Reviewerly/exHarmony huggingface dataset:
Evaluating the Emergence of Winning Tickets by Structured Pruning of Convolutional Networks

The recently introduced Lottery Ticket Hypothesis has created a new investigation front in neural network pruning. The hypothesis states that it is possible to find subnetworks with high generalization capabilities (winning tickets) from an over-parameterized neural network. One step of the algorithm implementing the hypothesis requires resetting the weights of the pruned network to their initial random values. More recent variations of this step may involve: (i) resetting the weights to the values they had at an early epoch of the unpruned network training, or (ii) keeping the final training weights and resetting only the learning rate schedule. Despite some studies have investigated the above variations, mostly with unstructured pruning, we do not know of existing evaluations focusing on structured pruning regarding local and global pruning variations. In this context, this paper presents novel empirical evidence that it is possible to obtain winning tickets when performing structured pruning of convolutional neural networks. We setup an experiment using the VGG-16 network trained on the CIFAR-10 dataset and compared networks (pruned at different compression levels) got by weight rewinding and learning rate rewinding methods, under local and global pruning regimes. We use the unpruned network as baseline and also compare the resulting pruned networks with their versions trained with randomly initialized weights. Overall, local pruning failed to find winning tickets for both rewinding methods. When using global pruning, weight rewinding produced a few winning tickets (limited to low pruning levels only) and performed nearly the same or worse compared to random initialization. Learning rate rewinding, under global pruning, produced the best results, since it has found winning tickets at most pruning levels and outperformed the baseline.
Vector-Based Back Propagation Algorithm of Supervised Convolution Neural Network

The primary goal of this paper is to analyze the impact of the convolution operation on the model performance. In this context, to avoid the mathematical complexities behind the Convolution Neural Network (CNN) model, the classical convolution operation is substituted by a new proposed matrix operation. The model considered is composed of one convolution layer in series with a set of fully connected hidden layers. The network parameters (filters, weights, and biases) are updated using the back propagation gradient descent algorithm. The model performance is improved through the variation of the width and height CNN hyper-parameters. MNIST data are considered here for the classification of handwritten numbers. With a simple modification of the CNN hyper-parameters using the new proposed matrix operation, a CNN performance of 98.83% was achieved.
The concept change and organization transformation of Vocational Education under the Internet thinking

Scientific development and technological innovation have a direct impact on the way of thinking, life and production of human beings, showing different development logic. The popularity of Internet technology has promoted the Internet + Internet plus stage and Internet economic stage, and has entered the Internet era, showing the Internet thinking. Faced with the Internet era, the modern vocational education system with Chinese characteristics needs to solve two core problems: building a modern vocational education system in different levels and classifications and serving the local economic and social development. To adapt to the Internet thinking, vocational education needs to grasp three directions: user thinking: standardized training gives way to students' personalized development; organizational form: campus center gives way to teaching practice center; development thinking: professional hard power gives way to comprehensive soft power.
SchenQL: Evaluation of a Query Language for Bibliographic Metadata

Information access needs to be uncomplicated, as users may not benefit from complex and potentially richer data that may be less easy to obtain. A user’s demand for answering more sophisticated research questions including aggregations could be fulfilled by the usage of SQL. However, this comes with the cost of high complexity, which requires for a high level of expertise even for trained programmers. A domain-specific query language could provide a straightforward solution to this problem. Although less generic, it is desirable that users not familiar with query construction are supported in the formulation of complex information needs. In this paper, we extend and evaluate SchenQL, a simple and applicable query language that is accompanied by a prototypical GUI. SchenQL focuses on querying bibliographic metadata while using the vocabulary of domain-experts. The easy-to-learn domain-specific query language is suitable for domain-experts as well as casual users while still providing the possibility to answer complicated queries. Query construction and information exploration is supported by the prototypical GUI. Eventually, the complete system is evaluated: interviews with domain-experts and a bipartite quantitative user study demonstrate SchenQL’s suitability and high level of users’ acceptance.
Study of the User's Eye Tracking to Analyze the Blinking Behavior While Playing a Video Game to Identify Cognitive Load Levels

As eye tracking studies are becoming more common, eye behavior is being explored in different scientific fields as it can be analyzed to detect changes in the mental state of users. In this way the development of new products or technologies could include eye tracking as an objective way of obtaining feedback from users. This paper presents an experiment that compares the eye behavior of users' blinks during interaction with two different levels of difficulty in a video game. The results of the experiment show that the participants' blink rate per minute was significantly reduced, according to the state-of-the-art normal blink behavior, during the two tasks. However, it is also shown that there is no significant difference between the samples taken, which is attributable to the fact that interaction with video games, in any difficulty, represents high cognitive load.
A Comparison of Manga Creators’ and Readers’ Focus Points about “Kawaii Characters”

The major purpose of this research is to compare the focus points on “kawaii character” between manga creators and readers. In order to find that what does manga creators focus on when they create “kawaii characters”, we interviewed 6 manga creators who are excel at “kawaii characters”. And for the readers, we conducted a survey using the Chi-squared test to find out which part of the characters is the readers most focus on when they judge a character as “kawaii character”. Through the research, we found that when manga creators create “kawaii characters”, they often pay more attention to the characteristic of the characters as the readers often focus on the characters’ faces when they judge a character as “kawaii character”.
Higher education services market: new trends in sustainable development

The transfer of higher education to a distance format as a new trend has increased the relevance of research on the problems of assessing the quality of higher education services and its sustainable development in the post-pandemic period. The article proposes a methodological approach that includes the authors’ modified Higher Education PERFormance (HEdPERF) model and importance–performance analysis. Testing of the methodology for the Russian university showed its advantages that is a comprehensive assessment of the higher education service quality. The transition to a mixed model of higher education caused the need to improve the methodology. The authors proposed to include in the questionnaire the characteristics of the quality of distance learning: correspondence of goals and content of online technology, the level of online interaction of students, conformity assessment procedures and the stated competence, clarity of assessment criteria students the opportunity to track the progress in the development of the online course, quality learning materials, quality of technical support the online technology, ease of use etc.
WD3-MPER: A Method to Alleviate Approximation Bias in Actor-Critic

Deep deterministic policy gradient has been successfully applied to continuous control problems, but its function approximation errors will cause the overestimation problem and limit its performance. Existing methods alleviate the overestimation problem. However, because taking the minimum value between a pair of critics for updates, the method sometimes underestimates the values. We propose a new algorithm, which uses weighted value of two critics to alleviate the underestimation and overestimation problems caused by function approximation error. Simultaneously, in order to improve the sampling efficiency of the algorithm, we propose an improved prioritized experience replay mechanism by modifying the priority definition instead of the original random sampling. Experiments show that, compared with two state-of-the-art algorithms, our algorithm has better performance on the MuJoCo continuous control tasks.
Neural Architecture Search Based on Particle Swarm Optimization

Neural architecture search can help researchers design excellent neural network structure. But it takes a lot of time, such as using a neural architecture search method based on reinforcement learning, which requires more than 3000 GPU hours to find an excellent architecture on the CIFAR-10. And in order to be able to use the back-propagation method during training, the architecture will be continuous. Therefore, we propose a neural network architecture search algorithm based on Particle Swarm Optimization (PSO) - PNAS. First, we need to train a super-net. Through random sampling during the super-net training process, only one path training is activated at a time, which greatly reduces the coupling between the super-net nodes. After the super-net training, we use the PSO algorithm to search the architecture of the neural network to find optimal architecture. Our PSO-based neural architecture search can achieve competitive speed compared to state-of-the-art models. Our PNAS search time is faster than GDAS 28% and the parameters are also less than GDAS.
Autonomous Driving System Design for Formula Racing

Abstract This paper mainly introduces automatic driving schemes and algorithms for formula racing car. Formula racing is a good platform for testing the extreme performance of vehicles, but there is a risk of harming the drivers. We propose an automatic driving scheme to avoid such problems. The solution we proposed integrates three modules: environment perception, path planning and vehicle control. Using lidar, camera and GPS/INS integrated navigation as sensors, an innovative data fusion method is proposed. This paper proposes a new path planning method, which uses Delaunay triangulation and topological map-based path search and evaluation to obtain the best path. Finally, a control model based on pure pursuit algorithm is used to control the vehicle precisely. The whole system is developed based on ROS which has a unique loose coupling. This scheme is proved to have high accuracy and good robustness by simulation analysis and multiple vehicle tests. The proposed solution has good portability, and it can also be applied to the field of unmanned passenger cars, robots and drones after appropriate modifications.
Modified Advanced Encryption Standard algorithm for fast transmitted data protection

Abstract With the exponential growth of digital data exchange over the computer network in recent times, protection of information content is becoming a major concern. There are many security risks, which can easily compromise the data transmitted over the network. Cryptography plays an important role in ensuring the security of digital data transmission over these unsafe networks,so in order to ensure secure and fast data transmitted over the network, an enhanced modification for Advanced Encryption Standard (AES) algorithm is proposed and implemented using additional key which generated using linear feedback shift register(LFSR), which provide an efficient technique to pseudo random number generation,also rounds number are decreased . The proposed method give promised result comparing with original AES algorithm result for different data type text, image, and video.
Improved Job Scheduling for Achieving Fairness on Apache Hadoop YARN

Enormous amounts of data are gathered from social media sites, mobile and other business environment. Analyzing the enormous amounts of big data becomes large workloads with distributed applications and the resources of a single machine are insufficient for this application. Hadoop YARN (Yet Another Resource Negotiator) enables running multiple applications over hadoop cluster to utilize the resources efficiently and provide the data parallel programming model. Hadoop YARN breaks up the performance of open source framework for distributed applications and performs job scheduling and monitoring together with storage, processing and analysis of big data on commodity hardware. Apache Hadoop provides for over 200 default parameter configuration settings for all type of clusters and applications. Of If the available parameters misconfigure, the one or more machines in the cluster may decrease the system performance. Appropriate tuning parameters configuration can increase the system performance. Tuning parameter configuration becomes the challenge of Apache Hadoop Framework for utilization of system resources efficiently. In this paper, YARN parameters tuning is done for improving the execution time and efficient job scheduling.
Considering Spherical Refraction in Visual Ocean Gas Release Quantification

Compared to traditional gas flow quantification methods, the stereo vision system has some advantages. However, underwater vision systems usually suffer from light refraction which can degrade the measurement accuracy from images. Cameras centered in spherical glass housings, dome ports, can theoretically avoid refraction, but misalignments in the dome create even more complex refraction effects than cameras behind flat glass windows. This paper introduces the spherical refraction model into a stereo vision gas flow quantification system. Also, this paper adds some contributions to an existing bubble quantification workflow for bubble size histogram and bubble volume estimation. First, the spherical glass dome port and the light propagation are modeled, and then the camera system is calibrated via underwater/in-air image pairs. Afterwards, the Epipolar Geometry Constraint is used to optimize the bubble matching. For volume estimation, an ellipsoid triangulation method is employed to improve ellipsoidal volume estimation. According to the calibration experiments and control experiments, the results show that the stereo vision gas flow quantification system can produce the volume of gas release accurately, which satisfies the requirements of long-term gas release monitoring in marine science.
Detection of Fake Online Reviews Using Semi-Supervised and Supervised Learning

Online reviews have an incredible effect on the present business and trade. The development of web-based business organizations has pulled in numerous buyers since they provide a scope of items on aggressive costs. The main aspect most buyers depends on while doing online shopping is the review of items for closing the choice of object. Basic leadership for the acquisition of online items generally relies upon reviews given by the clients. Henceforth, deft people or gatherings attempt to control item surveys for their advantages. In perspective on the impacts of these phony surveys, various systems to recognize these were proposed in the research. Because of reviews and its nature, this is hard to group these utilizing only one classifier. Henceforth, the present research discusses a classifier for dealing with identifying such phony reviews. The study also presents the text mining techniques both supervised and semi-supervised to identify counterfeit online reviews just as looks at the effectiveness of the two strategies on the datasets with hotel surveys.
Preprocessing Steps for Opinion Mining on Tweets

In current scenario, high accessibility to computational facilities encourage generation of large volume electronic data. Expansion of the data persuades researchers toward critical analyzation so as to extract maximum possible patterns for wiser decisiveness. Such analysis requires curtailing of text to a better structured format by preprocessing. This scrutiny focuses on implementing preprocessing in two major steps for textual data generated by dint of Twitter API. A NoSQL, document-based database named as MongoDB is used for accumulating raw data. Thereafter, cleaning followed by data transformation is executed on accumulated tweets. The results for this research demonstrate that even after application of cleaning, text contains some anomalies. Henceforth, data transformation follows the process of cleaning for this research. Ultimately, a preprocessed data suitable for opinion mining is generated.
IoT-Motion Sensor Device and Data Analysis : (Motion Detection and Step-Count Algorithm)

In the last one decade, with the advancement and developmental changes in artificial intelligence (AI) technology and the usage of smart-phones, motion-detection and step-count applications have increased the public attention. Because they are playing a significant role in the fields of device positioning, energy harvesting and facial and behavior recognition etc. These motion-detection sensors and smart-phone applications are playing a vital role in human's life. In this article, we are suggesting a novel peak detection motion algorithm to instantaneously detect foot movement and steps are counted by using Nordic Thingy device as a motion sensor by holding an android phone (Note 5A) in hand to save the walk data. The sensor device and unconstrained cell phones would have the ability to place not only arbitrary position but also alterable position. Depending on the foot-step motion periodicity and gyroscopes sensitivity; the projected algorithm extracts the features of time domain from a three dimensional (3D) angular velocity of a cell-phone with the help of Fast Fourier Transform (FFT) and classifies whether the sensor device with the smartphone of user sensed walk or not regardless of its placement or location of Thingy-sensor. The proposed algorithm is developed and experienced in real time environments for walking, running, climbing up and down stairs. It indicates promising results for walking at almost 99% accuracy. Contrasting, the conventional motion detection and step-count approaches, the errors are fixed in proposed algorithm and long distances do not affect its performance.
Avatar-Based Intellectual Managing for Innovation Technologies Transfer in Nationals Entrepreneurships of Armenia

Technology transfer is considered as one of the most important instruments of national and regional economic growth in such countries as world leaders such as the United States, Japan, the European Union, China, and others. The importance of developing this direction is not in doubt. It invests a lot of money, is supported at the legislative level. The activity of technology transfer centers is aimed at commercialization of the results obtained in different organizations of the world, ensuring the acceleration of solving technical problems of enterprises, improving the quality and reducing the cost of their products, and developing new types of products. The main goal of the Center is to facilitate the transfer of the Internet intellectual innovative technologies and blockchain technologies developed both in the Republic of Armenia and in the Armenian Diaspora to ensure sustainable growth of the economy, increase the competitiveness of industry, agriculture, science and education, tourism and business attractiveness Republic of Armenia and Artsakh Republic.
Conversations with Tom Johnson

Bernard Girard's book, Conversations avec Tom Johnson was published in French by Éditions Aedam Musicae in 2011 and is here translated into English for the first time by Christine Lucia. The original book comprised 137 pages of wide-ranging conversations held at various times, covering Johnson's music and career in some detail and organised into categories such as ‘The Man Who Counts', ‘Minimalisms', ‘Mathematics and Music', and ‘Opera'. The book also included two short essays on Johnson by musicologist Gilbert Delor and mathematician Franck Jedrzejewski, a brief Worklist and an Index that lists the more than 150 composers, performers, mathematicians and other key figures in Johnson's life discussed in the text. The translated text includes all but the Worklist and Index. The conversations range across six decades of new music and give unique insights into the development of musical minimalism, to which Johnson contributed as a critic in his early career in New York, and since he moved to Paris in 1982, as a composer. Discussions of Johnson and Cage, Feldman, Lucier, La Monte Young, Reich, Rzewski, Niblock and others reveal the diversity of minimalist aesthetics and Johnson’s uncompromising vision as a composer and artist. The translated text contributes to the literature in English on musical minimalism, new music and mathematics, computational science and combinatorics, and to our understanding of other relationships between music, science and art.
A video steganography using Hamming Technique for image Processing using optimized algorithm

Right now, steganography is gotten a standard choice for puzzle data correspondence. The introduction of the steganography computation relies upon the inserting effectiveness, installing payload, and power against assailants. At the present time, propose another video steganography estimation on the distinctive thing following computation and Hamming codes. The proposed figuring fuses four particular sections. In first place, the puzzle message is already processed, and Hamming codes (n, k) are given to convey the encoding message. Backing, a thought on different thing following figuring is applied on spread chronicles in order to recognize the territories of eagerness of the articles.
Increasing the Angular Resolution of Control and Measurement Systems in Signal Processing

The new method of digital signal processing is presented. The new signal processing technique is based on carrying parametrization problems with the help of data mining. It allows us to obtain the images of signal sources with angular superresolution. The high speed of the method, in contrast to many well-known methods, allows us to use it in realtime. The results of numerical experiments on mathematical models are presented.
Cyber Security Reactivity in Crisis Times and Critical Infrastructures

Technological and social developments lead to repositioning of criminal actions and adapted responses of Information Systems. On the invisible front, confrontations between national or international organizations and entities that are strictly economically motivated or supported by terrorist groups take place. The direct and indirect effects are difficult to predict due to the high degree of uncertainty of the phenomenon as a whole. The mobility of the relevant factors is quite high and that is why the algorithms use probabilistic models. The losses are quantified as a post factum effect of the events. The present study aims to present Data Mining and Analysis of possible impact that can be felt, starting from a review of actions against official institutions, transformations that occur in crises and finally, a set of proposals in support of alignment with common international standards.