nils-herrmann/clean_scirepeval huggingface dataset:
Context-aware query suggestion by mining click-through and session data

Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware - they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offine model-learning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence sufix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1:8 billion search queries, 2:6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions.
It’s All Fun and Games until Someone Annotates: Video Games with a Purpose for Linguistic Annotation

Annotated data is prerequisite for many NLP applications. Acquiring large-scale annotated corpora is a major bottleneck, requiring significant time and resources. Recent work has proposed turning annotation into a game to increase its appeal and lower its cost; however, current games are largely text-based and closely resemble traditional annotation tasks. We propose a new linguistic annotation paradigm that produces annotations from playing graphical video games. The effectiveness of this design is demonstrated using two video games: one to create a mapping from WordNet senses to images, and a second game that performs Word Sense Disambiguation. Both games produce accurate results. The first game yields annotation quality equal to that of experts and a cost reduction of 73% over equivalent crowdsourcing; the second game provides a 16.3% improvement in accuracy over current state-of-the-art sense disambiguation games with WordNet.
A content-based recommendation algorithm for learning resources

Automatic multimedia learning resources recommendation has become an increasingly relevant problem: it allows students to discover new learning resources that match their tastes, and enables the e-learning system to target the learning resources to the right students. In this paper, we propose a content-based recommendation algorithm based on convolutional neural network (CNN). The CNN can be used to predict the latent factors from the text information of the multimedia resources. To train the CNN, its input and output should first be solved. For its input, the language model is used. For its output, we propose the latent factor model, which is regularized by L 1-norm. Furthermore, the split Bregman iteration method is introduced to solve the model. The major novelty of the proposed recommendation algorithm is that the text information is used directly to make the content-based recommendation without tagging. Experimental results on public databases in terms of quantitative assessment show significant improvements over conventional methods. In addition, the split Bregman iteration method which is introduced to solve the model can greatly improve the training efficiency.
Improving Soft Pneumatic Actuator fingers through integration of soft sensors, position and force control, and rigid fingernails

Soft Pneumatic Actuators (SPAs) have recently become popular for use as fingers in robotic hands because of their inherent compliance, low cost, and ease of construction. We seek to overcome two key limitations which limit SPAs' abilities to grasp and manipulate objects: 1) Current SPAs lack position or force sensor feedback, which prevents controlling them precisely (e.g. to achieve a hand preshape or apply a specified pushing force), and 2) the tip of the SPA is compliant and has high friction against common surfaces, causing the SPA to stick against surfaces when grasping objects from above. To overcome the first limitation we propose methods to integrate soft eGaIn sensors into SPAs and controllers that use these sensors' feedback for position and force control. To overcome the second limitation, we explore embedding rigid fingernails into the tip of the SPA so that the finger does not stick against surfaces and can wedge under objects. Our experiments suggest that we can achieve low steady-state error and overshoot in position and force using feed-forward models that relate pressure, force, and curvature along with a PID controller. We also compare several fingernail designs and show that the best-performing design significantly outperforms having no fingernails when grasping a set of common objects from a table.
Modeling and animating eye blinks

Facial animation often falls short in conveying the nuances present in the facial dynamics of humans. In this article, we investigate the subtleties of the spatial and temporal aspects of eye blinks. Conventional methods for eye blink animation generally employ temporally and spatially symmetric sequences; however, naturally occurring blinks in humans show a pronounced asymmetry on both dimensions. We present an analysis of naturally occurring blinks that was performed by tracking data from high-speed video using active appearance models. Based on this analysis, we generate a set of key-frame parameters that closely match naturally occurring blinks. We compare the perceived naturalness of blinks that are animated based on real data to those created using textbook animation curves. The eye blinks are animated on two characters, a photorealistic model and a cartoon model, to determine the influence of character style. We find that the animated blinks generated from the human data model with fully closing eyelids are consistently perceived as more natural than those created using the various types of blink dynamics proposed in animation textbooks.
Nonparametric estimation of the likelihood ratio and divergence functionals

We develop and analyze a nonparametric method for estimating the class of f-divergence functionals, and the density ratio of two probability distributions. Our method is based on a non-asymptotic variational characterization of the f-divergence, which allows us to cast the problem of estimating divergences in terms of risk minimization. We thus obtain an M-estimator for divergences, based on a convex and differentiable optimization problem that can be solved efficiently. We analyze the consistency and convergence rates for this M-estimator given conditions only on the ratio of densities.
The Iron Cage Revisited : Institutional Isomorphism and Collective Rationality in Organizational Fields Author ( s ) :

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On the PMEPR of Binary Golay Sequences of Length  $2^{n}$

In this paper, some questions on the distribution of the peak-to-mean envelope power ratio (PMEPR) of standard binary Golay sequences are solved. For n odd, we prove that the PMEPR of each standard binary Golay sequence of length 2<sup>n</sup> is exactly 2, and determine the location(s), where peaks occur for each sequence. For n even, we prove that the envelope power of such sequences can never reach 2<sup>n+1</sup> at time points t ∈ {(v/2<sup>u</sup>)|0 ≤ v ≤ 2<sup>u</sup>, v,u ∈ N}. We further identify eight sequences of length 2<sup>4</sup> and eight sequences of length 2<sup>6</sup> that have PMEPR exactly 2, and raise the question whether, asymptotically, it is possible for standard binary Golay sequences to have PMEPR less than 2 - ϵ, where, ϵ > 0.
Product review summarization through question retrieval and diversification

Product reviews have become an important resource for customers before they make purchase decisions. However, the abundance of reviews makes it difficult for customers to digest them and make informed choices. In our study, we aim to help customers who want to quickly capture the main idea of a lengthy product review before they read the details. In contrast with existing work on review analysis and document summarization, we aim to retrieve a set of real-world user questions to summarize a review. In this way, users would know what questions a given review can address and they may further read the review only if they have similar questions about the product. Specifically, we design a two-stage approach which consists of question selection and question diversification. For question selection phase, we first employ probabilistic retrieval models to locate candidate questions that are relevant to a given review. A Recurrent Neural Network Encoder–Decoder is utilized to measure the “answerability” of questions to a review. We then design a set function to re-rank the questions with the goal of rewarding diversity in the final question set. The set function satisfies submodularity and monotonicity, which results in an efficient greedy algorithm of submodular optimization. Evaluation on product reviews from two categories shows that the proposed approach is effective for discovering meaningful questions that are representative of individual reviews.
Online Action Detection in Untrimmed, Streaming Videos - Modeling and Evaluation

The goal of Online Action Detection (OAD) is to detect action in a timely manner and to recognize its action category. Early works focused on early action detection, which is effectively formulated as a classification problem instead of online detection in streaming videos, because these works used partially seen short video clip that begins at the start of action. Recently, researchers started to tackle the OAD problem in the challenging setting of untrimmed, streaming videos that contain substantial background shots. However, they evaluate OAD in terms of per-frame labeling, which does not require detection at the instance-level and does not evaluate the timeliness of the online detection process. In this paper, we design new protocols and metrics. Further, to specifically address challenges of OAD in untrimmed, streaming videos, we propose three novel methods: (1) we design a hard negative samples generation module based on Generative Adversarial Network (GAN) framework to better distinguish ambiguous background shots that share similar scenes but lack true characteristics of action start; (2) during training we impose a temporal consistency constraint between data around action start and data succeeding action start to model their similarity; (3) we introduce an adaptive sampling strategy to handle the scarcity of the important training data around action start. We conduct extensive experiments using THUMOS’14 and ActivityNet. We show that our proposed strategies lead to significant performance gains and improve state-of-the-art results. A systematic ablation study also confirms the effectiveness of each proposed method.
Detecting Oriented Text in Natural Images by Linking Segments

Most state-of-the-art text detection methods are specific to horizontal Latin text and are not fast enough for real-time applications. We introduce Segment Linking (SegLink), an oriented text detection method. The main idea is to decompose text into two locally detectable elements, namely segments and links. A segment is an oriented box covering a part of a word or text line, A link connects two adjacent segments, indicating that they belong to the same word or text line. Both elements are detected densely at multiple scales by an end-to-end trained, fully-convolutional neural network. Final detections are produced by combining segments connected by links. Compared with previous methods, SegLink improves along the dimensions of accuracy, speed, and ease of training. It achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin. It runs at over 20 FPS on 512x512 images. Moreover, without modification, SegLink is able to detect long lines of non-Latin text, such as Chinese.
Coq: the world's best macro assembler?

We describe a Coq formalization of a subset of the x86 architecture. One emphasis of the model is brevity: using dependent types, type classes and notation we give the x86 semantics a makeover that counters its reputation for baroqueness. We model bits, bytes, and memory concretely using functions that can be computed inside Coq itself; concrete representations are mapped across to mathematical objects in the SSReflect library (naturals, and integers modulo 2n) to prove theorems. Finally, we use notation to support conventional assembly code syntax inside Coq, including lexically-scoped labels.  Coq definitions serve as a powerful "macro" feature for everything from simple conditionals and loops to stack-allocated local variables and procedures with parameters. Assembly code can be assembled within Coq, producing a sequence of hex bytes. The assembler enjoys a correctness theorem relating machine code in memory to a separation-logic formula suitable for program verification.
DEEP REINFORCEMENT LEARNING

Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it. Traditionally, such algorithms make decisions, i.e., select actions to execute, at every single time step of the agent-environment interactions. In this paper, we propose a novel framework, Fine Grained Action Repetition (FiGAR), which enables the agent to decide the action as well as the time scale of repeating it. FiGAR can be used for improving any Deep Reinforcement Learning algorithm which maintains an explicit policy estimate by enabling temporal abstractions in the action space. We empirically demonstrate the efficacy of our framework by showing performance improvements on top of three policy search algorithms in different domains: Asynchronous Advantage Actor Critic in the Atari 2600 domain, Trust Region Policy Optimization in Mujoco domain and Deep Deterministic Policy Gradients in the TORCS car racing domain.
Eavesdropping near-fi eld contactless payments : a quantitative analysis

This paper presents an assessment of how successful an eavesdropping attack on a contactless payment transaction can be in terms of bit and frame error rates, using an easily concealable antenna and low-cost electronics. Potential success of an eavesdropping attack largely depends on the correct recovery of the data frames used in the ISO 14443 standard. A near-field communication inductive loop antenna was used to emulate an ISO 14443 transmission. For eavesdropping, an identical inductive loop antenna as well as a shopping trolley modified to act like an antenna were used. The authors present and analyse frame error rates obtained with the authors equipment over a range of distances, up to 100 cm, well above the official maximum operating distance depending on the magnetic field strength.
Average cost temporal-difference learning

We propose a variant of temporal-di!erence learning that approximates average and di!erential costs of an irreducible aperiodic Markov chain. Approximations are comprised of linear combinations of "xed basis functions whose weights are incrementally updated during a single endless trajectory of the Markov chain. We present a proof of convergence (with probability 1) and a characterization of the limit of convergence. We also provide a bound on the resulting approximation error that exhibits an interesting dependence on the `mixing timea of the Markov chain. The results parallel previous work by the authors, involving approximations of discounted cost-to-go. ! 1999 Elsevier
An objective and subjective study of the role of semantics and prosodic features in building corpora for emotional TTS

Building a text corpus suitable to be used in corpus-based speech synthesis is a time-consuming process that usually requires some human intervention to select the desired phonetic content and the necessary variety of prosodic contexts. If an emotional text-to-speech (TTS) system is desired, the complexity of the corpus generation process increases. This paper presents a study aiming to validate or reject the use of a semantically neutral text corpus for the recording of both neutral and emotional (acted) speech. The use of this kind of texts would eliminate the need to include semantically emotional texts into the corpus. The study has been performed for Basque language. It has been made by performing subjective and objective comparisons between the prosodic characteristics of recorded emotional speech using both semantically neutral and emotional texts. At the same time, the performed experiments allow for an evaluation of the capability of prosody to carry emotional information in Basque language. Prosody manipulation is the most common processing tool used in concatenative TTS. Experiments of automatic recognition of the emotions considered in this paper (the "Big Six emotions") show that prosody is an important emotional indicator, but cannot be the only manipulated parameter in an emotional TTS system-at least not for all the emotions. Resynthesis experiments transferring prosody from emotional to neutral speech have also been performed. They corroborate the results and support the use of a neutral-semantic-content text in databases for emotional speech synthesis
An Algorithm for Training Polynomial Networks

We consider deep neural networks, in which the output of each node is a quadratic function of its inputs. Similar to other deep architectures, these networks can compactly represent any function on a finite training set. The main goal of this paper is the derivation of an efficient layer-by-layer algorithm for training such networks, which we denote as the Basis Learner. The algorithm is a universal learner in the sense that the training error is guaranteed to decrease at every iteration, and can eventually reach zero under mild conditions. We present practical implementations of this algorithm, as well as preliminary experimental results. We also compare our deep architecture to other shallow architectures for learning polynomials, in particular kernel learning.
Modifying the attitude of quadruped robot body against disturbances via data fusion

The purpose of this paper is to modify the attitude of quadruped robot body against disturbances via sensory feedback. For this goal Inertial Measurement Unit (a 3-axis gyroscope and a 3-axis accelerometer) and a 3-axis magnetometer are used. In order to use the data provided by IMU and magnetometer, it is required to design a filter to provide an estimation with proper accuracy. A Switching Extended Kalman Filter (SEKF) is used to fuse the sensory information and estimate the quadruped robot bodY attitude along with uncompensated gyro bias. In this filter, switching rule is used to separate the measurement model of EKF in the accelerated or non-accelerated motion of the robot body. Then, the body deviation is compensated using support legs based on the outputs of observer. Through numerical simulations, SEKF is compared with a nonlinear observer and its efficiency is shown. Finally, the robot body attitude is modified on a platform under ramp disturbances.
Treatment of angiokeratoma of Fordyce with long-pulse neodymium-doped yttrium aluminium garnet laser.

Angiokeratomas are typically asymptomatic, blue-to-red papules with a scaly surface located on the scrotum, shaft of penis, labia majora, inner thigh, or lower abdomen. The treatment of angiokeratomas may be necessary if they bleed and lead to patient anxiety.



To determine the safety and effectiveness of long-pulse 1,064 neodymium-doped yttrium aluminium garnet (Nd:YAG) laser for the treatment of angiokeratomas of Fordyce.



Ten consecutive patients with angiokeratoma of Fordyce were treated with long-pulse Nd:YAG laser in two to six sessions. The three authors independently assessed improvement of the lesion based on digital photographs taken before the treatment and 2 months after the end of the treatment.



Significant (>75%, <100%) and moderate (>50%, <75%) improvement was seen in six and two patients, respectively. Complete improvement was achieved in one patient. Transient swelling, purpura, bleeding, and some pain in the treated area were noted in all patients as short-term side effects. There were no permanent side effects.



The long-pulse Nd:YAG laser is a highly effective and safe treatment for angiokeratoma of Fordyce.
Semantic Integration: A Survey Of Ontology-Based Approaches

Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontologies. This paper provides a brief survey of the approaches to semantic integration developed by researchers in the ontology community. We focus on the approaches that differentiate the ontology research from other related areas. The goal of the paper is to provide a reader who may not be very familiar with ontology research with introduction to major themes in this research and with pointers to different research projects. We discuss techniques for finding correspondences between ontologies, declarative ways of representing these correspondences, and use of these correspondences in various semantic-integration tasks
Stereovision-Based Object Segmentation for Automotive Applications

Obstacle detection and classification in a complex urban area are highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The most difficult task for the system is to segment objects from varied and complicated background. In this paper, a novel position-based object segmentationmethod has been proposed to solve this problem. According to the method proposed, object segmentation is performed in two steps: in depth map (X-Z plane) and in layered images (X-Y planes). The stereovision technique is used to reconstruct image points and generate the depth map. Objects are detected in the depth map. Afterwards, the original edge image is separated into different layers based on the distance of detected objects. Segmentation performed in these layered images can be easier and more reliable. It has been proved that the proposed method offers robust detection of potential obstacles and accurate measurement of their location and size.