Reviewerly/exHarmony huggingface dataset:
Application of Statistical Analysis and Association Data Mining in College Students' Mental Health Intelligent Evaluation System

Application of statistical analysis and association data mining in the college students' mental health intelligent evaluation system is studied in this article. This paper uses the technology based methods to achieve the comprehensive analysis. In quantum mechanics, the microscopic particle has wave-particle duality, its motion is not the orbital motion, in the principle it is impossible to track the motion of the particle. This feature is to improve the statistical accuracy. Incremental data is different from static data. It is real-time and more complicated than general static data mining. Some transactions that happen accidentally in the original database are likely to happen frequently in the next time period. This feature is created to analyze the framework efficiently and implemented through the software.
Mixture ratio modeling of dynamic systems

Summary Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled‐data dependence. The extracted knowledge ultimately serves to a decision‐making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black‐box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high‐dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto‐regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data‐independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.
Big data evaluation model of football team cooperation based on entropy weight method

The success or failure of any team sport depends on the degree of team cooperation, and when calculating the degree of team cooperation, you need to refer to valid indicators. In football, a team game, there are many indicators, and it is very important to select the indicators that can have a key impact on the final result. Through correlation analysis in this paper, from a large amount of data indicators effective performance indicators are selected, and then through the entropy weight method to determine the weight of each performance indicators. the Pass Times Matrixes is introduced to describe the degree of cooperation between different players in the whole team. Finally built a team level for the objective, fair and comprehensive evaluation model, and with 17-18 premier league season Everton team's 38 games data has carried on the detailed analysis and practice. The correlation test between the scoring results of each competition and the results of the competition is carried out to demonstrate the correctness of the evaluation model.
Creation of Pinhole Camera for Solar Eclipse Using STEM Education

The STEM education was used in this study to develop the science skills of undergraduate students. They can generate the instrument for solar eclipse observation by themselves. It is corresponding to the evaluation which the overall is 4.75 that means the learning by doing process will help develop learners a lot. Furthermore, the overall satisfaction from the pinhole camera user is 4.84 that means the instrument was created from this process is easy for both usability and knowledge.
Identification of Sport News in Turkish Tweets Using Deep Learning Architectures

In social media and microblogging platforms, it is very popular to share news about sport activities from all around the world. This makes it important to extract information about sports out of text crawled from such platforms. In the scope of this paper, a binary classification is presented to identify tweets containing any information of sports in Turkish. To do so, firstly a dataset, composed of two categories (namely, sport and non-sport), is collected from eleven different Twitter news accounts. Afterwards, a preprocess phase takes place to remove the punctuation marks, the extra spaces, and the numeric characters. In the classification phase, accuracy values of four deep-learning architectures (namely, convolutional neural network, recurrent neural network, gated recurrent unit, and long short-term memory) are calculated to show the classification performances of each architecture. At last, the deep learning classification accuracy values are compared to the most commonly used supervised learning algorithms (namely, Naïve Bayes algorithm, Support Vector Machines, Random Forest, Dense Artificial Neural Network and Decision Tree).
Research on multi-target path planning of domestic mobile robot based on ant colony algorithm

In home mobile robots, in order to realize that the mobile robot can plan the shortest path traversing each task node when receiving multiple task commands at the same time, a multi-target path planning method based on ant colony algorithm is proposed. First, the algorithm models the operating environment of the mobile robot, and uses the visualization method and Dijkstra to find the minimum distance matrix between the points of the operating environment; then, the improved ant colony algorithm is used to find the order of traversing each node; and finally Generate a shortest path. The simulation results show that this method solves the problem that the traditional ant colony algorithm cannot solve the shortest distance problem because each node is only traversed once in the Traveling Salesman Problem, so that the robot can move to each target point quickly and safely.
Solving an Integer Program by Using the Nonfeasible Basis Method Combined with the Cutting Plane Method

For solving an integer linear programming problem, a linear programming (LP) relaxation problem is solved first. If the optimal solution to the LP relaxation problem is not integer, then some techniques to find the optimal integer solution is used. For solving the LP relaxation problem, the simplex method is performed. So, the artificial variables may be required which it means that the problem size will be expanded and wasted more computational time. In this paper, the artificial-free technique, called the nonfeasible basis method (NFB), is used combined with the cutting plane method for solving an integer linear programming problem, called the nonfeasible-basis cutting plane method (NBC). It performs in condensed table which is the smaller simplex tableau. From the computational results, we found that the computational time can be reduced.
Semantic Dependency Word Representation

We provide a novel model architecture for learning Chinese word representations with semantic dependency information. These representations can solve two problems, the first is to avoid polysemy, the second is to capture remote features. Our representations are learned from a Chinese semantic dependency model and a normal word embedding model (e.g.,CBOW or Skip-gram). Using CBOW or Skip-gram model, the word representations can get some semantic information from the position of words. Semantic dependency model can find different word pairs in the sentence and predict the semantic relationship between each word pair. The first word of each word pair represents the current word, and the second word represents the target word. By fusing the semantic information after semantic analysis into the basic word representations, we can get more accurate and dynamic word representations. More importantly, we have achieved advanced results when we use these word representations in sentiment classification task and subject classification task.
Online Video Stabilization Based on Converting Deep Dense Optical Flow to Motion Mesh

Video stabilization is very necessary for shaky videos. Until now, there are many offline methods (using both past and future frames) for stabilization. These methods have good results for stabilizing, but not be consistent with real applications. So inspired by the approach, first, we divide each frame into grids and calculate motion vectors at each vertex. Second, accumulating motion mesh across past frames to get motion curves. Finally, smoothing these curves to stabilize video. The difference of our proposed method is the way to calculate motion mesh. Instead of propagating motion vectors at feature points to mesh vertexes, we take advantage of the power of deep learning network to estimate dense optical flow, then convert it to motion mesh. Our experiment has shown that output videos of our online method (only using past frames) have stability scores which are competitive with offline methods. Our method is still effective where the similarity between two consecutive frames is low (due to fast camera, fast zooming, etc.), in this case feature-based methods have not achieved good results.
A Classification-Based Heuristic Approach for Dynamic Environments

Some of the earlier studies on dynamic environments focus on understanding the nature of the changes. However, very few of them use the information obtained to characterize the change for designing better solver algorithms. In this paper, a classification-based single point search algorithm, which makes use of the characterization information to react differently under different change characteristics, is introduced. The mechanisms it employs to react to the changes resemble hyper-heuristic approaches previously proposed for dynamic environments. Experiments are performed to understand the underlying components of the proposed method as well as to compare its performance with similar single point search-based hyper-heuristic approaches proposed for dynamic environments. The experimental results are promising and show the strength of the proposed heuristic approach.
Design of Remote Control Intelligent Vehicle System with Three-dimensional Immersion

The project uses 3D immersive technology to innovatively apply virtual reality technology to the monitoring field, and proposes the concept and technical route of remote 3D immersive intelligent control. A design scheme of a three-dimensional immersive remote somatosensory intelligent controller is proposed, which is applied to the remote three-dimensional immersive control of a crawler mobile robot, and the test and analysis of the principle prototype are completed.
Emergence of deep learning as a potential solution for detection, recovery and de-noising of signals in communication systems

Increase in communication devices demand for more intelligent and robust communication systems. In this work, we present our initial research on various aspects of combining deep learning in communication systems majorly for the task of signal detection, recovery and de-noising. In part one of the paper, we provide a comprehensive view on various aspects of applying deep learning in implicit channel estimation and signal detection: like the effect of different neural net complexity, activation functions, length of cyclic prefix, and the number of pilot symbols on the Symbol Error Rate (SER) with respect to increasing Signal to Noise Ratio (SNR). We have implemented a Recurrent Neural Network for the task of signal detection. In the second part, we introduce a new concept of implementing deep learning for signal recovery and de-noising in a communication system using auto-encoders.
Организация мониторинга удовлетворенности качеством образовательных услуг в системе "школа – университет – предприятие"

Burko Oksana Petrovna; Danilov Yuri Dmitrievich; Kudritskaya Elena Grigoryevna. Organization of monitoring satisfaction with the quality of educational services in the system school - university - enterprise
ReenactNet: Real-time Full Head Reenactment

Video-to-video synthesis is a challenging problem aiming at learning a translation function between a sequence of semantic maps and a photo-realistic video depicting the characteristics of a driving video. We propose a head-to-head system of our own implementation capable of fully transferring the human head 3D pose, facial expressions and eye gaze from a source to a target actor, while preserving the identity of the target actor. Our system produces high-fidelity, temporally-smooth and photo-realistic synthetic videos faithfully transferring the human time-varying head attributes from the source to the target actor. Our proposed implementation: 1) works in real time (~20 fps), 2) runs on a commodity laptop with a webcam as the only input, 3) is interactive, allowing the participant to drive a target person, e.g. a celebrity, politician, etc, instantly by varying their expressions, head pose, and eye gaze, and visualising the synthesised video concurrently.
Resource availability in the social cloud: An economics perspective

Abstract Social cloud has emerged as a case of sharing economy, where socially connected agents share their computing resources within the community. This paper considers the social cloud as an endogenous resource‐sharing network, where agents are involved in closeness‐based conditional resource sharing. This study focuses on (1) the impact of agents' decisions of link addition and deletion on their own local and global resource availability as well as on others' global resource availability (as spillover effects), (2) the role of agents' closeness in determining spillover effects, (3) agents' link addition behavior, and (4) stability and efficiency of the social cloud. The findings include the following: (i) Agents' decision of link addition (deletion) increases (decreases) their local resource availability. However, these observations do not hold in the case of global resource availability. (ii) In a connected network, agents experience either a positive or a negative spillover effect and there is no case with no spillover effects. Agents observe no spillover effects if and only if the network is disconnected with three or more components. Furthermore, an agent experiences negative spillover if there is no change in its closeness. Although an increase in the closeness of agents is necessary to experience positive spillover effects, the condition is not sufficient. (iii) We study the relation between agents' distance from each other, and their local as well as global resource availabilities. We prove that the local resource availability of an agent from another agent increases with decrease in the distance between them and that maximum local resource availability is obtained from the agent with the least closeness. Using these results, we discuss which agent to add a link to, so as to maximize the local resource availability. We discuss why such results are difficult to establish for global resource availability. However, in a two‐diameter network, we show that for an agent, link formation always increases the global resource availability. (iv) We also study resource‐sharing network formation and its efficiency in a strategic setting. We prove the existence of a pairwise stable network. Furthermore, we provide a set of conditions for a few prominent network structures (star, complete, wheel, and bipartite networks) to be pairwise stable. We show that the “connected in pairs, otherwise disconnected” network is better than a connected network, in terms of social welfare.
A Fast Discrete Homotopy Solution Method for Two Problems in Image Deconvolution

Blur and noise are commonly found in real-life images. Blur occurs in the acquisition whereas noise mostly occurs due to the signal converted into a digital representation due to the sampling process. Severe blur and noise in a given image results in resolution loss and restricts accurate post-processing assessments. To improve image restoration and on the same time build a fast variational framework we propose the incorporation of restarted homotopy analysis method as an alternative compared with costly direct methods. Through experiments, we demonstrate the effectiveness of our new technique.
Dependence of Number of Inputs and Weights during Training of FFANN

When the analysis of processing time series is done, learning becomes complex & consumes more time, hence various training algorithms are required that are accurate & robust. This paper compares performance in minimizing the error using twelve different variants of back propagation algorithms. The analysis is performed on different data sets from UCI machine learning repository and approximation functions. The weights are initialized by using random, Nguyen-Widrow and proposed weight initialization methods. After analysis it is found that our proposed methods give better results shown graphically also in this paper.
An Approach for Multi-human Pose Recognition and Classification Using Multiclass SVM

The aim of this paper is to recognize human activities and classify their activity using multi class support vector machine (SVM). There are many ways to predict human activity either vision based or wearable sensor based. In vision based, different types of sensors that can be used to address this task like a Kinect sensor. In this paper, a vision-based algorithm is proposed to identify multi-human activity so that we can predict the gesture Based on their action. The model is based on segmentation and filtering the environment and identifying human firstly, to identify frame based on semantic structure a dynamic distance separability algorithm is leading to divide a shot into sub shots for selecting appropriate key-frames in each sub shot by SVD decomposition. An adaptive filter is used for filtering process. Then to compose a feature vector, extraction of key poses is performed, and the meticulous Support vector Machine performs classification, and activity recognition. The application of this model is to recognize multi human and estimate their poses for classification. The future research area of this paperwork will be predicted abnormality of people’s health for medical purpose.
Direct Interpolation Algorithm Based on Corresponding Points in Rotary Scanning Serial Ultrasound Image

The rotating scanning imaging method leads to the result that the spatial resolution of the pixels in the fault is greater than that between the faults, which seriously affects the quality of 3D reconstruction. In this paper, we compared several interpolation methods and proposes an improved method based on the law of similar gray value of fault-related voxels, i.e. the characters of the approximate continuous distribution of gray information in three-dimensional space. The neighborhood window is taken on the two-dimensional image where the two adjacent points are located, and the average gray value of all the pixels in the window is used to replace the two. The point gray value method is used for interpolation, and the random irregular sampling ultrasonic image sequence with angular difference is converted into parallel image sequences. The experimental results show that the method can obtain clear regular volume data, realize the standardization of image data and lay a good foundation for 3D reconstruction of ultrasound images.
Nominal syntax with atom substitutions

Nominal syntax is a generalisation of first-order syntax that includes names, a notion of name binding and an elegant axiomatisation of alpha-equivalence, based on nominal set theory. However, it does not take into account non-capturing atom substitution, which is not a primitive notion in nominal syntax. We consider an extension of nominal syntax with non-capturing atom substitutions and show that matching is decidable and finitary but unification is undecidable in general. The proof of undecidability of unification is obtained by reducing Hilbert's tenth problem to unification of extended nominal terms. We provide a general matching algorithm and characterise a class of problems for which matching is unitary, giving rise to expressive and efficient rewriting systems.
Deceiving Eavesdroppers by Real Time Persistent Spoofing of Android Users’ Location Coordinates for Privacy Enhancement

Location-based services have exponentially escalated in the past few decades. They appear to be very practical, however, the location of user is constantly being tracked, hoarded, and monitored by tech giants without user knowledge and consent. Google keeps an eye on every motion of its users, irrespective of their security settings. For this study, various Android smartphones were carried through different places for one week without the availability of internet and location services. Later, thorough network traffic analysis revealed that the archived data collected by Google apps and services, contained location data as well. This data is transferred to Google as soon as the internet connection becomes available. Moreover, a detailed performance analysis of existing fake GPS location applications was conducted that revealed a plethora of weaknesses in their performance and none of them aimed to secure users' real location coordinates. In this paper, we present an interesting solution to obfuscate Android users' real location coordinates, even in off-line mode, thereby, guaranteeing location privacy.
Robust Collaborative Learning with Noisy Labels

Learning with curriculum has shown great effectiveness in tasks where the data contains noisy (corrupted) labels, since the curriculum can be used to re-weight or filter out noisy samples via proper design. However, obtaining curriculum from a learner itself without additional supervision or feedback deteriorates the effectiveness due to sample selection bias. Therefore, methods that involve two or more networks have been recently proposed to mitigate such bias. Nevertheless, these studies utilize the collaboration between networks in a way that either emphasizes the disagreement or focuses on the agreement while ignores the other. In this paper, we study the underlying mechanism of how disagreement and agreement between networks can help reduce the noise in gradients and develop a novel framework called Robust Collaborative Learning (RCL) that leverages both disagreement and agreement among networks. We demonstrate the effectiveness of RCL on both synthetic benchmark image data and real-world large-scale bioinformatics data.
A refined version of the inverse decomposition theorem for modular multiplicative inverse operators

Abstract Recently, we started in the IOP Conference Series [1, 2] and in [3] the study of modular multiplicative inverse operators in an algorithmic functional context on (ℤ /ϱ ℤ)* with ϱ &gt; 3, where it is possible to distinguish different formulas that break up these operators over (ℤ /ϱ ℤ)*, all this thanks to the so-called “inverse decomposition theorem (IDT)”. In this paper, we will study these issues a little deeper. Though we mainly focus the paper on derived for these operators a refined version of the (IDT), also appear on the scene some additional contributions. Finally, in a brief overview, we compare the differences that exist among the new result here established and the old ones. However, along with this, there are still plenty of questions to be answered in this research field.