nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Annotation of Structured Facts in Images

Motivated by the application of fact-level image understanding, we present an automatic method for data collection of structured visual facts from images with captions. Example structured facts include attributed objects (e.g., <flower, red>), actions (e.g., <baby, smile>), interactions (e.g., <man, walking, dog>), and positional information (e.g., <vase, on, table>). The collected annotations are in the form of fact-image pairs (e.g.,<man, walking, dog> and an image region containing this fact). With a language approach, the proposed method is able to collect hundreds of thousands of visual fact annotations with accuracy of 83% according to human judgment. Our method automatically collected more than 380,000 visual fact annotations and more than 110,000 unique visual facts from images with captions and localized them in images in less than one day of processing time on standard CPU platforms.
STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method

We present STRUCTURE HARVESTER (available at http://taylor0.biology.ucla.edu/structureHarvester/ ), a web-based program for collating results generated by the program STRUCTURE. The program provides a fast way to assess and visualize likelihood values across multiple values of K and hundreds of iterations for easier detection of the number of genetic groups that best fit the data. In addition, STRUCTURE HARVESTER will reformat data for use in downstream programs, such as CLUMPP.
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization

Many problems in artificial intelligence require adaptively making a sequence of decisions with uncertain outcomes under partial observability. Solving such stochastic optimization problems is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. In addition to providing performance guarantees for both stochastic maximization and coverage, adaptive submodularity can be exploited to drastically speed up the greedy algorithm by using lazy evaluations. We illustrate the usefulness of the concept by giving several examples of adaptive submodular objectives arising in diverse AI applications including management of sensing resources, viral marketing and active learning. Proving adaptive submodularity for these problems allows us to recover existing results in these applications as special cases, improve approximation guarantees and handle natural generalizations.
CAD and Optimization of Compact Wide-band Septum Polarizers

We describe a computer-aided optimization technique for the efficient and reliable design of compact wide-band waveguide septum polarizers (WSP). Wide-band performance is obtained by a global optimization which considers not only the septum section but also several step discontinuities placed before the ridge-to-rectangular bifurcation and the square-to-circular discontinuity. The proposed technique mnakes use of a dynamical optimization procedure which has been tested by designing several WSP operating in different frequency bands. In this work two examples are reported, one operating at Ku band and a very wideband prototype (3.4-4.2 GHz) operating in the C band. The component design, entirely carried out at computer level, has demonstrated significant advantages in terms of development times and no need of post manufacturing adjustments. The very satisfactory agreement between experimental and theoretical results further confirm the validity of the proposed technique.
Watch-n-patch: Unsupervised understanding of actions and relations

We focus on modeling human activities comprising multiple actions in a completely unsupervised setting. Our model learns the high-level action co-occurrence and temporal relations between the actions in the activity video. We consider the video as a sequence of short-term action clips, called action-words, and an activity is about a set of action-topics indicating which actions are present in the video. Then we propose a new probabilistic model relating the action-words and the action-topics. It allows us to model long-range action relations that commonly exist in the complex activity, which is challenging to capture in the previous works. We apply our model to unsupervised action segmentation and recognition, and also to a novel application that detects forgotten actions, which we call action patching. For evaluation, we also contribute a new challenging RGB-D activity video dataset recorded by the new Kinect v2, which contains several human daily activities as compositions of multiple actions interacted with different objects. The extensive experiments show the effectiveness of our model.
Anchor-free distributed localization in sensor networks

Many sensor network applications require that each node’s sensor stream be annotated with its physical location in some common coordinate system. Manual measurement and configuration methods for obtaining location don’t scale and are error-prone, and equipping sensors with GPS is often expensive and does not work in indoor and urban deployments. Sensor networks can therefore benefit from a self-configuring method where nodes cooperate with each other, estimate local distances to their neighbors, and converge to a consistent coordinate assignment. This paper describes a fully decentralized algorithm called AFL (Anchor-Free Localization) where nodes start from a random initial coordinate assignment and converge to a consistent solution using only local node interactions. The key idea in AFL is fold-freedom, where nodes first configure into a topology that resembles a scaled and unfolded version of the true configuration, and then run a force-based relaxation procedure. We show using extensive simulations under a variety of network sizes, node densities, and distance estimation errors that our algorithm is superior to previously proposed methods that incrementally compute the coordinates of nodes in the network, in terms of its ability to compute correct coordinates under a wider variety of conditions and its robustness to measurement errors.
Using quantitative analysis to implement autonomic IT systems

The software underpinning today's IT systems needs to adapt dynamically and predictably to rapid changes in system workload, environment and objectives. We describe a software framework that achieves such adaptiveness for IT systems whose components can be modelled as Markov chains. The framework comprises (i) an autonomic architecture that uses Markov-chain quantitative analysis to dynamically adjust the parameters of an IT system in line with its state, environment and objectives; and (ii) a method for developing instances of this architecture for real-world systems. Two case studies are presented that use the framework successfully for the dynamic power management of disk drives, and for the adaptive management of cluster availability within data centres, respectively.
A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging

In this paper, we present the design, fabrication and evaluation of a soft wearable robotic glove, which can be used with functional Magnetic Resonance imaging (fMRI) during the hand rehabilitation and task specific training. The soft wearable robotic glove, called MR-Glove, consists of two major components: a) a set of soft pneumatic actuators and b) a glove. The soft pneumatic actuators, which are made of silicone elastomers, generate bending motion and actuate finger joints upon pressurization. The device is MR-compatible as it contains no ferromagnetic materials and operates pneumatically. Our results show that the device did not cause artifacts to fMRI images during hand rehabilitation and task-specific exercises. This study demonstrated the possibility of using fMRI and MR-compatible soft wearable robotic device to study brain activities and motor performances during hand rehabilitation, and to unravel the functional effects of rehabilitation robotics on brain stimulation.
A multi-modal parcellation of human cerebral cortex

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
Behind Phishing: An Examination of Phisher Modi Operandi

Phishing costs Internet users billions of dollars a year. Using various data sets collected in real-time, this paper analyzes various aspects of phisher modi operandi. We examine the anatomy of phishing URLs and domains, registration of phishing domains and time to activation, and the machines used to host the phishing sites. Our findings can be used as heuristics in filtering phishing-related emails and in identifying suspicious domain registrations.
BPMN-Based Conceptual Modeling of ETL Processes

Business Intelligence (BI) solutions require the design and implementation of complex processes (denoted ETL) that extract, transform, and load data from the sources to a common repository. New applications, like for example, real-time data warehousing, require agile and flexible tools that allow BI users to take timely decisions based on extremely up-to-date data. This calls for new ETL tools able to adapt to constant changes and quickly produce and modify executable code. A way to achieve this is to make ETL processes become aware of the business processes in the organization, in order to easily identify which data are required, and when and how to load them in the data warehouse. Therefore, we propose to model ETL processes using the standard representation mechanism denoted BPMN (Business Process Modeling and Notation). In this paper we present a BPMN-based metamodel for conceptual modeling of ETL processes. This metamodel is based on a classification of ETL objects resulting from a study of the most used commercial and open source ETL tools.
Face recognition with enhanced local directional patterns

This paper presents a novel approach based on enhanced local directional patterns (ELDP) to face recognition, which adopts local edge gradient information to represent face images. Specially, each pixel of every facial image sub-block gains eight edge response values by convolving the local 3 3 neighborhood with eight Kirsch masks, respectively. ELDP just utilizes the directions of the most encoded into a double-digit octal number to produce the ELDP codes. The ELDP dominant patterns (ELDP) are generated by statistical analysis according to the occurrence rates of the ELDP codes in a mass of facial images. Finally, the face descriptor is represented by using the global concatenated histogram based on ELDP or ELDP extracted from the face image which is divided into several sub-regions. The performances of several single face descriptors not integrated schemes are evaluated in face recognition under different challenges via several experiments. The experimental results demonstrate that the proposed method is more robust to non-monotonic illumination changes and slight noise without any filter. & 2013 Elsevier B.V. All rights reserved.
Bayesian Poisson regression for crowd counting

Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this work, we analyze Poisson regression in a Bayesian setting, by introducing a prior distribution on the weights of the linear function. Since exact inference is analytically unobtainable, we derive a closed-form approximation to the predictive distribution of the model. We show that the predictive distribution can be kernelized, enabling the representation of non-linear log-mean functions. We also derive an approximate marginal likelihood that can be optimized to learn the hyperparameters of the kernel. We then relate the proposed approximate Bayesian Poisson regression to Gaussian processes. Finally, we present experimental results using Bayesian Poisson regression for crowd counting from low-level features.
Material-Efficient Permanent-Magnet Shape for Torque Pulsation Minimization in SPM Motors for Automotive Applications

This paper focuses on the design and analysis of a novel material-efficient permanent-magnet (PM) shape for surface-mounted PM (SPM) motors used in automotive actuators. Most of such applications require smooth torque with minimum pulsation for an accurate position control. The proposed PM shape is designed to be sinusoidal and symmetrical in the axial direction for minimizing the amount of rare earth magnets as well as for providing balanced axial electromagnetic force, which turns out to obtain better sinusoidal electromotive force, less cogging torque, and, consequently, smooth electromagnetic torque. The contribution of the novel PM shape to motor characteristics is first estimated by 3-D finite-element method, and all of the simulation results are compared with those of SPM motors with two conventional arched PM shapes: one previously reported sinusoidal PM shape and one step skewed PM shape. Finally, some finite-element analysis results are confirmed by experimental results.
Dynamic detection of inter-application communication vulnerabilities in Android

A main aspect of the Android platform is Inter-Application Communication (IAC), which enables reuse of functionality across apps and app components via message passing. While a powerful feature, IAC also constitutes a serious attack surface. A malicious app can embed a payload into an IAC message, thereby driving the recipient app into a potentially vulnerable behavior if the message is processed without its fields first being sanitized or validated. We present what to our knowledge is the first comprehensive testing algorithm for Android IAC vulnerabilities. Toward this end, we first describe a catalog, stemming from our field experience, of 8 concrete vulnerability types that can potentially arise due to unsafe handling of incoming IAC messages. We then explain the main challenges that automated discovery of Android IAC vulnerabilities entails, including in particular path coverage and custom data fields, and present simple yet surprisingly effective solutions to these challenges. We have realized our testing approach as the IntentDroid system, which is available as a commercial cloud service. IntentDroid utilizes lightweight platform-level instrumentation, implemented via debug breakpoints (to run atop any Android device without any setup or customization), to recover IAC-relevant app-level behaviors. Evaluation of IntentDroid over a set of 80 top-popular apps has revealed a total 150 IAC vulnerabilities — some already fixed by the developers following our report — with a recall rate of 92% w.r.t. a ground truth established via manual auditing by a security expert.
Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy

Detecting frauds in credit card transactions is perhaps one of the best testbeds for computational intelligence algorithms. In fact, this problem involves a number of relevant challenges, namely: concept drift (customers’ habits evolve and fraudsters change their strategies over time), class imbalance (genuine transactions far outnumber frauds), and verification latency (only a small set of transactions are timely checked by investigators). However, the vast majority of learning algorithms that have been proposed for fraud detection rely on assumptions that hardly hold in a real-world fraud-detection system (FDS). This lack of realism concerns two main aspects: 1) the way and timing with which supervised information is provided and 2) the measures used to assess fraud-detection performance. This paper has three major contributions. First, we propose, with the help of our industrial partner, a formalization of the fraud-detection problem that realistically describes the operating conditions of FDSs that everyday analyze massive streams of credit card transactions. We also illustrate the most appropriate performance measures to be used for fraud-detection purposes. Second, we design and assess a novel learning strategy that effectively addresses class imbalance, concept drift, and verification latency. Third, in our experiments, we demonstrate the impact of class unbalance and concept drift in a real-world data stream containing more than 75 million transactions, authorized over a time window of three years.
Understanding Tutor Learning : Knowledge-Building and Knowledge-Telling in Peer Tutors ’ Explanations and Questions Rod

Prior research has established that peer tutors can benefit academically from their tutoring experiences. However, although tutor learning has been observed across diverse settings, the magnitude of these gains is often underwhelming. In this review, the authors consider how analyses of tutors’ actual behaviors may help to account for variation in learning outcomes and how typical tutoring behaviors may create or undermine opportunities for learning. The authors examine two tutoring activities that are commonly hypothesized to support tutor learning: explaining and questioning. These activities are hypothesized to support peer tutors’ learning via reflective knowledge-building, which includes self-monitoring of comprehension, integration of new and prior knowledge, and elaboration and construction of knowledge. The review supports these hypotheses but also finds that peer tutors tend to exhibit a pervasive knowledge-telling bias. Peer tutors, even when trained, focus more on delivering knowledge rather than developing it. As a result, the true potential for tutor learning may rarely be achieved. The review concludes by offering recommendations for how future research can utilize tutoring process data to understand how tutors learn and perhaps develop new training methods.
Latest developments of high-frequency series load resonant inverter type built-in cooktops for induction heated all metallic appliances

This paper deals with the 1st generation prototype of one-stage boost-half bridge (B-HB) series load resonant (SLR) soft-switching high-frequency (HF) inverter with a lossless snubbing capacitor for a variety of induction heating (IH) appliances. The B-HB SLR HF inverter treated here is based upon a simple dual SLR frequency selection strategy changed automatically in accordance with various metal materials of IH loads. In the first place, the triple SLR frequency (three times of switching frequency) operated B-HB inverter is demonstrated for IH of non-magnetic and low resistivity metallic pans/utensils fabricated by aluminum, copper and multi-layer of aluminum and stainless steel. In the second place, the fundamental resonant frequency (switching frequency) operated B-HB SLR HF inverter for IH is also demonstrated of magnetic and high resistivity metallic pans/utensils fabricated by iron, iron cast and stainless steel. Finally, the principle of operation control, implemental and inherent unique features of the B-HB SLR HF inverter employing automatically dual resonant frequency selection scheme for a variety of IH metallic pans/utensils is described from an experimental point of view, along with its operating performance. This 1st generation HF SLR inverter type built-in IH cooktop with two ranges/three ranges has been put into practice in home energy utilizations in all electricity residential systems.
Adversarial Multi-Criteria Learning for Chinese Word Segmentation

Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improve the performance for each single criterion. However, it is interesting to exploit these different criteria and mining their common underlying knowledge. In this paper, we propose adversarial multi-criteria learning for CWS by integrating shared knowledge from multiple heterogeneous segmentation criteria. Experiments on eight corpora with heterogeneous segmentation criteria show that the performance of each corpus obtains a significant improvement, compared to single-criterion learning. Source codes of this paper are available on Github1.
Beyond Trending Topics: Real-World Event Identification on Twitter

User-contributed messages on social media sites such as Twitter have emerged as powerful, real-time means of information sharing on the Web. These short messages tend to reflect a variety of events in real time, making Twitter particularly well suited as a source of real-time event content. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events and non-event messages. Our approach relies on a rich family of aggregate statistics of topically similar message clusters. Large-scale experiments over millions of Twitter messages show the effectiveness of our approach for surfacing real-world event content on Twitter.
MODEC: Multimodal Decomposable Models for Human Pose Estimation

We propose a multimodal, decomposable model for articulated human pose estimation in monocular images. A typical approach to this problem is to use a linear structured model, which struggles to capture the wide range of appearance present in realistic, unconstrained images. In this paper, we instead propose a model of human pose that explicitly captures a variety of pose modes. Unlike other multimodal models, our approach includes both global and local pose cues and uses a convex objective and joint training for mode selection and pose estimation. We also employ a cascaded mode selection step which controls the trade-off between speed and accuracy, yielding a 5x speedup in inference and learning. Our model outperforms state-of-the-art approaches across the accuracy-speed trade-off curve for several pose datasets. This includes our newly-collected dataset of people in movies, FLIC, which contains an order of magnitude more labeled data for training and testing than existing datasets.