nils-herrmann/clean_scirepeval huggingface dataset:
Transfer Learning for Behavior Ranking

Intelligent recommendation has been well recognized as one of the major approaches to address the information overload problem in the big data era. A typical intelligent recommendation engine usually consists of three major components, that is, data as the main input, algorithms for preference learning, and system for user interaction and high-performance computation. We observe that the data (e.g., users’ behavior) are usually in different forms, such as examinations (e.g., browse and collection) and ratings, where the former are often much more abundant than the latter. Although the data are in different representations, they are both related to users’ true preferences and are also deemed complementary to each other for preference learning. However, very few ranking or recommendation algorithms have been developed to exploit such two types of user behavior.
 In this article, we focus on jointly modeling the examination behavior and rating behavior and develop a novel and efficient ranking-oriented recommendation algorithm accordingly. First, we formally define a new recommendation problem termed behavior ranking, which aims to build a ranking-oriented model by exploiting both the examination behavior and rating behavior. Second, we develop a simple and generic transfer to rank (ToR) algorithm for behavior ranking, which transfers knowledge of candidate items from a global preference learning task to a local preference learning task. Compared with the previous work on integrating heterogeneous user behavior, our ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show that our ToR algorithm performs significantly more accurately than the state-of-the-art methods in most cases. Furthermore, our ToR algorithm is very efficient in terms of the time complexity, which is similar to those for homogeneous user behavior alone.
Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information

Sequence-to-sequence (seq2seq) neural models have been actively investigated 1 for abstractive summarization. Nevertheless, existing neural abstractive systems 2 frequently generate factually incorrect summaries and are vulnerable to adversarial 3 information, suggesting a crucial lack of semantic understanding. In this paper, we 4 propose a novel semantic-aware neural abstractive summarization model that learns 5 to generate high quality summaries through semantic interpretation over salient 6 content. A novel evaluation scheme with adversarial samples is introduced to 7 measure how well a model identifies off-topic information, where our model yields 8 significantly better performance than the popular pointer-generator summarizer. 9 Human evaluation also confirms that our system summaries are uniformly more 10 informative and faithful as well as less redundant than the seq2seq model.
It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text

Word sense disambiguation (WSD) systems based on supervised learning achieved the best performance in SensEval and SemEval workshops. However, there are few publicly available open source WSD systems. This limits the use of WSD in other applications, especially for researchers whose research interests are not in WSD. In this paper, we present IMS, a supervised English all-words WSD system. The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers. By default, we use linear support vector machines as the classifier with multiple knowledge-based features. In our implementation, IMS achieves state-of-the-art results on several SensEval and SemEval tasks.
From Pose to Activity: Surveying Datasets and Introducing CONVERSE

We present a review on the current state of publicly available datasets within the human action recognition community; highlighting the revival of pose based methods and recent progress of understanding person-person interaction modeling. We categorize datasets regarding several key properties for usage as a benchmark dataset; including the number of class labels, ground truths provided, and application domain they occupy. We also consider the level of abstraction of each dataset; grouping those that present actions, interactions and higher level semantic activities. The survey identifies key appearance and pose based datasets, noting a tendency for simplistic, emphasized, or scripted action classes that are often readily definable by a stable collection of subaction gestures. There is a clear lack of datasets that provide closely related actions, those that are not implicitly identified via a series of poses and gestures, but rather a dynamic set of interactions. We therefore propose a novel dataset that represents complex conversational interactions between two individuals via 3D pose. 8 pairwise interactions describing 7 separate conversation based scenarios were collected using two Kinect depth sensors. The intention is to provide events that are constructed from numerous primitive actions, interactions and motions, over a period of time; providing a set of subtle action classes that are more representative of the real world, and a challenge to currently developed recognition methodologies. We believe this is among one of the first datasets devoted to conversational interaction classification using 3D pose Preprint submitted to Elsevier October 27, 2015 features and the attributed papers show this task is indeed possible. The full dataset is made publicly available to the research community at .
Authentication via Keystroke Dynamics

In an effort to confront the challenges brought forward by the networking revolution of the past few years, we present improved techniques for authorized access to computer system resources and data. More than ever before, the Internet is changing computing as we know it. The possibilities of this global network seem limitless; unfortunately, with this global access comes increased chances of malicious attack and intrusion. Alternatives to traditional access control measures are in high demand. In what follows we present one such alternative: computer access via keystroke dynamics. A database of 42 profiles was constructed based on keystroke patterns gathered from various users performing structured and unstructured tasks. We study the performance of a system for recognition of these users, and present a toolkit for analyzing system performance under varying criteria.
MEANS: A medical question-answering system combining NLP techniques and semantic Web technologies

The Question Answering (QA) task aims to provide precise and quick answers to user questions from a collection of documents or a database. This kind of IR system is sorely needed with the dramatic growth of digital information. In this paper, we address the problem of QA in the medical domain where several specific conditions are met. We propose a semantic approach to QA based on (i) Natural Language Processing techniques, which allow a deep analysis of medical questions and documents and (ii) semantic Web technologies at both representation and interrogation levels. We present our Semantic QuestionAnswering System, called MEANS and our proposed method for ‘‘Answer Search’’ based on semantic search and query relaxation. We evaluate the overall system performance on real questions and answers extracted from MEDLINE articles. Our experiments show promising results and suggest that a query-relaxation strategy can further improve the overall performance.
Reconstruction of the high urogenital sinus: early perineal prone approach without division of the rectum.

Reconstruction of the vagina and external genitalia in the infant is quite challenging, particularly when a urogenital sinus is associated with high confluence of the vagina and urethra. Many surgeons believe that children with such a malformation should undergo staged or delayed reconstruction, so that vaginoplasty is done when the child is older and larger. Vaginoplasty early in life is thought to be difficult due to patient size and poor visualization. The posterior sagittal approach has been beneficial for acquiring exposure to high urogenital sinus anomalies but it has been thought to require splitting of the rectum and temporary colostomy. We report a modification of this technique.



In the last 5 years all patients with urogenital sinus anomalies underwent reconstruction using a single stage approach regardless of the level of confluence. In 8 patients with a high level of confluence reconstruction was performed using a perineal prone approach. Exposure was achieved without division of the rectum. The operative technique is presented in detail.



This midline perineal prone approach has allowed excellent exposure of the high vagina even in infants. In all 8 patients reconstruction was done without difficulty and no patient required incision of the rectum or colostomy. This procedure did not preclude the use of a posteriorly based flap for vaginal reconstruction.



While patients with low confluence can be treated with single posteriorly based flap vaginoplasty, those with higher confluence may benefit from a perineal prone approach to achieve adequate exposure for pull-through vaginoplasty. This prone approach to the high urogenital sinus anomaly can be performed without division of the rectum, provides excellent exposure of the high confluence even in small children and does not preclude the use of posterior flaps for vaginal reconstruction.
Distributed Representations of Words and Documents for Discriminating Similar Languages

Discriminating between similar languages or language varieties aims to detect lexical and semantic variations in order to classify these varieties of languages. In this work we describe the system built by the Pattern Recognition and Human Language Technology (PRHLT) research center Universitat Politècnica de València and Autoritas Consulting for the Discriminating between similar languages (DSL) 2015 shared task. In order to determine the language group of similar languages, we first employ a simple approach based on distances with language prototypes with 99.8% accuracy in the test sets. For classifying intra-group languages we focus on the use of distributed representations of words and documents using the continuous Skip-gram model. Experimental results of classification of languages in 14 categories yielded accuracies of 92.7% and 90.8% when classifying unmodified texts and text with hidden named entities, respectively.
Estimating Attributes: Analysis and Extensions of RELIEF

In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very eecient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are veriied on various artiicial and one well known real-world problem.
Why Are Memristor and Memistor Different Devices?

This paper presents a circuit-theoretic foundation of the “memristor,” and clarifies why it is fundamentally different from a 3-terminal device with a similarly-sounding name called the “memistor.” Here we show that while the memristor is a basic 2-terminal circuit element based on classic nonlinear circuit theory, the memistor is an ad hoc 3-terminal gadget devised for one specific application, and does not qualify as a 3-terminal circuit element because it is impossible to predict its behavior when connected with other circuit elements.
Differential-drive CMOS rectifier for UHF RFIDs with 66% PCE at −12 dBm Input

A high efficiency differential CMOS rectifier circuit for UHF RFIDs was developed. The rectifier has a cross-coupled bridge configuration and is driven by a differential RF input. Differential-drive topology enables simultaneous low ON-resistance and small reverse leakage of diode-connected MOS transistors, resulting in large power conversion efficiency(PCE), especially under small RF input power conditions. The differential-drive rectifier was fabricated with 0.18-mum CMOS technology, and the measured performance was compared with those of other types of rectifiers. Dependence of the PCE on an input RF signal frequency and output loading conditions was also evaluated. 66% of PCE was achieved under conditions of 953 MHz, -12 dBm RF input and 10 KOmega DC output load. This is twice as large as that of the state-of-the-art rectifier circuit. The peak PCE increases with a decrease in operation frequency and with an increase in output load resistance.
Adversarial Objectives for Text Generation

Language models can be used to generate text by iteratively sampling words conditioned on previously sampled words. In this work, we explore adversarial objectives to obtain good text generations by training a recurrent language model to keep its hidden state statistics during sampling similar to what it has sees during maximum likelihood estimation (MLE) training. We analyze the convergence of these models and discuss the effect of the adversarial objective on word-level and character-level language models. We find that using an adversarial objective assists the MLE objective and results in faster convergence and lower validation perplexities for character-level language models. We also collect sentence quality ratings from human participants and show that character-level language models with the adversarial objective generates qualitatively better sentences than the standard character-level recurrent language model.
Image features that draw fixations

The ability to automatically detect ‘visually interesting’ regions in an image has many practical applications especially in the design of active machine vision systems. This paper describes a data-driven approach that uses eye tracking in tandem with principal component analysis to extract lowlevel image features that attract human gaze. Data analysis on an ensemble of image patches extracted at the observer’s point of gaze revealed features that resemble derivatives of the 2D Gaussian operator. Dissimilarities between human and random fixations are investigated by comparing the features extracted at the point of gaze to the general image structure obtained by random sampling in monte-carlo simulations. Finally, a simple application where these features are used to predict fixations is illustrated.
Fault investigations on die-cast copper rotors

It has been long known to the market that, the use of copper in the rotor in place of aluminium, taking advantage of copper's high conductivity, can be an economic solution for various applications, including electric vehicle, in the development of energy efficient motors. In comparison to aluminium, copper die-casting is extremely difficult to perform due to its high density, viscosity and melting point. Owing to these reasons, the copper die-cast rotors will evince several manufacturing problems. The objective of this paper is to identify the various defects in the manufacturing of copper die-cast rotor, and identify the lamination-coating-condition that sustains copper die-cast pressure and temperature. This paper evaluates the effectiveness of lamination coating on limiting the eddy current loss, and the recoating process to improve, in case the eddy current losses are in excess due to copper die-casting.
On IC traceability via blockchain

Traceability of ICs is important for verifying provenance. We present a novel IC traceability scheme based on blockchain. A blockchain is an immutable public record that maintains a continuously-growing list of data records secured from tampering and revision. In the proposed scheme, IC ownership transfer information is recorded and archived in a blockchain. This safe, verifiable method prevents any party from altering or challenging the legitimacy of the information being exchanged. However, we also need to establish correspondence between a record in a public database and the physical device, in an unclonable way. We propose an embedded physically unclonable function (PUF) to establish this correspondence The blockchain ensures the legitimacy of an IC's current owner, while the PUF safeguards against IC counterfeiting and tampering. Our proposed solution combines hardware and software protocols to let supply chain participants authenticate, track, trace, analyze, and provision chips during their entire life cycle.
Software-Specific Named Entity Recognition in Software Engineering Social Content

Software engineering social content, such as Q&A discussions on Stack Overflow, has become a wealth of information on software engineering. This textual content is centered around software-specific entities, and their usage patterns, issues-solutions, and alternatives. However, existing approaches to analyzing software engineering texts treat software-specific entities in the same way as other content, and thus cannot support the recent advance of entity-centric applications, such as direct answers and knowledge graph. The first step towards enabling these entity-centric applications for software engineering is to recognize and classify software-specific entities, which is referred to as Named Entity Recognition (NER) in the literature. Existing NER methods are designed for recognizing person, location and organization in formal and social texts, which are not applicable to NER in software engineering. Existing information extraction methods for software engineering are limited to API identification and linking of a particular programming language. In this paper, we formulate the research problem of NER in software engineering. We identify the challenges in designing a software-specific NER system and propose a machine learning based approach applied on software engineering social content. Our NER system, called S-NER, is general for software engineering in that it can recognize a broad category of software entities for a wide range of popular programming languages, platform, and library. We conduct systematic experiments to evaluate our machine learning based S-NER against a well-designed, and to study the effectiveness of widely-adopted NER techniques and features in the face of the unique characteristics of software engineering social content.
Women with hyperandrogenism in elite sports: scientific and ethical rationales for regulating.

The recent implementation by some major sports-governing bodies of policies governing eligibility of females with hyperandrogenism to compete in women's sports has raised a lot of attention and is still a controversial issue. This short article addresses two main subjects of controversy: the existing scientific basis supporting performance enhancing of high blood T levels in elite female athletes, and the ethical rationale and considerations about these policies. Given the recently published data about both innate and acquired hyperandrogenic conditions and their prevalence in elite female sports, we claim that the high level of androgens are per se performance enhancing. Regulating women with clinical and biological hyperandrogenism is an invitation to criticism because biological parameters of sex are not neatly divided into only two categories in the real world. It is, however, the responsibility of the sports-governing bodies to do their best to guarantee a level playing field to all athletes. In order not cloud the discussions about the policies on hyperandrogenism in sports, issues of sports eligibility and therapeutic options should always be considered and explained separately, even if they may overlap. Finally, some proposals for refining the existing policies are made in the present article.
Segmentation, Feature Extraction, and Multiclass Brain Tumor Classification

Multiclass brain tumor classification is performed by using a diversified dataset of 428 post-contrast T1-weighted MR images from 55 patients. These images are of primary brain tumors namely astrocytoma (AS), glioblastoma multiforme (GBM), childhood tumor-medulloblastoma (MED), meningioma (MEN), secondary tumor-metastatic (MET), and normal regions (NR). Eight hundred fifty-six regions of interest (SROIs) are extracted by a content-based active contour model. Two hundred eighteen intensity and texture features are extracted from these SROIs. In this study, principal component analysis (PCA) is used for reduction of dimensionality of the feature space. These six classes are then classified by artificial neural network (ANN). Hence, this approach is named as PCA-ANN approach. Three sets of experiments have been performed. In the first experiment, classification accuracy by ANN approach is performed. In the second experiment, PCA-ANN approach with random sub-sampling has been used in which the SROIs from the same patient may get repeated during testing. It is observed that the classification accuracy has increased from 77 to 91 %. PCA-ANN has delivered high accuracy for each class: AS—90.74 %, GBM—88.46 %, MED—85 %, MEN—90.70 %, MET—96.67 %, and NR—93.78 %. In the third experiment, to remove bias and to test the robustness of the proposed system, data is partitioned in a manner such that the SROIs from the same patient are not common for training and testing sets. In this case also, the proposed system has performed well by delivering an overall accuracy of 85.23 %. The individual class accuracy for each class is: AS—86.15 %, GBM—65.1 %, MED—63.36 %, MEN—91.5 %, MET—65.21 %, and NR—93.3 %. A computer-aided diagnostic system comprising of developed methods for segmentation, feature extraction, and classification of brain tumors can be beneficial to radiologists for precise localization, diagnosis, and interpretation of brain tumors on MR images.
On-Board Dual-Stereo-Vision for the Navigation of an Autonomous MAV

We present a quadrotor Micro Aerial Vehicle (MAV) equipped with four cameras, which are arranged in two stereo configurations. The MAV is able to perform stereo matching for each camera pair on-board and in real-time, using an efficient sparse stereo method. In case of the camera pair that is facing forward, the stereo matching results are used for a reduced stereo SLAM system. The other camera pair, which is facing downwards, is used for ground plane detection and tracking. Hence, we are able to obtain a full 6DoF pose estimate from each camera pair, which we fuse with inertial measurements in an extended Kalman filter. Special care is taken to compensate various drift errors. In an evaluation we show that using two instead of one camera pair significantly increases the pose estimation accuracy and robustness.