Miracle-dz/UniFAR-FTUs huggingface dataset:
IOT based Online Traffic Congestion Monitoring and Management System

: In today's world, traffic jams during rush hours is one of the major concerns. During rush hours, emergency vehicles like Ambulances, Police cars and Fire Brigade trucks get stuck in jams which leads to loss of many human lives. So we have done a project on IoT based online traffic congestion monitoring and management system ,which provides clearance to any emergency vehicle.
STRUM: A SEMANTIC TRUST-BASED MINER MODEL FOR DETECTING INFLUENTIAL SPREADERS IN SOCIAL NETWORKS

Social Networks have become embedded in every aspect of today people’s life. The success of online social networks has a significant role in shaping trust and influence among participants. They are continuing to gain ground every day, opening the opportunity for several research movements. Great attention is now given for identifying influential spreaders in many application domains as recommendation systems, viral marketing, epidemic control and others. Most of the recent research focused only on the network structure with its geospatial information disregarding the semantics underlying these networks. In this paper, we propose a semantic trust-based miner model (STRUM) that efficiently detects influential spreaders in social networks for enhancing recommendation services. STRUM considers both geospatial information and semantic information in defining each user in the social network. Different experiments have been conducted to verify and validate the proposed algorithm against the latest published using two real location based social networks namely; Brightkite and Weeplaces are used in the experiments.
Practicable Characterization of Systematic Heterogeneity

In public health, personalized medicine is the ideal. For example, an effective strategy for improving the health of a population is to measure the health of constituent subpopulations and intervene where the treatment is most needed. Alternatively, a member of a subpopulation presents with an ailment and relevant covariates are used to determine the appropriate treatment. Such strategies reasonably assume that there is heterogeneity in the effect of the treatment on health across subpopulations. However identification of heterogeneity tends to be expensive, understandably so due to the demands we are making of our data. Costs appear when having to make strong a priori assumptions about the number and identifying characteristics of the subpopulations across which the treatment effect differs, in the increased sample size required for the data to fill in gaps left by the absence of assumptions, and/or in the manual evaluation of large numbers of covariates. This dissertation discusses different approaches to reducing this cost. Chapter 1 compares a Lot Quality Assurance Sampling (LQAS) survey conducted in southwestern Uganda to an unaffiliated but coincident Demographic Health Survey (DHS) and shows that if we redefine our goal in terms of the programmatic decisions we need to make, we can come to the same conclusions at a fraction of the cost. In Chapter 2 I consider just how expensive it is to identify heterogeneity in the absence of a priori assumptions, and draw some general conclusions about the capabilities and limitations of extant modern methods of causal inference. I conclude with Chapter 3, where I leverage the insights of Chapter 2 to build a visualization application that facilitates the exploratory, hypothesis-generating analysis of treatment effect heterogeneity (TEH), particularly for large datasets where a manual evaluation of covariates is not practicable.
Face Authentication for High Level Security Application using LBQP

- Face recognition from the real data, capture images, sensor images and database images is challenging problem due to the wide variation of face appearances, illumination effect and the complexity of the image background. Face recognition is one of the most effective and relevant applications of image processing and biometric systems. In this paper we are discussing the face recognition methods, algorithms proposed by many researchers using artificial neural networks (ANN) which have been used in the field of image processing and pattern recognition. How ANN will used for the face recognition system and how it is effective than another methods will also discuss in this paper. There are many ANN proposed methods which give overview face recognition using ANN. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included, and also the performance analysis of different ANN approach and algorithm is analyzing in this research study. In most of these crimes the criminals were taking advantage of that hacking the information from commercial or academic access control system. The systems do not grant access by who we are, but by what we have, such as ID cards, keys, passwords, PIN numbers. These means they are really defining us or they just want to authenticate us. It goes without Permission of owner’s, duplicates, or acquires these identity means, he or she will be able to access our data or our personal property any time they want. Recently, technology became available to allow verification of true individual identity.
Label-Specific Document Representation for Multi-Label Text Classification

Multi-label text classification (MLTC) aims to tag most relevant labels for the given document. In this paper, we propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. LSAN takes advantage of label semantic information to determine the semantic connection between labels and document for constructing label-specific document representation. Meanwhile, the self-attention mechanism is adopted to identify the label-specific document representation from document content information. In order to seamlessly integrate the above two parts, an adaptive fusion strategy is proposed, which can effectively output the comprehensive label-specific document representation to build multi-label text classifier. Extensive experimental results demonstrate that LSAN consistently outperforms the state-of-the-art methods on four different datasets, especially on the prediction of low-frequency labels. The code and hyper-parameter settings are released to facilitate other researchers.
Exploring the scalability process of social innovation

PurposeSocial innovation is a recent theme, and the practices related to this area are characterized by punctual actions and projects restricted by time and space that make it difficult to develop strategies that can be sustained in this field. Therefore, one point that deserves to be highlighted in studies on social innovation is a matter of scalability. This paper aims to deal with a bibliometry whose objective was to map the existing studies about scalability of social innovation carried out in the Capes and EBSCOHost portals.Design/methodology/approachThis paper deals with a bibliometry. The topic researched in this bibliometry is scalability of social innovation. The databases chosen for this research were Portal Periódico Capes and EBSCOHost because they are the leading providers of search databases.FindingsA total of 42 papers were considered, distributed between 2002 and 2017. The analysis criteria for the study were origin (composed by year, author, country of origin, periodical and impact factor), focus of the investigations, justification, method and main techniques of research, contributions and theoretical advances and challenges and paths.Originality/valueAmong the main results found, one of them is that scalability is a topic that began to be researched recently, so that the USA and Brazil lead the research. Most of the studies focused on the scalability process and justified the importance of studies on the subject as a way to explore the potential of expanding the social impacts of a social innovation. Several studies have emphasized the role of networks as being quite positive for the scalability process and have been concerned with identifying factors that contribute to the scalability process. The challenge that most stood out among the papers was the financial sustainability of a social innovation. At the end, a research agenda was proposed.
Comparative analysis of data merging and fusion algorithms for the prediction of aerosol optical depth

Data fusion algorithms help extract information from “asynchronous” time series satellite data whereas data merging data help extract information from “synchronous” time series satellite data into a series of synthetic images by using the temporal, spatial, or even spectral properties. Such data fusion algorithms including Bayesian maximum entropy (BME) and spatial and temporal adaptive reflectance fusion model (STARFM) have greatly improved the coverage, enhancing data application potential with higher spatiotemporal resolution via multi-sensor earth observations. The goal of this study is to assess the utility of BME and modified BME algorithm with the aid of a data merging algorithm called Modified Quantile-Quantile Adjustment (MQQA), in comparison with STARFM for the retrieval of Aerosol Optical Depth in an urban environment. MQQA heavily counts on big data to support the systematic bias correction from “synchronous” time series satellite data. Such assessment of algorithmic efficiency needs to be carried out for both top of atmosphere reflectance and ground reflectance levels in support of the deep blue method for the retrieval of atmospheric optical depth at the ground level.
Throughput Maximization of NFV-Enabled Multicasting in Mobile Edge Cloud Networks

Mobile Edge Computing (MEC) reforms the cloud paradigm by bringing unprecedented computing capacity to the vicinity of end users at the mobile network edge. This provides end users with swift and powerful computing and storage capacities, energy efficiency, and mobility- and context-awareness support. Furthermore, Network Function Virtualization (NFV) is another promising technique that implements various network functions for many applications as pieces of software in servers or cloudlets in MEC networks. The provisioning of virtualized network services in MEC can improve user service experiences, simplify network service deployment, and ease network resource management. However, user requests arrive dynamically and different users demand different amounts of resources, while the resources in MEC are dynamically occupied or released by different services. It thus poses a significant challenge to optimize the performance of MEC through efficient computing and communication resource allocations to meet ever-growing resource demands of users. In this paper, we study NFV-enabled multicasting that is a fundamental routing problem in an MEC network, subject to resource capacities on both its cloudlets and links. Specifically, we first devise an approximation algorithm for the cost minimization problem of admitting a single NFV-enabled multicast request. We then develop an efficient algorithm for the throughput maximization problem for the admissions of a given set of NFV-enabled multicast requests. We third devise an online algorithm with a provable competitive ratio for the online throughput maximization problem when NFV-enabled multicast requests arrive one by one without the knowledge of future request arrivals. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.
Minimization of Assignment Problems

The assignment problem is a special type of linear programming problem and it is sub-class of transportation problem. Assignment problems are defined with two sets of inputs i.e. set of resources and set of demands. Hungarian algorithm is able to solve assignment problems with precisely defined demands and resources. Nowadays, many organizations and competition companies consider markets of their products. They use many salespersons to improve their organizations marketing. Salespersons travel form one city to another city for their markets. There are some problems in travelling which salespeople should go which city in minimum cost. So, travelling assignment problem is a main process for many business functions.
Continual Unsupervised Representation Learning

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowledge of task labels and boundaries. In this work, we propose an approach (CURL) to tackle a more general problem that we will refer to as unsupervised continual learning. The focus is on learning representations without any knowledge about task identity, and we explore scenarios when there are abrupt changes between tasks, smooth transitions from one task to another, or even when the data is shuffled. The proposed approach performs task inference directly within the model, is able to dynamically expand to capture new concepts over its lifetime, and incorporates additional rehearsal-based techniques to deal with catastrophic forgetting. We demonstrate the efficacy of CURL in an unsupervised learning setting with MNIST and Omniglot, where the lack of labels ensures no information is leaked about the task. Further, we demonstrate strong performance compared to prior art in an i.i. d setting, or when adapting the technique to supervised tasks such as incremental class learning.
Recommending Source Code Modifications by Using Association Rule Mining

あらまし 開発者がソースコードに対して修正を行う場合,修正が必要な全ての箇所を把握している必要がある. し かし,開発者は修正が必要な全ての箇所を必ずしも把握しているわけではないため,行うべき修正の一部が行われな い恐れがある. そのような問題を解決するため,開発者がファイルやメソッドに修正を加えた際に,それと同時に修 正を行うべきファイルやメソッドを推薦する研究が行われている. しかし,これらの研究には,どの行にどのような 修正を行うのかという具体的な情報については提示することができないという問題がある. そこで,本研究では,開 発者が修正を行った際にそれと同時に行われるべき修正の内容を提示する手法を提案する. この手法では,同じコミッ トで行われやすい修正の組があると仮定し,プログラム文レベルで修正間の相関ルールを作成する. そしてそのルー ルを参照することで,開発者が行った修正から同時に行われるべき修正の内容を候補として推薦する. キーワード 相関ルール,ソースコードリポジトリ,リポジトリマイニング,修正漏れ
Sentiment analysis for mining texts and social networks data: Methods and tools

Social networks (SNs) represent an established environment in which users share daily emotions and opinions. Therefore, they have become an essential source of big data related to sentiment/opinion sphere. Sentiment analysis (SA) aims to extract sentiments, emotions or opinions from texts, made available by different data sources like SNs. This review presents a depth study relative to the methods and the main tools for SA. The analysis was performed by defining four criteria and several variables to compare 24 tools with objective criteria. Specifically, the tools have been analyzed and tested to verify their usability, flexibility of use, and other specifications related to the type of analysis performed. The majority of tools can detect positive, negative, and neutral polarity, while few tools only detect positive and negative polarity. Moreover, seven tools were able to recognize emotions, and only one provides a visual map for geo‐referenced data. Except for one, remaining 23 tools offer service through the web interface. Finally, only nine tools provide both application program interfaces and a client for common programming languages to allow potential developer end‐users to integrate a specific SA tool into their application. Differently, from other recent surveys, the paper presents and discusses both methods and tools for analyzing texts and SN data sources to extract sentiment. Moreover, it contains a comprehensive comparison with other recent surveys. The comparative analysis of the tools completed according to objective criteria allows to highlight some limits on main tools that need to be faced with enhancing the end‐user experience.
Dynamic ICSP Graph Optimization Approach for Car-Like Robot Localization in Outdoor Environments

Localization has been regarded as one of the most fundamental problems to enable a mobile robot with autonomous capabilities. Probabilistic techniques such as Kalman or Particle filtering have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper presents an Interval Constraint Satisfaction Problem (ICSP) graph based methodology for consistent car-like robot localization in outdoor environments. The localization problem is cast into a two-stage framework: visual teach and repeat. During a teaching phase, the interval map is built when a robot navigates around the environment with GPS-support. The map is then used for real-time ego-localization as the robot repeats the path autonomously. By dynamically solving the ICSP graph via Interval Constraint Propagation (ICP) techniques, a consistent and improved localization result is obtained. Both numerical simulation results and real data set experiments are presented, showing the soundness of the proposed method in achieving consistent localization.
Temporal Segmentation for Faster and Better Learning

The human visual system faces an extraordinary challenge in building memories from a continuous stream of visual data without the opportunity to store it and process offline. This suggests a crucial role for visual attention in attending to specific moments in time. This project outlines key data and theories related to human temporal attention. The focus of this submission is on bridging the divide between the human visual system and artificial models by explicating segmentation mechanisms that accelerate the ability to learn the structure of the world through continuous vision. A computational model that simulates the temporal dynamics of visual attention in humans indicates a role for attention in on-demand temporal segmentation of incoming information at the sub-second scale. We predict that in humans this segmentation plays a key role in 1) simplifying visual information for learning about object kinds through compression 2) segmenting information from neighboring fixations and 3) encoding the temporal sequence of events. Such segmentation is likely to also play a key role in allowing artificial systems to learn from visual input in an online fashion, even though their specific temporal constraints are not shared with biology.
Compendium of Methods for the Microbiological Examination of Foods

Thank you for reading compendium of methods for the microbiological examination of foods 4th edition. As you may know, people have search numerous times for their favorite books like this compendium of methods for the microbiological examination of foods 4th edition, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their desktop computer.
VPGA: An SDN-based Location Privacy Zones Placement Scheme for Vehicular Networks

Making personal data anonymous is crucial to ensure the adoption of connected vehicles. One of the privacysensitive information is location, which once revealed can be used by adversaries to track drivers during their journey. Vehicular Location Privacy Zones (VLPZs) is a promising approach to ensure unlinkability. These logical zones can be easily deployed over roadside infrastructures (RIs) such as gas station or electric charging stations. However, the placement optimization problem of VLPZs is NP-hard and thus an efficient allocation of VLPZs to these RIs is needed to avoid their overload and the degradation of the QoS provided within theses RIs. This work considers the optimal placement of the VLPZs and proposes a geneticbased algorithm in a software defined vehicular network to ensure minimized trajectory cost of involved vehicles and hence less consumption of their pseudonyms. The analytical evaluation shows that the proposed approach is cost-efficient and ensures shorter response time.
Algebraic Series-Parallel-Based Switched-Capacitor DC–DC Boost Converter With Wide Input Voltage Range and Enhanced Power Density

This article presents an algebraic series–parallel (ASP) topology for fully integrated switched-capacitor (SC) dc–dc boost converters with flexible fractional voltage conversion ratios (VCRs). By elaborating the output voltage ( ${V} _{\textrm {OUT}}$ ) expression into a specific algebraic form, the proposed ASP can achieve improvements on both the charge sharing and bottom-plate-parasitic losses while maintaining the high topology and fractional VCR flexibility of conventional two-dimensional series–parallel (2DSP) converters. The proposed method consists of a generic ASP topology framework with systematic parameter determination for a precise converter implementation, and can theoretically surpass the power-conversion efficiency (PCE) of 2DSP converters. Fabricated in 65-nm bulk CMOS, we designed a fully integrated ASP-based SC rational boost converter by cascading with the Dickson topology, with a total of seven rational VCRs to boost an input voltage of 0.25–1 V to a 1-V output. Delivering a maximum loading power of 20.4 mW, the chip prototype achieves a peak efficiency of 80% at a power density of 22.7 mW/mm2.
RECOGNIZING HANDWRITTEN MATHEMATICAL EXPRESSIONS

This paper surveys the techniques to recognise handwritten mathematical characters in different expressions and to see the availability of systems that eventually understand and solve them. Humans are accustomed to writing mathematical expressions containing integrals, fractions, exponents or indices by hand. Entering each such expression into a computer is uncomfortable and tedious. Inspired by recent success in Neural Network Modelling, we discuss a proposed model that gives high accuracy in recognizing mathematical symbols and digits in different mathematical expressions using Neural Network Modelling for identification of offline handwritten expressions
ABC algorithm‐based trustworthy energy‐efficient MIMO routing protocol

This paper introduces artificial bee algorithm (ABC)‐based energy‐efficient protocol with security for the enhancement of lifetime of wireless sensor network. A trustworthy energy‐efficient routing MIMO (TEEM) technique is used by implementing MIMO technique in TEER protocol to isolate malicious nodes and to reduce the fading effects and interference in the network. The performance of ABC‐based trustworthy energy‐efficient MIMO routing (ABC‐TEEM) protocol is analysed. Alive node performance, residual energy, throughput analysis, and pocket loss rate of ABC‐TEEM protocol for WSN are computed and compared with the performance of existing TEEM protocol for various diversity orders.
An Approach Based on Active Constraint Strategy for Solving The Portfolio Optimization Problem

The mathematical model of portfolio optimization has been largely written in terms of minimizing the risk, given the return. The difficulty of this model is to deal with the quadratic programming model due to Markowitz. This situation has been overcome by the recent progress in algorithmic research, and the introduction of linear risk function. This paper deals with the portfolio selection problem with minimum transaction lots. A neighbourhood search algorithm based on active constrained strategy is proposed to solve the mixed integer programming model. The algorithm starts from the solution of the relaxed problem to find a solution which is close to the continuous solution.