Reviewerly/exHarmony huggingface dataset:
High-speed tracking based on multi-CF filters and attention mechanism

Abstract Recently, correlation filters and deep convolutional network show good performance for visual tracking. Many real-time and high accuracy tracking algorithms are realized; however, there are still some challenges to build a robust tracker. In this paper, we present a novel tracking framework named multi-attention filter (MAF) to solve some challenges for tracking like object drift in a long time, lack of training samples and fast motion. Our framework consists of two components, a basic CNN network to extract feature maps and a set of classifiers to distinguish between the target and the background. First, to solve the problem of object drift in a long time, a simple but effective evaluation mechanism is proposed to the framework, the evaluation mechanism checks the tracking results and corrects it when needed. In addition, the results from different classifiers are fused to predict the object location according to intersection over union. Second, to overcome the lack of training samples, MAF stores some positive and negative samples in two queues, one named long-term queue and another named short-term queue. Third, to deal with fast motion of the target, attention mechanisms including channel attention and location attention are added to the tracker. In our experiments on the popular benchmarks including OTB-2013 and OTB-2015. MFA achieves state of the art among trackers, and as a correlation filter framework, MAF is very flexible and has great rooms for improvement and generalization.
Multi-camera Background and Scene Activity Modelling Based on Spearman Correlation Analysis and Inception-V3 Network

A novel approach for background and scene activity modelling with spearman correlation analysis and customized deep learning model is introduced in this paper. It detects and gives correlated analytics between casual and temporal regional activities on the basis of similarities and primary dissimilarities in the same scene captured by several cameras. The experiment implement on four overlapped videos that are captured inside the hall from four cameras. Detected and analyzed by our model, 17.32% correlated co-occurrences is actual correlation among all videos. Rest of 82.68% of videos is background that shows similar and repetitive features in spearman rank tied result. Simulation results demonstrate that the proposed method can detect high correlation among all activities during the frame rate with tied features ability.
CSE: Complex-Valued System With Evolutionary Algorithm

Complex-valued system identification has the ability to provide the basis for system analysis.So as to demonstrate the potential and internal mechanism of the complex-valued system, this paper proposes a novel complex-valued hybrid evolutionary (CSE) algorithm to optimize the complex-valued expression model (CEM).Complex-valued gene expression programming (CVGEP) is proposed to optimize the architectures of the CEM.Complex-valued water wave optimization (CVWWO) is first proposed to optimize complex-valued coefficients and constants of the CEM.The two artificial complex-valued function approximation problems and non-minimum phase equalization problem are utilized to test the performance of the proposed algorithm.The results demonstrate that this method could identify complex-valued systems more correctly than complex-valued neural networks, which could obtain above 90% smaller mean-squared error (MSE) performance than other complex-valued models.With 10% Gaussian white noise, the CSE could identify accurate complex-valued structure and parameters containing coefficients and constants.The CVWWO has better convergence performance than complex-valued particle swarm optimization and crow search algorithm.
A Performance Driven Micro Services-Based Architecture/System for Analyzing Noisy IoT Data

The Internet of Things (IoT) technology presents a complex and challenging paradigm where a huge amount of noisy raw sensor data is collected in order to observe and detect critical events occurring on the system, and generate alarms when required. The biggest challenge of the IoT systems is that the systems collect a massive amount of uncertain data from diverse IoT devices connected through the network. In addition, some events are inferred from other events and uncertainty is propagated from parent events to the inferred events, which additionally contributes to overall system uncertainty. The observed complex events are a complex relationship of primitive events that are produced by IoT devices and collected in IoT systems. A survey performed on existing prior arts on quantifying uncertainty for complex events concluded that proposed existing solutions are unable to scale under heavy loads of incoming data. This paper presents a micro-service based notification methodology that uses complex event recognition (both complex event processing and probabilistic programming) to handle IoT systems uncertainty. In addition, the paper analyzes and recommends existing big data platforms for processing complex events in IoT systems. The current focus of our work includes research and development of the optimized deadline-based and cost-effective resource allocation algorithm in Apache Spark for Uncertain IoT Notification systems.
Effects of Automated Static Analysis Tools: A Multidimensional View on Quality Evolution

Leveraging and acting on the results of static analysis is a well-known best practice. Static analysis also is an integral part of software quality assurance which is highlighted by the inclusion of static analysis results in software quality models like ColumbusQM and Quamoco. Although there are studies that explore if tools for static analysis are used and how they are configured, few publications explore the longitudinal effects of acting on static analysis results on software and on the evolution of software. Especially effects on quality criteria, e.g., software quality metrics, defects, or readability are missing. With our research, we will bridge this gap and measure the effects of static analysis on software quality evolution. We will measure the effect the removal of code that generates static analysis warnings has on software quality metrics. Furthermore, we will measure long term effects on external quality attributes, e.g., reported issues and defects. Finally, we want to predict false positives of static analysis warnings by training predictive models on our collected data.
Fast Approximate Filtering of Search Results Sorted by Attribute

Several Web search services enable their users with the possibility of sorting the list of results by a specific attribute, e.g., sort "by price" in e-commerce. However, sorting the results by attribute could bring marginally relevant results in the top positions thus leading to a poor user experience. This motivates the definition of the relevance-aware filtering problem. This problem asks to remove results from the attribute-sorted list to maximize its final overall relevance. Recently, an optimal solution to this problem has been proposed. However, it has strong limitations in the Web scenario due to its high computational cost. In this paper, we propose ϵ-Filtering: an efficient approximate algorithm with strong approximation guarantees on the relevance of the final list. More precisely, given an allowed approximation error ϵ, the proposed algorithm finds a(1-ϵ)"optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of ϵ-Filtering against state-of-the-art competitors on two real-world public datasets. Experiments show that ϵ-Filtering achieves the desired levels of effectiveness with a speedup of up to two orders of magnitude with respect to the optimal solution while guaranteeing very small approximation errors.
Features of radioactive element distribution within the Arkhangelsk diamondiferous province: possible directions for development of isotope–radiogeochemical methods for kimberlite prospecting in complex landscape–geology and climate conditions of the subarctic zone

The effectiveness of traditional methods of searching for kimberlite pipes in the Arkhangelsk diamondiferous province (in the subarctic zone of Russia) has decreased greatly in recent years, and new methods of kimberlite exploration must therefore be developed. This study discusses new patterns of the distribution of natural radioactive isotopes within the Arkhangelsk diamondiferous province (Zolotitskoe kimberlite field), and the uranium isotopes 234 U and 238 U in particular. A variety of isotope radiogeochemical studies has shown that the kimberlite pipes are characterized by local radioisotope anomalies, on the surface and in exploration drill cores. These irregularities are clearly manifested in the formation of a non-equilibrium anomalous uranium isotope composition in the surrounding rocks and groundwater of the near-contact zone of the pipes. These uranium isotopes can be used to explore for kimberlites in other areas with similar complex landscape–geology and climate conditions of the subarctic zone.
Parameter Estimation and Statistical Test in Multivariate Adaptive Generalized Poisson Regression Splines

Abstract Poisson regression is a standard model for data counts that can be used to determine these factors. Equidispersion is assumptions that must be met in poisson regression. Equidispersion is a condition that the average of response variable is equal with the variance of the response variable. In real cases, there are overdispersion or underdispersion cases. Generalized Poisson Regression (GPR) is one of method that can handle cases of overdispersion or underdispersion. Multivariate Adaptive Regression Splines (MARS) is a nonparametric regression that can handle data whose behavior changes in sub-intervals, so that there is a knot point that indicates the occurence of changes in data behavior patterns. Multivariate Adaptive Generalized Poisson Regression Splines (MAGPRS) model is used as the development of the MARS and Generalized Poisson Regression. This research use Weighted Least Squares (WLS) with Berndt Hall Hall Husman (BHHH) algorithm to obtain parameter model estimator. Afterwards, get the test statistic on the model Multivariate Adaptive Generalized Poisson Regression Splines using Maximum Likelihood Ratio Test (MLRT). Finally, the application of MAGPRS model was carried out in the case of the number of Acute Respiratory Tract Infection in babies.
Cloud data analysis using a genetic algorithm‐based job scheduling process

Abstract Data analysis plays a major role in different research applications that require a large volume of data. Cloud computing can provide computer processing resources and device‐to‐device data sharing based on user requirements. The main goal of cloud computing is to allow users and enterprise of varying capabilities to store and process data in an efficient way and to access and distribute resources. However, a crucial problem in cloud computing is job scheduling for numerous users. Prior to the implementation of job scheduling, jobs must be categorized according to degree of criticalness, privacy and time required. Based on the experimental results, the combination of tasks was successfully determined by the processor. In heterogeneous multiprocessor systems, customized job scheduling is highly critical for obtaining optimal job performance. In this paper, an evolutionary genetic algorithm was used for obtaining better results in job scheduling, thereby improving performance in the cloud system in this regard. The genetic algorithm‐based job scheduling process introduced minimizes the investment in time through effective allocation of user requests in order to enhance the overall efficiency of the system.
Towards Similarity-Aware Constraint-Based Recommendation

Constraint-based recommender systems help users to identify useful objects and services based on a given set of constraints. These decision support systems are often applied in complex domains where millions of possible recommendations exist. One major challenge of constraint-based recommenders is the identification of recommendations which are similar to the user’s requirements. Especially, in cases where the user requirements are inconsistent with the underlying constraint set, constraint-based recommender systems have to identify and apply the most suitable diagnosis in order to identify a recommendation and to increase the user’s satisfaction with the recommendation. Given this motivation, we developed two different approaches which provide similar recommendations to users based on their requirements even when the user’s preferences are inconsistent with the underlying constraint set. We tested our approaches with two real-world datasets and evaluated them with respect to the runtime performance and the degree of similarity between the original requirements and the identified recommendation. The results of our evaluation show that both approaches are able to identify recommendations of similar solutions in a highly efficient manner.
Understanding Brand Consistency from Web Content

Brands produce content to engage with the audience continually and tend to maintain a set of human characteristics in their marketing campaigns. In this era of digital marketing, they need to create a lot of content to keep up the engagement with their audiences. However, such kind of content authoring at scale introduces challenges in maintaining consistency in a brand's messaging tone, which is very important from a brand's perspective to ensure a persistent impression for its customers and audiences. In this work, we quantify brand personality and formulate its linguistic features. We score text articles extracted from brand communications on five personality dimensions: sincerity, excitement, competence, ruggedness and sophistication, and show that a linear SVM model achieves a decent F1 score of $0.822$. The linear SVM allows us to annotate a large set of data points free of any annotation error. We utilize this huge annotated dataset to characterize the notion of brand consistency, which is maintaining a company's targeted brand personality across time and over different content categories; we make certain interesting observations. As per our knowledge, this is the first study which investigates brand personality from the company's official websites, and that formulates and analyzes the notion of brand consistency on such a large scale.
Mobile Localization Techniques Oriented to Tangible Web

We implemented a system able to locate people indoor, with the purpose of providing assistive services. Such approach is particularly important for the Art, for providing information on exhibitions, art galleries and museums, and to allow the access to the cultural heritage patrimony to people with disabilities. The system may provide also very important information and input to elderly people, helping them to perceive more deeply the reality and the beauty of art. The system is based on Beacons, very small and low power consumption devices, and Human Body Communication protocols. The Beacons, Bluetooth Low Energy devices, allow to obtain a position information related to predetermined reference points, and through proximity algorithms, locate a person or an object of interest. The position obtained has an error that depends from the interferences present in the area. The union of Beacons with Human Body Communication, a recent wireless technology that exploits the human body as a transmission channel, makes it possible to increase the accuracy of localization. The basic idea is to exploit the localization derived from Beacons to start a search for an electrical signal transmitted by the human body and to distinguish the position according to the information contained in the signal. The signal is transmitted by capacitance to the human body and revealed by a special resonant circuit (antenna) adapted to the microphone input of the mobile device.
Two Neighborhood Semantics for Subintuitionistic Logics

This investigation is concerned with weak subintuitionistic logics interpreted over neighborhood models introduced by the authors in 2016. The two types of neighborhood semantics introduced in that article are compared and their relationship is clarified. Thereby modal companions for various logics are recognized. Specifically, a logic is found which has basic monotonic logic with necessitation as its modal companion. Many of the extensions of the basic logics are discussed and characterized.
A Method for Scalable First-Order Rule Learning on Twitter Data

We propose a method for scalable first-order rule learning on large-scale Twitter data. By learning rules, probabilistic inference queries can be executed to reason over the data to ascertain its veracity. Our method employs a divide-and-conquer approach, graph-based modeling, and data parallel processing during rule learning using a commodity cluster to overcome the problem of slow structure learning on large-scale Twitter data. The first-order predicates (constructed on the posts) are first partitioned in a balanced way by pivoting around users to reduce the chance of missing relevant rules. By constructing a weighted graph and applying graph partitioning, balanced partitions of the ground predicates can be created. Each partition is then processed using an existing structure learning approach to get the set of rules for that partition. We report a preliminary evaluation of our method to show that it offers a promising solution for scalable first-order rule learning on Twitter data.
Research on Multi-modal Interactive Control for Quadrotor UAV

Due to the changeable tasks and environments of unmanned aerial vehicle (UAV), the single modal interactive control is unable to satisfy users' requirements. To solve this problem, a multi-modal interactive control method for Quadrotor UAV is proposed in this paper, which integrates eye movement, gesture and voice. Firstly, single modal interactive control of UAV is realized. SMI eye tracker, Kinect gesture recognizer and iFlytek voice input software are used to capture the interactive information respectively. Then, a seamless modal switching method between three interaction types is proposed. Experimental results show that the system guarantees high accuracy of single modal interactive control. Besides, the system can achieve seamless switching between the three channels, which brings more natural and efficient interactive experience to operators.
Maximum Likelihood Estimation in a Parametric Stochastic Trajectory Model

In this work, we consider maximum likelihood estimation of parameters in a stochastic trajectory model. The velocity paths are generated from an Ornstein-Uhlenbeck process and thus revert to a latent expected value. In addition to this expected velocity, parameters that specify the reversion characteristics and the process noise covariance determine the behaviour of typical trajectories of the model. Estimation of these parameters from trajectory samples facilitates learning of patterns and training of predictive models using trajectory data, e.g., automatic identification system (AIS) messages transmitted by vessels. We propose a six-degrees-of-freedom parameterisation and investigate the identifiability of these parameters using the Cramér-Rao bound matrix which we estimate using Monte Carlo methods. We demonstrate that some parameter configurations of interest are identifiable and their maximum likelihood estimate can be found using iterative optimisation algorithms. We demonstrate the efficacy of this approach on both simulated and real data.
Typomorphic Characteristics of Pyrites from the Shuangwang Gold Deposit, Shaanxi, China: Index to Deep Ore Exploration

The large Shuangwang gold deposit (&gt;80 t gold) is located in the Western Qinling Orogen (WQO) of central China. It is an orogenic-type gold deposit hosted in an NW-extending breccia belt in the Devonian Xinghongpu Formation. Gold mineralization of the Shuangwang deposit is featured by hydrothermal breccia ores with strata fragments cemented by hydrothermal minerals dominated by ankerite, quartz, and pyrite with minor amounts of calcite and albite. Pyrite is the major gold-hosting sulfide and the most abundant ore mineral. Crystal habits, thermoelectricity, and trace-element composition of pyrites from the main ore-forming stage of the Shuangwang gold deposit were studied by microbinocular, BHTE-06 thermoelectric coefficient measuring instrument, and high-resolution inductively coupled plasma mass spectrometry (HR-ICP-MS). Spatial distribution of the above data for pyrites was delineated by contour maps of morphology index, P-type frequency, and primary halo elements (e.g., supraore halo elements Ba and Sb; near-ore halo elements Pb, Zn, and Cu; and subore halo elements Co, Mo, and Bi). Based on the above results, four target areas (areas between prospecting lines 0 and 1, between lines 14 and 18 below orebody KT9; areas between prospecting lines 30 and 34, between lines 44 and 46 below orebody KT8) were put forward for deep gold exploration in the future. These targets are consistent with the depth extrapolation of proven gold orebodies, indicating the practicality of typomorphic characterization of pyrites as vector to deep/concealed gold orebodies. The effectiveness of the pyrite typomorphic parameter for deep gold prediction seems to be chemical composition, crystal habits, and then thermoelectricity.
Implementing a Library for Probabilistic Programming Using Non-strict Non-determinism

Abstract This paper presents PFLP , a library for probabilistic programming in the functional logic programming language Curry. It demonstrates how the concepts of a functional logic programming language support the implementation of a library for probabilistic programming. In fact, the paradigms of functional logic and probabilistic programming are closely connected. That is, language characteristics from one area exist in the other and vice versa. For example, the concepts of non-deterministic choice and call-time choice as known from functional logic programming are related to and coincide with stochastic memoization and probabilistic choice in probabilistic programming, respectively. We will further see that an implementation based on the concepts of functional logic programming can have benefits with respect to performance compared to a standard list-based implementation and can even compete with full-blown probabilistic programming languages, which we illustrate by several benchmarks.
Solr Integration in the Anserini Information Retrieval Toolkit

Anserini is an open-source information retrieval toolkit built around Lucene to facilitate replicable research. In this demonstration, we examine different architectures for Solr integration in order to address two current limitations of the system: the lack of an interactive search interface and support for distributed retrieval. Two architectures are explored: In the first approach, Anserini is used as a frontend to index directly into a running Solr instance. In the second approach, Lucene indexes built directly with Anserini can be copied into a Solr installation and placed under its management. We discuss the tradeoffs associated with each architecture and report the results of a performance evaluation comparing indexing throughput. To illustrate the additional capabilities enabled by Anserini/Solr integration, we present a search interface built using the open-source Blacklight discovery interface.