Reviewerly/exHarmony huggingface dataset:
A multivocal literature review on record management potential components in CRUD operation for web application development

In recent years, web application frameworks have been widely practised by many developers to increase programming productivity as the frameworks are more flexible, rapidly built using CRUD operation, MVC-based, secure and most of them are published under an open-source license which will reduce the final cost of development. Although the CRUD automation in the web application framework boosts the development process, there are many important aspects of a web application absent from the CRUD output. Therefore, this multivocal literature review investigates the record management aspects that are required in modern WA and the perceived benefit of integrating the record management aspect into CRUD operation. The study extracted 284 publications from respectable scientific resources and the grey resources literature created by WA development practitioners outside academic mediums. After a detailed review process, only 14 scientific primary studies and 13 gray studies were considered for this review based on defined inclusion and exclusion criteria. The review shows that the most important aspects required in WA are search, role-based access control, retention, appraisal, search, audit trail, digital archiving, sharing, reporting, inactive files management and several other features. These important aspects have been analyzed and characterized according to its function and features. The method and procedure for integrating the specified aspect into CRUD operation are identified and discussed. Integrating and implementing the specified record management features into CRUD operation will boost the WA development productivity by producing more features as a standard output with integrated record management functions.
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy

Recent years, local differential privacy (LDP) has been adopted by many web service providers like Google [23], Apple [33] and Microsoft [15] to collect and analyse users’ data privately. In this paper, we consider the problem of discrete distribution estimation under local differential privacy constraints. Distribution estimation is one of the most fundamental estimation problems, which is widely studied in both non-private and private settings. In the local model, private mechanisms with provably optimal sample complexity are known. However, they are optimal only in the worst-case sense; their sample complexity is proportional to the size of the entire universe, which could be huge in practice. In this paper, we consider sparse or approximately sparse (e.g. highly skewed) distribution, and show that the number of samples needed could be significantly reduced. This problem has been studied recently [1], but they only consider strict sparse distributions and the high privacy regime. We propose new privatization mechanisms based on compressive sensing. Our methods work for approximately sparse distributions and medium privacy, and have optimal sample and communication complexity.
Investigation of the Effect of Augmented and Virtual Reality Applications in E-Learning on Students' Use of Microscopes

determine the effects of applications developed with augmented and virtual reality technologies on subjects that require the use of microscopes in the e-learning process in science lessons on students' academic achievement, course engagement and self-efficacy beliefs in microscope use.The research was conducted in a quasi-experimental design with three groups, two experimental and one control.In this process, the teaching practices towards the control group (CG) were conducted with the use of the course presentations prepared by the researchers and the content of the Education Information Network related to the subjects covered in the research.While the students in the augmented reality experimental group (AREG) were taught with the same course presentations and augmented reality microscope application (MikrosAR), the students in the virtual reality experimental group (VREG) were taught with the same course presentations and virtual reality microscope application (MikrosAR2).In the study, data were collected through an academic achievement test, a scale of self-efficacy belief in microscope use, a course engagement scale and an observation form.The relationship between pre-test and post-test scores was performed using Kruskal-Wallis one-way analysis of variance.Observation data obtained from learning environments were calculated as frequency and presented by digitizing.Correlation analysis was applied to determine the relationship levels of the variables discussed in the study.According to the results, it was seen that there was no significant difference between the groups in terms of academic achievement, and all the materials used in the groups related to the subject had an equal effect on the students in the e-learning environment.Furthermore, it was observed that students in VREG had significantly higher self-efficacy beliefs in microscope use than students in AREG, therefore, the use of virtual reality technology in the e-learning environment had a positive effect on students' self-efficacy beliefs in microscope use.In addition, it was concluded that the students had a high level of productive interaction in practices, and no difference was observed between the groups in their participation in class.Moreover, it can be stated that course engagement in the e-learning environment is effective on the academic achievement of the student.
Joint Learning of Feature Extraction and Cost Aggregation for Semantic Correspondence

Establishing dense correspondences across semantically similar images is one of the challenging tasks due to the significant intra-class variations and background clutters. To solve these problems, numerous methods have been proposed, focused on learning feature extractor or cost aggregation independently, which yields sub-optimal performance. In this paper, we propose a novel framework for jointly learning feature extraction and cost aggregation for semantic correspondence. By exploiting the pseudo labels from each module, the networks consisting of feature extraction and cost aggregation modules are simultaneously learned in a boosting fashion. Moreover, to ignore unreliable pseudo labels, we present a confidence-aware contrastive loss function for learning the networks in a weakly-supervised manner. We demonstrate our competitive results on standard benchmarks for semantic correspondence.
Behavioral embedding for continuous user verification in global settings

In recent years, user identity verification techniques based on mobile touch interaction are becoming more reliable and prominent. These techniques can be integrated in many types of mobile applications and help preventing illegitimate access to information and storage done by impostors. Such techniques usually rely on binary classification algorithms building models for each user in the system, that require other users data in order to build ones model and in case the other users' data does not exists, a model cannot be built.In addition using a method that generates a model for each user in the system pose a significant challenge when scaling up to real-world systems. The main drawback with having a great number of models resides in the fact that it is difficult to debug, analyze, update, and fine-tune each individual model. Thus, we introduce a method for generating a unified global model that can verify the identity of every user in the system. Our most fundamental challenge is to preserve the unique behavior of each user in one unified model. Having a global model that was built prematurely also has privacy preserving characteristics, when compared to methods that require other users data. The core idea of our method is a novel behavioral embedding layer that captures and embeds each user's unique behavior and enables it to be used within global settings. We compared our method to several state-of-the-art techniques in experiments with over 9,000 users on 85 different devices. Our method achieves 0.918 AUC and 15.6% EER in efficient global settings, bypassing the second-best method by a large margin of 0.119 on AUC and 11.7% on EER.
Multi aspects based requirements prioritization for large scale software using deep neural lagrange multiplier

In multi aspect based software (MABS) aspects like business values, benefits, cost, time, penalty and risk on business are evaluated. Requirement prioritization (RP) is contemplated as a portion of Requirements Engineering and is pivotal decision-making activity. In existing RP techniques, MABS aspects are not taken into consideration. The objective of this paper is to develop a new RP method called, Deep Neural Lagrange Multipler-based Multi-aspect Large Scale Software Requirement Prioritization (DLM-MLSRP). The method consists of four different layers, i.e., one input layer, two hidden layers and one output layer. The requirement specification acquired from the customer forms as input to the input layer. The first hidden layer performs requirement selection via Criteria Hypothesis formulation. The second hidden layer performs Pair-wise Assessment by means of Lagrange Multipler Eigen-based function. Finally, the requirement prioritization matrix forms the output layer. The performance of our method is evaluated based on four paramters, first one is RP time, time efficiency of DLM-MLSRP method was found to be 24% and 36% better than that of SRP Tackle and IFS respectively. Second parameter is RP accuracy and our method shows 98.33% accuracy as compared with 96.6% and 93.33% of other two methods, third paprameter is sensitivity and our method shows 0.88 compared to 0.85 and 0.81 of other two methods. The final parameter that we consider is specificity and our results show improvement of specificity of DLM-MLSRP method by 8% and 20% compared to other two methods.
Secure Automatic Question Paper Generation with the Subjective Answer Evaluation System

In this current world, people moves towards automation process to reduce the time and easy to work, so that a need for automation in education system is very essential now days. Education is a vital role for career development and also for personal development. At present, preparing a question paper based on the syllabus and evaluating the answer sheet is hectic for the teachers. Manually preparing the question paper and evaluating the answer consume more time and manpower. This paper describes secure automatic question paper generation and also evaluates the answer which is computerizing in subjective format. For the automatic question paper generation, the admin has to create a database which is the form of question with bloom’s taxonomy. By clicking the generate button, the question will generated automatically. To send the question paper safely to authorized person the cryptography were utilized. For evaluating the answer, admin has to build the question as well as keyword in the database, when the users attend the examination, it automatically evaluate answer with the keyword matching algorithm. By using this system will reduce the human error and also consume less time to generate question and as well as evaluating the answer.
Sparse non-negative matrix factorization for uncertain data clustering

We consider the problem of clustering a set of uncertain data, where each data consists of a point-set indicating its possible locations. The objective is to identify the representative for each uncertain data and group them into k clusters so as to minimize the total clustering cost. Different from other models, our model does not assume that there is a probability distribution for each uncertain data. Thus, all possible locations need to be considered to determine the representative. Existing methods for this problem are either impractical or have difficulty to handle large-scale datasets due to their pairwise-distance based global search strategy and expensive optimization computation. In this paper, we propose a novel sparse Non-negative Matrix Factorization (NMF) method which measures the similarity of uncertain data by their most commonly shared features. A divide-and-conquer approach is adopted to remarkably improve the efficiency. A novel diagonal l0-constraint and its l1 relaxation are proposed to overcome the challenge of determining the representatives. We give a detailed analysis to show the correctness of our method, and provide an effective initialization and peeling strategy to enhance the ability of processing large-scale datasets. Experimental results on some benchmark datasets confirm the effectiveness of our method.
Detection of lung tumor using SVM and Bayesian classification

Lungs being an important organ in the respiratory system, it is prone to many chronic diseases involving tumor cells. These lung tumors are treatable, if diagnosed at early stage. Among lung tumors, the non-small cell category is irresponsive even for chemotherapy treatment when diagnosed at later stage. This work concentrates on improving the diagnosis of non-small tumor cells at early stage through image processing techniques. The CT image of lungs is used for discriminating the tumor cells from healthy non-tumor cells. Upon using computer aided image processing techniques, the level of accuracy in assessing the tumor cells can be improved. Initially, the noise present in the CT image is removed using Wiener filter by improving the signal to noise ratio. The vascular structures in the image are removed and possible tumor cells are segmented from other healthy cells using region growing technique. After extracting the features, the Support Vector Machine and Naïve Bayesian techniques are used for classifying the tumor cells and healthy cells.
Internet of Things Device Identification Algorithm considering User Privacy

The Internet of Things has become the third wave of the information industry and cloud computing, big data, and Internet technologies. Among the many identification technologies used in the Internet of Things, radiofrequency identification technology is undoubtedly one of the most popular methods today. It is replacing the traditional contact IC card and becoming a new trend of smart cards. At the same time, a large amount of data is generated in the IoT environment. A lot of data involve user privacy, and users do not have good control over these data. Collecting and utilizing these data on the basis of protecting user privacy have become an important problem to be solved urgently. With the implementation of the strategy of rejuvenating the country through science and education, major colleges and universities are developing rapidly through enrollment and expansion, which also brings inconvenience to campus security management. Although the traditional campus all-in-one card system can guarantee the security identity of people entering and leaving, it does not reasonably integrate and utilize this information, resulting in waste of information resources and, to a certain extent, the problem of user privacy leakage. To solve the above problems, a new system was developed to integrate resources to identify users. To protect the privacy data of Internet of Things users, a specific solution using blockchain technology is proposed; for the identity authentication problem of Internet of Things users, the identity authentication based on the public key address of the blockchain is used on the chain, and the group signature is used off the chain. The identity authentication method solves the contradiction between anonymity and traceability in blockchain application scenarios. The simulation results show that the system not only considers user privacy but also has extremely important practical significance for the promotion of Internet of Things and RF applications.
Face Detection and Recognition Techniques Analysis

The face is one of the foremost widely physical attributes used in biometrics systems for human identification. Biometric authentication has gained a lot of interest and attention recently. The field of biometrics can range from complicated tasks such as video police investigations to simple ones such as access to social media accounts or unlocking mobile phones. There are several face recognition and detection algorithms. They are based mostly on two types of computer vision schools. The first type of school is the traditional techniques which use descriptive hand-designed features to capture the characteristics and then machine learning strategies to learn classifiers. On the other hand, deep learning algorithms perform the separated two tasks in one stage where the network can learn the features and the classifiers. The face verification technique contains two stages, the first stage is face detection, and the second stage is face recognition. Face detection addresses the issue of the face&#x0027;s localization within the entire image to determine the bounding box of individual faces. Face recognition measures the similarity of the localized faces in the image with stored templates in a particular database to identify the personality of the detected faces. This paper provides a comprehensive study and analysis of face detection and recognition techniques. Each stage is analyzed by studying the suggested algorithms in the literature and providing strengths and weaknesses. Additionally, we discuss the challenges and the problems which make the detection and recognition tasks very complicated.
Alleviating Representational Shift for Continual Fine-tuning

We study a practical setting of continual learning: fine-tuning on a pre-trained model continually. Previous work has found that, when training on new tasks, the features (penultimate layer representations) of previous data will change, called representational shift. Besides the shift of features, we reveal that the intermediate layers’ representational shift (IRS) also matters since it disrupts batch normalization, which is another crucial cause of catastrophic forgetting. Motivated by this, we propose ConFiT, a fine-tuning method incorporating two components, cross-convolution batch normalization (Xconv BN) and hierarchical fine-tuning. Xconv BN maintains pre-convolution running means instead of post-convolution, and recovers post-convolution ones before testing, which corrects the inaccurate estimates of means under IRS. Hierarchical fine-tuning leverages a multi-stage strategy to fine-tune the pre-trained network, preventing massive changes in Conv layers and thus alleviating IRS. Experimental results on four datasets show that our method remarkably outperforms several state-of-the-art methods with lower storage overhead.
A Joint Model for Extracting Latent Aspects and Their Ratings From Online Employee Reviews

The personal description of a company associated with job satisfaction, company culture, and opinions of senior leadership is available on workplace community websites. However, it is almost impossible to read all of the different and possibly even contradictory reviews and make an accurate overall rating. Therefore, extracting aspects or sentiments from online reviews and the corresponding ratings is an important challenge. We collect online anonymous employees’ reviews from Glassdoor.com which allows people to evaluate and review the companies they have worked for or are working for. Here, we propose a joint rules-based model which combines the numerical evaluation reflected in the form of 1–5 stars, and the reviewed context to extract aspects. The model first inputs the five aspects with the initial word sets that are manually screened, and expands the aspect keyword sets through bootstrapping semi-supervised learning, and then uses latent rating regression to obtain the aspect score and aspect weight to update the corresponding score. Our experimental evaluation has shown better results as compared with an unsupervised learning of the latent Dirichlet allocation. The results could not only help companies understand their strengths and weaknesses, but also help job seekers apply for companies.
Helmholtz vibrations in bowed strings

For almost 160 years, it has been known that Helmholtz oscillations, unique to vibrating strings in bowed instruments (violin, cello, etc.), have two distinct regimes: "slip" and "stick." During the slip regime, the force at the bow-string interaction is attributed to friction between the sliding bow hair and the vibrating string, with a friction coefficient that decreases with increasing relative velocity. Yet the hair-string interaction during the stick regime is less understood. We propose that the interaction force during the stick regime is proportional to the product of the longitudinal acoustic impedance of the bow hair to the relative bow-string velocity. We validate this hypothesis by solving the string's differential equation of motion, including an enhanced formulation to avoid parasitic high-frequency oscillations. This physical model enables us to analyze, in real time, the characteristics of the Helmholtz oscillations, including the string shape, excitation of harmonics, Schelleng ripples, and string energy, showing that the bowed string gains energy during the stick regime and loses energy during the slip regime.
An Efficient Medical Image Encryption Using Magic Square and PSO

Encryption is essential for protecting sensitive data, especially images, against unauthorized access and exploitation. The goal of this work is to develop a more secure image encryption technique for image-based communication. The approach uses particle swarm optimization, chaotic map and magic square to offer an ideal encryption effect. This work introduces a novel encryption algorithm based on magic square. The image is first broken down into single-byte blocks, which are then replaced with the value of the magic square. The encrypted images are then utilized as particles and a starting assembly for the PSO optimization process. The correlation coefficient applied to neighboring pixels is used to define the ideal encrypted image as a fitness function. The results of the experiments reveal that the proposed approach can effectively encrypt images with various secret keys and has a decent encryption effect. As a result of the proposed work improves the public key method's security while simultaneously increasing memory economy.
A CNN-MPSK Demodulation Architecture with Ultra-Light Weight and Low-Complexity for Communications

Modulation is an indispensable component in modern communication systems and multiple phase shift keying (MPSK) is widely studied to improve the spectral efficiency. It is of great significance to study the MPSK modulations of symmetric phases in practice. Based on convolutional neural networks (CNNs), we propose a generic architecture for MPSK demodulation, referred to as CNN-MPSK. The architecture utilizes a single-layer CNN and a pooling trick to crop network parameters. In comparison with conventional coherent demodulation, the CNN-MPSK eliminates three modules, i.e., carrier multiplication, bandpass filter and sampling decision. Thus, we can avoid π-inverted phenomenon from the multiplication of two carrier waves with different phases, as the carrier multiplication is not employed. In addition, we can reduce errors introduced by sampling decision. Furthermore, we conduct bit-error-rate tests for binary-PSK, 4PSK, 8PSK, and 16PSK demodulation. Experimental results reveal that the performance of CNN-MPSK is almost the same to that of conventional coherent demodulation. However, the CNN-MPSK demodulation reduces computational complexity from O(n2) to O(n) as compared to the latter one. Additionally, the proposed scheme can be readily applied for demodulation of non-symmetric MPSK constellations that maybe distorted by linear and nonlinear impairments in communication systems.