nils-herrmann/clean_scirepeval huggingface dataset:
Farthest-Point Heuristic based Initialization Methods for K-Modes Clustering

The k-modes algorithm has become a popular technique in solving categorical data clustering problems in different application domains. However, the algorithm requires random selection of initial points for the clusters. Different initial points often lead to considerable distinct clustering results. In this paper we present an experimental study on applying a farthest-point heuristic based initialization method to k-modes clustering to improve its performance. Experiments show that new initialization method leads to better clustering accuracy than random selection initialization method for k-modes clustering.
Truncated Importance Sampling for Reinforcement Learning with Experience Replay

Reinforcement Learning (RL) is considered here as an adaptation technique of neural controllers of machines. The goal is to make Actor-Critic algorithms require less agent-environment interaction to obtain policies of the same quality, at the cost of additional background computations. We propose to achieve this goal in the spirit of experience replay. An estimation method of improvement direction of a changing policy, based on preceding experience, is essential here. We propose one that uses truncated importance sampling. We derive bounds of bias of that type of estimators and prove that this bias asymptotically vanishes. In the experimental study we apply our approach to the classic ActorCritic and obtain 20-fold increase in speed of learning.
Computer Vision: A Modern Approach

The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.
Using plane + parallax for calibrating dense camera arrays

A light field consists of images of a scene taken from different viewpoints. Light fields are used in computer graphics for image-based rendering and synthetic aperture photography, and in vision for recovering shape. In this paper, we describe a simple procedure to calibrate camera arrays used to capture light fields using a plane + parallax framework. Specifically, for the case when the cameras lie on a plane, we show (i) how to estimate camera positions up to an affine ambiguity, and (ii) how to reproject light field images onto a family of planes using only knowledge of planar parallax for one point in the scene. While planar parallax does not completely describe the geometry of the light field, it is adequate for the first two applications which, it turns out, do not depend on having a metric calibration of the light field. Experiments on acquired light fields indicate that our method yields better results than full metric calibration.
Interactive Neural Translation Assistance for Human Translators

We present the first user study on neural interactive translation prediction. Our neural translation aid was built into existing online translation software. Depending on the prefix already typed by the user the system suggests a list of continuations. We assessed the impact of the system on human translators in a user study with both professional and non-professional translators. The analysis was done with respect to translation speed and translation accuracy as assessed by human judges. We find that our neural translation aid enables relatively fast translations and does not compromise on quality.
On the Benefit of Incorporating External Features in a Neural Architecture for Answer Sentence Selection

Incorporating conventional, unsupervised features into a neural architecture has the potential to improve modeling effectiveness, but this aspect is often overlooked in the research of deep learning models for information retrieval. We investigate this incorporation in the context of answer sentence selection, and show that combining a set of query matching, readability, and query focus features into a simple convolutional neural network can lead to markedly increased effectiveness. Our results on two standard question-answering datasets show the effectiveness of the combined model.
On Different Facets of Regularization Theory

This review provides a comprehensive understanding of regularization theory from different perspectives, emphasizing smoothness and simplicity principles. Using the tools of operator theory and Fourier analysis, it is shown that the solution of the classical Tikhonov regularization problem can be derived from the regularized functional defined by a linear differential (integral) operator in the spatial (Fourier) domain. State-ofthe-art research relevant to the regularization theory is reviewed, covering Occam's razor, minimum length description, Bayesian theory, pruning algorithms, informational (entropy) theory, statistical learning theory, and equivalent regularization. The universal principle of regularization in terms of Kolmogorov complexity is discussed. Finally, some prospective studies on regularization theory and beyond are suggested.
Using Ontology and Data Provenance to Improve Software Processes

Provenance refers to the origin of a particular object. In computational terms, provenance is a historical record of the derivation of data that can help to understand the current record. In this context, this work presents a proposal for software processes improvement using a provenance data model and an ontology. This improvement can be obtained by process data execution analysis with an approach called PROV-Process, which uses a layer for storing process provenance and an ontology based on PROV-O.
A Constant-Factor Approximation Algorithm for the k-Median Problem

We present the first constant-factor approximation algorithm for the metric k-median problem. The k-median problem is one of the most well-studied clustering problems, i.e., those problems in which the aim is to partition a given set of points into clusters so that the points within a cluster are relatively close with respect to some measure. For the metric k-median problem, we are given n points in a metric space. We select k of these to be cluster centers, and then assign each point to its closest selected center. If point j is assigned to a center i, the cost incurred is proportional to the distance between i and j. The goal is to select the k centers that minimize the sum of the assignment costs. We give a 6 23 -approximation algorithm for this problem. This improves upon the best previously known result of O(log k log log k), which was obtained by refining and derandomizing a randomized O(log n log log n)-approximation algorithm of Bartal.
Cool English: a Grammatical Error Correction System Based on Large Learner Corpora

This paper presents a grammatical error correction (GEC) system that provides corrective feedback for essays. We apply the neural sequence-to-sequence model, which is frequently used in machine translation and text summarization, to this GEC task. The model is trained on EF-Cambridge Open Language Database (EFCAMDAT), a large learner corpus annotated with grammatical errors and corrections. Evaluation shows that our system achieves competitive performance on a number of publicly available testsets.
Precision CMOS current reference with process and temperature compensation

This paper presents a new first-order temperature compensated CMOS current reference. To achieve a compact architecture able to operate under low voltage with low power consumption, it is based on a self-biasing beta multiplier current generator. Compensation against temperature is achieved by using instead of an ordinary resistor two triode transistors in parallel, acting as a negative and a positive temperature coefficient resistor, that generate a proportional to absolute temperature and a complementary to absolute temperature current which can be directly added to attain a temperature compensated current. Programmability is included to adjust the temperature coefficient and the reference current magnitude over process variations. Results for a 0.18 μm CMOS implementation show that the proposed 500 nA reference operate with supplies down to 1.2 V accomplishing over a (-40 to +120°C) range temperature drifts below 120 ppm/°C.
When Lyrics Outperform Audio for Music Mood Classification: A Feature Analysis

This paper builds upon and extends previous work on multi-modal mood classification (i.e., combining audio and lyrics) by analyzing in-depth those feature types that have shown to provide statistically significant improvements in the classification of individual mood categories. The dataset used in this study comprises 5,296 songs (with lyrics and audio for each) divided into 18 mood categories derived from user-generated tags taken from last.fm. These 18 categories show remarkable consistency with the popular Russell’s mood model. In seven categories, lyric features significantly outperformed audio spectral features. In one category only, audio outperformed all lyric features types. A fine grained analysis of the significant lyric feature types indicates a strong and obvious semantic association between extracted terms and the categories. No such obvious semantic linkages were evident in the case where audio spectral features proved superior.
Information Feedback, Targeting, and Coordination: An Experimental Study

We experimentally study the role of information targeting and its effect on coordination in a multi-threshold public goods game. To the best of our knowledge, ours is the first to consider this problem. In a lab setting, we consider four treatments, one in which no information is provided and three others that vary in whom we provide the information to: a random sample of subjects; those whose contributions are below the average of their group, and those whose contributions are above the average of their group. We find that random provision of information is no better than providing no information at all. More importantly, the average contributions improve with targeted treatments. Coordination waste is also lower with targeted treatments. The insights from this research may also be relevant to management in contexts such as piracy, teendrinking, among others, where positively or negatively affecting coordination between consumers is of interest.
Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image

We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework's stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes.
Sinr , Rsrp , Rssi and Rsrq Measurements in Long Term Evolution

The four basic Radio Resource Management (RRM) measurements in Long Term Evolution (LTE) system are Channel Quality Indicator (CQI), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Carrier Received Signal Strength Indicator (RSSI). A measurement of channel quality represented by Signal to Interference plus Noise Ratio (SINR) is used for link adaptation along with packet scheduling, whereas RSRP and RSRQ are needed for making handover decision during intra-eUTRAN (evolved Universal Terrestrial Random Access Network) handover in LTE. In this paper, some practical measurement results recorded from a live LTE network of Australia using a commercial measurement tool namely NEMO Handy are analysed to verify the possible relationships among SINR, RSRP, RSSI and RSRQ as well as to evaluate the effects of SNR on throughput. In addition, the intraeUTRAN handover events occurred during the test period within the test area are studied. The analysis yields some useful information such as: if the SINR is good for a measurement slot, higher throughput is achieved; RSRP and SNR are proportional to each other on average; and lesser is the difference between RSSI and RSRP, better is the RSRQ – each of which is consistent with theory. All the measurement results are evaluated using computer programs built on MATLAB platform.
Reading Comprehension with Deep Learning

We train a model that combines attention with multi-perspective matching to perform question answering. For each question and context pair in SQuAD, we perform an attention calculation over each context before extracting features of the question and context, matching them from multiple perspectives. Whilst we did not have time to perform a hyper-parameter search or incorporate other features into our model, this joint model obtained an F1 score of 22.5 and an EM score of 13.0, outperforming our baseline model without attention, which attained an F1 score of 19.4 and an EM score of 10.0. In the future, we would like to implement an early stopping feature and a co-dependent representation of each question and context pair.
Abnormal driving behavior detection for bus based on the Bayesian classifier

As the primary transportation in the urban traffic, bus plays a significant role in road safety. It would cause severe casualties if an accident occurred. To improve the safety of bus driving, we classify the specific types of latent abnormal driving behavior, which include sudden braking, lane changing casually, quick turn, fast U-turn and long time parking, and propose a method to identify the abnormal driving behavior of the bus. Firstly, we collect the acceleration, orientation and timestamp bus driving data through smartphone's accelerometer and orientation sensor. After de-nosing the collected data, we extract features in thirteen dimensions and train the naive Bayesian classifier, which is employed to detect and identify abnormal driving behaviors. The experimental results have been compared with support vector machine, and shows that the naive Bayesian classifier has an better performance than support vector machine on detecting and identifying various types of the abnormal bus driving behavior with the accuracy at 98.40%.
Printed Drug-Delivery Systems for Improved Patient Treatment.

The use of various types of printing technologies offer potential solutions for personalized medicine and tailored dosage forms to meet the needs of individual treatments of the future. Many types of scenario for printed dosage form exist and the concepts include, on the simplest level, accurately deposited doses of drug substances. In addition, computer design allows endless opportunities to create suitable geometries with tailored functionality and different levels of complexity to control the release properties of one or multiple drug substances. It will take some time to convert these technological developments in printing to better treatments for patients, because challenges exist. However, printing technologies are developing fast and have the potential to allow the use of versatile materials to manufacture sophisticated drug-delivery systems and biofunctional constructs for personalized treatments.
Discovering influential authors in heterogeneous academic networks by a co-ranking method

Research in ranking networked entities is widely applicable to many problems such as optimizing search engines, building recommendation systems and discovering influential nodes in social networks. However, many famous ranking approaches like PageRank are limited to solving this problem in homogeneous networks and are not applicable to heterogeneous networks. Faced with this problem, we propose a co--ranking method to evaluate scientific publications and authors. This novel approach is a flexible framework based on a set of customized rules taking into account both topological features of networks and the included citations. The approach ranks authors and publications iteratively and uses the results of each round to reinforce the ranks of authors and publications. Unlike traditional approaches to assessing publication, which require a great number of citations, our method lowers this requirement. This co--ranking approach has been validated using data collected from DBLP and CiteSeer, and the results suggest that it is effective and efficient in ranking authors and publications based on limited numbers of citations in heterogeneous networks and that it has fast convergence.
Differences in kinematics and electromyographic activity between men and women during the single-legged squat.

Numerous factors have been identified as potentially increasing the risk of anterior cruciate ligament injury in the female athlete. However, differences between the sexes in lower extremity coordination, particularly hip control, are only minimally understood.



There is no difference in kinematic or electromyographic data during the single-legged squat between men and women.



Descriptive comparison study.



We kinematically and electromyographically analyzed the single-legged squat in 18 intercollegiate athletes (9 male, 9 female). Subjects performed five single-legged squats on their dominant leg, lowering themselves as far as possible and then returning to a standing position without losing balance.



Women demonstrated significantly more ankle dorsiflexion, ankle pronation, hip adduction, hip flexion, hip external rotation, and less trunk lateral flexion than men. These factors were associated with a decreased ability of the women to maintain a varus knee position during the squat as compared with the men. Analysis of all eight tested muscles demonstrated that women had greater muscle activation compared with men. When each muscle was analyzed separately, the rectus femoris muscle activation was found to be statistically greater in women in both the area under the linear envelope and maximal activation data.



Under a physiologic load in a position commonly assumed in sports, women tend to position their entire lower extremity and activate muscles in a manner that could increase strain on the anterior cruciate ligament.
Faceted Search

We live in an information age that requires us, more than ever, to represent, access, and use information. Over the last several decades, we have developed a modern science and technology for information retrieval, relentlessly pursuing the vision of a “memex” that Vannevar Bush proposed in his seminal article, “As We May Think.” Faceted search plays a key role in this program. Faceted search addresses weaknesses of conventional search approaches and has emerged as a foundation for interactive information retrieval. User studies demonstrate that faceted search provides more effective information-seeking support to users than best-first search. Indeed, faceted search has become increasingly prevalent in online information access systems, particularly for e-commerce and site search. In this lecture, we explore the history, theory, and practice of faceted search. Although we cannot hope to be exhaustive, our aim is to provide sufficient depth and breadth to offer a useful resource to both researchers and practitioners. Because faceted search is an area of interest to computer scientists, information scientists, interface designers, and usability researchers, we do not assume that the reader is a specialist in any of these fields. Rather, we offer a self-contained treatment of the topic, with an extensive bibliography for those who would like to pursue particular aspects in more depth.
Gaze-Adaptive Above and On-Surface Interaction

We explore the combination of above-surface sensing with eye tracking to facilitate concurrent interaction with multiple regions on touch screens. Conventional touch input relies on positional accuracy, thereby requiring tight visual monitoring of one's own motor action. In contrast, above-surface sensing and eye tracking provides information about how user's hands and gaze are distributed across the interface. In these situations we facilitate interaction by 1) showing the visual feedback of the hand hover near user's gaze point and 2) decrease the requisite of positional accuracy by employing gestural information. We contribute input and visual feedback techniques that combine these modalities and demonstrate their use in example applications. A controlled study showed the effectiveness of our techniques for manipulation tasks against conventional touch, while the effectiveness in acquisition tasks depended on the amount of mid-air motion, leading to our conclusion that the techniques can benefit interacting with multiple interface regions.
Multiobjective Optimization of Ultraflat Magnetic Components With PCB-Integrated Core

In future applications, e.g., in ultra-flat OLED lamp drivers or flat screen power supplies, ultra-flat ac/dc and dc/dc converter systems are highly demanded. Therefore, the design and implementation of a printed circuit board (PCB)-integrated flyback transformer for a 1-mm-thin single-phase power factor correction rectifier is under investigation. In this paper, first an overview on several integration methods is given. It is shown that the PCB integration of magnetic cores allows us to achieve the required thickness of 1 mm and a high energy density. In a next step, the design and the realization of ultra-flat magnetic components with PCB-integrated cores are discussed in detail. The presented multi-objective design procedure determines the inductor and/or transformer setup optimal with respect to minimal losses and/or minimal footprint area; for this purpose, all required electrical, magnetic, and geometrical parameters of the magnetic component are considered in the design process. Furthermore, all specific implications entailed by the PCB-integrated core, e.g., the core setup, anisotropic core losses, the interleaving of windings, or an accurate reluctance model are treated. Finally, experimental results are used to verify the design procedure.