Reviewerly/exHarmony huggingface dataset:
Inter-Vehicle Position Estimation For NLOS Condition In The Persistence Of GPS Outages

The main focus of thesis work addresses one of the functional key points of Cooperative Collision Warning application which is an accurate estimation of the range data of neighboring vehicles during persistent GPS outages under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Cooperative Collision Warning, based on vehicle-to-vehicle radio communications and GPS systems, is one promising active safety application that has attracted considerable research interest. One of the severe estimation error is due to NLOS that can be mitigated by applying biased Kalman filter on range measurements. For our algorithm these inter-vehicle distances are measured from using one of the radio-based ranging techniques. Main objective is to establish an accurate map of positions for neighboring vehicles in the persistance of GPS outages. GPS outages can be possible in multipath environments where NLOS component is introduced to the true range measurements. These position estimates mainly depend on two factors: (i) Preprocessed inter-vehicle distances (range data is processed from biased Kalman filter); (ii) Road constraints (the vehicle uncertainty is more in the direction of road than the uncertainty in the direction opposite the road); This thesis suggests smoothing and mitigating the NLOS for radio-based ranging measurements under multipath conditions. In order to find accurate positions of neighboring vehicles an extended Kalman filter is implemented along with road constraints. Unbiased Kalman filter, biased Kalman filter and extended Kalman filter performances are experimentally verified using Matlab simulation tool with random number of vehicles at unknown random distinct positions in some physical region along a section of road for vehicular environment.
Correction to: The middle Miocene palynofloras of the Salihpaşalar lignite mine (Yatağan Basin, Southwest Anatolia): environmental characterisation and comparison with palynofloras from adjacent basins

Fig. 10 LM overview (a, d, g, j, m), SEM overview (b, e, h, k, n) and SEM detail (c, f, i, l, o, p) micrographs of Amaranthaceae, Droseraceae, Polygonaceae, Sapotaceae and Oleaceae pollen. a–cDroserasp. (S153636). d–fPersicariasp./Persicarioipollenites pliocenicus(S153634). g–i Amaranthaceae gen. Indet. 3 (S153636)
Upgraded Segmentation of Histopathological Images for classification of intraductal breast lesions

Cancer among women and the second most common cancer in the world is Breast Cancer(BC). This type of cancer-initiating from breast tissue, mostly from the inner region of milk ducts. The current progress in high-throughput and getting of digitized histological studies have made it possible to use histological pattern with image analysis to facilitate disease classification using computer-aided technology (CAT). The practice of analysis has become a part of the routine clinical discovery of breast cancer. In fact, CAT has become recent research subjects in the diagnostic of medical imaging and radiology. The vast increase in the capability of image acquisition and computational power in recent decades has prompted the development of several image segmentation algorithms. For the analysis of histopathological images, the automatic dissection of cell nuclei is an important stage. Its prime objective is to determine the exact location of the nuclei and boundary points of the cells. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.). In the proposed method, additional k means clustering algorithm used for evaluating segmentation algorithms. Here demonstrate the in proposed methods over the state-of-the-art system in performance measures.
Target Tracking Using Reinforcement Learning and Neural Networks

Target tracking is a process that may find applications in different domains such as video surveillance, robot navigation and human computer interaction. In this work we have considered the problem of tracking a moving object in a multi agent environment. The environment is a rectangular space bounded by walls. The first agent is the target and it moves randomly in the space. The second agent should follow the target, keeping as close as possible without crashing with it. It uses sensors to detect the position of the target. The sensor readings give the distance and the angle from the target. We use reinforcement learning to train the tracker to detect any change in the movement of the target and stay within a certain range from it. Reinforcement learning is a form of machine learning in which the agent learns by interacting with the environment. By doing so, for each action taken, the agent receives a reward from the environment, which is used to determine positive or negative behaviour. The goal of the agent is to maximise the total reward received during the interaction. This form of machine learning has applications in different areas, such as: game solving with the most known game being AlphaGO; robotics, for design of hard-to engineer behaviours; traffic light control, personalized recommendations, etc. The sensor readings may have continuous values, making a very large state space. We approximate the value function using neural networks and use different reward functions for learning the best policy.
Design and Implementation of Strong Authentication Model to Improve Performance of Cloud Computing in Hybrid Cloud Environment

Cloud computing became a part of everyday life. We allow us to support the device with accessibility across platforms regardless of the location. It has become vulnerable to several security threats because of public environment and internet participation. At this point the attack by hackers can break down the security and leak the information. Data is recognized in the cloud computing as an important corporate asset which must be safeguarded from unwanted internal or external threats. Data encryption provides data protection on sensitive data but also raises computation and memory overhead during large data processing on cloud.Cloud computing also requires low overhead operation to keep the computation as soon as possible. To authenticate the client we apply modified Kerberos protocol.
A Literature Research on Machine Learning Techniques used for Training Annotated Corpus

The development of research in the annotation area is growing. Researchers perform annotation task using various forms of datasets such as text, sound, images, and videos. Various algorithms are used to perform tasks. The purpose of this survey is to find out algorithms that are often used by researchers to perform annotation tasks, especially on text data. The literature surveys thirteen research papers on text annotation from the last 5 years. The results of this review indicate that SVM is the algorithm used for all three annotation methods: manual, automatic and semi-automatic annotation, with a significant accuracy above 80%. The result of this survey will be referred by the authors as the basis for subsequent research that will be conducted, especially in the semi-automatic annotation method.
Analisis penerapan system application and product in data processing (SAP) pada sistem inventory logistik pada PT. Haier Sales Indonesia, Jakarta Utara

Rapid economic development causes increasingly fierce business competition. Moreover, the development of increasingly sophisticated technology, companies are competing to use technology to increase competitive advantage and efficiency in the company. One of the most popular information technology investments today is the Application of Systems and Products in Data Processing (SAP), systems that do business using a single database that can be accessed by all divisions within the company. The purpose of this research is to study and discuss the application of Systems and Products in Data Processing (SAP) conducted at PT. Haier Sales Indonesia. This study uses qualitative methods through in-depth interviews with IT Managers and Logistics Managers and warehouse administration staff using Pareto diagrams and fishbone diagrams. The results of the evaluation of the application of Application Systems and Products in Data Processing (SAP) in Inventory Logistics that occurred at PT. Haier Sales Indonesia is getting better, all data from every activity has been documented and integrated into the system much faster than the manual process that has been done before and can produce accurate reports. Suggestions are given in order to continue training in SAP Systems and Applications for users in developing SAP Systems and Applications.
Retraction Note to: Location Privacy with Dynamic Pseudonym-Based Multiple Mix-Zones Generation over Road Networks

The Editor-in-Chief has retracted this article because it shows significant overlap with previously published articles [1,2]. This article is therefore redundant. None of the authors agree to this retraction.
A Speculative Model to Explore Data Hub and Quality of Service in IAAS Cloud Computing Systems

Cloud server ranch the specialists is a key issue in context on the one of a kind and heterogeneous systems that can be applied, associating from the VM circumstance to the union with various fogs. Execution appraisal of dissipated figuring establishments is required to predict and assess the cash sparing favored circumstance of a framework portfolio and the relating thought of affiliation experienced by customers. The experience of the customers accepts a crucial activity to envision the structure utilized. Such assessments are not practical by reenactment or on the field experimentation, by ethicalness of the fantastic number of parameters that must be investigated. In this work, a logical model is presented, considering Stochastic Prize Nets (SRNs), that is both versatile to show structures made out of thousands of benefits and versatile to address different methodologies and cloud-express systems. A couple of display estimations are portrayed and evaluated to investigate the lead of a cloud server ranch: use, openness, holding up time, and responsiveness. These estimated attributes are concerned to be major factors for assessing the performance analysis of the system. An adaptability examination is also given to consider load impacts. Finally, a usual philosophy is shown that, starting from the possibility of system limit, can help structure managers to fortunately set the server ranch parameters under different working conditions. These conditions are said to be feasible for the functioning of the system without any hassles.
Alternation Special Edition 24 (2019) 285-307285Print ISSN 1023-1757; Electronic ISSN: 2519-5476; DOIhttps://doi.org/10.29086/2519-5476/2019/sp24.2a13Hate Speech Crimes in Cyberspace: A Criminological/Communicological Investigation of Social Media in South Africa–Understanding the Cyber-mind

This contribution examines hate speech in the cyberspace in South Africa from both a Criminological and Communication Studies perspective.From the field of Criminology, legislation and interpretation of unlawfulness, intent and incitement of hate speech are examined.From the field of Communication Studies conceptualisations surrounding fragmented identities, Goffman's dramaturgy and the Spiral of Silence theory are used to analyse hate speech in the cyberspace through a cross-disciplinary interpretation.A qualitative, thematically orientated methodology is employed which allows for, not only understanding of online communication and meta-theoretical conceptualisations of the cyber-mind, but classification in terms of legal aspects surrounding hate speech.Data were collected from social media postings nationally over the past year, utilising a convenience sample; and classified into two cohorts, (1) postings from the ordinary person; and (2) postings from politicians.Findings are interpreted against legal conceptualisations of hate speech whilst utilising the above communication theories for the provision of a meta-interdisciplinary-perspective.Findings highlight the difference between threats posted on social media by ordinary people and is compared to instruction posted by 1
Research on the Innovative Pattern of Education for Children

This paper analyses the development of preschool education professional dance courses, researches education in the teaching process of problem, and researches the cause of the problem in order to explore a more perfect solution, aiming at promoting normal preschool children's dance curriculum innovation model, and improving the quality of teaching of preschool education dancing courses. At the present stage, people's standards for early childhood education have been constantly enhanced, and the comprehensive development of children is the educational goal pursued by many parents. The traditional early childhood education model can no longer meet the current educational needs. Therefore, it is imperative to innovate and improve the learning model of preschool education professional dance class.
Формирование и развитие научно-исследовательского потенциала молодежи в вузе

One of the priorities of higher education institutions is the formation and development of research potential of students. The article presents a brief analysis of negative trends that characterize modern processes in Russian science and the involvement of young people in it. This article examines the essence, content and structure of students' research potential. A theoretical analysis of the concepts «potential», «research potential of students», «research activity» is given. The authors highlight the components of the student's research potential and its formation at the university, and describe the basic processes of formation of research subjectivity and research competencies. The paper also reflects the results of a sociological study aimed at identifying the readiness of students and teachers for research activities, their satisfaction with working conditions.
Signal Extraction for Classification of Noisy Images Compressed using Autoencoders

The world is experiencing an increasing boom in computer vision. This is more and more used in many domainssuch as robotics, medicine, industry, security systems, etc. In this context, Deep Neural Networks (DNNs) havegreat capabilities and are widely used. Convolutional Neural Networks (CNNs) present a particular class of DNNsthat is most commonly leveraged to analyzing visual imagery. However, CNN performances completely dependon two main issues. The first issue is related to the quality of the images generated by capture cameras. All imagescaptured by remote sensors and modern imaging systems are practically noisy, which can prevent the image frombeing correctly classified and identified by a CNN. The second issue is the throughput available for the transmissionof the large amount of data between capture sensors and units processing CNNs. A seamless transmission can beensured by compression techniques that help reducing the size of data, while affording the required quality forcomputer vision algorithms. Since lossy compression of noise-free and noisy images differ from each other, thiswork firstly raises the question of CNNs resilience to noisy images compression using the particular autoencoders.We secondly propose a method that aims to improve this resilience so that CNNs can achieve better classificationperformances. The compressed noisy images are passed, as a test set, along a model that is learnt from a noisedataset. The subtraction of the so captured noise from the noisy images is then performed to extract the usefulsignal to classify. This will be first work, where we learn the autoencoder from the noise sample, and not the noisysample, while denoising. Obtained results prove the efficiency of the proposed method.
Cyber Delinquency: Issues And Challenges Under Indian Legal System

Children are the shoulders which carry a nation to the future. It is important for a nation to develop that its youth are on the right tracks. But even if sometime a child shows a behavior which is uncalled for a society and delinquent in nature that does not means that he is no longer fit to be a part of the society. Children are a fragile mind and can be put on the right course of development through proper guidance. This paper aims at discussing the reason why minors are more and more involving themselves into committing offences with the aid of technology. Technology which is aimed at their development is in some case proving to be there enemy. The paper further focuses on various types of cyber crimes and how our legal system can control cyber crimes by minors.
Metaclassifiers for Predicting the Robotic Navigational Performance

Prediction of robot steps can be used in path exploring problems and application of data mining techniques, enhances navigational direction of robots. In this paper the proposed method is validated on the data sets using multi classification algorithms with four types of movement classes like Action-ahead, Sharp right turn, small in degree-right turn, and small in degree left turn in a separate layer. We obtain the results based on Meta classifiers’ accuracies tabulated. A layered approach is followed for obtaining the more accurate multi-classification.
A Lightweight Authentication Method in Perception Layer of IoT Through Digital Watermarking

Introduction of IoT (Internet of Things) has enjoyed vigorous support from governments and research institutions around the world, and remarkable achievements have been obtained till date. IoT systems collect the voluminous amount of data in real time from hospitals, battlefield and daily living environment which is related to privacy and security of people. So, securing collected sensitive data is one of the major challenges in the development of IoT systems. Authenticating the source of collected data is utmost important because the adversary may act as a source which may lead to a breach in security and privacy of people using the IoT network. IoT devices are resource scarce so lightweight methods for network security and privacy need to develop to achieve future development goals. In this paper, a novel lightweight node to node authentication scheme based on watermark is proposed to solve the contradiction between the security and restricted resources of perception layer. To improve the security, Proposed scheme usage node identity and the number of neighbours as input to generate the watermark and use the watermark to calculate the embedding positions which makes node authentication based on temporal dynamics of sensing network. The generated watermark is embedded in fixed size message digest generated using the variable message as input into a low-cost one-way hashing algorithm LOCHA. The embedded bits of watermark extracted at the receiving node and matched to check the authenticity of the sender node. The security analysis and simulations of the proposed scheme show that it can be a good candidate to ensure the authentication of the resource constraint devices which are integral part of Internet of Things at low cost.
Performance Analysis of Distance Metric for Content Based Image Retrieval

Content based image retrieval uses different feature descriptors for image search and retrieval. For image retrieval from huge image repositories, the query image features are extracted and compares these features with the contents of feature repository. The most matching image is found and retrieved from the database. This mapping is done based on the distance calculated between feature vector of query image and the extracted feature vectors of images in the database. There are various distance measures used for comparing image feature vectors. This paper compares a set of distance measures using a set of features used for CBIR. The city-block distance measure gives the best results for CBIR.
Planning Catering Business Model for E-Commerce SOA Based: AU-OR!

The population and the many activities of people in big cities like Jakarta make catering become one of the services that are needed by the community. Society is one reason that catering services are needed. The need to order catering has increased to become an opportunity in the food service business. But in practice, it is difficult to find information about the catering that is needed information or marketing from the seller of the service that is felt lacking. The purpose of this study is to design e-commerce applications for catering businesses based on service-oriented architecture (SOA) so that they can integrate several service selling entities and help people find easier food service. The benefit of this research is finding an e-commerce application design for the catering business, can get catering information more easily and can make catering reservations easier. The method used in this research is observation on the respondent who are a catering consumer and business canvas model from the observation. The results of this study are SWOT Analysis, Business Model Canvas, and the design of e-commerce prototypes for catering businesses that can be used in catering industry which will going to e-commerce industry.
Time Conserving Multi-Label Classification System by Incorporating Pyramid Data Structure

Data classification is one of the evergreen research areas of data analysis. Numerous data classification approaches exist in the literature and most of the classification systems are based on binary and multi-class classification. Multi-label classification system attempts to suggest multiple labels for a single entity. However, it is complex to attain a better multi-label classification system. Taking this as a challenge, this work proposes a multi-label classification system, which extracts the features of both entities and labels. The relationship between them are organised in the pyramid data structure. As the features are organized effectively, the interrelated labels are present in the same tier. This feature makes it simple for suggesting multiple labels for a single entity. The performance of this work is analysed over three different datasets and compared against existing approaches in terms of precision, recall, accuracy and time consumption.
Detection of Tumor in Ultrasound Images

Breast carcinoma is one of the most commonly occurring problem among women, which even leads to the end of the victim. Sonogram is widely used in imaging breast abnormalities. The paper is mainly concentrated in detecting lesion in breast sonogram images. The proposed scheme automatically identify the abnormalities without manual intervention. The ultrasound image is preprocessed by equalizing the histogram of the image followed by Gaussian filtering. The filtered image is then segmented using watershed technique which identifies the abnormal region both benign and malignant.