Reviewerly/exHarmony huggingface dataset:
New Vehicle System Identification Based On Magnetic Feature Map

Vehicle detection is one of the important solutions for transposition system problems and can be utilised for different applications. Vehicles consist of deferent iron parts that lead to having a magnetic behaviour or map. This paper presents a new vehicle detection and identification system using magnetic sensors. A specific new system has been designed consisting of a non-magnetic material frame. Four magnetic sensors to detect the magnetic fields around the vehicle have been fixed in the frame in suitable places. Each sensor signal has been measured and transmitted to a PC for storing, analysing and identifying. Signals from magnetic sensors detected when the vehicle passes through the frame. A new database has been built employing the designed identification system used as reference data or reference signals. Two approaches have been used to analyse and identify the vehicle type. The first approach uses normalised cross-correlation depending on Fast Fourier Transform to find the similarity between magnetic sensors signals and the reference signals. The second approach uses a matching pattern approach in which the signals from sensors converted to an image and make a matching between the two patterns to identify the vehicle. The accuracy of the first approach of vehicle identification reached 90%, and the accuracy for the second approach reached 95%. These two approaches, compared with other approaches, gave more accurate results to identify the type of vehicle and distinguish between vehicles of the same type and give a signature to each type/model of the vehicles. Other approaches just detect the vehicle and identify if it is a small or big vehicle.
Securing Technology and Government Services Enrollment - Stage

The world is going digital, this application could be a drop of the digital ocean. This application integrates Digital Locker and all government services. The Digital Locker helps to store all the documents safely and using this app one can easily apply to almost all the government services. As it is an integrated app, the documents that are needed for the application are fetched directly from the Digital Locker instead of uploading it each and every time. This application reduces time complexity, as all the services, and documents are available at one place itself and this can be used for dual purpose.
Experimental Study on the Digital Color Video Using Absolute Difference and Piecewise Mapping Techniques

Video processing acts as a major role in various applications and it easy to record at anytime and anywhere in the today's world. Visually pleasing videos are generally required for most of the video applications like medical imagery, high definition video photography, and liquid crystal display and surveillance systems such that these videos have a reduced noise and enhanced contrast. The proposed method improves the contrast of the video by giving importance to the color details and provides visually acceptable result as compared to various other existing methods. The algorithm adopts ROAD and adaptive piecewise mapping function to automatically enhance the contrast of the video.
Detecting Faulty Sensors by Analyzing the Uncertain Data Using Probabilistic Database

In today's era, data is stored for later analysis but most of the significant applications such as WSN, and Medical Science are producing uncertain data. Still, the recorded uncertain data can be analyzed to produce probabilistic answers but the conventional DBMS are designed based on First Order Logic so they are unable to store and process data with some uncertainty or missing values. At the same time, it is not beneficial to delete uncertain data it may affect the result. To deal with uncertain data, Different research groups at the world's renowned institutes developed the Probabilistic DBMS. Like a research group at Oxford University has developed the MayBMS: A probabilistic database management system to analyze the uncertain data. But before using the probabilistic DBMS to manage the uncertain data, the uncertainty in the data should be calculated using probability theory to know the correctness of each record. The purpose of writing this research paper is to find a way to measure the uncertainty available in the data before managing it. Because the management of uncertain data is the second phase, the first thing is to know the correctness or falseness of each available record in the dataset.
The Architecture of the Self-Driving Car Project at Innopolis University

This paper describes the software and hardware platform used by the Innopolis University team to conduct investigations in the field of self-driving vehicles. The paper also describes basic technical solutions and the grounds for decisions for each key subsystems: localization, route planning, control, object perception and prediction of their behavior. The developed software and hardware platform was implemented for KAMAZ truck and KIA electric car navigation in various navigation scenarios. The article presents the results of testing of the platform for both of those vehicles.
An explicit track continuity algorithm based on the GM-PHD filter with adaptive birth intensity

Due to its low computational cost, the Gaussian mixture (GM) implementation of the probability hypothesis density (PHD) filter is a good candidate for multi-target tracking (MTT), especially in real-time processing applications. In this study, we propose an explicit track continuity algorithm based on the GM-PHD filter with unknown target birth intensity for online MTT. In this approach, the identity labels of Gaussian components are divided into three classes, and the labels of confirmed components are unchanged through the overall filtering process, this can provide explicit MTT. Based on the measurements not associated with the predicted components, the adaptive target birth intensity is obtained. The simulation results demonstrate that the proposed approach can achieve explicit MTT under the unknown target birth intensity scenes.
Moving Towards Program Comprehension in Software Development: A Case Study

The goal of this paper aims at promoting program comprehension in computer science, in particular software engineering and object-oriented programming courses. The research methodology is evaluated through an empirical study which involves an active participation audience of undergraduate and graduate computer science students. With no previous knowledge on program comprehension, participants were explicitly instructed to use their computing skills and own strategies to comprehend a set of Java programs of different difficulty levels. We did not impose on the participants any specific paradigm to comprehend the source code. We conducted three case studies with two groups of students, and the results were promising, despite of the fact that students had no previous knowledge of program comprehension. The results of this study shows that participants identified four dimensions to comprehend the source code - namely input/output activities, visual scanning, computer science knowledge, and application domain. In addition, the results provided strong evidence for the validation of the hypotheses that we formulated earlier before conducting the case studies. Another factor worth mentioning is the indentation of the code which might play a role in debugging.
Sentimental Analysis (Opinion Mining) in Social Network by Using Svm Algorithm

Web discussions are as often as possible utilized as stages for the trading of data and assessments just as publicity dispersal. The client produced content on the web develops quickly right now age. The transformative changes in innovation utilize such data to catch just the client's substance lastly the valuable data are presented to data searchers. The majority of the current research on content data preparing, centers in the genuine area as opposed to the assessment space. Content mining assumes a fundamental job in online gathering feeling mining. Be that as it may, feeling mining from online discussion is significantly more troublesome than unadulterated content procedure because of their semi organized qualities. Order dependent on opinions has become another outskirts to content mining network. The assignment of assumption arrangement is to decide the semantic directions of words, sentences or records. Notion investigation is about conclusion mining. Break down feelings, attributes and assessments of clients about any items, subjects, or issue. For the popular feeling, web is turning into a spreading and exceptionally wide stage where online gatherings, social locales, websites and different destinations contains sentiment and audit of individuals in type of remarks and posted messages. Presently a days the information acquired from these destinations, online journals and remarks and publication is helpful for advertising research. Right now propose an extraction method to score the audits and condense the suppositions to end client. In light of conclusions mined it is chosen as whether to break down the slant of client feed backs and furthermore channel the sentiments dependent on client areas. This venture for the most part centers on giving a system to mining the feelings utilizing nonexclusive client centered surveys utilizing common language preparing steps. We can actualize this system progressively situations and furthermore improve the precision in feeling mining in python structure.
A Perspective View of Mining Rules with Ant Colony Optimization Technique:Ant-miner

In these days of computational technology, rule based classifiers in data mining are gaining popularity and provides a good platform for researchers to use different metaheuristics techniques to solve non-deterministic problems in real-world scenario. This study provides the implementation of ant based algorithms in the classification task of data mining. We have done an experimental study by taking ten datasets and calculated predictive accuracy measures of Ant-miner, cAnt-miner, JRIP and PART as well as non-statistical measurement tests to validate the results. This study shows that Ant based algorithms outperforms over JRIP as well as PART algorithms.
Solar Irradiance Estimation at Certain Location Using Artificial Neural Network and ASHRAE Clear-Sky Model

Solar energy is one of the renewable energies with great potential in tropical countries including Indonesia. Harvesting solar energy through a photovoltaic system has a major challenge due to its intermittency and uncertainty as well as the unavailability of the measured data at every location. The purpose of this study is to estimate the solar irradiance at certain location by employing the ASHRAE Clear-Sky model and the local weather information through an Artificial Neural Network (ANN) algorithm. The ASHRAE Clear-Sky model is used as the basis of maximum irradiance which will be calibrated by considering the local weather information. The estimation model is developed by using a backpropagation algorithm of ANN. The proposed method is simulated at the department of electrical engineering Universitas Indonesia. The result shows that the proposed method can provide an accurate estimation of solar irradiance with the mean absolute error in the three different test days are 71.44 in day 1, 43.24 in day 2, and 60.23 in day 3,also the average from mean absolute error of these days is 58.30.
Application of Deep Learning in Human-computer Interaction Design of Rehabilitation Equipment

Rehabilitation equipment design, which combines rehabilitation medicine, structural mechanics, biomechanics, computer science and ergonomics, has become a promising research direction in recent years. Rehabilitation equipment belongs to medical devices, and its main function is to assist patients in rehabilitation training. While deep learning technology can be used in data analysis, feature extraction, human-computer interaction and many other fields. As rehabilitation equipment has strong characteristics in human-computer interaction(HCI), this paper will focus on the HCI design of rehabilitation equipment and expounds the practical significance of deep learning technology in its design process.
Recent Advances in Geomathematics in Croatia: Examples from Subsurface Geological Mapping and Biostatistics

Geomathematics is extremely important in geosciences, particularly in the geology. The key for any geomathematical analysis is the definition of a typical model to be applied for further prognosis, either through deterministic or stochastic approaches. The selection of the appropriate procedure is presented in this paper. Two different geomathematical subfield datasets were used in subsurface geological mapping and palaeontology and different biostatistics applications, representing important geomathematical subfields in the Croatian geology. The different subsurface interpolation methods tested, validated and recommended for application were used to obtain the best possible outcome in reservoir modelling, in the cases with small datasets. Cross-validation may be chosen as the main selection criteria, applied to the Croatian part of the Pannonian Basin System (CPBS). Recent advances in biostatistics applied in palaeontology and case studies from Croatia are also presented, where biometric studies are of significant importance in fossil biota. Data, methods and problems in geosciences are vast subjects, and address a wide spectrum of fundamental science. Because geology includes subsurface and surface geology, and very different datasets regarding variable and number of data, we have chosen here two representative case study groups with original samples from Northern Croatia. Subsurface mapping has been presented on limited petrophysical datasets from the Northern Croatian, Miocene, hydrocarbon reservoirs. Biostatistics have been presented on very different samples, allowing us to achieve paleoenvironmental reconstructions of the size of relevant fossils, such as dinosaurs or other species and their paleoenvironments. All examples highlight examples of the valuable application of geomathematical tools in geology. The results, cautiously validated and correlated with other, non-numerical (indicator, categorical) geological knowledge, are of enormous assistance in creating better geological models.
Deciphering the Myth About Non-Compliance and Its Impact on Cyber Security and Safety

The use of computers and sophisticated technologies are on the rise, and organizations are constantly looking for ways to invest in technologies to stay ahead of the competitive market. As such, cyber security and safety measures have been put in place by the organizations to protect them from attacks and to ensure that products and services are safe. However, managing cyber security and safety is becoming more challenging in today's business because people are both a cause of cyber security incidents as well as a key part of the protection from them. It is however that non-compliance with policies and directives are major security breaches. What is not well known, however, are the reasons behind the non-compliance behaviours. This chapter seeks to explore the reasons behind the non-compliance behaviours by use of compliance assessment model (CAM). The chapter reviews a case study in a health centre and systematically assesses the reasons behind the non-compliance behaviour by using the CAM model.
A Novel Method for Small Object Tracking Based on Super-Resolution Convolutional Neural Network

In this paper, we propose a novel tracking algorithm that can work robustly in small object tracking. The algorithm is inspired by recent advances in Super-Resolution Convolutional Neural Network (SRCNN). In contrast to most existing trackers which simply combine super-resolution (SR) and tracking, a deep network was proposed to better utilize SR features from different layers for small object tracking, which is rarely investigated in previous works. Specifically, we train a designed SRCNN offline to acquire SR features that are more robust against low resolution. Afterwards, this is followed by network weight transfer from offline training to the super-resolution tracker (SRT). The whole network can be tuned online to fit appearance variation. The SRT tracker employs a compact CNN structure without expanding layers or deconvolution layers and thus reduces the computational cost. Therefore, the tracking model achieves real-time performance while still maintaining good performance. The proposed method is evaluated on public tracking benchmarks, the simulation shows that SRT significantly increase the accuracy of tracking small objects.
A Camera Calibration Method Based on Real-Time Video

Camera calibration is one of the most important steps in computer vision. This paper presents a camera calibration algorithm based on real-time video. Video key frames are extracted by inter-frame difference algorithm, and camera calibration is carried out by key frames extracted from video. The influence of background on key frame extraction is eliminated. Moreover, the calibration process is self-calibrated, which makes the process more convenient, fast, and practical. The uncertainty of selecting images is artificially eliminated and the results of camera calibration are improved.
A new technique for Diffie-hillman key exchange protocol security using random image generation

The rapid development of technology by using the internet as a medium to transfer many types of data has led to the appearance of new problems, such as intrusion, hacking and other problems and providing the security for the progress using these algorithms. The Diffie-hillman algorithm is one of the key exchange protocols that used for the security of information, but by the passing time, the change of security progress led to the occurrence of many problems due to the using Diffie-Hellman protocol in key exchange such as Man-in-the-Middle attack and Discrete logarithm attack. So, to solve these problems and to obtain better security, it needs to generate a random colour image(NxN) for each encryption, then uses an image generated to extract keys by applying the XOR function.
Privacy Preserving Data Publishing of Multiple Sensitive Attributes by using Various Anonymization Techniques

The enhancement in digital era speeds up the process of aggregating the massive amount of information from various sectors of governments, diverse sections of healthcare unit, multiple organizations as well as from individuals. This aggregated data's release is essential for the betterment of researchers, varied occupations, and individuals etc. This gives rise for necessitate releasing and exchanging of assembled data. However, when information is in native form, it carries some crucial sensitive facts about human beings and/or organizations. If such information is disclosed, personal and/or organizational privacy may be threatened. Therefore, Privacy Preserving Data Publishing (PPDP) comes up with tools and techniques which describe how to publish valuable facts along with its privacy protection. Thus, it is inevitable to alter the data before its release with the aim to persist its privacy without jeopardize its utility. This is achieved by varied anonymization schemes. In point of fact, datasets comprise of distinct kinds of Multiple Sensitive Attributes (MSAs) (which can be numerical and/or categorical). Anonymization done for only Single Sensitive Attribute is not having any importance in practical scenarios. On that account, it is significant that, while operating the highly dimensioned data, the association amidst these MSAs is sustained along with the efficient privacy preservation of Mixed (numerical as well as categorical) MSAs. This paper concentrates mainly on analysing different schemes proposed in literature for PPDP of MSAs.
Emotional State Estimation by Dialogue History and Sentence Distributed Representation

The paper presents a novel approach to predict human's emotion in the dialogue. We target the scenario dialogue corpora. The corpora are annotated by some subjects with emotion tag. We use sentence embedding by Bidirectional Encoder Representations from Transformers as versatile and rich feature. By acquiring multi-context information from dialogue text, the accuracy of emotion prediction can be improved. As an experiment by machine learning algorithm such as deep neural networks for the proposed method, we achieved higher accuracy than the method using embedding feature from utterance itself.
Presentation and interaction of Internet of Things data based on augmented reality

In recent years, AR (Augmented Reality) technology has gradually entered people’s horizons and has become a research hotspot in the field of image processing. At the same time, the mobility and interactivity of AR have also been valued. This paper studies the interaction technology in the AR scene, and proposes a scheme to detect the user’s gaze at the cone perspective. The Leapmotion body sensor was used for gesture extension, and the hardware connection and data transmission scheme of Leapmotion and Holo Lens was designed. Based on the three input modes of sight, gesture and voice, the interaction logic between the user and the system in the AR scene is designed. The network of the Internet of Things is dominated by wireless sensor networks (Wireless Sensor Networks). This paper studies the problem of congestion in wireless sensor network sensing data transmission. WSNs nodes need to transmit the sensed data or preprocessed sensed data to sink nodes. When the data packets (or packets) received by a sensor node exceed its upper limit for forwarding or processing data packets, the excess data packets need to be buffered. When the memory of cached packets is full, excess packets are discarded, causing buffer overflows and causing node-level congestion. This paper proposes an energy balance strategy based on feedback control. The feedback control algorithm is used to adjust the node’s transmit power to realize the redistribution of node load in the network, solve the problem of node energy imbalance in wireless sensor networks, and further extend the sensor network.
Designing for Immersion

Although immersive realities are not new, new applications, more available development tools, and expanding frontiers require educators to better understand how to design in these areas. This chapter develops a guiding pathway for exploration and development within a very broadly defined field of technology and of study. It establishes the range of variables that need to be considered and elucidates a systematic, applied, yet unfettered way for discovering the new potential. With scenarios, immersions, interactions and role-playing, shared and solo experiences now possible, imagination and creativity can move designs well beyond present text, image, and video limitations by using elements of gaming, storytelling, and conversation. Envisioning and designing for these environments is challenging; audience integration, assessment, and evaluation will be required throughout. However, since learning can reach beyond past boundaries, educators must use persistence when they move into these new realms, documenting and sharing their experiences, stumbles-along-the-way, and victories.
XNORAM: An Efficient Computing-in-Memory Architecture for Binary Convolutional Neural Networks with Flexible Dataflow Mapping

In this paper, an energy-efficient computing-inmemory architecture for binary convolutional neural networks, called XNORAM, is proposed. The XNORAM employs 6T feature cells and 10T weight cells to form one XNORAM column. Multiplexed XNOR operations are embedded in each column. To address the data reuse in convolutional neural networks, flexible dataflow mapping is supported on XNORAM to minimize the external data access. To verify the architecture, we design a 4-KB XNORAM prototype in 65nm CMOS technology. It achieves a throughput of 18. 5GOPs at 100-MHz clock rate and 1.0-V power supply. XNOR-AlexNet is performed on the design achieving 39.86 TOPS/W and 4.63 GOPS/KB utilization with only 1.3% accuracy loss comparing to the original XNOR-Net result on GPUs.