nils-herrmann/clean_scirepeval huggingface dataset:
Real-Time Detection of Anomalous Taxi Trajectories from GPS Traces

Trajectories obtained from GPS-enabled taxis grant us an opportunity to not only extract meaningful statistics, dynamics and behaviors about certain urban road users, but also to monitor adverse and/or malicious events. In this paper we focus on the problem of detecting anomalous routes by comparing against historically “normal” routes. We propose a real-time method, iBOAT, that is able to detect anomalous trajectories “on-the-fly”, as well as identify which parts of the trajectory are responsible for its anomalousness. We evaluate our method on a large dataset of taxi GPS logs and verify that it has excellent accuracy (AUC ≥ 0.99) and overcomes many of the shortcomings of other state-of-the-art methods.
An extension of statistical decision theory with information theoretic cost functions to decision fusion: Part II

This paper is the second of two papers that examine statistical decision theory for the case where the cost function is an information theoretic cost function. Part I reviewed the information theoretic decision rule that results from the information theoretic function as well as some of its unique characteristics. It was shown that information theoretic decisions are represented by characteristic probability distributions and that the optimal decision is the one with the smallest Kullbach–Leibler statistical distance from the measurement-based probability distribution. The information theoretic decision function is less sensitive to the assignment of prior probabilities and the abstraction levels of the decision and hypothesis sets when compared to Bayesian decision theory. This second part develops an entirely new decision fusion rule from the decision rule of Part I. The decision fusion rule is restructured to produce a recursive algorithm that has similarities with the Kalman filter. The performance characteristics of the recursive algorithm and a handful of extensions are compared to a recursive Bayesian decision fusion algorithm through the analysis of a simple decision problem.
Community detection methods can discover better structural clusters than ground-truth communities

Community detection emerged as an important exploratory task in complex networks analysis across many scientific domains. Many methods have been proposed to solve this problem, each one with its own mechanism and sometimes with a different notion of community. In this article, we bring most common methods in the literature together in a comparative approach and reveal their performances in both real-world networks and synthetic networks. Surprisingly, many of those methods discovered better communities than the declared ground-truth communities in terms of some topological goodness features, even on benchmarking networks with built-in communities. We illustrate different structural characteristics that these methods could identify in order to support users to choose an appropriate method according to their specific requirements on different structural qualities.
Cytokines and chemokines in uveitis: is there a correlation with clinical phenotype?

Uveitis is a general term for intraocular inflammation and includes a large number of clinical phenotypes. As a group of disorders, it is responsible for 10% of all registered blind patients under the age of 65 years. Immune-mediated uveitis may be associated with a systemic disease or may be localized to the eye. The pro-inflammatory cytokines interleukin (IL)-1beta, IL-2, IL-6, interferon-gamma and tumor necrosis factor-alpha have all been detected within the ocular fluids or tissues in the inflamed eye together with others, such as IL-4, IL-5, IL-10 and transforming growth factor-beta. The chemokines IL-8, monocyte chemoattractant protein-1, macrophage inflammatory protein (MIP)-1alpha, MIP-1beta and fractalkine are also thought to be involved in the associated inflammatory response. There have been a number of studies in recent years investigating cytokine profiles in different forms of uveitis with a view to determining what cytokines are important in the inflamed eye. This review attempts to present the current state of knowledge from in vitro and in vivo research on the inflammatory cytokines in intraocular inflammatory diseases.
Learning-based seat belt detection in image using salient gradient

Seat belt detection is a critical challenge in traffic video surveillance. To tackle this challenge, an automatic seat belt detection approach is proposed in this paper. The proposed approach yields three significant steps. First, an edge detection method is used to extract edge features of the input image. Second, a salient gradient map is constructed by considering salient gradient features in the image. Finally, the obtained salient gradient map is incorporated into a machine learning approach to perform binary decision on whether the input image yields seat belt or not. Experimental results are provided to demonstrate the superior performance of the proposed approach.
Medical Diagnosis using Back propagation Algorithm

Artificial Intelligence has always lent a helping hand to the practitioners of medicine for improving medical diagnosis and treatment then, paradigm of artificial neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. A lot of Applications tried to help human experts, offering a solution. This paper describes a optimal feed forward Back propagation algorithm. Feedforward back propagation neural network is used as a classifier to distinguish between infected or non-infected person in both cases. However, Traditional Back propagation algorithm has many shortcomings. Learning often takes long time to converge, and it may fall into local minima. One of the possible remedies to escape from local minima is by using a very small learning rate, which slows down the learning process. The back propagation algorithm presented in this paper used for training depends on a multilayer neural network with a very small learning rate, especially when using a large training set size. It can be applied in a generic manner for any network size that uses a back propagation algorithm and achieved the best performance with the minimum epoch (training iterations) and training time.  Artificial Neural Network, Back propagation algorithm, Medical Diagnosis, Neural Networks.
Privacy-Aware Role-Based Access Control

In this article, we introduce a comprehensive framework supporting a privacy-aware access control mechanism, that is, a mechanism tailored to enforce access control to data containing personally identifiable information and, as such, privacy sensitive. The key component of the framework is a family of models (P-RBAC) that extend the well-known RBAC model in order to provide full support for expressing highly complex privacy-related policies, taking into account features like purposes and obligations. We formally define the notion of privacy-aware permissions and the notion of conflicting permission assignments in P-RBAC, together with efficient conflict-checking algorithms. The framework also includes a flexible authoring tool, based on the use of the SPARCLE system, supporting the high-level specification of P-RBAC permissions. SPARCLE supports the use of natural language for authoring policies and is able to automatically generate P-RBAC permissions from these natural language specifications. In the article, we also report performance evaluation results and contrast our approach with other relevant access control and privacy policy frameworks such as P3P, EPAL, and XACML.
Augmented Reality Interface for Teleoperation via Internet ) , one of the characteristics Design and Manipulation in Augmented Reality of Fanuc 200 iC Robot

In this paper, an AR (Augmented reality) system based on a multimodal user interface is proposed and its core techniques are described. The objective of this work is to realize certain task that operators can use the virtual robot to ameliorate the interaction with the real world using AR environment. VC++ and OpenGL are used to create this application. AR applications (ARToolKit) is used to detect the real-time position and orientation of camera and to facilite the operator’s observation and operation. Experiments prove that the system is reliable and efficient. Simulation result is obtained and it is very useful for designers to ameliorate the interaction with the real world.
Deadlock avoidance policies for automated manufacturing cells

Although the typical process-layout manufacturing environment is susceptible to deadlocks , the problem of deadlock resolution in this context has only lately been undertaken by the scientiic community. Previous studies have found that deadlock avoidance method-ologies seem to be the most appropriate for this particular context. Unfortunately, in the general case, these methods suuer from high computational complexity which results in heuristic solutions and/or reduced performance. Taking the position that any solution to the problem should be scalable and provably correct, this paper proposes an analytical framework for designing deadlock avoidance policies for a subclass of Resource Allocation Systems (RAS). Speciically, this subclass is characterized by the fact that jobs in the system are deened by deterministic job-step sequences with every step in the sequence requiring a single unit of the system resources. Job-step models are appropriate for the study of the deadlock problem in the context of the Automated Manufacturing Cell (AMC). The functionality of the framework is demonstrated by using it to prove the correctness of two avoidance policies: the Resource Upstream Neighborhood (RUN) policy, developed in the authors' research program and rst presented in 10], and the B-K Deadlock Avoidance Algorithm (BKDAA), presented in 3].
An Accurate and Real-time Self-blast Glass Insulator Location Method Based On Faster R-CNN and U-net with Aerial Images

The location of broken insulators in aerial images is a challenging task. This paper, focusing on the self-blast glass insulator, proposes a deep learning solution. We address the broken insulators location problem as a low signal-noise-ratio image location framework with two modules: 1) object detection based on Fast R-CNN, and 2) classification of pixels based on U-net. A diverse aerial image set of some grid in China is tested to validated the proposed approach. Furthermore, a comparison is made among different methods and the result shows that our approach is accurate and real-time. Keywordsinsulators; location; aerial images; deep learning; real-time; faster r-cnn; U-net
Mining and Forecasting of Big Time-series Data

Given a large collection of time series, such as web-click logs, electric medical records and motion capture sensors, how can we efficiently and effectively find typical patterns? How can we statistically summarize all the sequences, and achieve a meaningful segmentation? What are the major tools for forecasting and outlier detection? Time-series data analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability.
 The objective of this tutorial is to provide a concise and intuitive overview of the most important tools that can help us find patterns in large-scale time-series sequences. We review the state of the art in four related fields: (1) similarity search and pattern discovery, (2) linear modeling and summarization, (3) non-linear modeling and forecasting, and (4) the extension of time-series mining and tensor analysis. The emphasis of the tutorial is to provide the intuition behind these powerful tools, which is usually lost in the technical literature, as well as to introduce case studies that illustrate their practical use.
What Is the Range of Surface Reconstructions from a Gradient Field?

We propose a new technique for edge-suppressing operations on images. We introduce cross projection tensors to achieve affine transformations of gradient fields. We use these tensors, for example, to remove edges in one image based on the edge-information in a second image. Traditionally, edge suppression is acieved by setting image gradients to zero based on thresholds. A common application is in the Retinex problem, where the illumination map is recovered by suppressing the reflectance edges, assuming it is slowly varying. We present a class of problems where edge-suppression can be a useful tool. These problems involve analyzing images of the same scene under variable illumination. Instead of resetting gradients, the key idea in our approach is to derive local tensors using one image and to transform the gradient field of another image using them. Reconstructed image from the modified gradient field shows suppressed edges or textures at the corresponding locations. All operations are local and our approach does not require any global analysis. We demonstrate the algorithm in the context of several applications such as (a) recovering the foreground layer undervarying illumination, (b) estimating intrinsic images in non-Lambertian scenes, (c) removing shadows from color images and obtaining the illumination map, and (d) removing glass relections.  This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.  2006 201 Broadway, Cambridge, Massachusetts 02139
Autonomously Acquiring Instance-Based Object Models from Experience

A key aim of current research is to create robots that can reliably manipulate objects. However, in many applications, general-purpose object detection or manipulation is not required: the robot would be useful if it could recognize, localize, and manipulate the relatively small set of specific objects most important in that application, but do so with very high reliability. Instance-based approaches can achieve this high reliability but to work well, they require large amounts of data about the objects that are being manipulated. The first contribution of this paper is a system that automates this data collection using a robot. When the robot encounters a novel object, it collects data that enables it to detect the object, estimate its pose, and grasp it. However for some objects, information needed to infer a successful grasp is not visible to the robot’s sensors; for example, a heavy object might need to be grasped in the middle or else it will twist out of the robot’s gripper. The second contribution of this paper is an approach that allows a robot to identify the best grasp point by attempting to pick up the object and tracking its successes and failures. Because the number of grasp points is very large, we formalize grasping as an N-armed bandit problem and define a new algorithm for best arm identification in budgeted bandits that enables the robot to quickly find an arm corresponding to a good grasp without pulling all the arms. We demonstrate that a stock Baxter robot with no additional sensing can autonomously acquire models for a wide variety of objects and use the models to detect, classify, and manipulate the objects. Additionally, we show that our adaptation step significantly improves accuracy over a non-adaptive system, enabling a robot to improve its pick success rate from 55% to 75% on a collection of 30 household objects. Our instance-based approach exploits the robot’s ability to collect its own training data, enabling experience with the object to directly improve the robot’s performance during future interactions.
Behavior Programming Language and Automated Code Generation for Agent Behavior Control

Behavior-based agents are becoming increasingly used across a variety of platforms. The common approach to building such agents involves implementing the behavior synchronization and management algorithms directly in the agent’s programming environment. This process makes it hard, if not impossible, to share common components of a behavior architecture across different agent implementations. This lack of reuse also makes it cumbersome to experiment with different behavior architectures as it forces users to manipulate native code directly, e.g. C++ or Java. In this paper, we provide a high-level behavior-centric programming language and an automated code generation system which together overcome these issues and facilitate the process of implementing and experimenting with different behavior architectures. The language is specifically designed to allow clear and precise descriptions of a behavior hierarchy, and can be automatically translated by our generator into C++ code. Once compiled, this C++ code yields an executable that directs the execution of behaviors in the agent’s sense-plan-act cycle. We have tested this process with different platforms, including both software and robot agents, with various behavior architectures. We experienced the advantages of defining an agent by directly reasoning at the behavior architecture level followed by the automatic native code generation.
Ontology-Based Approach for Semantic Description and the Discovery of e-Learning Web Services

The creation of the e-learning web services are increasingly growing. Therefore, their discovery is a very important challenge. The choice of the e-learning web services depend, generally, on the pedagogic, the financial and the technological constraints. The Learning Quality ontology extends existing ontology such as OWL-S to provide a semantically rich description of these constraints. However, due to the diversity of web services customers, other parameters must be considered during the discovery process, such as their preferences. For this purpose, the user profile takes into account to increase the degree of relevance of discovery results. We also present a modeling scenario to illustrate how our ontology can be used.
Security Testing for Chatbots

Services like chatbots that provide information to customers in real-time are of increasing importance for the online market. Chatbots offer an intuitive interface to answer user requests in an interactive manner. The inquiries are of wide-range and include information about specific goods and services but also financial issues and personal advices. The notable advantages of these programs are the simplicity of use and speed of the search process. In some cases, chatbots have even surpassed classical web, mobile applications, and social networks. Chatbots might have access to huge amount of data or personal information. Therefore, they might be a valuable target for hackers, and known web application vulnerabilities might be a security issue for chatbots as well. In this paper, we discuss the challenges of security testing for chatbots. We provide an overview about an automated testing approach adapted to chatbots, and first experimental results.
Complex multifault rupture during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand

On 14 November 2016, northeastern South Island of New Zealand was struck by a major moment magnitude (Mw) 7.8 earthquake. Field observations, in conjunction with interferometric synthetic aperture radar, Global Positioning System, and seismology data, reveal this to be one of the most complex earthquakes ever recorded. The rupture propagated northward for more than 170 kilometers along both mapped and unmapped faults before continuing offshore at the island’s northeastern extent. Geodetic and field observations reveal surface ruptures along at least 12 major faults, including possible slip along the southern Hikurangi subduction interface; extensive uplift along much of the coastline; and widespread anelastic deformation, including the ~8-meter uplift of a fault-bounded block. This complex earthquake defies many conventional assumptions about the degree to which earthquake ruptures are controlled by fault segmentation and should motivate reevaluation of these issues in seismic hazard models.
Greedy Transition-Based Dependency Parsing with Stack LSTMs

We introduce a greedy transition-based parser that learns to represent parser states using recurrent neural networks. Our primary innovation that enables us to do this efficiently is a new control structure for sequential neural networks—the stack long short-term memory unit (LSTM). Like the conventional stack data structures used in transition-based parsers, elements can be pushed to or popped from the top of the stack in constant time, but, in addition, an LSTM maintains a continuous space embedding of the stack contents. Our model captures three facets of the parser's state: (i) unbounded look-ahead into the buffer of incoming words, (ii) the complete history of transition actions taken by the parser, and (iii) the complete contents of the stack of partially built tree fragments, including their internal structures. In addition, we compare two different word representations: (i) standard word vectors based on look-up tables and (ii) character-based models of words. Although standard word embedding models work well in all languages, the character-based models improve the handling of out-of-vocabulary words, particularly in morphologically rich languages. Finally, we discuss the use of dynamic oracles in training the parser. During training, dynamic oracles alternate between sampling parser states from the training data and from the model as it is being learned, making the model more robust to the kinds of errors that will be made at test time. Training our model with dynamic oracles yields a linear-time greedy parser with very competitive performance.
Cancer RNA-Seq Nexus: a database of phenotype-specific transcriptome profiling in cancer cells

The genome-wide transcriptome profiling of cancerous and normal tissue samples can provide insights into the molecular mechanisms of cancer initiation and progression. RNA Sequencing (RNA-Seq) is a revolutionary tool that has been used extensively in cancer research. However, no existing RNA-Seq database provides all of the following features: (i) large-scale and comprehensive data archives and analyses, including coding-transcript profiling, long non-coding RNA (lncRNA) profiling and coexpression networks; (ii) phenotype-oriented data organization and searching and (iii) the visualization of expression profiles, differential expression and regulatory networks. We have constructed the first public database that meets these criteria, the Cancer RNA-Seq Nexus (CRN, http://syslab4.nchu.edu.tw/CRN). CRN has a user-friendly web interface designed to facilitate cancer research and personalized medicine. It is an open resource for intuitive data exploration, providing coding-transcript/lncRNA expression profiles to support researchers generating new hypotheses in cancer research and personalized medicine.
Ch2R: A Chinese Chatter Robot for Online Shopping Guide

In this paper we present a conversational dialogue system, Ch2R (Chinese Chatter Robot) for online shopping guide, which allows users to inquire about information of mobile phone in Chinese. The purpose of this paper is to describe our development effort in terms of the underlying human language technologies (HLTs) as well as other system issues. We focus on a mixed-initiative conversation mechanism for interactive shopping guide combining initiative guiding and question understanding. We also present some evaluation on the system in mobile phone shopping guide domain. Evaluation results demonstrate the efficiency of our approach.