Reviewerly/exHarmony huggingface dataset:
Abnormal Electromagnetic Signal Detection Based on Time Series Analysis

Nowadays, mobile wireless devices are becoming more and more indispensable. The wide use of wireless devices provides great convenience for people's communication in public and private. In the condition, the significance of wireless security cannot be ignored. However, because there is still a lack of supervision and control on wireless signals, it is difficult to detect and locate those malicious signals accurately in time, which poses great threats on social stability and people's property and privacy. With the proposed software-defined radio (SDR) concept, some new methods for analyzing wireless signal have emerged, but there are several researchers applying SDR method on electromagnetic signals analysis. Moreover, various data analysis algorithms also allow us to analyze electromagnetic signals more flexibly and efficiently. In this paper, the FM broadcast signal frequency bands are dynamically tracked, and the dynamic characteristics of the signal are deeply explored and analyzed. Considering that some small signals have strong attenuation and high burstiness characteristics, we propose an abnormal signals detection method based on time series analysis which can identify such small signals in time. The results show that the detection algorithm can detect such small signals well, and has considerable reference value for the discovery of abnormal signals and subsequent tracking.
Cost-free Transfer Learning Mechanism

End-to-end deep learning for video action recognition is always a data-hungry task because training data with detailed annotations will never be sufficient for such a difficult problem. To fully explore the potential of existing labeled categories, we propose a new transfer learning mechanism deeply digging the local and global relationships between different action categories. As for local relation mining, a weakly-constrained hierarchy structure is built to prioritize the correlation for action pairs. And for global, the consistency of joint distribution of visual and semantic feature for all action categories is measured by the maximum mean discrepancy. As far as we know, this is the first time to explicitly explore the local and global relationships between video action categories. The experimental results show that the proposed relationships of action categories can improve the classification results on most representative models without any extra annotation. Surprisingly, the boost is even more obvious when the training samples are few. We achieve state-of-art experimental results on well-known datasets.
Speaker Recognition Method Based on Statistical Features of Spectrograms and CNN

This study proposes a technology for obtaining stable pronunciation features for speaker recognition using a statistical strategy that utilizes the physiological characteristics of human pronunciation and bionic cognitive processes of a speaker's voice. Due to its characteristics of reflecting the frequency information in a speaker's voice, a spectrogram is employed to analyze the samples. An individual's voice signal is first split into short-duration voice segments and a logarithmic spectrogram is then generated for each of the segments. To collect the energy information for each logarithmic spectrogram at a particular frequency, the linear superposition method is employed to obtain an energy distribution map for the different frequency components in the speaker's voice. Next, a certain number of the spectrograms are superimposed as a group to reduce the sample size and to accurately obtain stable pronunciation features. Finally, features of the spectrograms are used to train a convolutional neural network for speaker recognition. Experimental results show that the proposed method exhibits high stability and recognition rates.
A Bayesian discriminant analysis method under semiparametric density ratio models

We propose a semiparametric Bayesian discriminant analysis method. Statistical simulation shows that when the hypothesis of normal distribution is satisfied, our method is comparable to and even slightly better than the traditional method; when the hypothesis of normal distribution is not satisfied, our method is obviously better than the traditional one. We also apply our method to analyze a real data set and find that our method is better than the traditional one. Finally, we point out that implementation of our method is easy since the usual polytomous logistic regression procedures in many statistical softwares can be employed.
Persuasive Assistant Study—Moderator Variables

The persuasivePersuasive systems assistant study presented 72 participants with a persuasive to-do list application that was controlled via a spoken dialogue assistant. The investigated moderator variables were trait reactance, social agency, and involvement. Results show that trait reactance only moderated the effects of negative cognitions, while social agency interacts with both components of state reactanceState reactance. No moderating effect of involvement could be shown. Furthermore, the study showed that state reactanceState reactance can improve the prediction accuracy of the subjects’ global opinion about the technical device.
Experimental Study of Copper Alloys by Electronic Speckle Pattern Interferometry

Electronic Speckle Pattern interferometry (ESPI) is a non-destructive optical method for studying surface deformations.It relies on the interference between diffusely reflected light from the test object and a reference beam.This is one of the most sensitive interferometric technique, so that we can measure sub-micron level displacements either in plane or out of plane.In this study, experimental investigation of vibration behaviour of square copper alloys plates by electronic speckle pattern interferometry is employed.Resonant frequencies and corresponding mode shape are obtained experimentally using the introduced method.Frequencies of resonant vibrational modes depend on elastic properties of material, especially on Young's modulus E, Poisson ratio μ and density of material ρ.This fact gives us in principle the possibility to estimate the elastic constant of materials from the vibrational measurement.The numerical calculations by the finite element method are performed and the results are compared to the experimental measurements.
A Backward Learning Algorithm in Polynomial Echo State Networks

Recently, the polynomial echo state network (PESN) has been proposed to incorporate the high order information of input features. However, there are some redundant inputs in PESN, which results in high computational cost. To solve this problem, a backward learning algorithm is designed for PESN, which is denoted as BL-PESN for short. The criterion for input features removing is designed to prune the insignificant input features one by one. The simulation results illustrate that the proposed approach has better prediction accuracy and less testing time than other ESNs.
First year undergraduate electronic engineering students from a Peruvian university learn to program with Python

The programming language Python, due to its syntax, is nowadays considered to be the best choice to introduce students to programming. Therefore, during the first semester of the year 2018, an introductory course to programming with Python was given at the University of Sciences and Humanities in Lima, Peru. The course was targeted to first year undergraduate students from the professional school of electronic engineering of the aforementioned institution. In the following paper, the design and methods followed and used, respectively, throughout the course are discussed. Furthermore, the outlook of the students as well as the lessons gained by the professor on how to further improve this course for the coming semesters are reported.
Real-Time Hand Palm Detection and Tracking Augmented Reality Game Using Lucas Kanade Optical Flow Combined with Color Blob Detection

Games have become an increasingly widespread part of society because of the spread and use of mobile devices. But playing games for a long time can simulate a negative impact on health such as hemorrhoids caused by sitting within a long period. The body's metabolism will decrease due to lack of activity when playing games. Many games on the market today are usually played in a sitting position and only move a finger. But with the development of technology, games can now be played by moving other limbs. One example is to combine the Augmented Reality, Hand Tracking, and Hand Gesturing technology so that players are required to move their limbs, such as hands, head, and body to play the game. The Hand Tracking algorithm used is the Lucas Kanade Optical Flow algorithm combined with Color Blob Detection, while the Convex Hull algorithm is used to recognize Hand Gesture. The results of this study indicate that by applying the above three algorithms for Hand Tracking and Hand Gesturing in Android games, it has a profound effect on lighting and background color, using background colors that contrast with skin color and adequate lighting, Hand Tracking and hand movements can work well.
Database Exploration for Microbial Information

Several countries on different continents have collections of microbial cultures; these collections have a lot of information, because a single microorganism can collect a great amount of information in several researches. Regularly, microbiology researchers want to explore databases to find new knowledge, relationships, similarities, etc. However, information systems with traditional HTML forms offer poor access to data and limited interaction. Therefore, new computer tools that allow interactive database exploration for this type of information are necessary. This paper presents a new approach to interaction in database exploration. This proposal uses a semantic representation to obtain a quick, efficient and intuitive way to access information. Our proposal is to transform the semantics contained in a conceptual scheme of a database in a structure composed by visual elements with meaning in the interface. This new way of interaction offers a field of research that can allow the creation of generic architectures for database exploration for use in various types of scientific databases.
Extending Convolutional Pose Machines for Facial Landmark Localization in 3D Point Clouds

In this work we address the problem of landmark localization in 3D point clouds by extending the convolutional pose machine (CPM) architecture to facilitate landmark localization in 3D point clouds. Making use of PointNet++,we are able to construct an architecture that is invariant tothe ordering of an input point cloud. The sequential CPM architecture facilitates allows initial heatmaps to be iteratively refined in a series of point convolutional stages to yield robust landmark predictions. We propose to evaluate our approach for 3D facial landmark localization on benchmark face databases, BU-3DFE, BP4D-Spontaneous and BP4D+. The robustness of the approach to the size of the input point cloud will be assessed, and the contribution of the CPM stages will be evaluated in an ablation study.
An Improved Active Incremental Fine-Tuning Method Using Outlier Detection Based on the Normal Distribution

In the machine learning field, the greater the amount of data it can acquire, the higher its performance is. However, especially for medical field, its data set acquisition itself is more difficult, coupled with privacy considerations, it is even more difficult to obtain this few data sets. Therefore, data augmentation is a feasible solution. Data augmentation refers to increasing the number of data sets effectively through reasonable expansion of current data sets in order to improve the performance of the model. This paper aims to eliminate noisy data by using an improved AIFT method and the outlier detection method based on the normal distribution. In this paper, some medical phenomena and noise in the experiment can be considered as normal distribution because of the excellent nature of the normal distribution. For the newly generated candidate samples, the existed noisy data could be removed by the outlier detection method. The improved method preserves the data with large probability distribution, and abandon the data of the small probability distribution, thus to solve the problem that the accuracy of the AIFT method is impacted by the noise generated in the newly generated preferred samples. The performance of improved AITF has been greatly promoted compared with the original method.
Construction and Implementation of Big Data Framework for Crop Germplasm Resources

Based on understanding the application of big data and the research status of crop germplasm resources, this paper proposes a system architecture that is suitable for crop germplasm resources big data. Among them, the overall architecture of germplasm resources is elaborated through six functional modules, including data source, data integration, data processing, data application, big data operation and maintenance platform, and data management and security. The logical functional architecture specification was formulated and the technical implementation and selection are defined. The technical implementation framework describes the technical implementation of germplasm resources big data, and jointly supports the construction and operation of germplasm resources big data. Finally, a verification system is established to verify the feasibility of the big data system framework for germplasm resources.
Usage of Fractal Dimension for Identification of Man-Made Objects

Automatic vectorization of aerospace images causes redundancy of node points of man-made objects' contours. Usage of Hough parametric space for reduction of approximation points, unlike the piecewise linear method, allows receiving more accurate representation of contours, but its computer realization is rather time-consuming. In the paper in order to reduce time and to increase efficiency of the process of vectorization of images of ultrahigh spatial resolution the possibility of usage of fractal dimension as an additional criterion for objects classification is investigated.
Instantaneous Articulate Seeker For Cryptographic Cloud Storage

Cloud computing has become a major computing service platform in the computer science field. However it has drawbacks and challenges related to the privacy of data on cloud. Even though scholars researched over it, proper solutions have not yet been proposed that facilitate searching over encrypted data stored on cloud server. Data breach is also one major threat to cloud storage. In this project, we provide a phrase search scheme using bloom filter to search encrypted data from cloud server using multiple keywords. Besides, we also introduce the Proxy Re-encryption scheme that enables data owner to provide access rights to their desired data users. This scheme assures high speed searching and faster response time at a low cost of communication.
Facies Analysis of Eocene Sediment of Umuahia Area, Southeastern Nigeria

This research work is the detailed facies analysis of the depositional environments and paleogeographic setting of the Eocene sedimentary sequence (Ameki Formations) exposed in the Umuahia area and paleoclimate during that periods. The study area was mainly concentrated around Amaudara inUmuahia South and Ekeoha in Umuahia North. And the co-ordinate are as follows,location-1 0.5°30.80N, 0.7°26.93E, location-2 0.5°30.39N, 0.7°26.62E, location-3 0.5° 32.83N, 0.7°27.24 E and location-4 0.5°32.19 N, 0.7°26.13 E.&#x0D; The aim of the study is to analyze the detailed sedimentary facies and describe the depositional environment in other to predict the depositional environment of the Eocene sediment (Ameki Formation) of the study area, which is underlain by rock unit of Ameki and predominately contains Laterite, mudstone, siltstone, claystone, sandstone and shale and Burrows were identified.&#x0D; The rock sequence consist of reddish lateritic material, highly weathered mudstone capped with ripped bedded kaolinite clay unit, light grey claystone, cross-bedded sandstone with claystone, whitish sandstone, siltystone, fine-medium grained sandstone with pockets of mudclast capped with ferruginized ground and dark grey shale.&#x0D; On the basis of gross lithology, sand-silt-clay percentage, color, texture and assemblage of sedimentary structure, eight distinct lithofacies type were recognized, grey shale facie (Gs), clay stone facie (Cs), cross-bedded sandstone facie (Cbs), mudstone facie (Mf), lateritic facie (Lf), mudstone facie (Bms), ferruginized sandstone facie (Fsf), sandstone facie (Bsf) are recognized within the lithosuccesion.&#x0D; From the analysis, the facies are grouped into two facie association on the basis of grain size. The Fine-grained facies association (FFA) which consist of Gs, Cbs, Cs, Mf and Fst and the Medium to Fine-grained facies association (MFA) which also consist of Bms, Bsf and Lf. It also shows medium grained sand, moderately sorted to well sorted sandstone, Skewness ranged from symmetrical to positive skewed and kurtosis showed leptokurtic.&#x0D; Deduction from facies analysis and grain size analysis shows that Ameki Formation consist of foraminifera and Mollusca which indicate that Ameki Formation was deposited in the estuarine(Marine) environment.
A Priori-based Adaptive Repair Method for Mural Images

Dunhuang Mogao Grottoes murals have a huge scale and a long history. Due to the joint influence of natural and human factors, murals have suffered serious damage. The murals have suffered from diseases such as armoring, falling off, smoking, cracking, fading and discoloration. How to repair these murals is a key problem. Aiming at the above-mentioned mural diseases, this chapter focuses on the mural noise removal, mural missing content completion and other aspects of repair research, and proposes a priori-based adaptive mural image restoration algorithm, which combines local total variation and variational regularization based on non-local graph to solve the ill-posed problems in image processing field. The algorithm is iteratively updated by the current image calculation. The experimental results show that our method achieves the same quality as the existing methods, but it has better robustness on Dunhuang murals which are complex in noise type.
CSI2: Cloud Server Idleness Identification by Advanced Machine Learning in Theories and Practice

Studies show that virtual machines (VMs) in cloud are easily forgotten with non-productive status. This incurs unnecessary cost for cloud tenants and resource waste for cloud providers. As a solution to this problem, we present our Cloud Server Idleness Identification (CSI2) system. The CSI2 system collects data from the servers in cloud, performs analytics against the dataset to identify the idle servers, then provides suggestions to the owners of the idle servers. Once the confirmation from the owners are received, the idle servers are deleted or archived. We not only design and implement the CSI2 system, but also bring it alive into production environment. How to accurately identify the idleness in cloud is the challenging part of this problem, because there is a trade-off between the cost saving and the user experience. We build a machine learning model to handle this challenge. In addition to that, we also build an advanced tool based on Bayesian optimization (BO) to help us finely tune the hyperparameters of the models. It turns out that our finely tuned models works accurately, successfully handling the aforementioned conflict, and outperforms its predecessors with a F1 score of 0.89.
Kinematic Motion Control for Robot Mobile Manipulators as Fire Fighters

Abstract The purpose of this research is to make mobile manipulator robot as firefighting, to derivate kinematical formulation for mobile robot manipulator system, beside that is to make control system of such robot. The design and experiments were conducted in a mobile manipulator study as a firefighter. The result of this unmanned robot unmanned robot design can still carry the appropriate the standard sprinkle of firefighting system. The design of mobile manipulutor mechanism is done by using 3 SolidWorks 2010 design software design. The process of making robot begins with mobile robot as base frame and then installed the robot manipulator. Direct control method was done in this research by using arduino mega as interface system. The conclusion of this research is that robot can move with direct control and autonavigation for tracking trajectory case as firefighting system.
E-GSM: An Artifact-Centric Language for Process Monitoring

One important issue in process monitoring is being able to promptly identify when the process is not executed as expected.
Why Your Neural Network is Still Singular and What You Can Do About It

We investigate the effects of neural network regularization techniques. First, we reason formally through the effect of dropout and training stochasticity on gradient descent. Then, we conduct classification experiments on the ImageNet data set, as well as regression experiments in the OneNow Reinforcement Learning data set. A network layer's weight matrix is quantified via Singular Value Decomposition and Conditioning ratios. Our regression network appeared to be well conditioned. However, we find that learning for large-scale classification applications is likely to be capped by poor conditioning. We propose approaches that may prove breakthroughs in learning, providing early evidence. We introduce a gradient perturbation layer, a method to maximize generalization experimentally. Our numerical analysis showcases the opportunity to introduce network circuitry compression, relying on the principal components of a layer's weights, when conditioning peaks. Generally, we propose conditioning as an objective function constraint.
PURAN: Word Prediction System for Punjabi Language News

This paper presents an outline of the PURAN: A state-of-the-art word prediction system for Punjabi language news. Word prediction systems are used to increase the user text composition rate while typing the text. Brief background of the various approaches utilized in the development of word prediction systems, while discussing the various factors affecting the development of such systems is provided. This paper also elaborates the word prediction system architecture in detail. The system performance was tested on Keystroke saving, Hit ratio, Average rank and Average keystrokes benchmark metrics. The paper demonstrates that the PURAN has achieved highest Hit ratio in Regional news genre followed by National news genre by making lowest average keystrokes in the said categories of news. The results show that system has achieved 88.38% Average Hit ratio with 51.42% Average keystroke saving for N = 10.
Online Hot Topic Discovery and Hotness Evaluation

In this paper, by analyzing the inadequacies of traditional TF-IDF(Term Frequency-Inverse Document Frequency) method and taking into account the factors of the location information, named entity and feature term burstiness, we put forward an improved weight calculation formula i.e., a new TF-IDF to update the feature term weight in real time. In this way, the accuracy of news representation model can be improved to some extent. Incremental k-means clustering based on time window and multi-center topic model is proposed to tackle topic center drift problem, reduce the error caused by inadequate topic model, and therefore, improve the clustering accuracy. At last, we defined an improved energy accumulation formula. And based on media attention, topic competition, topic burstiness magnitude and topic cohesiveness, we constructed a topic hotness evaluation model to quantify the topic hotness and therefore to better distinguish the hot topics from the cold topics. The experimental results demonstrated the effectiveness of our approaches and models.