Miracle-dz/UniFAR-FTUs huggingface dataset:
Visualization of Hierarchical Information Based on Venn Diagrams

In order to visualize large-scale hierarchical information, an approach for tree visualization using nested circles is presented. The brother nodes of the tree are represented by the circles tangent to each other. The tree nodes at different levels are represented by nested circles. The properties of nodes are described by circle′s sites, sizes and colors. Circles at the same levels are packed closely together and no overlapping. The circle packing is very fast and robust. The complexity of circle packing algorithm is nearly liner. It is suitable for visualizing large information. This visualization provides not only good overviews for large-scale information but also the details for each node. Both the complex hierarchical structures and the properties of leaf nodes are obvious. The tree visualization is used to large file systems. Examples show this approach is robust, efficient and easy to use.
Realisations and variants of have to: what corpora can tell us about usage-based experience

This paper explores the potential of corpus data to account for language users’ mental representations of a high-frequency item that is prone to phonetic reduction. We present an analysis of the realisations of semi-modal have to in spoken American English, which is a candidate for to-contraction (compare wanna and gotta) but has no clearly established contracted form. The study therefore focusses on the potential reduction of to and its conditioning by speech rate and phonetic environment. Three variants are extracted from the data, a citation form [hævtʊ] (or [hæftʊ]), a ‘schwa variant’ [hævtə] (or [hæftə]), and a reduced form that is akin to to-contraction [hævɾə] (‘havda’). The first two can alternate freely, but are subject to preferences based on the following sound, while the contracted form is strongly tied to rapid speech. These results suggest that the citation form and schwa variant are generally stored exemplars of have to, while ‘havda’ is more weakly represented in the system (as an outcome of on-line articulatory reduction). In this it clearly differs from conventionalised contractions such as gotta and wanna. On the methodological level, the study shows that thorough analysis of spoken corpus data provides insights into exemplar representations, though experimental hypothesis testing is also necessary.
Parallel Algorithm for a Nonlocal Diffusion Model Applied to a 3D Input Domain

In this paper we analyze two methods to optimize the numerical computations for the diffusion term in a nonlocal reaction-diffusion equation in case of a 3D input domain. The results are studied in the context of applying the equation to 3D image processing tasks like image restoration and segmentation. It is important to find an optimal way of computing the numerical approximation for diffusion term because, due to its integral formulation, the diffusion is computational intensive especially if the numerical analysis takes place on a large input domain with three dimensions.
An efficient graph-based algorithm for discovering association rules

The algorithm for discovering association rules based on graph only scans the database once to construct an association graph and then traverses the graph to generate all frequent itemsets. It costs too much time in proving the candidate frequent itemsets to be really frequent. An improved algorithm was proposed. The improvements were renumbering the frequent 1 item in the support degree descending order and utilizing the Apriori property to prune the redundant extended items which were used to generate the candidate frequent itemsets. Experiment results show that the improved algorithm prune the redundant candidate frequent itemsets when the minimum support degree is small, and the performance is improved.
Using haptic feedback to increase seat belt use of service vehicle drivers.

The legacy of research on increasing seat belt use has focused on enactment of seat belt legislation, public education, high-visibility police enforcement, and seat belt reminder systems. Several behavioral programs have produced large, sustained increases in seat belt use, and several of these techniques have been employed on a communitywide basis to increase seat belt use. Innovative technologies may add to the success realized by the highvisibility enforcement model and elevate the seat belt use rate further.
Task decomposition and scheduling in large-scale network management

With large-scale network management becoming more complicated, neither centralized network management nor generic agent-based network management could satisfy the increasing demands. A three-tier network management framework based on multi-agent was provided, which gave multi-agent the collaboration environment. In this framework, a task decomposition algorithm based on task dependence relationship was proposed, according which scheduling method was produced for multi-agent considering the characteristic of network management function. The theoretic analysis and experimental results reveal that using task decomposition method can decrease tasks execution time and save network bandwidth in large-scale network management.
Exploring protein flexibility during docking to investigate ligand-target recognition

Ligand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue. The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline. Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes.
A Survey on Vanet Technologies

Recent advancement in wireless communication technologies and automobiles have enabled the evolution of Intelligent Transport System (ITS) which addresses various vehicular traffic issues like traffic congestion, information dissemination, accident etc. Vehicular Ad-hoc Network (VANET) a distinctive class of Mobile ad-hoc Network (MANET) is an integral component of ITS in which moving vehicles are connected and communicate wirelessly. Wireless communication technologies play a vital role in supporting both Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication in VANET. This paper surveys some of the key vehicular wireless access technology standards such as 802.11p, P1609 protocols, Cellular System, CALM, MBWA, WiMAX, Microwave, Bluetooth and ZigBee which served as a base for supporting both Safety and Non Safety applications. It also analyses and compares the wireless standards using various parameters such as bandwidth, ease of use, upfront cost, maintenance, accessibility, signal coverage, signal interference and security. Finally, it discusses some of the issues associated with the interoperability among those protocols.
A Source-Location Privacy Preservation Protocol in Wireless Sensor Networks Using Source-Based Restricted Flooding

Wireless sensor networks are widely deployed to monitor valuable objects.Attackers can find out the source node by hop-by-hop backtracking strategy.So,we need provide source anonymity for sensor networks.This paper examines that the phantom source nodes generated by the existing source location privacy preservation protocols are near the real source.So they can not protect the real source node well.As such,this paper proposes a protocol called PUSBRF(Source Location Privacy Preservation Protocol in Wireless Sensor Network Using Source-Based Restricted Flooding). And based on PUSBRF,it proposes another protocol called EPUSBRF(Enhancement Source Location Privacy Preservation Protocol in Wireless Sensor Network Using Source-Based Restricted Flooding)under the consideration of an attacker with enhancement visual ability.Simulation results show that compared to the existing protocols,the new protocols provide strong source location privacy preservation and the average safety period is increased by nearly one order of magnitude.
A Mechanism for Highly-Available OLTP Systems based on Paxos Consensus

Demand for highly available (HA) on-line transaction processing system has been increasing these years. However, it is relatively difficult to make modern multi-layered systems HA because the HA mechanisms of each layer need to be coordinated carefully. In addition, service outage is dependent on failure detector which requires some amount of time until it detects a failure. We propose a mechanism that provides end-to-end HA for modern On-Line-Transaction-Processing (OLTP) systems. In our mechanism, when the primary system did not respond in a specific time, client reroutes a request to a secondary system without waiting for failure detection. Primary database server replicates transaction logs to secondary database server using PAXOS consensus algorithm. Primary and secondary database servers avoid duplicate transaction commits while they allow duplicate executions of a transaction by multiple application servers. We applied this mechanism to a Web application and evaluated it to show that it can be used for general Web applications.
Big data platform for health and safety accident prediction

Purpose The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy. Design/methodology/approach The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services. Findings The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain. Originality/value This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.
Identification, Implementation and Validation of Authentication and Authorization pattern in Distributed System using Spring Security Framework

-Software Security is an important part of Software development as the risk from attackers is constantly evolving through increased exposure. The main drawback in current approach is more concerned towards software requirement and not towards the security requirement of the software in SDLC. There are various techniques to identify the vulnerabilities which can be eliminated with the help of security pattern in software development life cycle. But the main concern is about which pattern is more efficient and can eliminate the vulnerabilities efficiently. The Security pattern must be applied in the Design phase and validated using USE TOOL that all the threats are mitigated. But Main aim is to identify the J2EEE pattern which can be implemented in spring Security Framework with the help of available Classes of Spring Security and this framework is more concern about Authentication and Authorization. So we are also focusing on both of this area. After identification of Security pattern it is tested in Design phase and checked Using USE TOOL whether it is mitigating all the threat and finally it is implemented in Spring Security Framework.
K2 Algorithm based Text Detection with an Adaptive Classifier Threshold

In natural scene images, text detection is a challenging study area for dissimilar content-based image analysis tasks. In this paper, a Bayesian network scores are used to classify candidate character regions by computing posterior probabilities. The posterior probabilities are used to define an adaptive threshold to detect text in scene images with accuracy. Therefore, candidate character regions are extracted through maximally stable extremal region. K2 algorithm-based Bayesian network scores are learned by evaluating dependencies amongst features of a given candidate character region. Bayesian logistic regression classifier is trained to compute posterior probabilities to define an adaptive classifier threshold. The candidate character regions below from adaptive classifier threshold are discarded as non-character regions. Finally, text regions are detected with the use of effective text localization scheme based on geometric features. The entire system is evaluated on the ICDAR 2013 competition database. Experimental results produce competitive performance (precision, recall and harmonic mean) with the recently published algorithms.
Modeling the Effects of HUD Visual Properties and Configurations Modeling the Effects of HUD Visual Properties and Configurations on a Multi-Dimensional Measure of Clutter on a Multi-Dimensional Measure of Clutter

The objectives of this study were to: validate a multidimensional measure of display clutter for advanced head-up displays (HUDs) incorporating enhanced and synthetic vision systems (EVS/SVS); assess the influence of HUD configuration on pilot perceptions of display clutter and flight performance; and model clutter scores in terms of visual display properties. Eighteen pilots flew a flight simulator in a landing approach using three different sets of HUD configurations (low, medium, or high clutter). Pilot ratings of overall display clutter and its underlying dimensions were recorded along with flight performance measures (deviations from localizer, glideslope, altitude and approach speed). A display image analysis software application was used to measure the visual properties of HUDs. The multidimensional measure of clutter showed internal consistency with high correlations of overall perceived clutter. Calculated clutter scores were sensitive to the various HUD configurations and in agreement with prior display classification (based on the new measure). There was a trend for the extremes of display clutter to cause less stable performance. High clutter displays were associated with cognitive complexity of flight tasks and low clutter was associated with a lack of display information. Multiple linear regression models of perceived clutter were developed based on HUD visual properties.
User Identification of Pseudonyms without Identity Information Exposure in Access Federations

The concept and design of access federations have been widely accepted and their world-wide deployment is in progress. In an access federation, control of user information (personal identification information) is a key issue in its operation in terms privacy. Pseudonym is proposed and implemented as a solution to this problem. We consider a case where the requirement of privacy protection by using pseudonyms and that of user identification for service providing conflict with each other. In this paper, we propose a "counting server" for identification of different pseudonyms or social identities. SPs can use this identification information to provide special services such as student discount and limit of use. We also show an implementation of this scheme on Shibboleth/SAML platforms. Related protocols are designed, another SAML engine are provided on SP, and counting server is provided. Furthermore, we analyze this scheme, and proves the security properties.
Multi-Label Latent Spaces with Semi-Supervised Deep Generative Models

Expert labeling, tagging, and assessment are far more costly than the processes of collecting raw data. Generative modeling is a very powerful tool to tackle this real-world problem. It is shown here how these models can be used to allow for semi-supervised learning that performs very well in label-deficient conditions. The foundation for the work in this dissertation is built upon visualizing generative models’ latent spaces to gain deeper understanding of data, analyze faults, and propose solutions. A number of novel ideas and approaches are presented to improve single-label classification. This dissertation’s main focus is on extending semi-supervised Deep Generative Models for solving the multi-label problem by proposing unique mathematical and programming concepts and organization. In all naive mixtures, using multiple labels is detrimental and causes each label’s predictions to be worse than models that utilize only a single label. Examining latent spaces reveals that in many cases, large regions in the models generate meaningless results. Enforcing a priori independence is essential, and only when applied can multi-label models outperform the best single-label models. Finally, a novel learning technique called open-book learning is described that is capable of surpassing the state-of-the-art classification performance of generative models for multi-labeled, semi-supervised data sets.
How to make a telephone speech corpus

The telephone speech corpus is the basis of developing a Human-machine interaction system designed for communication and mobile internet. The main problem nowadays for constructing a qualified speech corpus is lack of a standard scheme. This research tries to find a standardization program which can make the corpus be established more efficiently and be used or shared easier. The specifications of constructing a speech corpus are also introduced in the paper. Finally, a telephone speech corpus, TSC973, be exemplified to illuminate the standardization program.
Second Order Online Active Learning based Malicious Web Classification

: Active learning is an exceptional instance of semi-directed ML in which a training calculation can intuitively inquiry the client (or some other data source) to acquire the ideal yields at new information focuses. This is more testing than regular online learning undertakings since the student not just needs to figure out how to viably refresh the classifier yet additionally needs to choose when is the best time to query the mark of an approaching occurrence given restricted name spending plan. Login accreditations or record information by sending as an authentic component or individual in email or other correspondence channels which assailant endeavors to learn fragile information in a kind of blackmail called Phishing. To improve the execution of on-line learning classifiers, an upgraded on-line learning procedure is proposed. They potentially use URL data to choose whether the URL interfaces with poisonous pages. The versatile methodology, genuinely characteristic and compelling which can be handle by an epic structure of Cost Sensitive Online Active Learning (CSOAL) for vindictive identification.
Data Protection Guidelines for Protecting Privacy of Users on Social Media

Social Media interaction is growing at a rapid pace. Every smart phone user has at least 1 social media account. Social media websites offer th*eir users what seem like free services. These free services are then paid for in kind when the social media websites use their personal information to aide their business objectives. In this paper, I have included survey results of a user awareness survey that I conducted among people from various demographics. The total number of respondents to that survey was 404. I have used Facebook as the subject of my survey and will be using the results to generalize to other social media websites. The survey showed that people are ignorant of the ways in which their personal data might be misused. Another section of my paper will consist of a summary of our current IT Act, its limitations in extending their jurisdiction to OSNs (Online Social Networks) and a growing need for India to have a dedicated Personal Data Protection Act. Lastly, I would like to propose some framework or guidelines on which our Personal Data Protection Act can be compiled.
1 Specialization and Career Dynamics: Evidence from the Indian Administrative Service

In this article, we attempt to resolve the tension between two sets of conflicting hypothesizes about the role of specialization in workers’ careers. Some scholars argue that specialization is a net benefit that allows workers to get ahead, while others argue that broad experience across several domains is the only way to be truly exceptional. We use remarkably rich longitudinal data on the careers of Indian Administrative Service officers, members of the Republic of India’s elite bureaucratic service to test both these hypotheses. We find that specialization benefits officers throughout their career. However, multiple theoretical accounts are consistent with this effect. One account suggests that specialization benefits workers because it gives them skills that make them more productive, while another suggests that specialization is a signal of ability and promise. Our analysis indicates that both accounts are valid, but apply at different stages of officers’ careers. The skills acquired through specialization matter more in the later career; in the early career, specialization is mainly a signal.