nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server

This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel black box variational algorithm that is an alternative to expectation propagation (EP). In contrast to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server, implementing a distributed asynchronous version of SNEP, which allows scalable and robust Bayesian learning in cases where a dataset is stored in a distributed manner across a cluster. An independent Monte Carlo sampler is run on each compute node which targets an approximation to the global posterior distribution given all data across the whole cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of neural networks.
Predicting Motivations of Actions by Leveraging Text

Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.
On the Quantitative Analysis of Decoder-Based Generative Models

The past several years have seen remarkable progress in generative models which produce convincing samples of images and other modalities. A shared component of many powerful generative models is a decoder network, a parametric deep neural net that defines a generative distribution. Examples include variational autoencoders, generative adversarial networks, and generative moment matching networks. Unfortunately, it can be difficult to quantify the performance of these models because of the intractability of log-likelihood estimation, and inspecting samples can be misleading. We propose to use Annealed Importance Sampling for evaluating log-likelihoods for decoder-based models and validate its accuracy using bidirectional Monte Carlo. The evaluation code is provided at https:// github.com/tonywu95/eval_gen. Using this technique, we analyze the performance of decoder-based models, the effectiveness of existing log-likelihood estimators, the degree of overfitting, and the degree to which these models miss important modes of the data distribution.
When Are Tree Structures Necessary for Deep Learning of Representations?

Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture. But there have not been rigorous evaluations showing for exactly which tasks this syntax-based method is appropriate. In this paper we benchmark recursive neural models against sequential recurrent neural models (simple recurrent and LSTM models), enforcing apples-to-apples comparison as much as possible. We investigate 4 tasks: (1) sentiment classification at the sentence level and phrase level; (2) matching questions to answer-phrases; (3) discourse parsing; (4) semantic relation extraction (e.g., component-whole between nouns). Our goal is to understand better when, and why, recursive models can outperform simpler models. We find that recursive models help mainly on tasks (like semantic relation extraction) that require associating headwords across a long distance, particularly on very long sequences. We then introduce a method for allowing recurrent models to achieve similar performance: breaking long sentences into clause-like units at punctuation and processing them separately before combining. Our results thus help understand the limitations of both classes of models, and suggest directions for improving recurrent models.
Rectangle detection based on a windowed Hough transform

The problem of detecting rectangular structures in images arises in many applications, from building extraction in aerial images to particle detection in cryo-electron microscopy. This paper proposes a new technique for rectangle detection using a windowed Hough transform. Every pixel of the image is scanned, and a sliding window is used to compute the Hough transform of small regions of the image. Peaks of the Hough image (which correspond to line segments) are then extracted, and a rectangle is detected when four extracted peaks satisfy certain geometric conditions. Experimental results indicate that the proposed technique produced promising results for both synthetic and natural images.
Polluino: An efficient cloud-based management of IoT devices for air quality monitoring

The Internet of Things paradigm originates from the proliferation of intelligent devices that can sense, compute and communicate data streams in a ubiquitous information and communication network. The great amounts of data coming from these devices introduce some challenges related to the storage and processing capabilities of the information. This strengthens the novel paradigm known as Big Data. In such a complex scenario, the Cloud computing is an efficient solution for the managing of sensor data. This paper presents Polluino, a system for monitoring the air pollution via Arduino. Moreover, a Cloud-based platform that manages data coming from air quality sensors is developed.
GraphDocExplore: A Framework for the Experimental Comparison of Graph-based Document Exploration Techniques

Graphs have long been proposed as a tool to browse and navigate in a collection of documents in order to support exploratory search. Many techniques to automatically extract different types of graphs, showing for example entities or concepts and different relationships between them, have been suggested. While experimental evidence that they are indeed helpful exists for some of them, it is largely unknown which type of graph is most helpful for a specific exploratory task. However, carrying out experimental comparisons with human subjects is challenging and time-consuming. Towards this end, we present the GraphDocExplore framework. It provides an intuitive web interface for graph-based document exploration that is optimized for experimental user studies. Through a generic graph interface, different methods to extract graphs from text can be plugged into the system. Hence, they can be compared at minimal implementation effort in an environment that ensures controlled comparisons. The system is publicly available under an open-source
Hand gestures to control infotainment equipment in cars

Controlling multiple devices while driving steals drivers' attention from the road and is becoming the cause of accidents in 1 out of 3 cases. Many research efforts are being dedicated to design, manufacture and test Human-Machine Interfaces that allow operating car devices without distracting the drivers' attention. A complete system for controlling the infotainment equipment through hand gestures is explained in this paper. The system works with a visible-infrared camera mounted on the ceiling of the car and pointing to the shift-stick area, and is based in a combination of some new and some well-known computer vision algorithms. The system has been tested by 23 volunteers on a car simulator and a real vehicle and the results show that the users slightly prefer this system to an equivalent one based on a touch-screen interface.
Approximate clustering without the approximation

Approximation algorithms for clustering points in metric spaces is a flourishing area of research, with much research effort spent on getting a better understanding of the approximation guarantees possible for many objective functions such ask-median,k-means, and min-sum clustering. This quest for better approximation algorithms is further fueled by the implicit hope that these better approximations also yield more accurate clusterings. E.g., for many problems such as clustering proteins by function, or clustering images by subject, there is some unknown correct “target” clustering and the implicit hope is that approximately optimizing these objective functions will in fact produce a clustering that is close pointwise to the truth. In this paper, we show that if we make this implicit assumption explicit—that is, if we assume that any capproximation to the given clustering objective Φ is ǫ-close to the target—then we can produce clusterings that are O(ǫ)close to the target, even for valuesc for which obtaining a c-approximation is NP-hard . In particular, fork-median and k-means objectives, we show that we can achieve this guarantee for any constant c > 1, and for the min-sum objective we can do this for any constant c > 2. Our results also highlight a surprising conceptual difference between assuming that the optimalsolution to, say, the k-median objective isǫ-close to the target, and assuming that anyapproximately optimal solution isǫ-close to the target, even for approximation factor say c = 1.01. In the former case, the problem of finding a solution that is O(ǫ)close to the target remains computationally hard, and yet for the latter we have an efficient algorithm.
Invariant Human Action Recognition Using Histograms of 3 D Joints

In this paper, we present a novel approach for human action recognition with histograms of 3D joint locations (HOJ3D) as a compact representation of postures. We extract the 3D skeletal joint locations from Kinect depth maps using Shotton et al.'s method . The HOJ3D computed from the action depth sequences are reprojected using LDA and then clustered into k posture visual words, which represent the prototypical poses of actions. The temporal evolutions of those visual words are modeled by discrete hidden Markov models (HMMs). In addition, due to the design of our spherical coordinate system and the robust 3D skeleton estimation from Kinect, our method demonstrates significant view invariance on our 3D action dataset. Our dataset is composed of 200 3D sequences of 10 indoor activities performed by 10 individuals in varied views. Our method is real-time and achieves superior results on the challenging 3D action dataset. We also tested our algorithm on the MSR Action3D dataset and our algorithm outperforms Li et al.  on most of the cases.
Exploring Cyberbullying and Other Toxic Behavior in Team Competition Online Games

In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.
Qilin: Exploiting parallelism on heterogeneous multiprocessors with adaptive mapping

Heterogeneous multiprocessors are increasingly important in the multi-core era due to their potential for high performance and energy efficiency. In order for software to fully realize this potential, the step that maps computations to processing elements must be as automated as possible. However, the state-of-the-art approach is to rely on the programmer to specify this mapping manually and statically. This approach is not only labor intensive but also not adaptable to changes in runtime environments like problem sizes and hardware/software configurations. In this study, we propose adaptive mapping, a fully automatic technique to map computations to processing elements on a CPU+GPU machine. We have implemented it in our experimental heterogeneous programming system called Qilin. Our results show that, by judiciously distributing works over the CPU and GPU, automatic adaptive mapping achieves a 25% reduction in execution time and a 20% reduction in energy consumption than static mappings on average for a set of important computation benchmarks. We also demonstrate that our technique is able to adapt to changes in the input problem size and system configuration.
Enabling and Exploiting Flexible Task Assignment on GPU through SM-Centric Program Transformations

A GPU's computing power lies in its abundant memory bandwidth and massive parallelism. However, its hardware thread schedulers, despite being able to quickly distribute computation to processors, often fail to capitalize on program characteristics effectively, achieving only a fraction of the GPU's full potential. Moreover, current GPUs do not allow programmers or compilers to control this thread scheduling, forfeiting important optimization opportunities at the program level. This paper presents a transformation centered on Streaming Multiprocessors (SM); this software approach to circumventing the limitations of the hardware scheduler allows flexible program-level control of scheduling. By permitting precise control of job locality on SMs, the transformation overcomes inherent limitations in prior methods.
 With this technique, flexible control of GPU scheduling at the program level becomes feasible, which opens up new opportunities for GPU program optimizations. The second part of the paper explores how the new opportunities could be leveraged for GPU performance enhancement, what complexities there are, and how to address them. We show that some simple optimization techniques can enhance co-runs of multiple kernels and improve data locality of irregular applications, producing 20-33% average increase in performance, system throughput, and average turnaround time.
Design and Implementation of Probabilistic Programming Language Anglican

Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We introduce the programming language Anglican, outline our design choices, and discuss in depth the implementation of the Anglican language and runtime, including macro-based compilation, extended CPS-based evaluation model, and functional representations for probabilistic paradigms, such as a distribution, a random process, and an inference algorithm.
 We show that a probabilistic functional language can be implemented efficiently and integrated tightly with a conventional functional language with only moderate computational overhead. We also demonstrate how advanced probabilistic modelling concepts are mapped naturally to the functional foundation.
Model-based hand pose estimation via spatial-temporal hand parsing and 3D fingertip localization

In this paper we present a novel vision-based markerless hand pose estimation scheme with the input of depth image sequences. The proposed scheme exploits both temporal constraints and spatial features of the input sequence, and focuses on hand parsing and 3D fingertip localization for hand pose estimation. The hand parsing algorithm incorporates a novel spatial-temporal feature into a Bayesian inference framework to assign the correct label to each image pixel. The 3D fingertip localization algorithm adapts a recently developed geodesic extrema extraction method to fingertip detection with the hand parsing algorithm, a novel path-reweighting method and K-means clustering in metric space. The detected 3D fingertip locations are finally used for hand pose estimation with an inverse kinematics solver. Quantitative experiments on synthetic data show the proposed hand pose estimation scheme can accurately capture the natural hand motion. A simulated water-oscillator application is also built to demonstrate the effectiveness of the proposed method in human-computer interaction scenarios.
Glasses with haptic feedback of gaze gestures

We introduce eyeglasses that present haptic feedback when using gaze gestures for input. The glasses utilize vibrotactile actuators to provide gentle stimulation to three locations on the user's head. We describe two initial user studies that were conducted to evaluate the easiness of recognizing feedback locations and participants' preferences for combining the feedback with gaze gestures. The results showed that feedback from a single actuator was the easiest to recognize and also preferred when used with gaze gestures. We conclude by presenting future use scenarios that could benefit from gaze gestures and haptic feedback.
Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias

Many standard computer vision datasets exhibit biases due to a variety of sources including illumination condition, imaging system, and preference of dataset collectors. Biases like these can have downstream effects in the use of vision datasets in the construction of generalizable techniques, especially for the goal of the creation of a classification system capable of generalizing to unseen and novel datasets. In this work we propose Unbiased Metric Learning (UML), a metric learning approach, to achieve this goal. UML operates in the following two steps: (1) By varying hyper parameters, it learns a set of less biased candidate distance metrics on training examples from multiple biased datasets. The key idea is to learn a neighborhood for each example, which consists of not only examples of the same category from the same dataset, but those from other datasets. The learning framework is based on structural SVM. (2) We do model validation on a set of weakly-labeled web images retrieved by issuing class labels as keywords to search engine. The metric with best validation performance is selected. Although the web images sometimes have noisy labels, they often tend to be less biased, which makes them suitable for the validation set in our task. Cross-dataset image classification experiments are carried out. Results show significant performance improvement on four well-known computer vision datasets.
An axiomatic approach for result diversification

Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diversify results to improve user satisfaction. In such a setting, the probability ranking principle-based approach of presenting the most relevant results on top can be sub-optimal, and hence the search engine would like to trade-off relevance for diversity in the results.
 In analogy to prior work on ranking and clustering systems, we use the axiomatic approach to characterize and design diversification systems. We develop a set of natural axioms that a diversification system is expected to satisfy, and show that no diversification function can satisfy all the axioms simultaneously. We illustrate the use of the axiomatic framework by providing three example diversification objectives that satisfy different subsets of the axioms. We also uncover a rich link to the facility dispersion problem that results in algorithms for a number of diversification objectives. Finally, we propose an evaluation methodology to characterize the objectives and the underlying axioms. We conduct a large scale evaluation of our objectives based on two data sets: a data set derived from the Wikipedia disambiguation pages and a product database.
Compiler-Agnostic Function Detection in Binaries

We propose Nucleus, a novel function detection algorithm for binaries. In contrast to prior work, Nucleus is compiler-agnostic, and does not require any learning phase or signature information. Instead of scanning for signatures, Nucleus detects functions at the Control Flow Graph-level, making it inherently suitable for difficult cases such as non-contiguous or multi-entry functions. We evaluate Nucleus on a diverse set of 476 C and C ++ binaries, compiled with gcc, clang and Visual Studio for x86 and x64, at optimization levels O0–O3. We achieve consistently good performance, with a mean F-score of 0.95.
Technical accuracy of optical and the electromagnetic tracking systems

Thousands of operations are annually guided with computer assisted surgery (CAS) technologies. As the use of these devices is rapidly increasing, the reliability of the devices becomes ever more critical. The problem of accuracy assessment of the devices has thus become relevant. During the past five years, over 200 hazardous situations have been documented in the MAUDE database during operations using these devices in the field of neurosurgery alone. Had the accuracy of these devices been periodically assessed pre-operatively, many of them might have been prevented. The technical accuracy of a commercial navigator enabling the use of both optical (OTS) and electromagnetic (EMTS) tracking systems was assessed in the hospital setting using accuracy assessment tools and methods developed by the authors of this paper. The technical accuracy was obtained by comparing the positions of the navigated tool tip with the phantom accuracy assessment points. Each assessment contained a total of 51 points and a region of surgical interest (ROSI) volume of 120x120x100 mm roughly mimicking the size of the human head. The error analysis provided a comprehensive understanding of the trend of accuracy of the surgical navigator modalities. This study showed that the technical accuracies of OTS and EMTS over the pre-determined ROSI were nearly equal. However, the placement of the particular modality hardware needs to be optimized for the surgical procedure. New applications of EMTS, which does not require rigid immobilization of the surgical area, are suggested.
Neural correlates of trust.

Trust is a critical social process that helps us to cooperate with others and is present to some degree in all human interaction. However, the underlying brain mechanisms of conditional and unconditional trust in social reciprocal exchange are still obscure. Here, we used hyperfunctional magnetic resonance imaging, in which two strangers interacted online with one another in a sequential reciprocal trust game while their brains were simultaneously scanned. By designing a nonanonymous, alternating multiround game, trust became bidirectional, and we were able to quantify partnership building and maintenance. Using within- and between-brain analyses, an examination of functional brain activity supports the hypothesis that the preferential activation of different neuronal systems implements these two trust strategies. We show that the paracingulate cortex is critically involved in building a trust relationship by inferring another person's intentions to predict subsequent behavior. This more recently evolved brain region can be differently engaged to interact with more primitive neural systems in maintaining conditional and unconditional trust in a partnership. Conditional trust selectively activated the ventral tegmental area, a region linked to the evaluation of expected and realized reward, whereas unconditional trust selectively activated the septal area, a region linked to social attachment behavior. The interplay of these neural systems supports reciprocal exchange that operates beyond the immediate spheres of kinship, one of the distinguishing features of the human species.