Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Diversity Selection Combining on Radio Frequency for Image Transmission

Abstract This paper describes a simulation of the image transmission system with diversity selection combining (DSC) method on radio frequency. DSC is used to obtain multiple data streams corresponding to the transmitted image at the receiver in order to combat errors during image transmission on wireless channels. These individual image data streams are combined to form a composite image with higher perceptual quality. When DSC is used, it is expected that it can affect the number of errors, so that the signal transmitted to the wireless channels becomes more robust against interference caused by transmission channels. By using DSC technique, it can increase the quality of image transmission at the receiver. The result of the experiment which was carried out to test bit error rate (BER) and peak signal to noise ratio (PSNR) on Lena image with rate 0.8 bpp by using DSC on radio frequency in Rayleigh fading channel showed that there was a decrease in BER at the average of 0.0152 or 3.39%, while for PSNR there was an increase at the average of 1.6737 dB or 6.73%when comparing to the system without DSC.
On the primitivity of the AES-128 key-schedule

The key-scheduling algorithm in the AES is the component responsible for selecting from the master key the sequence of round keys to be xor-ed to the partially encrypted state at each iteration. We consider here the group [Formula: see text] generated by the action of the AES-128 key-scheduling operation, and we prove that the smallest group containing [Formula: see text] and all the translations of the message space is primitive. As a consequence, we obtain that no linear partition of the message space can be invariant under its action.
Modified Grey Wolf Optimization based Advanced Encryption Standard based Cryptographic Technique for Digital Images

In recent times, cryptography is widely used to achieve secure data transmission over insecure communication channels. With the increased requirement of confidentially and privacy of image transmission, an effective encryption technique becomes essential. This article introduces a novel Modified Grey Wolf Optimization-based Advanced Encryption Standard (MGWO-AES) based Cryptographic Technique for Digital Image Security. The presented MGWO-AES model uses multilevel discrete wavelet transform (DWT) for image decomposition where the input image is classified as RGB components to examine the basic colors of every image portion. Besides, the encryption process takes place using the AES technique. The MGWO algorithm is utilized for the optimal selection of encryption keys. To investigate the effective performance of the presented MGWO-AES model, a series of experiments were conducted and results are examined under distinct dimensions.
Pedestrian Behavior Analytics on Dashcam Videos in Chaotic Environments

Pedestrian safety is a major challenge in geographies where the traffic is not well managed, such as developing nations. Pedestrians often lose their lives when moving vehicles or buses hit them, for example, nearly half the road mishaps in Delhi involve such scenarios. We envision that video analytics on dashboard cameras can solve this problem if the pedestrians could be detected and tracked within permissible limits to alert the vehicle drivers. In this paper, we propose a novel end-to-end pipeline for multi-pedestrian detection and tracking their trajectories by leveraging multi-scale aggregate channel features in a tracking-by-detection paradigm and temporal trajectory analysis for pedestrian trajectory characterization to identify if the pedestrian is on a potential collision course with the vehicle. We evaluate this framework on a novel dataset collected from dashboard cameras with frontal view of Delhi roads with unlaned and chaotic traffic conditions. The problem of multi-pedestrian detection, tracking and characterizing pedestrian behavior on the road becomes challenging because of the motion of camera mounted on-board the vehicle and different traffic scenarios on Delhi roads. Extensive experiments demonstrate that the proposed method achieves promising results over several pedestrian detection baselines on the collected dataset with an average precision of 94.71% in detection and an accuracy of 86.48% in pedestrian behavior classification.
Other Neural Networks

The present chapter discusses neural networks other than the feedforward neural networks (see Chap. 2) and the mutually connected neural networks (see Chap. 4). Section 5.1 describes the self-organizing map (SOM) and Sect. 5.2 the radial basis function network.
Using Bézier-Ball Function to Interpolate Positive Real Data and it's Application

This paper proposes a function that can interpolate real data as well as preserving the nature of the data. A rational cubic Bézier function expressed in terms of Ball control points and weights is presented. Four shape parameters are proposed which provides more freedom to the user to control the shape of the data. Sufficient conditions needed to preserve its positivity are presented. The degree of smoothness attained achieved C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> continuity. Three different real data are used in the application which are real data of oxygen level in gas, real data of a conductance obtained from experiments and real data consists of the output of a pressurizer inside a Pressurized Water Reactor. The output measures the water level of the pressurizer when the inputs which are the temperature and heat loss are varied. The results show that the proposed method managed to produce smooth interpolants as well as preserving the positivity of the data.
Combining Faster-RCNN and Convolutional Siamese Network for Aerial Vehicle Tracking

Drones bring convenience to humans as well as threats. According to the characteristics of drones, it is imperative to develop corresponding defense systems to detect, track, and strike intrusive drones. Based on the twin network research in the field of target tracking, this paper proposes a detection and tracking fusion algorithm, using new amplification methods to solve the problem of poor detection accuracy of small targets, and using progressive labeling strategies to solve the problem of difficulty in acquiring labeled data sets. The engineering method alleviates the problem of poor generalization ability of the algorithm under some extreme conditions, Comparing the performance of the proposed algorithm with the typical target detection algorithm on the UAV data set, the efficiency of the algorithm is verified.
Relicensing Combined Datasets

Metadata portals offer access to Open Data, which is increasingly published on the basis of RDF vocabularies and ontologies. Individual datasets include URIs and descriptions of licenses under which the datasets are published. Licenses can be defined by attributes of the types permission, requirement and prohibition. This paper focuses on the problem of relicensing datasets that include multiple other open datasets published under different licenses. If combined datasets are republished, a chosen license must comply with the original licenses of each dataset. The approach is based on mapping licenses to deontic interpretations and combining them using an OR-composite operator. The result of the operator is used to determine matching licenses from a Knowledge Base containing license information. The approach is evaluated based on the Creative Commons License Compatibility Chart.
WITHDRAWN: A Customized Profile-Aware news system for filtering and summarizing

A significant amount of news material consists of information on the Internet. In Web information, a lot of interest has been devoted to web news tips, filtering, and review, focused at identifying interesting information and analyzing informative user information. We implement our research into the development of the Information Extraction and Summary Process. An integrated part of learning triggers a user interest model and suggests user specific news. An expert system of key words is retained and offers an actual overview to represent the specific matter of web information and the desires of the reader. Not interested and unwanted news content that is unrelated to the news platform is filtered out. Key terms that retrieve the main description of the news Through linguistic sequences, information is retrieved to represent semantic connections between words and Providing refined data, such as a description Versions of the source text and highlights key words in the text that are significant.
Quantizer Design to Exploit Common Information in Layered and Scalable Coding

This paper considers a layered coding framework with a relaxed hierarchical structure, tailored to serve content at multiple quality levels, where a key challenge is the conflict between coding optimality at each layer and efficient use of storage and networking resources. The prevalent approach of storing and transmitting independent copies for each quality level, is highly wasteful in resources. The alternative of conventional scalable coding incurs the notorious “scalability penalty” at the enhancement layers, due to its rigid structure. The approaches pursued in this work involve a layered coding framework, wherein information common to one or more subsets of the quality levels is first extracted and transmitted, and then complemented by individual (quality level specific) bit streams. This framework ensures that no redundant or irrelevant information is sent to any decoder, enables achieving all intermediate operating points between the two extremes of conventional scalable coding versus independent coding, and hence mitigates the layered coding penalty. Joint design of common and individual layers ensures that all extracted common information is fully usable by the target decoders, as needed to approach optimality. Simulation results for practically important sources, confirm the superiority of the proposed framework.
The effect of the Covid 19 pandemic on the development of the knowledge graph as an integrated recovery accelerator

Abstract The spread of the 2019 Corona virus Disease pandemic (Covid-19) resulted in many organizational entities being forced to redesign business processes, reducing the cost of reducing working hours, to terminating employment (layoffs). The informal sector has the worst impact. Thus, both the government and the private sector make various efforts in overcoming problems of social impact that arise. One of them is through the formation of the Task Force for the Acceleration of Handling Covid19 (covid19.go.id) as a single contact crisis center. However, the extent of the impact that arises will result in preventive, curative and promotive efforts at risk of not being handled properly. The implementation of knowledge management based on graphs (knowledge graph) will enable agile integration between various sources of information. The Graph Data Model captures the relationships of various different entities by connecting them through the edge of information extracted from various heterogeneous sources. After the data is represented in a graphical format, there are various analytic graph techniques for asking multi-hop (multi-node) relationships between entities in the knowledge graph that is built. In addition, graphs allow users to visualize data quickly, interactively and exploratively for analysis.
The influence of the reading concept map number head together (Remap-NHT) learning model on the communication skills students of eleven grade science SMAN 1 sampang

Communication skills are needed to achieve success in learning because students will easily communicate various matters relating to learning material, in written form or verbally so that the level of academic ability possessed by students, so that students’ academic ability level also increases. One of the learning model that can help student communication skills is the learning models Number Heads Together (NHT). This study aims to determine the effect of the Remap-NHT learning model on class XI students of SMAN 1 Sampang’s communication skills. This research uses a quasi-experimental design method with a pretest- posttest nonequivalent control group design. The population in this study were all students of class XI IPA SMAN 1 Sampang. The sample of this research is class XI IPA 5 and class XI IPA 6. The class used as a control class is XI IPA 5 and the experimental class XI IPA 6. Data analysis used to analyze data communication skills is Anacova. This study’s results are that there is an effect of the Reading Concept Map Number Head Together (Remap-NHT) learning model on communication skills with an increase of 72.41% of students in class XI Science of SMAN 1 Sampang. The next studies, to add a class positive control is by learning model NHT and remapped in order to compare the learning Reading Concept Map Number Heads Together (Remap-NHT), Reading Concept Map (Remap), Number Heads Together (NHT) and conventional so the Communication skills can be measured optimally.
Design of a Three-Dimensional Cultivation Device for Home Sprouts Based on SolidWorks Simulation Analysis

In order to better meet the needs of home sprouts cultivation and improve the convenience and appreciation of user management, a three-dimensional cultivation device for home sprouts was designed. The device consists of a frame structure, a cultivation system, a water supply system, and a light supplement system, a control system and other components. A simulation analysis was carried out using SolidWorks, and the results showed that the strength of each part can meet the force requirements, the irrigation water can flow into each cultivation trough smoothly, and the water supply in each trough is even. The prototype application test results show that: it can meet the cultivation requirements of sprouts, and the cultivation effect is better than ordinary three-dimensional cultivation devices, and the management convenience, ornamental, and comprehensive user satisfaction are also better than ordinary three-dimensional cultivation devices. The device can provide a certain reference value for related designs, and has broad promotion prospects in the field of home sprout vegetable cultivation.
ABCD: A Compact Object Detector Based on Channel Quantization and Tensor Decomposition

Object detection and tracking are critical computer vision tasks because of the broad needs in society; however, deep neural network-based methods cost many computational resources that hinder them from real scene applications. Quantization is a widely adopted technique to reduce the storage space and memory footprint which makes deep learning models more energy-efficient and resource-friendly. Traditional network quantization methods directly quantize neural networks layer-wise, which means the parameters in different channels take the same quantization range. In this paper, we propose a low-bit learning method for convolutional neural network object detector quantization. Different from previous methods, we quantize the detector channel-wisely to avoid accuracy loss in the low-bit framework. We use progressive quantization, progressive batch normalization fusion, and cut the unnecessary long-tail weights and activations to reduce quantization loss. Moreover, based on the object detector and long short-term memory network (LSTM), we develop a high-performance tracking system. We leverage the tensor decomposition to compress weights in LSTM for getting a higher compression ratio. Experiments are conducted on public datasets and our infrared aerial dataset for object detection and tracking. The experimental results show that our approach obtains better performance compared with the state-of-the-art methods in terms of accuracy and compression ratio.
Scene text spotting based on end-to-end

Aiming at the problem that the traditional OCR processing method ignores the inherent connection between the text detection task and the text recognition task, This paper propose a novel end-to-end text spotting framework. The framework includes three parts: shared convolutional feature network, text detector and text recognizer. By sharing convolutional feature network, the text detection network and the text recognition network can be jointly optimized at the same time. On the one hand, it can reduce the computational burden; on the other hand, it can effectively use the inherent connection between text detection and text recognition. This model add the TCM (Text Context Module) on the basis of Mask RCNN, which can effectively solve the negative sample problem in text detection tasks. This paper propose a text recognition model based on the SAM-BiLSTM (spatial attention mechanism with BiLSTM), which can more effectively extract the semantic information between characters. This model significantly surpasses state-of-the-art methods on a number of text detection and text spotting benchmarks, including ICDAR 2015, Total-Text.
Research on Passive Method of Doppler Coefficient of Underwater High Speed Moving Target

Doppler estimation is an important issue for passive positioning of flank arrays. In practical applications, Doppler compensation is an effective method to correct positioning errors caused by Doppler. To obtain the Doppler coefficient difference, this paper proposes two estimation methods: a method based on frequency domain cross-correlation (Hereinafter referred to as FDC method) and a method based on feature extraction (Hereinafter referred to as FE method). FDC method obtains the cross-correlation peak position of each subband Fourier series module of the receiving signal. FE method matches the pole position of the frequency domain waveform between different array elements and calculates the frequency shift to obtain the curve of the relationship between frequency and frequency shift. This paper comparatively analyzes the estimation error and computation amount of ambiguity function method (Hereinafter referred to as AF method), FDC method and FE method. Compared with AF method, the FDC and FE methods can achieve Doppler estimation and effectively reduce the estimation computation cost.
Capacity Assessment and Simulation of Terminal Area under the Influence of Military Activities

Military activities have a great impact on the capacity of terminal area. In the paper, the area affected by military activities can be represented by flight restricted area. On the basis of aircraft maneuverability analysis, the design method of flight restricted area is given. The characteristics and defects of traditional algorithms at home and abroad are analyzed. Particle Swarm optimization (PSO) algorithm is introduced to solve the bottleneck of terminal area capacity affected by military activities. The simulation shows that the method can effectively evaluate the airspace capacity under the influence of military activities from a quantitative perspective.
A Novel Fuzzy Logic Based Controller for Measuring the Effectiveness of Training for Placement

It is a well-known fact that the placements are crucial in the professional life of a graduating student. The need for high quality output from the academia necessitates the students to be equipped with the requisite skill set to be industry-ready. This paper briefs about the training sessions done at SDMCET-Dharwad, for aiding the students in campus placement drives. Also, this paper deliberates a unique approach based on fuzzy logic for measuring the effectiveness of training sessions in terms of various dimensions namely program pacing, quality of content covered and interaction with students. The fuzzy controller is built using Mamdani and Sugeno style of inference engines. The design and simulations have been done using MATLAB and Simulink softwares.
Immersive Virtual Reality for Teaching Problem Solving in Networking

We describe work-in-progress in immersive virtual reality (iVR) to support teaching problem solving in networking. Technical feasibility was established for a system which integrates a microworld simulator on the back end with a studio-like iVR front end for networking. Early prototypes and user testing for single-user VR and collaborative asymmetric VR are discussed.
Fusion of classifiers based on physical activities data from smartphone user

The behavior recognition of mobile phone users is based on different types of smartphone sensors. The accelerometer is one of these sensors which can represent the person activity. In this paper, in order to recognize and classify the physical activities of user's smartphones, we will use the Dempster-Shafer (DS) theory of belief functions. After applying decision tree machine learning algorithm on each of the three axis of movement, we will fuse different combinations of these results. Then, we will fuse different machine learning algorithms applied on three axis of movement together. We will show that using a classifier of each attribute give better results than applying on all attributes.
Analysis of Behavioral Patterns for Social Virtual Reality Based Active Learning

The purpose of this study is to implement a virtual reality (VR) classroom by exchanging certain social signals which can be observed during an active learning (AL) setting. It is not clear how to do AL effectively in a VR classroom because it is difficult to exchange social signals on avatars. This paper describes a pilot experiment and suggested that social signals such as facing behavior, body tilted forward and backward, and hand movement on memo are important to convey the intention to the other participants. The outcomes are expected to be used to realize the social VR based AL.
Fast Training of POI Recommendation Models Using Gradient Compression

Point-of-Interest (POI) recommendation plays an important role in location-based services. Regarding POI recommendation as a trajectory prediction problem, attention-based models have shown promising achievements to tackle this problem. With the rapid development of LBSN applications, modern training datasets and POI recommendation models are becoming increasingly large and complicated, which results in time-consuming training procedures. Existing solutions adopt data parallel training for POI models, but they still suffer from the high latency of inter-device data communication. To address this issue, we propose a fast training method for POI recommendation models. It conducts a layer-wise top-k gradient compression to reduce the overhead of data transfer. Meanwhile, it modifies the Adam optimizer with an error feedback mechanism to avoid the decrease of POI recommendation accuracy resulting from gradient compression. Experimental results show that our methods can achieve similar even better accuracy than uncompressed methods while the communication time is shorter.
PENGGUNAAN NEURO FUZZY PADA SISTEM MONITORING KETINGGIAN AIR SUNGAI

Flood is a natural phenomenon that usually occurs in an area that is often flowed by rivers. The impact of flooding can be reduced if the community is better prepared to face the coming flood. One of the solution is to create a flood early warning system. In this study, the concept of early warning system is a river water level sensor that consist of 2 conductor plates that will produce a capacitance value when drowned into the water with a certain depth. These water level measurement values will use 3 different methods. Furthermore, those three methods will be compared to determine the best method that produces a small measurement error. The three methods are linear regression, polynomial regression, and neuro fuzzy. The results show that the measurement error which generated by linear regression was 25.45%, the measurement error that generated by polynomial regression was 13.61%, and the measurement error that generated by neuro fuzzy was 3.76%. According to these error values, the study finds that neuro fuzzy is the best method for water level measurement.