Reviewerly/exHarmony huggingface dataset:
Facial Recogition for CASIA-Face V5 in Virtue of Convolutional Neural Networks

Since it takes too long time for computer to train to recognize individual under SVM and other traditional algorithms. in this study, this paper used a better model called Convolutional Neural Network (CNN) as the main technology to reduce the training time for computer. This paper showed every step of my proposed model openly and transparently. Eleven of my own pictures are mixed with CASIA-FaceV5 dataset since Asian people are similar to each other, so it would help with exercising the accuracy of the CNN model. This paper used the file called haarcascade_frontalface_default.xml to extract people’s face for further training. Sequential mode in Keras was used for better operating. More specifically, this paper also used ReLu function as the activation function for better result. Stochastic gradient descent (SGD) was employed with momentum to minimize the loss function. Finally, computer camera was used for taking the picture and visualize the final result. Experimental results indicate that my CNN model achieves a satisfactory performance that it can provide accurate feedback for recognition.
Multivariate Anomaly Detection in Discrete and Continuous Telemetry Signals Using a Sparse Decomposition into a Dictionary

This paper presents some research works based on the PhD thesis of B. Pilastre (B. Pilastre, Estimation Parcimonieuse et Apprentissage de Dictionnaires pour la détection d’Anomalies Multivariées dans des Données Mixtes de Télémesure Satellite, PhD Thesis of the university of Toulouse, Nov. 6, 2020.), supported by CNES and Airbus Defence & Space, on a new Anomaly Detection algorithm based on a sparse decomposition into a DICTionary (ADDICT). The proposed method addresses two main challenges related to anomaly detection for satellite telemetry parameters, namely the multivariate processing of these parameters and the mixed continuous and discrete nature of the data. Different variations of the ADDICT algorithm, referred to as C-ADDICT and W-ADDICT, have been investigated differing by the data decomposition term defined using a linear combination of the atoms or its convolutional equivalent. The resulting ADDICT, C-ADDICT and W-ADDICT algorithms have been evaluated on a small representative dataset containing satellite anomalies with an available ground-truth and have shown competitive results with respect to the state-of-the-art. They have also been tested on industrial use-cases, especially regarding online processing (i.e., sequential learning taking into account the feedback of users). The results of these tests are presented in this paper.
Attention Meets Involution in Visual Tracking

In visual tracking, both convolution and attention are widely employed for feature enhancement and fusion. However, convolution is not adept at modeling global dependencies of samples due to operation on local neighbors, while attention focuses mostly on global dependencies and little on local dependencies.It is intrinsically infeasible to trivially combine both to integrate global and local information.A novel model, involution, was recently proposed, whose kernels are different in spatial extent but share across channels, making it possible to take advantage of both convolution and attention.We propose an Att_Invo model that uses attention mechanism to generate involution kernels, so as to take into account both global and local dependencies of samples.To improve the performance, we develop strategies of backbone network modification, template updating, and regression of bounding box distributions. Our tracker is tested on three benchmarks. Experimental results show that it is competitive with state-of-the-art trackers.
Chukonu

Apache Spark is a widely deployed big data analytics framework that offers such attractive features as resiliency, load-balancing, and a rich ecosystem. However, there is still plenty of room for improvement in its performance. Although a data-parallel system in a native programming language significantly improves performance, it may require re-implementing many functionalities of Spark to become a full-featured system. It is desirable for native big data systems to just write a compute engine in native languages to ensure high efficiency, and reuse other mature features provided by Spark rather than re-implement everything. But the interaction between the JVM and the native world risks becoming a bottleneck. This paper proposes Chukonu, a native big data framework that re-uses critical big data features provided by Spark. Owing to our novel DAG-splitting approach, the potential Spark integration overhead is alleviated, and its even outperforms existing pure native big data frameworks. Chukonu splits DAG programs into run-time parts and compile-time parts: The run-time parts are delegated to Spark to offload the complexities due to feature implementations. The compile-time parts are natively compiled. We propose a series of optimization techniques to be applied to the compile-time parts, such as operator fusion, vectorization, and compaction, to significantly reduce the Spark integration overhead. The results of evaluation show that Chukonu has a speedup of up to 71.58X (geometric mean 6.09X) over Apache Spark, and up to 7.20X (geometric mean 2.30X) over pure-native frameworks on six commonly-used big data applications. By translating the physical plan produced by SparkSQL into Chukonu programs, Chukonu accelerates Spark-SQL's TPC-DS performance by 2.29X.
The Dilemma and Prospects of Deep Learning

Deep learning algorithms have emerged in many cutting-edge sciences. Its simple structure and high performance make it a valuable object. However, with its application in various fields, its understanding, vulnerability, sample cost, and other issues have also been exposed. For example, in natural language understanding, deep learning could not truly understand the meaning of words. Moreover, in recognition of text and images, deep learning is easily attacked by adversarial samples. Moreover, the problem of sample cost required for deep learning is also revealed. This paper reviews these problems and proposes some possible solutions to the problems in the future. The current dilemma of deep learning research is that there is no clear explanation for the shortcomings of deep learning. The significance of the research on such problems is: whether it is to change the direction or continue to optimize and solve the weaknesses, deep learning can be brought to a new height, making it a straightforward, efficient, and stable tool.
Query Disambiguation to Enhance Biomedical Information Retrieval Based on Neural Networks

Information Retrieval Systems (IRS) use a query to find the relevant documents. Often the query term can have more than one sense; this is known as the ambiguity problem. This problem is a cause of poor performance in IRS. For this purpose, Word Sense Disambiguation (WSD) specifically deals with choosing the right sense of an ambiguous term, among a set of given candidate senses, according to its context (surrounding text). Obtaining all candidate senses is therefore a challenge for WSD. Word Sense Induction (WSI) is a task that automatically induces the different senses of a target word in different contexts. In this work, we propose a biomedical query disambiguation method. In this method, WSI use K-means algorithm to cluster the different contexts of ambiguous query term (MeSH descriptor) in order to induce the different senses. The different contexts are the sentences extracted from PubMed containing the target MeSH descriptor. To represent sentences as vectors, we propose to use a contextualized embeddings model “Biobert”. Our method is derived from the intuitive idea that the correct sense in the one having the high similarity among the candidate senses of an ambiguous term with its context. The conducted experiments on OHSUMED test collection yielded significant results.
Intra-Shard and Inter-Shard Collaborative Incentive Scheme for Shard-Based Permissionless Blockchain

Shard-Based blockchain technology is currently one of the effective ways to improve the throughput of permissionless blockchain. By distributing transactions into multiple shards, the validators in each shard verify transactions and reach in consensus in parallel, thereby increasing system throughput and scalability. However, most of the existing Shard-Based Blockchain schemes lack an incentive scheme that can promote mutual cooperation among the nodes in the shard and participate in consensus as much as possible, which leads some selfish nodes to adopt strategies that are not conducive to the maintenance of the blockchain system in order to obtain higher profits, thereby endangering the stability and security of the entire blockchain system. In this paper, we introduce the concept of node reputation to evaluate node behavior, and establish the correlation between reputation and profit. In that case, nodes with higher reputation can get more rewards, thereby achieving the fairness of reward distribution. In addition, we use the N-player static game model in game theory to analyze the changes in utility corresponding to different strategies for node, and design a dynamic transaction distribution scheme through genetic algorithm(GA) to ensure that the majority of nodes participate in consensus and shard liveness. Our research results show that our solution can better improve incentive fairness and liveness compared to the general transaction uniform distribution scheme.
Cloud in the Wallet

The creation of a Cloud computing solution for individuals or businesses has proven to be essential in meeting the ever-increasing demand of the public in search of low-cost online storage services. A private cloud server is a solution to many concerns regarding the security and costs of the service. Considering that a microprocessor like Raspberry Pi can perform complex operations, and at the cloud maintenance level can handle performance with low latency, the output of it would certainly allow an efficient storage solution. This report describes the analysis and design of Cloud in Wallet—a Cloud computing system that offers reliable, secure, and cheap storage service.
A Decision Making Tool for Mathematics Curricula Formal Verification

The authors have been working on mathematics curriculaCurricula organization for a long time. For instance, the first author is one of the advisors of the Ph.D. Thesis of the second author, that is closely related to this topic. We have recently developed a theoretical proposal of formalizationFormalization and verificationVerification of mathematics curriculaCurricula that is focused on allowing their verificationVerification using techniques borrowed from artificial intelligenceArtificial intelligence. Mathematics curriculaCurricula can be addressed due to the hierarchical nature of their contents. The underlying idea is that, if an “official curriculumCurricula” exists (provided, for instance, by the educational authorities), it is possible to automatically check the completeness and soundness of a given “development of the official curriculumCurricula” (for instance, a textbook or a project-based learning proposal). Now a computer tool, inspired by rule based expert system verificationVerification, that is, by well-known processes that check the logical consistency of the knowledgeKnowledge underlying the rules, is presented.
College Counselor Information Management Platform Based on Web and Mobile Application

With the rapid development of economy and society, the popularization of computer is also rapid. It can be said that current social development has entered the information age. At present, many schools and enterprises have begun to use computers and mobile application networks to perform related tasks or manage information work platforms. Similarly, the information of college counselors can be managed by work management platform. This paper first briefly introduces the theoretical knowledge of Web and mobile applications; Secondly, it introduces the structure of the university counselor information management platform based on Web and mobile application. Finally, the network structure, main technologies and how to serve the platform are introduced in detail. According to the construction of the work management platform in this paper, on the basis of web and mobile application algorithm, the information work management platform of college counselors has made the work and allocation of counselors a lot of convenience, among which the highest reached 91.3%.
Advantages and Factors Affecting the Adoption of Cloud Computing

In Cloud Computing, the system is generally fitted with a set of IT infrastructure, platform and the relevant applications supplied via Internet, with low cost of installation and network access to a shared configurable computing pool of resources and pay-by-use services. Although cloud computing has a great potential, literature indicates that factors affecting its adoption and the benefits leveraged from it have not been largely examined among empirical studies. Hence, in this study the authors explain the advantages and factors that affect the drive of cloud computing adoption. This paper is organized in the following way: In section one, the paper provides the introduction to the topic. this is followed by section two that contains cloud computing information in detail and section three that provides a description of the advantages that can be reaped from its use. Section four provides the factors that affect cloud computing adoption or the barriers to its implementation and finally, section five provides the paper conclusion and recommendations for future studies.
Architecture Reduction of a Probabilistic Neural Network by Merging K–Means and K–Nearest Neighbor Algorithms

Probabilistic neural network (PNN) has a sizable structure since it requires all training records in the activation of its hidden layer. Therefore, in order to manage large data classification problems, it is recommended to minimize its inner design. In this paper, the algorithm for the PNN’s architecture reduction is presented. It is organized as follows. First, a k-means data clustering is conducted and the obtained centers are stored. Next, one selects a single nearest neighbor to the determined centers considering each class separately. The pattern neurons of a PNN are then established using both (i) the clusters centers and (ii) the records closest to the obtained centroids. The algorithm is applied to the classification tasks of seven repository data sets. Utilized PNN is trained by means of four training procedures with different kernel functions in each case. A 10–fold cross validation method is applied to assess the performance of the original and reduced networks. The results are also compared to the ones provided by methods existing in literature. It is shown that in the majority classification cases, it is possible to achieve a higher accuracy of reduced PNN compared to the original network and the approaches introduced in literature.
An Efficient Natural Merge Sort Incorporating Array Decomposition

We present a new sorting algorithm that comes up with some modifications on Knuth’s NaturalMergeSort to make it faster, more selective and less spaceconsuming. The way we optimize it is by taking advantage of both ascending and descending runs, i.e., increasing the potential of the decomposition method compared to the existing old algorithm. We have also implemented our sorting algorithm in C++ and experimented with some random and manually prepared datasets that resulted in up to 1.9% and 97.5% run-time improvement over NaturalMergeSort’s average-case and worst-case respectively; Also up to 17.5% improvement over MergeSort ’s average-case. The performance and efficiency of our algorithm have been recorded and presented in graphical form by comparing time and space complexity with other competitor sorting algorithms.
A Computational Intelligence Approach for Cancer Detection Using Artificial Neural Network

In cancer detection, early diagnosis of cancer is necessary for cancer research. To come off with the solution, these problems correlate biomedical or bioinformatics with the field of computational intelligence approaches. The computational intelligence tools help to identify cancer-affected patients, level of risk, i.e., high or low in the cancer patient, detection of principal feature for the presence of cancerous cell. Inspired from the previous research of machine learning, in this research diagnosis, authors have tried to detect cancerous cell in breast cancer data using artificial neural network.
Fidelity Homogenous Genesis Recommendation Model for User Trust with Item Ratings

Abstract The ever-increasing volume of cloud services has created a service targeting issue. The mechanisms of recommenders address the issue by allowing consumers to easily access services that match their preferences. A recommendation is a regularly utilized function in recommender systems to assist users in swiftly narrowing their choices and making sensible judgments from a large amount of knowledge. In this document, design a “Fidelity Homogenous Genesis Recommendation Model” for user trust along with item ratings. The key for addressing data sparsity is how accurately the likely values of unoccupied cells are estimated. For sparsity reduction of the user-item matrix, we employ a similar prior case rationale technique mixed with average filling. This phase will aid in the later computation of user and item similarity. Genesis: the autonomous map technique was used to clustering the user-item matrix for similar users, followed by an optimization process to generate sub-optimal clusters with a more balanced number of users in each. Based on actual grid computing, the User-Item Privacy Marmalade Technique considers all trustworthy neighbors to be available after optimization. Based on the filtered item set, the trust weighting approach is intended to compute trust similarity among users. To locate trustworthy users, the filtered item set traverses all users in trust networks. In particular, a user trust neighbor set that has comparable preferences and matches with a target user and can be derived through user trust dispersion features in a trusted network. As a result, the proposed algorithm was able to give a novel recommendation model that was guided by user trust as well as item ratings.
Comparative Study of Service-Based Sentiment Analysis of Social Networking Sites Fanatical Contents

The proliferation of mobile web services (MWS) for sentiment analysis makes it hard to identify the best MWS for sentiment analysis of social networking sites’ fanatical contents. This paper carries out a comparative study of service-based sentiment analysis of social networking sites’ fanatical contents. This is achieved by cleaning, transformation, and reduction of fanatical contents from the publicly available social media dataset, and multiple MWS are selected for comparison using the application programming interface (API) key of the MWS. To evaluate the service-based sentiment analysis, standard measures such as accuracy, precision, recall, and f-measures of sentiment result for each MWS are used. The result shows that Dandelion SA performs better in terms of accuracy (72.5%) and recall (76.9%), while Wingify SA performs better in terms of precision (88.6%) and f-measure (75.5%), though AlchemyAPI offers the most crucial elements in analyzing sentiments such as emotion, relevance score, and sentiment type. The outcomes of this paper will benefit the sentiment analysis service developers, sentiment analysis service requesters as well as other researchers in the social media fanatical content domain.
Rank-Based Mixture Models for Temporal Point Processes

Temporal point process, an important area in stochastic process, has been extensively studied in both theory and applications. The classical theory on point process focuses on time-based framework, where a conditional intensity function at each given time can fully describe the process. However, such a framework cannot directly capture important overall features/patterns in the process, for example, characterizing a center-outward rank or identifying outliers in a given sample. In this article, we propose a new, data-driven model for regular point process. Our study provides a probabilistic model using two factors: (1) the number of events in the process, and (2) the conditional distribution of these events given the number. The second factor is the key challenge. Based on the equivalent inter-event representation, we propose two frameworks on the inter-event times (IETs) to capture large variability in a given process—One is to model the IETs directly by a Dirichlet mixture, and the other is to model the isometric logratio transformed IETs by a classical Gaussian mixture. Both mixture models can be properly estimated using a Dirichlet process (for the number of components) and Expectation-Maximization algorithm (for parameters in the models). In particular, we thoroughly examine the new models on the commonly used Poisson processes. We finally demonstrate the effectiveness of the new framework using two simulations and one real experimental dataset.
Enabling public transport system for visually impaired people

To reduce the problem faced by visually impaired people, the system has been developed by users and the bus module to establish communication between the user and a bus to reduce the problem facing visually impaired people. The user sends a request to the cloud when the user gives a one-digit number as an entrance. The cloud compares the request number to the bus number with the closest user and bus location and provides user and bus driver information. The user is informed by voice replay, when the desired bus reaches the bus stop and also user gets the bus location information. When the bus module received information, a buzzer is given and the user's position is displayed in the LCD on the bus that the bus driver can notify that the visually impaired people are waiting for the bus to enter at the specific location.
Partial channel-Noisy Continuous Evolution for Efficient Neural Architecture Search

To shorten the time of neural architecture search, in this paper, we improve the Continuous Evolution for Efficient Neural Architecture Search method (CARS). The Differentiable Architecture Search (DARTS) search space used by CARS has the defects of excessive memory and computational overhead when jointly training Supernetworks. Therefore this paper Partially-Connected DARTS, samples a small number of Supernetworks, i.e., operates on a subset of channels, which reduces the redundancy of the search space and shortens the search time from 21.37GPU days to 15.47GPUdays. Aiming at the defect that the unfair advantage of skip-connections in exclusive competition for the DARTS search space can easily lead to performance collapse, this unfair ad-vantage is hindered by injecting unbiased random noise into the output of skip operations. The results show that the proposed PN-CARS has an excellent search effect and is more stable.