Reviewerly/exHarmony huggingface dataset:
A Novel Enhanced Nonnegative Matrix Factorization Method for Face Recognition

Nonnegative matrix factorization (NMF), distinguished from the approaches for holistic feature representation, is able to acquire meaningful basis images for parts-based representation. However, NMF does not utilize the data-label information and usually achieves undesirable performance in classification. To address the above-mentioned problem of NMF, this paper proposes a new enhanced NMF (ENMF) method for facial image representation and recognition. We seek to learn powerfully discriminative feature by a label-based regularizer which describes the relationship between the data. It is desired that minimizing the regularizer makes the data from the same class have high similarity and the data from different classes have low similarity. This good property will contribute to improving the performance of NMF. Therefore, we propose an objective function of ENMF by incorporating the label-based regularizer into the loss function. Subsequently, we find the stationary point of the constructed auxiliary function by means of Cardano’s formula and derive the update rules of our ENMF algorithm. The convergence of the proposed ENMF is both theoretically sound and empirically validated. Finally, the proposed ENMF method is successfully applied to face recognition. Four publicly available face datasets, namely AR, Caltech 101, Yale, and CMU PIE, are chosen for evaluations. Compared with the state-of-the-art NMF-based algorithms, the experimental results illustrate that the proposed ENMF algorithm achieves superior performance.
Application of Big Data Computer Figure Image Auxiliary Technology in Modern Home Design

With the continuous development of computer technology, virtual reality technology has become one of the focuses in the field of computer science. With the growing maturity of virtual reality technology, driven by commercial interests, web 3D technology is more and more widely used in the Internet. In addition to 3D games, more and more 3D products are emerging. These 3D products enable users to get a better experience through various 3D special effects applications. According to the needs of virtualization and user experience of home design, through the in-depth research of computer graphics and image assistance technology, this paper constructs the home design in daily life in the virtualized three-dimensional scene, so that users can carry out home design according to their own ideas in two-dimensional and three-dimensional space. Based on network 3D modeling and visualization technology, the interactive roaming of virtual scene is realized, and users can dynamically control the scene in real time, so that the design purpose can be realized faster and better.
Empirical standards for repository mining

The purpose of scholarly peer review is to evaluate the quality of scientific manuscripts. However, study after study demonstrates that peer review neither effectively nor reliably assesses research quality. Empirical standards attempt to address this problem by modelling a scientific community's expectations for each kind of empirical study conducted in that community. This should enhance not only the quality of research but also the reliability and predictability of peer review, as scientists adopt the standards in both their researcher and reviewer roles. However, these improvements depend on the quality and adoption of the standards. This tutorial will therefore present the empirical standard for mining software repositories, both to communicate its contents and to get feedback from the attendees. The tutorial will be organized into three parts: (1) brief overview of the empirical standards project; (2) detailed presentation of the repository mining standard; (3) discussion and suggestions for improvement.
Deep Squeeze and Excitation-Densely Connected Convolutional Network with cGAN for Alzheimer’s Disease Early Detection

Personalised medicine is a new approach that ensure a tolerant and optimal diagnosis for the patient basing on his own data and its profile information such as life style, medical history, genetic data, behaviours, and his environment. These data is vital to predict the potential disease progression. Extracting insights from these heterogeneous data is a challenging task. Brain disorders such as neurodegenerative diseases detection and prediction is still an open challenge for research. The early prediction of these diseases is the key solution to prevent their progression. Deep learning methods has shown an outstanding performance on the brain diseases diagnosis such as the Alzheimer’s disease (AD). In this paper we present two contributions. Firstly, we adopt a conditional generative adversarial network for data augmentation based on two datasets the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Open Access Series of Imaging Studies (OASIS) as a solution for the shortage of health information. Additionally, we present a new method for an early detection of the Alzheimer’s disease based on the combination of the Densenet 201 network and the Squeeze and Excitation network. Furthermore, we compare our approach with the traditional Densenet 201, Squeeze and Excitation network and other networks. We figure out that our approach yields best results over these networks. We reach an accuracy of 98%.
Image Restoration and Noise Reduction with Context-Dependent Wavelet Graph and ADMM Optimization

We represent the image noise reduction and restoration problems as context-dependent graphs and propose algorithms to derive the optimal graphs by the alternating direction method of multipliers (ADMM) method. An image is spatially decomposed into smooth regions and singular regions, consisting of edges and textures. The graph representing a smooth region is defined in the image domain, while that representing a singular region is defined in the wavelet domain. The optimal graphs are formulated as the solutions of constrained optimization problems over sparse graphs, where the sparseness is imposed on the edges. The graphs on the wavelet domain are solved in a hierarchical layer structure. The convergence and complexity of the algorithms have been studied. Simulation experiments demonstrate that the results of our algorithms are superior to the state-of-the-art algorithms for image noise reduction and restoration.
A Conceptual Model for Conformance with Accessible Gamification

There currently remains limited Gamification awareness and training for developers on WCAG conformance. Studies indicate an increased interest from developers to raise their acceptance, awareness, and technical abilities for designing accessible digital products. This article explores and presents a conceptual module to improve web developers’ capabilities and knowledge of accessible digital design. By leveraging the standards put forth by WCAG 2.0, developers can create accessible content for users who identify with various forms of abilities. Four primary principles comprise WCAG 2.0 and 12 standards, and 12 standards deliver fundamental objectives as best practices for developers. These guidelines were employed for gaming content design and development, permitting users to regulate reality and Gamification associations whereas immersing in the game. The goal is to apply diverse processes for each stage of the game to allow challenges and motivation for users to determine novel processes while understanding the guidelines. Assistive Technology was used to navigate each stage. To suit independence or self-reliance, the conceptual model supports players’ personalization while completing the game activities. Likewise, employing complex, advanced, and reward dashboards satisfies the proficiency component, and social network communications to other players provide the opportunity for interconnectedness. The conceptual model presented in this paper underpins Gamification and the potential to incorporate evidence-based accessibility principles developed by W3C. The previous examinations focused on instruments (e.g., software, feature, components) to achieve WCAG conformance. This examination presents a distinction from prior studies as this conceptual model recognizes consciousness and self-determination as the initial starting point.
Fpc: feature map pruning using channel attention mechanism

Neural networks have the problems of excessive number of parameters and computation due to data noise and redundant filters, which limits the application of neural networks. A feature map pruning method using channel attention (FPC) is proposed to address the problem of excessive accuracy loss of existing pruning methods. Experiments show that the feature graph pruning method using channel attention has better pruning effect with the same accuracy loss.
Penerapan Agile Project Management Pada Pembuatan Sistem E-Warga Taman Cibodas Lippo Cikarang

The implementation of E-Government technology in government at this time has not been evenly applied in all regions, especially in the lowest government, namely RT (Rukun Tetangga).Currently administrative services in the RT001 RW017 area of Taman Cibodas Lippo Cikarang Housing are still using conventional method, by utilizing the E-Warga system technology, it is expected to change the conventional and more efficient way.By using the Agile Management System method and the Scrum model framework, the creation of this E-Warga system will be faster in the manufacturing process because it divides the project into several parts called sprints.The results of the research show that the creation of the E-Warga system takes 8 weeks by dividing the time into 5 sprints.The evaluation of the sprint results is carried out after completing 1 sprint which has been determined by the priority scale previously.With the presence of the E-Warga system, RT management and residents can be faster and more efficient in administrative activities in the area of RT001 RW017, Taman Cibodas Housing, Lippo Cikarang.
CryptO’Roulette A Smart Approach To Modern Encryption

In a symmetric-key cryptosystem, where the secret key is known to both the sender who encrypts the message (let’s call her Alice) and the receiver who decrypts the message (let's call him Bob). Since the secret key is very important so it cannot be disclosed to any third party that is why it should not be exchanged through any public form of communication because if through any means these keys get leaked all the encrypted messages in the future will be compromised [1].We know that the advent of technology has increased the risks of data thefts and compromising data integrity. So, to secretly exchange the key both the parties must hold some sort of private meeting, therefore they need to establish some private communication channel. This is a difficult task practically speaking in terms of internet communications. [6] These days, the utilization of the Internet is developing quickly throughout the planet, and security is turning into a significant public concern. Already security was a significant issue for military applications yet presently the application region has been improved as more correspondence happens on the web. Cryptography is a computer science platform intended to give security to senders and receivers to communicate and recover secret data about an uncertain channel through a cycle called Encryption/Decryption. Cryptography guarantees that the message ought to be sent without change and that only authorized individuals can open and peruse the message [10].In this paper, we tried to randomize the encryption process so that the data becomes more secure and less vulnerable to hacker attacks. We will use the top 4 best existing encryption algorithms like AES, TripleDES, Rabbit, etc., and use them to randomly encrypt the different segments of the message.
Directed Motivational Currents in L2: Exploring the Effects on Self and Communication. Nourollah Zarrinabadi, Saeed Ketabi, and Mansoor Tavakoli (eds.) (2019). Cham (Switzerland): Springer. ISBN: 2193-7648 . pp. xvii-118

Over half of a century, second language (L2) motivation theory has been heatedly discussed and continuously developed by L2 teachers, language practitioners, applied linguists and psycholinguists who attempted to examine the effects of motivational variables on L2 learners’ learning outcomes. Recently, the state-of-the-art development of L2 motivation theory is the idea of Directed Motivational Currents (DMC), which refers to “a prolonged period of engagement in a series of tasks which are pleasant mainly because they transport the person towards a highly valued goal” (p.3). As a speculative empirical endeavor in this regard, this book aimed to examine the existence of DMC to gain a better understanding of how a DMC was triggered and launched, importantly, this book also discussed the impact of DMC on L2 learners’ individual differences, e.g., self-concept, self-confidence, autonomy and willingness-to-communicate (WTC).
Luckin Opens a New Era

This article selects Luckin coffee, the legendary coffee company in the industry, as the research object, analyzes the important role of big data in cost management during its forced delisting and recovery process, aiming to provide help for the digital revolution of the coffee industry. The article takes SWOT model which is used to comprehensively analyze the current competitive situation of the enterprise, identify its advantages and disadvantages, so as to accurately drive the upgrade of cost management strategy. The last part, according to the above analysis put forward the enterprise expectation and industry outlook. By discussing Luckin coffee's comeback in the background of digital economy, this paper concludes that big data, as the most active element in the new round of industrial revolution.
S-CPM: Semantic-Similarity Cluster based Privacy Preservation Model with Cell Generalization Principle

Timely data analysis on a wide variety and a large volume of data unveil valuable information or new insights.The analysis results could be used to innovate new avenues in health care service, business and e-service, etc.However, releasing, storing and reusing sensitive data to third parties results in breaching the data privacy of the individual.To combat privacy breach invasion, privacy-preserving techniques such as suppression, generalization and encryption-based privacy models have been proposed in the literature.The widely used privacy preservation model k-anonymity model prevents record-linkage invasions but fails to satisfy monotonicity property.It has more data distortion and fails to defend semantic-similarity, closeness, nearest-neighborhood data privacy breaches.Moreover, existing approaches are not scalable for the large-scale data set.The paper proposes a semantic similarity two-phase cluster based privacy preservation model.The proposed model considers both numerical and categorical attribute values for data anonymization.Two-phase clustering contains two phases.In the first phase, the t-centroid clustering algorithm is designed and used to partition a set of transaction records of data set D into a set of t-centroids based on the Euclidean distance between transaction records.In the second phase, the neighborhood-aware hierarchical clustering algorithm is designed.It is used to split a set of transaction records within clusters based on neighborhood aware attribute values.Two-phase clustering operations are carried out in parallel and scalable for Big Data sets.The proposed privacy model relies on cell generalization to combat records linkage and semantic-similarity, closeness, nearest-neighborhood privacy breach invasion.All experiments are carried out on two different datasets: Income-Census (KDD) and Bank Credit Card dataset.The experimental results demonstrate that the proposed privacy model can combat privacy breach invasion with cell generalization principles.The proposed privacy model is scalable and time efficient for large-scale data sets.
Simulation of Two‐Qubit Gates with a Superconducting Qudit

In article number 2100500, Xinsheng Tan, Yang Yu, and co-workers use a four-level superconducting quantum loop to simulate a two-qubit quantum system. By mapping the energy levels of the two-qubit system to a non-composite system, simulations of several quantum gate operations, including the CNOT gate and the Hadamard gate, are completed in a convenient way. Furthermore, the authors generate entangled states to verify the log2 entropic inequality. The results indicate that the superconducting qudit is indeed a huge quantum platform for quantum information.
Semantic analysis of the concept of «quality of physical training of cadets»

The article follows an actual and under-researched problem of assessing the quality of physical training of cadets of educational organizations of the Ministry of Internal Affairs of Russia. Semantic analysis of scientific publications has been based on the key unit «the quality of physical training of cadets». The author defined the semantic field of the studied frequency of occurring keywords, ranked by relevance. It is revealed that the included words are most often found in the organization of the learning process, without highlighting the specific foundations for assessing and diagnosing the quality of physical readiness of future employees. The formed array of phrases consisting of two words highlighted by the semantic processing system is determined by the following factors: the process of physical training and the assessment of the quality of physical training of cadets. The degree of interrelation of these groups was defined as 5/4. It should be noted that the quality of physical training of cadets lacks analysis in the scientific, methodological and educational literature; a multi-level assessment of the quality of physical readiness of future employees for professional activity is not presented.
HB+-MHT: Lightweight and Efficient Data Integrity Verification Scheme for Cloud Virtual Machines

With the rapid development of cloud computing, cloud storage is widely used. In the cloud environment, users’ virtual machine system mirrors and data are stored in the cloud server. The escape of virtual machines and Trojan virus attacks make it challenging to ensure the integrity of virtual machine systems. Trusted computing is expensive to randomly verify data integrity and does not adapt to dynamic data changes. Provable data integrity is a potential solution to this problem. Merkle Hash Tree (MHT) model is widely adopted in provable data integrity. Although MHT requires only a small amount of evidence for verification, the verifier’s number of hash calculations and the server’s efficiency of evidence query are not optimal. Moreover, the verification frequency of each piece of data is not considered by MHT. Properly handling these factors can improve the actual verification performance. In this paper, a lightweight and efficient data integrity verification approach called HB+-MHT is proposed for the tenant virtual machine (TVM) in cloud computing. In HB+-MHT, the Huffman hash tree scheme is used for small file verification to ensure that the hot file has a shorter path, which reduces the required amount of evidence for verification. Meanwhile, the B+ hash tree scheme is used for big files verification, which can effectively reduce evidence query time and hash calculation times. The experimental results show that the scheme proposed in this paper can perform data integrity verification well, with reduced computing and storage overhead.
Pre-training with Phonetics and glyphs for Chinese Spelling Correction

Spelling error correction is a challenging task. Extensive approaches nowadays either use rule-based statistics and language models or sequence-to-sequence deep learning methods. In this paper, we propose a new end-to-end CSC model that uses powerful pre-training and fine-tuning methods to integrate phonetic features, stroke features into the language model. The selected tokens are masked according to the confusion set using similar characters, instead of using a fixed token mask as in BERT [10]. in addition to the prediction of the characters themselves, this paper introduces phonetic prediction to learn knowledge of spelling errors at the phonetic level. In addition to this, we add a probability weight in order to balance error detection and error correction throughout the framework. experimental results show that the model achieves a significant improvement over the SIGHAN dataset and outperforms previous state-of-the-art methods.
Temperature Prediction Using a Neofuzzy Neuron Approach

In this paper it’s presented a temperature prediction application using a modified neofuzzy neuronbased approach. This approach is an easy and accurate method for obtaining prediction results using climatic measurements from the previous days. The variables used for building the model are Temperature, Humidity, Dew Point, Wind speed, Pressure, Rain and Solar Radiation. It’s also presented the obtained results for temperature prediction in Ibarra, Ecuador using three years data.
Using a Pangeo platform on Azure to tackle global environmental challenges&amp;#160;

&amp;lt;p&amp;gt;The Pangeo project enables interactive, reproducible and scalable environmental research to be carried out using an integrated data-computational platform. Here we demonstrate a few examples that utilise a Pangeo platform on Microsoft Azure supported by the Met Office where global environmental challenges are explored and tackled collaboratively. They include: (1) Analysing and quantifying drivers of low rainfall anomalies during boreal summer in Indonesian Borneo using causal inference and causal network to identify key teleconnections, and their possible changes under a warming climate, which will contribute to seasonal forecasting efforts to strengthen prevention and control of drought and fire multihazards over peatlands in the study region; (2) Quantifying and communicating uncertainty in volcanic ash forecasts; and (3) Exploring the cascading effects that follow the degradation and recovery of Caribbean coral reefs.&amp;lt;/p&amp;gt;
Dependence Research on Multi-Layer Convolutions of Images

Convolutions are important structures in deep learning. However, theoretical analysis on the dependence among multi-layer convolutions cannot be found until now. In this paper, the image pixels before, in, and after multi-layer convolutions are of modified multifractional Gaussian noise (mmfGn). Thus, their Hurst parameters are calculated. Based on these, we applied mmfGn model to analyze the dependence of gray levels of multi-layer convolutions of the image pixels and demonstrate their short-range dependence (SRD) or long-range dependence (LRD), which can help researchers to design better network structures and image processing algorithm.