Reviewerly/exHarmony huggingface dataset:
Stationary Object Detection using RetinaNet and Kalman Filter

Detection of objects is the most popular research topic nowadays. In this regard Convolution neural network gives a direction to achieve the goal. But detection of the Stationary objects on a live camera become more challenging due to the non-rigid movement of the object. Also, most of the time stationary objects appear to be focal loss in the time of detection. So using CNN for those cases will make the scheme fragile. In this paper, Image segmentation and Kalman filter are used to rectify the focal loss to make the scheme more accurate. Here RetinaNet is used for the implementation of a better object detection scheme. As a result, it is observed that the use of RetinaNet makes the stationary object detection more accurate and the results are acceptable compared to the state of the art model.
Deepfake Audio Detection with Neural Networks Using Audio Features

In this paper, a speech spoofing detection system based on Convolutional neural networks using different audio features has been proposed to classify the human speech and synthetic voice, Worst-case scenarios can develop using deepfake audios as threat to assets and image of a person, it can also become a threat to the whole country by unethical uses intended for loss of other party. Using a small voice clip of a person an attacker can develop similar voices. Every audio signal can be represented on a 2D graph plotted by mathematical calculations. The processing of audios into CNN requires a lot of computation, to make a system that can detect deepfake voices with much less computation by conversion of audios to images of audio features (Spectrogram, MFCC, FFT, STFT) and then obtaining the array values as a numeric format which are most suitable to feed. Different approaches for feeding data to model are applied for prediction individually as well as in a concatenated approach.
Hybrid FA-GA Controller for Path Planning of Mobile Robot

Recently, in the path planning of mobile robots, navigation in complex areas are still a challenging task using different AI techniques. One such problem of navigation is solved here using the firefly algorithm and the genetic algorithm as a hybrid approach. The proposed approach efficiently handles the sensory information and converts this into taking the accurate decision for solving the challenges of navigation such as obstacle avoidance and target seeking in a static environment. The proposed approach not only ensures path safety but also ensures path optimality on account of navigational parameters such as path length and navigational time. The developed approach has been tested in the simulation environment using the MATLAB software and in the real-time environment using the Khepera robot. The simulation and real-time results in presence of multiple obstacles are presented for the validation of the proposed FA-GA hybrid controller and obtained results are satisfactory in terms of path optimization.
A Big Data Based Analysis of Accurate Operation for User Multidimensional Value Identification

In the development of Internet technology, the state grid enterprises of electric power began to use big data analysis technology to identify the multi-dimensional value of users at the same time of technological innovation, and put forward more accurate marketing operation countermeasures. Because the electricity customers in the electricity market belong to a relatively large group, so the analysis service based on the actual electricity consumption of customers, power demand and other content can not only provide effective basis for the actual management decision, but also improve the operation quality and efficiency of the computer system. Therefore, on the basis of understanding the functions and technical implementation of precision marketing platform based on big data technology, this paper conducts in-depth research on the power butler service model of residential customers based on cluster analysis of user types, and finally conducts empirical analysis on the basis of constructing ADTM-AI model. The results show that the stochastic forest classification method is more suitable to identify the multi-dimensional value of users in the state grid of electric power, and can provide effective basis for the accurate operation of the actual system.
Achieving Fast Convergence and High Efficiency Using Differential Explicit Feedback in Data Center

Since most flows are short-lived in data center networks, fast convergence becomes very important to help the short flows effectively utilize high bandwidth. Though current explicit feedback-based transport control protocols (TCPs) provide fast convergence via fine-grained congestion information from customized switches, they unavoidably incur large traffic overhead for widely existing small packets in data center applications, resulting in suboptimal network efficiency. To solve this issue, we propose a datacenter TCP based on <b>D</b>ifferential <b>E</b>xplicit <b>C</b>ongestion <b>N</b>otification, called DECN, to achieve fast convergence without any traffic overhead. Specifically, DECN feeds rate difference between the target and current rate back to the source by using multiple consecutive packets. Besides, we propose an enhanced version DECN* which obtains the optimal number of consecutive packets according to the packet loss rate. The experimental results of NS2 simulation and testbed implementation show that DECN and its enhanced version DECN* achieve comparable fast convergence as XCP without incurring any extra feedback overhead. Compared with the state-of-the-art explicit feedback-based TCPs, they reduce the flow completion time by up to 34&#x0025; in typical data center applications.
An Economy-Oriented GPU Virtualization With Dynamic and Adaptive Oversubscription

GPU is becoming attractive around multiple academic and industrial area because of its massively parallel computing ability. However, there are still some obstacles which the GPU virtualization technologies should overcome to reach their maturity. These obstacles mainly include the problem of resource allocation strategy to guarantee possible higher yield. This shortage has already become an obvious barrier to the practical GPU usage in the cloud for satisfying business and academical requirements. There are many mature pieces of research in the area of oversubscribed cloud computing to enhance economic efficiency. However, the study on GPU oversubscription is almost blank for the just started use of GPU in cloud computing. This paper introduces gOver, an economy-oriented GPU resource oversubscription system based on the GPU virtualization platform. gOver is able to share and modulate GPU resource among workloads in an adaptive and dynamic manner, guaranteeing the QoS level at the same time. We evaluate the proposed gOver strategy with designed experiments with specific workload characteristics. The experimental results show that our dynamic GPU oversubscription solution improves the economic efficiency by 20% over traditional GPU sharing strategy, and outperforms the static oversubscription method by much better stability in QoS control.
Study of Diagnosis and Improvement Index System of Higher Vocational Classroom Teaching Based upon AHP

Under the background of diagnosis and improvement of teaching work in higher vocational colleges, it is urgent to construct a set of scientific and complete evaluation index system of diagnosis and improvement in classroom teaching so as to improve the quality of classroom teaching. This paper constructs an evaluation index system for the diagnosis and improvement of classroom teaching, it is composed of five first-class evaluation indexes such as classroom teaching goal, classroom teaching design, classroom teaching resources, classroom teaching organization, classroom teaching quality and 18 second-class indexes such as clarity of teaching goal. This paper carries out practical research on the diagnosis and improvement of the evaluation index system of classroom teaching, obtains the diagnosis conclusion of classroom teaching, and puts forward the direction of classroom teaching optimization based on the diagnosis conclusion, it provides a certain reference for higher vocational colleges to carry out the work of classroom teaching diagnosis and reform. The practice shows that the diagnosis and improvement of the evaluation index system of classroom teaching in higher vocational colleges are highly operative, can measure the quality of classroom teaching comprehensively, and can promote the quality of classroom teaching effectively.
Seafloor Classification based on Sub-bottom Profiler Data using Random Forest

Research on Seafloor classification and recognition is of great significance. Sub-bottom profiler is a kind of equipment used to prospect seabed shallow profiler. Its echo signal has rich sediment characteristics and can be used for seabed sediment classification. Based on the data characteristics of dual frequency sub-bottom profiler, this paper accurately extracts the echo intensity sequence containing rich characteristics of seabed sediments by using high-frequency data and threshold detection method. Dynamic and static methods are used to collect sediment data of gravel, silt and cement in the lab testing pond, The dynamic data set, static data set are classified by random forest algorithm. The classification accuracy of the optimized classification model is 98.15&#x0025;, 85.67&#x0025;. It proved that this method can be effectively used in the classification of seabed sediment.
Z-Face Sketch : Automatic Placement of Face Composites for Composite Sketches

The face composite sketching has been in use as the substitute as well as the complement to the forensic sketches by law enforcement artists. However, it requires manual methods to make proper placements for the various types of face composites which is a time-consuming process. This paper has introduced the Z-Face Sketch for the automatic positioning and scaling of face composites. The mechanics of Z-Face Sketch has taken the proportions and the positions of face composites relative with other types of face composites with human anatomy data into account. Z-Face Sketch also allows the users to readjust the positions of face composites as needed by users. Z-Face sketch has been designed as web application to be used on-line on PC or mobile devices. The image results from Z-Face Sketch are comparable with the photographs of volunteers and the manual results.
Particle-balanced context-based filtering for hypothesis maintenance in sparse sensor coverage situations

In this paper, a method for ensuring the maintenance of multiple hypotheses in the presence of context data is proposed. In many practical context-based tracking problems where particle filtering is used, the filtering distribution is distinctly multimodal. Several of the state hypotheses may be lost owing to resampling of a finite number of particles, when the target leaves sensor coverage for several timesteps. This is especially the case where there is no sensor coverage in areas of the state space where particle density is low, and tracking is confined to narrow pathways, such as narrow roads and alleyways. The approach followed in this paper is to cluster particles into hypotheses using expectation maximisation of a multivariate Gaussian mixture, and to ensure that the number of particles per cluster is maintained using modified resampling. When no measurements are received for extended periods, two criteria are used to modify resampling to ensure hypothesis maintenance. This first adjusts resampling probabilities such that each hypothesis or cluster has roughly the same number of particles. The second adjusts resampling probabilities such that each hypothesis or cluster has a number of particles proportional to the narrowest dimension of the cluster (minimum eigenvalue of the cluster). This ensures that the particle density of each hypothesis remains roughly the same over all the hypotheses. The particular application will dictate which criterion is the most suitable.
Optimized Design of English Learning Environment Based on Deep Neural Network

The learning environment is an important support condition for learning and an important variable affecting learning, so it is an important research content of learning theory, and the understanding of the learning environment is also developing and changing as the educational theory continues to develop. The new curriculum standards for college English demand teachers alter the original conventional way of pedagogy and optimize students’ mode of learning. In teaching practice, the learning atmosphere exerts an extremely influential influence on the smooth implementation of teaching activities and the healthy development of students’ minds and bodies. Deep learning is a hot research topic among machine learning areas in recent years, and deep belief networks as a pioneer in constructing such deep structures. Also, deep neural network (DNN) has been a hot research topic in the field of artificial intelligence and big data analysis in recent years. A DNN-based English learning environment optimization design is put forward in this paper, focusing on the problems of the English learning environment in colleges and the causes of the problems, and exploring strategies to optimize the English learning environment in colleges in order to promote the normal development of English teaching in colleges. The experimental results show that the DNN can improve the overall recognition rate of fault identification and fault location by 13% and 25% on average compared with the other two algorithms, so the deep learning can extract features directly from the original samples and overcome the defect that the neural network is easy to fall into local optimum, and obtain better results. The optimization of the learning method will help to realize the education concept of “human-oriented and comprehensive development,” and help to stimulate students’ enthusiasm, initiative, and exploration in learning.
Crypto Genetic Approach in Information Security

The present work focuses on a combined crypto genetic algorithm to ensure high level of confidential information security. At the data transmission stage of the information system, the algorithm starts with generating two arbitrary non-coprime numbers, determining a least common multiple and a great common divisor, and producing private and public keys. First, the information is encrypted with a public key to obtain the initial cipher. The initial cipher is additionally manipulated by applying operators of the genetic algorithm. Final cipher is produced. At the receiver, the final cipher first is manipulated by operators of the genetic algorithm. The initial cipher is to restored. Then, a private key is applied to obtain the transmitted information. The main contribution of the present study is the introduction of random generated two non-coprime numbers in the determination of the public and private key at the beginning of the algorithm, which enriches the asymmetric crypto technique and expanses substantially the application area of asymmetric RSA crypto codes.
Development of a customizable interactions questionnaire (CIQ) for evaluating interactions with objects in augmented/virtual reality

As new methods for interacting with systems are being developed for use within augmented or virtual reality, their impact on the quality of the user's experience needs to be assessed. Although many instruments exist for evaluating the overall user experience or the computer interface used to complete tasks, few provide measures that can be used to evaluate the specific forms of interaction typically used in these environments. This paper describes the development of a customizable questionnaire for measuring the subjective user experience that focuses on the quality of the interactions with objects in augmented reality/virtual reality (AR/VR) worlds, which we are calling the Customizable Interactions Questionnaire, or (CIQ). The final questionnaire measures five factors that are related to user satisfaction while using the system: quality of interactions, assessment of task performance, comfort, quality of sensory enhancements, and consistency with expectations.
Homography Superposition of the Television Image for Multicam Machine Vision by Data of the Environmental Test

The homography superposition of television images is an important issue for multicam machine vision. The feature of this issue is parameters estimation by data of the environmental test. The trouble of data processing of the environmental test is point matching. The full-scan method defines the huge amount of matching. The processing time does not meet the requirements of video surveillance. Two developed procedures reduce the amount of matching. The homography superposition algorithm is developed by these procedures. It allows estimating the parameters by the acceptable for multicam machine vision time.
Hierarchical Sampling for the Visualization of Large Scale-Free Graphs

Graph sampling frequently compresses a large graph into a limited screen space. This paper proposes a hierarchical structure model that partitions scale-free graphs into three blocks: the core, which captures the underlying community structure, the vertical graph, which represents minority structures that are important in visual analysis, and the periphery, which describes the connection structure between low-degree nodes. A new algorithm named hierarchical structure sampling (HSS) was then designed to preserve the characteristics of the three blocks, including complete replication of the connection relationship between high-degree nodes in the core, joint node/degree distribution between high- and low-degree nodes in the vertical graph, and proportional replication of the connection relationship between low-degree nodes in the periphery. Finally, the importance of some global statistical properties in visualization was analyzed. Both the global statistical properties and local visual features were used to evaluate the proposed algorithm, which verify that the algorithm can be applied to sample scale-free graphs with hundreds to one million nodes from a visualization perspective.
Linear Complexity of New Binary Sequence Derived From Polynomial Quotients Modulo p in General Case and Their Generalizations

Pseudorandom sequences with large linear complexity have been widely applied in electronic countermeasures, mobile communication and cryptography.Linear complexity is considered as a primary security criterion to measure the unpredictability of pseudorandom sequences.This paper presents the linear complexity and minimal polynomial of a new family of binary sequences derived from polynomial quotients modulo an odd prime p in general case.Results indicate that the sequences have high linear complexity, which means they can resist the linear attack against pseudo-noise or stream ciphers.Moreover, we generalize the result to the polynomial quotients modulo a power of p in general case.Finally, we design a Gpqs stream cipher generator based on the generalized binary pseudorandom sequences.
Diseases Video Recommender System using Keyword-Based Vector Space on Youtube and Vimeo

Digital health solutions can be done in various ways, one of which is by searching for information on the internet. However, when someone searches on a search engine, the videos that are displayed are only videos based on keywords, without considering what kind of videos the user likes. Meanwhile, when searching for videos on YouTube, the recommended videos are only videos found on YouTube, so the range of recommended videos is limited. To overcome this problem, we build a web-based video recommender system about diseases that is more organized with a wider range of videos taken from YouTube and Vimeo. In addition, the system not only recommends videos based on the searched keywords but also recommends videos based on videos that are liked by users. The YouTube and Vimeo APIs are used to retrieve videos about the disease being searched for. We use content-based filtering for the recommendation process. Keyword-based vector space does some tasks: 1) converts the title and description of a video into a vector space, 2) calculates the cross product of the term frequency, 3) determines the proximity of the title using cosine similarity. The test results show that the average performance is 92.67% according to the purpose of the recommendation system made, namely novelty and relevance.
Analyzing Students' Writing Error in Comparison Between Male and Female Students

This study aims to compare the errors made by the male and female students who learn English as their second language. This research is classified as qualitative study because the data in this study is in the form of words and the investigators are human beings. The participants of this study comprised 30 students at one of the universities in West Nusa Tenggara. They consisted of 15 men and 15 women. This study's data was gathered through a writing test. This was used to know the students’ errors in writing English. After collecting the data, the authors analyzed the data in four steps; identifying the errors, describing the errors, comparing male and female errors, and explaining the error. The results show that there are similarities and differences in the errors produced by the male and female students. The similarities are a missing verb, subject and verb agreement, problems with plural, double verbs, problems with prepositions, problems with the indefinite article, problems with pronoun, and problems with spelling. The differences are missing a missing a subject, missing a subject and a verb, Capital issues, Verb after modal auxiliary, problems with collocation, problem with a conjunction, and misorder of the word.
Efficient Detection of COVID-19 Exposure Risk

In this demo paper, we present the new module of our HealthDist system that performs contact tracing in a privacy-preserving manner and considers the COVID-19 exposure risk. This is achieved by answering a new spatio-temporal query, dubbed ST-Aggregate Join, which calculates the COVID-19 exposure risk of an individual on their devices. It utilizes a special-purpose access structure to record the trajectories of users on their devices and optimize the ST-Aggregate Join processing. We demonstrate interactively using a smartphone application how our system can provide effective contact tracing within a university campus. We also illustrate how our new module is working through an intuitive web interface that shows the exposure risk of a person by coloring the trajectory of the infected person and the person(s) in high risk in a preloaded real dataset.
High Performance UAV Data Retrieval Algorithm Based on Bit Vector Segmented Hash

This paper mainly studies the application of a high-performance retrieval algorithm under massive data. To solve the challenges faced by the growing UAV patrol data, nearly 0.5TB per month, we put forward a high-performance query algorithm for massive patrol data. Based on bit-vector hash table, the algorithm achieves high-performance data retrieval by adapting the data structure, such as inspection location, voltage class and defective part. Experiment shows the algorithm can achieve data retrieval with low delay, high reliability and low memory occupation under tens of millions of data, meeting the growing demand for UAV data management.
A Novel Low-rank and Sparse Decomposition Algorithm Based on Laplacian Distribution

The principal component pursuit (PCP) method has an excellent performance in foreground/background separation, but this method is also acknowledged to have some drawbacks: 1) the poor robustness; 2) the choice of balancing parameters is a tricky matter. To address these problems, we propose a new low-rank and sparse decomposition model based on the nuclear norm and Laplacian scale mixture. This model uses the Laplacian scale mixture to approximate the sparse term to improve the robustness of PCP and reduce the difficulty of adjusting parameters. Experimental results show that our approach is more effective than the PCP algorithm.