Reviewerly/exHarmony huggingface dataset:
Entrepreneurial Skills Development through Distance Learning

Distance learning is becoming more and more popular with students of the Russian Federation. However, since this form of education is relatively new for our country, its organization has some drawbacks that need correction in order to increase the effectiveness of such education.The purpose of this research is to determine whether students of distance learning are confident in the effectiveness of their education, as well as in their competitiveness with full-time students and readiness for entrepreneurship. It is necessary to find out directly from entrepreneurs whether graduates will be able to compete in the labor market.Two groups of respondents were surveyed: 426 students of public and private universities of Kazan and 35 local entrepreneurs. The survey was conducted in order to determine the prospects of distance learning from all possible aspects: both from the future applicant and from the potential employer.The results showed students' positive attitude towards distance learning. It was confirmed by students' confidence in their successful employment and readiness to compete in the market. However, half of the respondents were not ready for their own business activities. The reason for this may be the lack of adequate educational materials, lack of easy access to university libraries and lack of practice during the educational process. Moreover, the favorable attitude of entrepreneurs towards students of this form of education was revealed, since they find skills and experience more important than the specifics of the educational form. However, most entrepreneurs are not ready to do business with graduates of distance learning. Insufficient practical training of students may also be the reason for this.These developments can significantly affect the quality of distance learning. The administration of all universities should ensure that all students are provided with the necessary educational materials, as well as organize compulsory internships for undergraduates in each department where it has not been implemented yet.
Outlier Geometric Angle Detection Algorithm

Massive logs are generated in telecommunication networks. It is a challenge to analyze abnormal information in the big data logs quickly and effectively. We present a new outlier detection algorithm based on Unsupervised Learning Algorithm by geometric angle scanning judgment. First, calculate geometric center of measured data and several observation points around the measured data. Outliers can be segregated from normal area by density contrast method by angle based calculation. Results show that outlier geometric angle detection (OGAD) algorithm can separate anomaly from measured data effectively, and improve the accuracy of anomaly identification.
Graph-Based Transform with Weighted Self-Loops for Predictive Transform Coding Based on Template Matching

This paper introduces the GBT-L, a novel class of Graph-based Transform within the context of block-based predictive transform coding. The GBT-L is constructed using a 2D graph with unit edge weights and weighted self-loops in every vertex. The weighted selfloops are selected based on the residual values to be transformed. To avoid signalling any additional information required to compute the inverse GBT-L, we also introduce a coding framework that uses a template-based strategy to predict residual blocks in the pixel and residual domains. Evaluation results on several video frames and medical images, in terms of the percentage of preserved energy and mean square error, show that the GBT-L can outperform the DST, DCT and the Graph-based Separable Transform.
Measuring the Sum-of-Squares Indicator of Boolean Functions in Encryption Algorithm for Internet of Things

Encryption algorithm has an important application in ensuring the security of the Internet of Things. Boolean function is the basic component of symmetric encryption algorithm, and its many cryptographic properties are important indicators to measure the security of cryptographic algorithm. This paper focuses on the sum-of-squares indicator of Boolean function; an upper bound and a lower bound of the sum-of-squares on Boolean functions are obtained by the decomposition Boolean functions; some properties and a search algorithm of Boolean functions with the same autocorrelation (or cross-correlation) distribution are given. Finally, a construction method to obtain a balanced Boolean function with small sum-of-squares indicator is derived by decomposition Boolean functions. Compared with the known balanced Boolean functions, the constructed functions have the higher nonlinearity and the better global avalanche characteristics property.
An Optimum Shift-and-Weighted brightness mapping for low-illumination image restoration

Images captured under low-illumination environments often impose difficulties in revealing objects of interest. An effective approach, Optimum Shift-and-Weighted Brightness Mapping, is here proposed that can optimally enhance the image for higher brightness, information content, and colour vividness. Specifically, the input-output brightness mapping is determined by a shifted spline curve and a larger amplification is allowed for low-brightness pixels. A weighting function is further applied such that high brightness pixels are preserved. The final enhanced image is obtained by inserting the extracted high frequency components from the original input to the brightness boosted image. The algorithm is adaptive to image contents where parameters are optimized using the efficient golden section search instead of relying on user specified coefficients. Experimental results, from a large set of test images, showed that better quality images could be obtained on a variety of low-illumination scenarios as compared to several recent approaches.
The Teaching Reform of Higher Vocational Business English Course Informationization on the Background of Education Informationization 2.0

With the extensive application of new technologies like Internet, cloud-computing and big data, the impact of information technology on education is becoming increasingly significant. The information-based teaching, the integration of information technology and curriculums have become the hot focus of education. In order to meet the needs of the times better, the teaching of higher vocational courses is bound to face significant changes. This paper, guided by the background of education informationization 2.0, explores the informationized-teaching reform of business English course in higher vocational colleges to promote students to "be able to learn", "desire to learn", "joy to learn" and "persist to learn", and to improve the teaching efficiency, so as to be helpful for the development of higher vocational education.
Efficient Ring Signature Scheme Without Random Oracle from Lattices

Among post-quantum alternatives, latticebased cryptography is the most promising one, due to its simple operations, reduction from aver-age-case to worstcase hardness, and supporting of rich functionalities. Ring signature enables a user to sign anonymously on behalf of an adaptively chosen group, and has multiple applications in anonymous e-voting, anonymous authentication, whistle blowing etc. However, most lattice-based ring signature schemes were constructed in the random oracle model from lattice basis delegation and they suffer large verification key sizes as a common disadvantage. This work proposes an efficient ring signature scheme from lattice basis delegation without random oracle based on the extended split-SIS problem, whose security is approximately as hard as the worst-case SIVP problem. Our scheme is proved to be anonymous and existentially unforgeable under latticebased assumptions. Finally, the verification key size is significantly reduced to a small constant, instead of increasing linearly with the number of ring members.
Quantum Communication Protocols Based on Hybrid Entanglement of Light

Hybrid entanglement of light combines two quantum information paradigms, the particle-like and the wave-like encodings, together. We report on recent experiments that engage this resource for two quantum communication protocols: remote state preparation and quantum steering.
Analysis of Local Binary Pattern for Facial Expression Recognition Using Patch Local Binary Pattern on Extended Cohn Kanade Database

Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. In this paper, we explore the effect of representing the information in facial expression recognition using local patch local binary pattern (LBP). The processed information with accurate representation of different expressions can discriminate and improve the overall facial expression recognition accuracy. The objective of this paper is to analyze the effect of using local region patch to represent facial features with local binary pattern as the feature extraction method. In our experiment, first facial landmark is being detected using cascade linear regression, followed by alignment and normalization. The LBP feature extraction is being performed on the holistic image, followed by patch of mouth and eyes. At the final stage support vector machine (SVM) has been used as a classifier to examine the recognition rate. The results are being validated using the extended Cohn-Kanade database. From the analysis, it is found that using local region LBP can significantly reduce the number of features to be fed into SVM. Hence the processing time is improved.
Fall recognition system using feature selection and SVM: an empirical study

In this paper we are interested in improving the accuracy of recognition when a person is fallen and when he is lying on the floor. It represents some difficulties, because the same postures exist in both actions and they happen in seconds. In our proposal the visual information acquired by the Kinect device is used to evaluate if a non-intrusive system is capable of detecting falls with high precision. A set of features, which describe the human shape and its orientation are used to be selected by a Genetic Algorithm (GA) and Principal Component Analysis (PCA). The two set of features are used for detecting the postures when the person is closing to the floor and Support Vector Machine (SVM) is used as a classifier. After detecting the falling posture, the velocity and acceleration are used, in order to distinguish when the person is fallen or lying on the floor. The obtained results suggest that the success of detection of fall depends on the selected features, and the genetic algorithm is a good technique to select them, when compared with PCA. The main contribution of this paper is to improve the accuracy of the fall detection event selecting features using a GA and PCA and incorporating acceleration and speed measurement obtained only through visual means. The public database TST Fall detection data set v2 [1] was used used in the experiments.
Deploying external bandwidth guaranteed media server clusters for real-time live streaming in media cloud

The cloud-based media streaming service is a promising paradigm for multimedia applications. It is attractive to media streaming service providers, who wish to deploy their media server clusters in a media cloud at reduced cost. Since the real-time live streaming service is both a bandwidth-intensive and quality-sensitive application, how to optimize the internal bandwidth utilization of a data center network (DCN) as well as guarantee the external bandwidth of the real-time live streaming application, is a key issue of deploying virtual machine (VM)-hosted media server cluster in a media cloud. Therefore, in this study, we propose an external-bandwidth-guaranteed media server cluster deployment scheme in media cloud. The approach simultaneously considers the outside bandwidth requirement of a tree-based media server cluster for live streaming and the intra-bandwidth consumption of a DCN. The proposed scheme models the optimal problem as a new terminal-Steiner-tree-like problem and provides an approximate algorithm for placing the media servers. Our evaluation results show that the proposed scheme guarantees the external bandwidth requirement of a real-time live streaming application, at the same time, greatly reduces the intra-bandwidth utilization of a media cloud with different DCN structures.
Assessing Programming Language Impact on Software Development Productivity Based on Mining OSS Repositories

This study is to investigate the impact of high-level, generalpurpose, programming languages on software development productivity and quality. In particular, a comparison is to be made between scripting languages and traditionally compiled, system programming ones to examine differences, if any. The data obtained for the research is from open source repositories gathered from Github. The results are going to be based on the analysis of possibly the largest open source dataset through examining a population of 15,000 projects and by including a sample of 4349 projects, where a main language can be identified. The investigation, so far, has revealed considerable differences in productivity between the two language groups.
High-performance Gesture Recognition System

discrete wavelet transformWith advances in technology, motion sensing has been applied in a wide range of devices.The system used for motion sensing captures images via a camera and analyzes the acquired images, also known as human-computer interaction, wherein gesture-controlled machines are considered as the most convenient kind and are most commonly used.However, the existing gesture recognition algorithm often has a long computation time and uses a huge amount of memory, preventing the algorithm from being implemented in embedded systems.To alleviate these problems, in this paper, we propose to reduce the computation time of the system by employing the lifting-based discrete wavelet transform (DWT).The different frequency bands featured in a lifting-based discrete wavelet can swiftly distinguish the face region from the hand region, reduce the image resolution, and thus reduce memory usage.In addition to recognizing gestures in a swift and accurate manner, the gesture recognition approach proposed in this study is also compatible with embedded systems.Our experimental results suggest that the proposed approach can reduce the execution time by up to 66% while achieving a high identification rate.
Comparison of Various Techniques for Emergency Vehicle Detection using Audio Processing

This chapter explains comparison of the various machine learning techniques for the classification of emergency vehicle detection. In the method of machine learning, we use the feature as the input layer and based on the requirement we add hidden layers. Sire detection is the process of identifying the presence of an emergency vehicle on the roads by the siren sound it produces using audio processing. The issue of the siren detection has been assaulted with a procedure chipping away at various levels. The chapter discusses the basic siren detection using the pitch detection method. Pitch is the basic fundamental frequency of the sound or audio signal. Emergency signals all in all have an occasional example which rehashes over a brief length of time. Moreover, they additionally have a place with particular frequency groups which loans them the high pitch sound that they are typically connected with.
Collective Interaction Filtering Approach for Detection of Group in Diverse Crowded Scenes

Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast.Thus, it is significant in the future video surveillance systems.Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior.Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations.Most existing studies on group detection are scene-specific.Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes.Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories.This approach is able to deduce people interaction with the Expectation-Maximization algorithm.The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other.It also tackles grouping consistency between frames.Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results.
Two person Interaction Recognition Based on Effective Hybrid Learning

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction.The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video.Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data.In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data.Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a stateof-the-art detector for image classification).We broaden a semi-supervised learning method combined with an active learning method to improve overall performance.Numerous types of two-person interactions exist in the real world, which makes this a challenging task.In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex.We show that our trained model with an active semi-supervised learning architecture gradually improves the performance.In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy.Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset.We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.
Channel Coding Optimization for Visually Pleasant QR Codes : Invited Presentation

Quick Response (QR) codes are widely used in publicity campaigns to connect offline and online content. However, the black and white QR codes structure represents a constraint for their integration into publicity campaigns. In this paper, an algorithm to generate visually enhanced QR codes is proposed. The algorithm aims at blending the black and white QR code with a color image. Taking advantage of the error correction codes used in the standard QR code encoding, the original binary QR code is modified without affecting the decoding rate. Gauss-Jordan elimination is utilized to generate this new binary QR code whose structure is more similar to the color image. Finally, the luminance of the blended image is optimized to minimize a metric of visual quality subject to a constraint in the maximum probability of decoding error.
Category Specific Prediction Modules for Visual Relation Recognition

Object classification in an image does not provide a complete understanding of the information contained in it.Visual relation information such as "person playing with dog" provides substantially more understanding than just "person, dog".The visual inter-relations of the objects can provide substantial insight for truly understanding the complete picture.Due to the complex nature of such combinations, conventional computer vision techniques have not been able to show significant promise.Monolithic approaches are lacking in precision and accuracy due to the vastness of possible relation combinations.Solving this problem is crucial to development of advanced computer vision applications that impact every sector of the modern world.We propose a model using recent advances in novel applications of Convolution Neural Networks (Deep Learning) combined with a divide and conquer approach to relation detection.The possible relations are broken down to categories such as spatial (left, right), vehicle-related (riding, driving), etc.Then the task is divided to segmenting the objects, estimating possible relationship category and performing recognition on modules specially built for that relation category.The training process can be done for each module on significantly smaller datasets with less computation required.Additionally this approach provides recall rates that are comparable to state of the art research, while still being precise and accurate for the specific relation categories.
Repetition cat-qubits: fault-tolerant quantum computation with highly reduced overhead

We present a 1D repetition code based on the so-called cat qubits as a viable approach toward hardware-efficient universal and fault-tolerant quantum computation. The cat qubits that are stabilized by a two-photon driven-dissipative process, exhibit a tunable noise bias where the effective bit-flip errors are exponentially suppressed with the average number of photons. We propose a realization of a set of gates on the cat qubits that preserve such a noise bias. Combining these base qubit operations, we build, at the level of the repetition cat qubit, a universal set of fully protected logical gates. This set includes single-qubit preparations and measurements, NOT, controlled-NOT, and controlled-controlled-NOT (Toffoli) gates. Remarkably, this construction avoids the costly magic state preparation, distillation, and injection. Finally, all required operations on the cat qubits could be performed with slight modifications of existing experimental setups.