios.
O estado do conhecimento sobre competências digitais docentes: tendências e perspectivas internacionais

This article aims to analyze the current situation of international scientific production on digital teacher competences.For that, a meta-analysis of the articles retrieved in the Portal of Periodical of CAPES was carried out.It is important to mention that we chose to search the CAPES Journal Portal because it allowed the access, at the time of data collection for this study, to full texts of articles from more than 38,000 international periodicals and 126 databases (such as Proquest, EBSCO, Science Direct, Emerald, Web of Science and Web of Knowledge) with document summaries in all areas of knowledge.We chose the exhaustion rule of Bardin ( 2011), and we do not define a priori temporal cut-off for data collection.After data collection, 175 articles were retrieved, of which 37 were considered valid to compose the corpus of this study.The conclusions reveal that researchers' greatest interest is in digital teacher competencies in a broader way and that little is studied about pedagogical transposition, for example.The findings also corroborate previous studies regarding the multiple interpretations of the understanding of what is digital competence.There is also evidence in the analyzed literature of the use of more than one terminology, including in the same text, indistinctly and synonymously, to refer to digital skills.
Secure Cloud Storage of Text and Image Files by Giving Access Control to Users

Cloud computing is extensively used but it’s not completely trustworthy. It can be in terms of protecting or securing the confidential data from malicious attacks as well as from cloud providers. Also once the data is deployed on cloud, the user has no control over it. So rather than deploying the original text file the user encrypts the file and then uploads on the cloud. When this file needs to be shared with other it can be done using Cipher text Policy Attribute Based Encryption (CP-ABE). This gives the central access to the owner and secures the data from external attacks. Besides, the cloud provider serves both as the payee of resource consumption fee, lacking the transparency to data owners. So to reduce these factors a specific encryption strategy is been used and the time for encryption is also taken into account. The files are also encrypted by the priority that are given by the user during the file uploading section. A text file of size 1GB with the CP-ABE encryption strategy requires 174 seconds whereas encryption done using AES is 473 seconds and that of RSA is 330 seconds. Hence, this dissertation work proposes methodology to secure or protect encrypted cloud storage from Economic Denial of Sustainability attacks and give an efficient time constraint approach.
Encryption Techniques for Different Messenger Applications

With the advancements in number of technologies, communication has taken a bigger leap. A number of messenger applications have been developed to exchange data and information through the Internet. This data is very private and can be vulnerable to security attacks. Hence, it must be protected with certain encryption technique to keep the information confidential and away from unauthorized access. In this paper, a brief study is done on different encryption techniques in messenger applications and the conclusions are presented.
Minimizing Makespan Time in Cloud Computing using Heuristic Elasticity based Dynamic Task Scheduling Algorithms

Cloud computing technique is rapidly spreading in all over the world due to its pay per usage, anywhere any time property.Most of the IT [23] firms and business are using this technique with the help of web based application and tools over the internet As a result of growing large number of users, cloud service providers and applications in the cloud is growing quite rapidly due to the problem of preserving QoS parameters such as throughput, reliability, availability, elasticity and makespan time, etc. Here, with the aid of ERPD (elastic resource provisioning / deprovisioning) and scalability mechanism, we have proposed, built and developed an algorithm to balance the load dynamically between virtual machines for optimizing resource usage and minimizing the make-pan time in the cloud environment.The experimental results compared to the SJF, FCFS algorithm, and the Min-Min algorithm and this new proposed algorithm showed better performance.
Using empirical mode decomposition and Kullback-Leibler distance for online handwritten signature verification

The signature is one of the most accepted biometric modalities in the world. In this paper, we present a new method for online handwritten signature based on the empirical mode decomposition (EMD). After extracting each of the signature coordinates, a phase of preprocessing and normalisation is carried out. Then, the features of the signatures are extracted by using the EMD. After that, three similarity measures are used to match the signatures between them. The database used in our work is the one used in SVC (2004). Experimental results confirm the effectiveness of our approach and show the level of its reliability. Finally, the proposed method gives an EER of 2.03% and allows high rates of recognition compared to other approaches.
Autonomous Rover Controlled By Nao

In this project, a new approach of a Humanoid robot controlled mobile platform is presented. The robot we are using is Nao by Aldebaran Robotics. The main reason for choosing Nao is for its flexibility and versatility. Nao’s wide range of movements allows it to perform a steering mechanism in a real world. The Driving behavior for Nao is done by using NAOqi python SDK and through using Raspberry Pi controller interface. We use the HC-SR04 ultrasonic sensor for obstacle range detection in the navigation system to avoid the obstacle in real time. The system architecture and algorithms used in each stage are described in this project.
A New Digital Image Steganography Approach Based on The Galois Field GF(pm) Using Graph and Auto

In this paper, we introduce concepts of optimal and near optimal secret data hiding schemes.We present a new digital image steganography approach based on the Galois field ( ) m GF p using graph and automata to design the data hiding scheme of the general form 2 ( , , log ) for binary, gray and palette images with the given assumptions, where , , , k m n N are positive integers and p is prime, show the sufficient conditions for the existence and prove the existence of some optimal and near optimal secret data hiding schemes.These results are derived from the concept of the maximal secret data ratio of embedded bits, the module approach and the fastest optimal parity assignment method proposed by Huy et al. in 2011 and 2013.An application of the schemes to the process of hiding a finite sequence of secret data in an image is also considered.Security analyses and experimental results confirm that our approach can create steganographic schemes which achieve high efficiency in embedding capacity, visual quality, speed as well as security, which are key properties of steganography.
Effectiveness of Using Guided Peer Code Review to Support Learning of Programming Concepts in a CS2 Course: A Pilot Study

Learning of Programming Concepts in CS2 Course: A Pilot Studyretention or sharing among students.To accomplish this, guided PCR sessions were conducted where students were asked to follow a provided checklist in order to systematically check for programming errors when reading source code on line-to-line basis. Research methodology:We designed and implemented four different PCR sessions over the course of four weeks in one CS2 classroom.During each week, students were given a piece of code covering a specific data structure and were asked to review and find errors in the code.The provided code pieces were seeded with five categories of errors: initialization/declaration, method call/ definition, array/linkedList/trees/, output and flow of control.We analyzed data gathered from the guided PCR sessions, reflection sessions conducted after each PCR session, work conducted by students (assignments, quizzes, and exams completed as part of the course), and a feedback survey conducted at the end of study. Contribution:This paper provides empirical evidence of the impact the guided PCR had on CS2 students' understanding of programming concepts.While prior research has examined the effects of PCR on students' motivation to learn and course attendance, it lacks evidence concerning the usefulness of guided PCR at improving students' conceptual knowledge in their courses.We also provide evidence on how PCR can be used to enable knowledge sharing among students and foster in-class discussion.We also hope that this study will lead to the development of a more robust error checklist and a larger repository of programs that are seeded with realistic defects and can be used by other CS1/CS2 instructors.
Predictors of Malaria Incidence in Rural Eastern Indonesia

In Indonesia, the largest number of malaria cases was contributed to by Eastern Indonesia.The study aimed to analyze the predictors of malaria incidence in rural Eastern Indonesia.This study analyzes the 2018 Indonesia Basic Health Survey data.In the final stage binary logistic regression was used to determine the incidence of malaria incidence in 86,382 respondents.Variables analyzed included malaria incidence, regional/province, age, gender, educational level, work type, marital status, wealth status, and health insurance.The analysis found that people who lived in rural Maluku and rural North Maluku had a lower risk of experiencing malaria than those who lived in rural areas of East Nusa Tenggara.People in rural West Papua and rural Papua have a higher risk than those who live in rural East Nusa Tenggara.Men were 1.107 times more likely than women to have malaria incidence.People with primary school education were 1.237 times more likely to develop malaria than those without education.People with a college education were 1.440 times more likely to develop malaria than those without education.People who have a wealth of poorer status are 0.804 times more likely to have malaria than those who are poorest.People who have the richest wealth status are 0.851 times more likely to have malaria than those who are the poorest.There were 4 variables that have proven to be significant as predictors of incidence in rural Eastern Indonesia, namely regional/province, gender, education, and wealth.
Motivation in first year engineering students of the online course “Computer Programming in Python” in the context of a pandemic by COVID-19

Teaching-learning has changed drastically since the coronavirus pandemic and motivation in a pandemic context where students have had to radically change their learning system. The main goal of this research is to identify the relationship between the motivation of students in an online course of Computer Programming in Python, in the context of pandemic with sociodemographic variables (Internet connection, number of people living at home, employment status) and school variables (number of parallel subjects, grades and time spent). 75 first year engineering students participated and motivation was measured through the EME scale that provides Internal Motivation, External Motivation and Amotivation. The results indicate that there are no mean differences between motivation and socio-demographic variables, while significant differences are found in the Amotivation factor compared to students’ final grades and the total time taken to complete the learning units. The course in question took place in the second semester of 2020, which ended in January 2021, given the irregularity of the semester
Facial Expression Recognition

This paper aims to incorporate facial expression recognition needs and applications. The expression of the face is the mode of non-verbal communications between the verbal and no verbs. It reflects or fills a human perspective and its mental state. A major research initiative was conducted over two decades to develop human computer interaction. This paper comprises introduction to the recognition method for facial emotions, implementation, comparison of common techniques of facial expression recognition and phases for automated systems for facial identification. The Paper aims to Sight faces from any image, extract facial expression (eyes and lips) and classify them into six emotions (happy, fear, anger, disgust, neutral and sadness. The Coaching information is skillful a series of filters and processes and is eventually characterized through a Support Vector Machine (SVM), refined victimization Grid Search. Facial Expression are most commonly used for interpretation of human’s emotion. There is a range of different emotions in two categories: positive emotion and negative emotion. There are four types of generally using system: Face detection, extraction, classification and recognition. In Existing System, it is not so much identified extract emotion of a persona summary of existing phases, methods and data sets of Facial Emotion Recognition (FER). FER is recognized in the sphere of computer vision and machine learning for decades and is an essential topic. In most applications such as healthcare, teaching, criminal research, human-robot interface (HRI), etc., automated FERs are useful.
Publisher Correction: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Enhancing User Profile Accuracy for Personalized Web Systems

Personalized Web Applications aim to improve the user's browsing experience by offering customized products and services based on his preferences and needs. A key feature of a successful personalization system is building profiles that accurately express the real interests and needs of each user. In this work, we focus on creating accurate, complete and dynamic profiles by capturing and tracking the users’ browsing activities. Moreover, we implement techniques to increase the accuracy of the retrieved user profiles by collecting more browsing data, identifying the most important concepts and removing irrelevant ones, and the number of levels from the concept hierarchy in the reference ontology that we should use to efficiently represent the users’ reel interests and needs. The result is a complete, dynamic, and accurate user profile that can be used to give users better-personalized browsing experience.
Using Airborne Gamma-Ray Spectrometry for Mapping Radioactive Zones in G. Kadabora Area, Central Eastern Desert, Egypt

This research aims at mapping potential radioactive zones in G. kadabora area, Central Eastern Desert, Egypt using airborne gamma-ray spectrometry data.Various processing techniques were applied to delineate potential radioactive zones in the studied area including; False Color Composite (FCC), Principal Component Analysis (PCA) and K-Means classification which represents their robustness in extracting known and potential anomalous areas.The results revealed that the highest radioactivity in the G. Kadabora area, Central Eastern Desert is associated with the central.G. Kadabora pluton which can be subdivided into three parts where the northern part of the pluton is the most promising part.The detailed investigation concluded that radioactivity in the current study area is controlled lithologically and structurally where Pegmatite, Syanogranite and Monzogranite represent the lithological control while N-S structural trend represents the structural control.
Explainable ASP

Despite its proven relevance, ASP (answer set programming) suffers from a lack of transparency in its outputs. Much like other popular artificial intelligence systems such as deep learning, the results do not come with any explanation to support their derivation. In this paper, we use a given answer set as guidance for a simplified top-down procedure of answer set semantics developed by Satoh and Iwayama to provide not only an explanation for the derivation (or non-derivation) of the atoms, but also an explanation for the consistency of the whole answer set itself. Additionally, we show that a full use of the Satoh-Iwayama procedure gives an explanation of why an atom is not present in any answer set.
Smart Mobile Identity Detection from Captured Images

Most of the cybercrime are originated from abuse of mobile phones. If there is a possibility to identify the persons who abuse mobile phones, then the task of Cybercrime, Information forensic and Defense department is getting easier. Here the authors proposed a technique to identify the International Mobile Equipment Identity (IMEI) number, time stamp andgeographical location of the smartphone/mobile. Whenever the mobile camera is used the identity information such as IMEI number, timestamp and geolocation are embedded in the captured image automatically. For that, a mobile application was developed and deployed as a mobile service. By decoding the image, the embedded identity information of that mobile phone is revealed. Using this information it is easy to identify the person/s.
Secure Login System using MD5 and AES Attribute Based Encryption Algorithm

The cryptographic hash work and symmetric encryption make it hard to break Passwords. Secure secret word stockpiling is a crucial perspective in framework dependent on secret word verification, which is as yet the most broadly utilized confirmation system, notwithstanding its some security imperfections. So basically, this work is based on providing security to the systems. Right now, propose a secret word verification structure that is intended for secure secret word stockpiling and could be effectively coordinated into existing confirmation frameworks. In our system, first, the got plain secret key from a customer is worked out a cryptographic hash work. At that point; the hashed secret word is changed over into a negative secret word. At last, the negative secret word is encoded into an Encrypted Negative Password (ENP) utilizing a symmetric-key calculation, and multi-emphasis encryption could be utilized to additionally improve security. The cryptographic hash work and symmetric encryption make it hard to split passwords from ENPs.We are going to use message digest i.e MD5 and AES algorithm for this purpose. Besides, there are loads of comparing ENPs for a given plain secret key, which makes precomputation assaults infeasible. The calculation multifaceted nature investigations and examinations show that the ENP could oppose query table assault and give more grounded secret word insurance under lexicon assault. It merits referencing that the ENP doesn't present additional components other than this, the ENP could in any case oppose precomputation assaults. We are giving shading coding framework just as key logger idea secret key reason. This shading code framework is hard to break to third person. In key lumberjack the keypad of framework is mix, each time it will change the grouping of catches of 0-9 numbers,subsequent to logging the client one otp will send to client email just as the key squence of the keypad will send on client email. By utilizing this otp and key grouping client will login to framework and it will do the further exchange process. This framework will valuable in future for any financial framework or any verification reason will be utilized.
Erythrocyte Classification using Multi Layer Perceptron, Naïve Bayes Classifier, RBF Network and SVM

Several diseases can be diagnosed based on the appearance of abnormal erythrocytes, among others anaemia and thalassemia. Process of examination peripheral blood smear manually is time-consuming and subjective. Currently, the process of examination peripheral blood smear by laboratory assistants can be assisted with digital image processing technology so that it can speed up the examination time and avoid subjectivity. This research begins with the process of microscopic image acquisition, then preprocessing, segmentation, feature extraction and classification. The microscopic image acquisition is carried out using an additional special camera on a microscope. In this study, we used peripheral blood smear of thalassemia patients and healthy individuals. We convert the RGB image to grayscale image and perform the median filtering in the preprocessing stage. In the segmentation stage, we used the watershed distance transform method. As a segmentation result, we got 7108 erythrocyte images consisting of nine types of erythrocytes. In feature extraction, we used shape, color and texture characteristics to represent erythrocytes. The combination of these three features is used as classifier’s input. One crucial stage in digital image processing technology is object classification. In this study, erythrocyte classification is done by comparing four types of the classifier to determine the best classifier performance in this case. Multi-Layer Perceptron (MLP), Naïve Bayes classifier, RBF Network, and SVM used as classifiers in this study. Experimental results showed that MLP got the highest performance with 89.6% accuracy, 89.3% precision and 89.6% recall. Furthermore SVM came in second place, followed by RBF Network and Naïve Bayes classifier.