Reviewerly/exHarmony huggingface dataset:
Empirical Research on the Status Quo of Teachers of Common Required Courses in Vocational Higher Education Institutions

In order to build a high-quality team of teachers in vocational higher education institutions, and investigate the current situation of teachers of common required courses in vocational higher education institutions of Hainan Province, an empirical research is conducted. A questionnaire survey was carried out in aspects of satisfaction of teaching and scientific research, professional quality, work saturation and teacher’s job satisfaction. The research results shows that the professional quality of teachers of common required courses cannot significantly influence the satisfaction of teaching and scientific research; Job saturation can positively influence teaching and research satisfaction; teaching and research satisfaction can positively influence teachers’ job satisfaction.
SNMFNet

PCANet is a deep learning network that uses an orthogonal factorization form (i.e., principal component analysis, PCA) to learn the filter bank. It shows superior performance for image classification task. However, PCA only considers the statistics of the image patches while it does not consider the part-based matrix factorization (i.e. Non-negative Matrix Factorization). In this paper, we proposed to replace the PCA algorithm with the Semi-Non-Negative Matrix Factorization (SNMF) algorithm and to construct the SNMFNet. Specifically, first, we propose a seminon- negative matrix factorization algorithm, which uses the l2 norm regularizer to restrict base vectors to substitute the nonnegative constraint on basis matrix. Second, we introduce the SNMFNet, which uses the nonorthogonal matrix factorization algorithm to replace the orthogonal matrix factorization such as PCANet. Experimental results show that our proposed SNMFNet achieves superior performance to PCANet on several benchmark datasets.
Some Artificial Intelligence Driven Algorithms For Mobile Edge Computing in Smart City

Smart mobile devices can share computing workload with the computer cloud that is important when artificial intelligence tools support computer systems in a smart city. This concept brings computing on the edge of the cloud, closer to citizens and it can shorten latency. Edge computing removes a crucial drawback of the smart city computing because city services are usually far away from citizens, physically. Besides, we introduced a neuro-evolution approach for supporting smart infrastructures. Solutions related to using Tweeter’s blogs by smart city apps are presented, too. Finally, the design principles of differential evolution for smart city are analyzed.
Correcting and Redesigning Metadata for the Excavation of an Archaeological Site

Libraries have a long tradition on cataloging with consistency. Other domains try to follow their paradigm, but there are many challenges to be addressed. In this paper we examine the metadata design and implementation for the excavation of an archaeological site. We start by discussing with the excavation personnel, to understand the procedures and the metadata involved. We examine the current metadata scheme and the way it was used to fill in all metadata element values and we classify the obvious errors found in them. We reveal additional required functionality and propose an improved scheme. In the poster we expose our methodology and findings.
Algorave: Dancing to Algorithms

A brief reflection on the founding and development of algorave, events where musicians and visualists create music using algorithms, usually through live coding: live manipulation of algorithms as code. The essay reflects on the now-traditional role of projected code, the experience of performing at an algorave, and the role of code as creative material in embodied music improvisation.
A User Profile Analysis Framework Driven by Distributed Machine Learning for Big Data

In recent years, big data has become the new focus of attention from all walks of life. The valuable information contained in big data becomes the driving force for people to process and analyze big data. Big data analytics helps enterprises to take better decisions to improve business output. As a user description tool, user profile is widely used in various fields. However, it is difficult to deal with large-scale datasets using traditional methods since the established processes was not designed to handle large volumes of data. In this paper, we propose a user profile analysis framework using machine learning approach which apply advanced machine learning programs to solve industrial scale problems. And this approach can be effective to speculate real and potential needs of various groups of users and precisely extract individual characteristics and group generality. By introducing high-level data parallel framework, the process of large-scale data processing can be executed efficiently. We use real-world data to validate the effectiveness of the proposed framework.
Letter Recognition and Drawing Using Pioneer 3dx Robot

Robots in general can be defined as intelligent machines responsible for accomplishing specific task. Through this paper, we are going to design a robot capable of drawing letters based on an input sensory data. This can help people who want to draw words or decorate the courts or fields. It is built by pioneer 3dx combined with different types of sensors like Kinect camera and LIDAR. It will be able to recognize different handwriting by matching what is extracted from the image to database. As a result, it will draw the letters using its WidowX arm. Twenty-six letters were compiled in to specific codes to build up a database for matching stage. The robot was able to analyze and draw the letters perfectly.
A Lightweight Certificateless User Authentication Scheme for Mobile Environment

Nowadays, smartphone applications are the most widespread in our daily lives. These applications raised several security concerns such as authentication, key agreement, and mutual authentication. Accordingly, the researchers have been presented several user authentication schemes based on the identity-based cryptography (IBC) and certificateless cryptography (CLC). Smartphones considered as limited resources devices, thus, it needs lightweight protocols. However, the existing schemes are suffering from high computational costs especially the one that depends on CLC. In this paper, a lightweight certificateless user authentication scheme based on the elliptic curve cryptography (ECC) is introduced. The proposed scheme has the lowest computation costs comparing with the existing certificateless user’s authentication protocols. Furthermore, The proposed scheme is secure under the computational Diffie-Hellman (CDH) Problem and the elliptic curve discrete logarithm problem (ECDLP). Indeed, the proposed scheme is suitable to use in the mobile client-server environment and the Internet of things (IoT) applications.
Inverse problems in signal processing : Functional optimization, parameter estimation and machine learning

Inverse problems arise in any scientific endeavor. Indeed, it is seldom the case that our senses or basic instruments, i.e., the data, provide the answer we seek. It is only by using our understand ...
Anti-Tailing AB-Door Detection Based on Motion Template Algorithm

The product of anti-tailing AB-Door is a kind of high-tech product among the security entrance guard systems, which is widely demanded in the high-end application fields of finance (banks), security (prisons), official business, general places(factories) where anti-submerged crime exists. The main task for the system to function well is the accuracy of human's detection. This paper introduces an effective system to detect multi-person between AB-door based on Motion History Image (MHI) and motion segmentation. The direction of movement is then calculated depends on MHI which is essential for the following operation for the doors. In order to solve the problem that people cannot be detected when no movement occurs, we adopt novel original Token and Separation Degree Analysis algorithm to optimize our method. Because of the complexity of natural scene, a perfect Intelligent Video System tends to be based on different algorithm policies to deal with a variety of situations unpredicted beforehand. We tried to conquer the statistics of person number together with the disturbance of half-transparent glasses, free poses, light changing and imaging distortion in a tiny fixed area between two doors, which were supposed to contain at most single. We measured the performance of the algorithm in the above scenarios which demonstrated high accuracy and strong adaptability.
The Continuum in Constructive and Intuitionistic Mathematics

In constructive mathematics, a problem is counted as solved only if an explicit solution can, in principle at least, be produced. Thus, for example, “There is an x such that P(x)” means that, in principle at least, we can explicitly produce an x such that P(x). If the solution to the problem involves parameters, we must be able to present the solution explicitly by means of some algorithm or rule when given values of the parameters. That is, “for every x there is a y such that P(x, y) means that, we possess an explicit method of determining, for any given x, a y for which P(x, y). This leads us to examine what it means for a mathematical object to be explicitly given.
SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.
Towards a Stable Graph Representation Learning Using Connection Subgraphs

This chapter studies the problem of learning large-scale graph representations (a.k.a. embeddings) that encode the relationships among distinct nodes. The learned representations generalize over various tasks, such as node classification, link prediction, and recommendation. Learning node representations aims to map proximate nodes close to one another in a low-dimensional vector space. Thus, embedding algorithms pursue to preserve local and global network structure by identifying node neighborhoods. However, many existing algorithms generate embeddings that fail to preserve the network structure and are unstable. That is, the embeddings yield from multiple runs on the same graph are different. Therefore, on one side of the spectrum, such algorithms seem to be suitable for single graph-related tasks, like node classification; however, on the other side, these algorithms cannot fit multi-graph problems. In this chapter, we propose a novel stable graph representation learning using connection subgraphs (GRCS) algorithmic framework, which aims to learn graph representations using connection subgraphs, where analogy with electrical circuits has been employed. The connection subgraphs are known to be very beneficial in different real-world networks, such as social networks, biological networks, citation networks, co-authorship networks, terrorism networks, and others, as they address the proximity among each two non-adjacent nodes, which are abundant in real-world networks, by maximizing the amount of flow between them. Although a subgraph captures proximity between two non-adjacent nodes, the formation of the subgraph accounts for connections with immediate neighbors as well. In addition, using connection subgraphs, we address the issues of high-degree nodes, and take advantage of weak ties and use the meta-data that have been neglected by embedding baseline algorithms. We demonstrate the efficacy and robustness of GRCS over existing representation learning algorithms on a node classification task using data sets from various domains. GRCS is robust to noise; its performance is either as good as or better than that of the state-of-the-art algorithms.
Research on Quality Assessment of Network Video Based Frame Freezing Duration

Delivery service of high quality video streaming is a big challenge for providers of network video due to there is huge video data need to be delivered in time and the network condition is complex and dynamic, which have a severe impact on fluency of playing video. The frame freezes as one of the main factors affects the fluency of playing video severely. A novel model of no-reference network video quality assessment is proposed to improve the process of video quality monitoring. The assessment model is constructed according to frame freeze duration, concentrative degree of frame freeze and complexity of video frame content. The frame freeze duration and concentrative degree of frame freeze are acquired by frame rate; the number and location of frame freeze events are obtained from analyzing the frame freeze events occurred in playing video. The complexity of video frame content is calculated by coding bit-rate, the type of macro-block and the motion vector, which obtained from payload information of video streaming. The tests are performed on a set of video sequences with different spatial and temporal information. Compared with G.1070 model, the Pearson Linear Correlation Coefficient of the proposed model is increased by average of 0.041, and the Root Mean Square Error reduced by average of 0.049. Test results indicate that the proposed model has a better performance in assessing frame freeze in network video.
A Color Separation Algorithm with Multi-Levels Grayscale for Glass Printing

This paper proposes a color separation algorithm with multi-levels grayscale for glass printing. Different from the existing algorithms without grayscale for glass printing, we separate the original image into several color separation layers with given grayscale levels, which can be easily applied to n grayscale levels' case. The number of color separation layers for one type of ink is considered before color separation, and grayscale levels of each pixel are varied by value of corresponding layers. After handling the transparency channel, original image is separated through dithering and the related color separation layers are written based on the grayscale intervals. Moreover, we put forward three methods of dividing grayscale intervals by using RGB values or grayscale values. The experiments show that original image can be appropriately separated into multi-levels grayscale and vividly expressed with a small average value.
A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training

In this work, a novel modification on the standard Levenberg-Marquardt (LM) algorithm is proposed for eliminating the necessity of the validation set for avoiding overfitting, thereby shortening the training time while maintaining the test performance. The idea is that training points with smaller magnitudes of training errors are much liable to cause overfitting and that they should be excluded from the training set at each epoch. The proposed modification has been compared to the standard LM on three different problems. The results shown that even though the modified LM does not use the validation data set, it reduces the training time without compromising the test performance.
THE CONSTITUTIVE FUNCTIONS OF SYMBOLIC FORMS OF CULTURE E. CASSIRER

The main instrument of human interaction with the world is called by Cassirer as symbol. It is the ability of a person to create and operate with symbols, that the thinker attributes as a significant feature of the human essence. Character of creation is not representative of human activity in reality, but constitutive. In other words, the person does not reflect the objects and processes of the world in consciousness, and the consciousness itself projects a certain symbolic meaning of reality. The symbol outlines of the procedure for defining an object or phenomenon, and in this its transcendental nature. All symbolic forms differ in their specificity (meaningful, expressive-representational, logical- verbal, etc.), but they all have a similar structure. The structure of the symbolic form is differentiated by the type of connection between speculative and empirical, and includes expression (perception), representation (contemplation), pure meaning (concept). That is, the formation of a corpus of culture occurs as a dialectical process of abstracting information of empirical experience, which in turn is determined by abstract symbolic forms. Indicative in this respect is the postulation that not individual pieces of empiricism are compared, but abstract forms denoting and defining perception. This act occurs at the stage of transformation of perception into imagination.
A weighted split Bregman iteration for adaptive fractional order total variation model

Image denoising is an important branch in the process of image processing which has been widely used in various fields. This paper proposes a weighted split Bregman iteration (WSBI) algorithm for adaptive fractional order total variation model, which provides an effective method to deal with the image denoising problem. A weight coefficient w is added to the split Bregman iteration (SBI) algorithm. Compared with the ordinary SBI algorithm, this improved algorithm can achieve faster convergence and higher (PSNR) of the image by experiments. This algorithm also can well preserve the texture details of the image and avoid the staircase artifact.
HiSeqGAN: Hierarchical Sequence Synthesis and Prediction

High-dimensional data sequences constantly appear in practice. State-of-the-art models such as recurrent neural networks suffer prediction accuracy from complex relations among values of attributes. Adopting unsupervised clustering that clusters data based on their attribute value similarity results data in lower dimensions that can be structured in a hierarchical relation. It is essential to consider these data relations to improve the performance of training models. In this work, we propose a new approach to synthesize and predict sequences of data that are structured in a hierarchy. Specifically, we adopt a new hierarchical data encoding and seamlessly modify loss functions of SeqGAN as our training model to synthesize data sequences. In practice, we first use the hierarchical clustering algorithm, GHSOM, to cluster our training data. By relabelling a sample with the cluster that it falls to, we are able to use the GHSOM map to identify the hierarchical relation of samples. We then converse the clusters to the coordinate vectors with our hierarchical data encoding algorithm and replace the loss function with maximizing cosine similarity in the SeqGAN model to synthesize cluster sequences. Using the synthesized sequences, we are able to achieve better performance on high-dimension data training and prediction compared to the state-of-the-art models.
Human Behavior Recognition: An l1 – ls KSVD-Based Dictionary Learning and Collaborative Representation-Based Classification

This work presents a new idea for human behavior recognition based on dictionary learning algorithm and collaborative representation-based classification approach. In this paper, we have proposed an l1 – ls-based KSVD algorithm for learning a dictionary and collaborative representation is used in the classification phase for this problem. The performance of our proposed idea for human behavior recognition problem establishes the superiority of our new idea.
Self-Attention Networks for Text-Independent Speaker Verification

In this paper, we present a self-attention based model for text-independent speaker verification task and a novel variant of the triplet loss. Conventional convolutional neural networks (CNNs) used in speaker verification tasks need very deep layers to realize considerable performance. In our proposed model, the self-attention mechanism could easily capture long-range dependencies, thus achieves better representational capability with fewer parameters. Based on triplet loss, we propose a novel triplet selection method, which makes the training more efficient and achieves significant performance enhancement. Text-independent speaker verification experiments on AISHELL-2 corpus shows that the proposed model with the improved loss function decreases the verification equal error rate (EER) by 16.81% relatively compared with the state-of-the-art ResNet-like model with common triplet loss, while the proposed model has fewer parameters and requires lower computational cost.
Improved Cuckoo Algorithm for Adaptive Adjustment of Discovery Probability

The Cuckoo Search Algorithm (CS) is a bio-inspired new swarm intelligent optimization algorithm. Aiming at the slow convergence speed and weak search ability of CS algorithm in the late stage of search, an improved cuckoo algorithm (APCS) for detecting the adaptive adjustment of probability parameters is proposed. Firstly, the state discriminant parameter P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> is calculated by Pareto optimal solution and then the balance parameter P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eb</sub> is calculated by exploring-exploiting the equilibrium state, finally the adaptive dynamic adjustment of the found probability P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eb</sub> of the bird egg is realized. The performance of the two algorithms is analyzed by 10 benchmark functions in 10 and 30 dimensions. The results show that the convergence speed, optimization ability, stability and computation time of APCS algorithm are better than CS algorithm.
Child Abusing and Reporting System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.