nils-herrmann/clean_scirepeval huggingface dataset:
Watching sex on television predicts adolescent initiation of sexual behavior.

Early sexual initiation is an important social and health issue. A recent survey suggested that most sexually experienced teens wish they had waited longer to have intercourse; other data indicate that unplanned pregnancies and sexually transmitted diseases are more common among those who begin sexual activity earlier. The American Academy of Pediatrics has suggested that portrayals of sex on entertainment television (TV) may contribute to precocious adolescent sex. Approximately two-thirds of TV programs contain sexual content. However, empirical data examining the relationships between exposure to sex on TV and adolescent sexual behaviors are rare and inadequate for addressing the issue of causal effects.



We conducted a national longitudinal survey of 1792 adolescents, 12 to 17 years of age. In baseline and 1-year follow-up interviews, participants reported their TV viewing habits and sexual experience and responded to measures of more than a dozen factors known to be associated with adolescent sexual initiation. TV viewing data were combined with the results of a scientific analysis of TV sexual content to derive measures of exposure to sexual content, depictions of sexual risks or safety, and depictions of sexual behavior (versus talk about sex but no behavior).



Initiation of intercourse and advancement in noncoital sexual activity level, during a 1-year period.



Multivariate regression analysis indicated that adolescents who viewed more sexual content at baseline were more likely to initiate intercourse and progress to more advanced noncoital sexual activities during the subsequent year, controlling for respondent characteristics that might otherwise explain these relationships. The size of the adjusted intercourse effect was such that youths in the 90th percentile of TV sex viewing had a predicted probability of intercourse initiation that was approximately double that of youths in the 10th percentile, for all ages studied. Exposure to TV that included only talk about sex was associated with the same risks as exposure to TV that depicted sexual behavior. African American youths who watched more depictions of sexual risks or safety were less likely to initiate intercourse in the subsequent year.



Watching sex on TV predicts and may hasten adolescent sexual initiation. Reducing the amount of sexual content in entertainment programming, reducing adolescent exposure to this content, or increasing references to and depictions of possible negative consequences of sexual activity could appreciably delay the initiation of coital and noncoital activities. Alternatively, parents may be able to reduce the effects of sexual content by watching TV with their teenaged children and discussing their own beliefs about sex and the behaviors portrayed. Pediatricians should encourage these family discussions.
Steering Output Style and Topic in Neural Response Generation

We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoderdecoder based language generation. This capability is desirable in a variety of applications, including conversational systems, where successful agents need to produce language in a specific style and generate responses steered by a human puppeteer or external knowledge. We decompose the neural generation process into empirically easier sub-problems: a faithfulness model and a decoding method based on selectivesampling. We also describe training and sampling algorithms that bias the generation process with a specific language style restriction, or a topic restriction. Human evaluation results show that our proposed methods are able to to restrict style and topic without degrading output quality in conversational tasks.
Negation-Aware Framework for Sentiment Analysis in Arabic Reviews

Sentiment analysis deals with identifying polarity orientation embedded in users' comments and reviews. It aims at discriminating positive reviews from negative ones. Sentiment is related to culture and language morphology. In this paper, we investigate the effects of language morphology on sentiment analysis in reviews written in the Arabic language. In particular, we investigate, in details, how negation affects sentiments. We also define a set of rules that capture the morphology of negations in Arabic. These rules are then used to detect sentiment taking care of negated words. Experimentations prove that our suggested approach is superior to several existing methods that deal with sentiment detection in Arabic reviews.
CETR: content extraction via tag ratios

We present Content Extraction via Tag Ratios (CETR) - a method to extract content text from diverse webpages by using the HTML document's tag ratios. We describe how to compute tag ratios on a line-by-line basis and then cluster the resulting histogram into content and non-content areas. Initially, we find that the tag ratio histogram is not easily clustered because of its one-dimensionality; therefore we extend the original approach in order to model the data in two dimensions. Next, we present a tailored clustering technique which operates on the two-dimensional model, and then evaluate our approach against a large set of alternative methods using standard accuracy, precision and recall metrics on a large and varied Web corpus. Finally, we show that, in most cases, CETR achieves better content extraction performance than existing methods, especially across varying web domains, languages and styles.
Finding the k shortest simple paths: A new algorithm and its implementation

We describe a new algorithm to enumerate the k shortest simple (loopless) paths in a directed graph and report on its implementation. Our algorithm is based on a replacement paths algorithm proposed by Hershberger and Suri [2001], and can yield a factor Θ(n) improvement for this problem. But there is a caveat: The fast replacement paths subroutine is known to fail for some directed graphs. However, the failure is easily detected, and so our k shortest paths algorithm optimistically uses the fast subroutine, then switches to a slower but correct algorithm if a failure is detected. Thus, the algorithm achieves its Θ(n) speed advantage only when the optimism is justified. Our empirical results show that the replacement paths failure is a rare phenomenon, and the new algorithm outperforms the current best algorithms; the improvement can be substantial in large graphs. For instance, on GIS map data with about 5,000 nodes and 12,000 edges, our algorithm is 4--8 times faster. In synthetic graphs modeling wireless ad hoc networks, our algorithm is about 20 times faster.
A Cyclic Weighted Median Method for L1 Low-Rank Matrix Factorization with Missing Entries

A challenging problem in machine learning, information retrieval and computer vision research is how to recover a low-rank representation of the given data in the presence of outliers and missing entries. The L1-norm low-rank matrix factorization (LRMF) has been a popular approach to solving this problem. However, L1-norm LRMF is difficult to achieve due to its non-convexity and non-smoothness, and existing methods are often inefficient and fail to converge to a desired solution. In this paper we propose a novel cyclic weighted median (CWM) method, which is intrinsically a coordinate decent algorithm, for L1-norm LRMF. The CWM method minimizes the objective by solving a sequence of scalar minimization sub-problems, each of which is convex and can be easily solved by the weighted median filter. The extensive experimental results validate that the CWM method outperforms state-of-the-arts in terms of both accuracy and computational efficiency.
Process-centric business intelligence

The purpose of this paper is to deliver an insight into the interaction effects of process-oriented management and business intelligence (BI).  The paper takes up publications from the fields of BI and business process management and analyzes the state-of-the-art of process-centric business intelligence (PCBI). To highlight the potentials and limitations of the concept, two exemplary use cases are presented and discussed in depth. Furthermore, a vision for the technical implementation is sketched.  PCBI is found to play an important role in an organization’s strive for competitiveness. The concept’s potential benefits are significant. However, the overall levels of adoption and maturity of the concept within real-world organizations appear to be rather low at the moment.  The paper discusses solely two exemplary use cases – the most that could be done within the scope of a journal publication. Therefore, the explanatory power and the representativeness of the results need to be scrutinized in detail.  The paper highlights the practical significance of PCBI. It therefore represents a useful source of information for both practitioners and academics who are interested in improving the efficiency and effectiveness of an organization’s information supply in support of its processes.  The paper motivates, describes, and analyzes the concept of PCBI. Furthermore, it provides examples of the concept’s adoption and benefits from a practitioner’s point of view.
A 4.39–5.26 GHz LC-Tank CMOS Voltage-Controlled Oscillator With Small VCO-Gain Variation

A wide band CMOS LC-tank voltage controlled oscillator (VCO) with small VCO gain (KVCO) variation was developed. For small KVCO variation, serial capacitor bank was added to the LC-tank with parallel capacitor array. Implemented in a 0.18 mum CMOS RF technology, the proposed VCO can be tuned from 4.39 GHz to 5.26 GHz with the VCO gain variation less than 9.56%. While consuming 3.5 mA from a 1.8 V supply, the VCO has -113.65 dBc/Hz phase noise at 1 MHz offset from the carrier.
Accounting Conservatism , the Quality of Earnings , and Stock Returns

When a firm practices conservative accounting, changes in the amount of its investments can affect the quality of its earnings. Growth in investment reduces reported earnings and creates reserves. Reducing investment releases those reserves, increasing earnings. If the change in investment is temporary, then current earnings is temporarily depressed or inflated, and thus is not a good indicator of future earnings. This study develops diagnostic measures of this joint effect of investment and conservative accounting. We find that these measures forecast differences in future return on net operating assets relative to current return on net operating assets. Moreover, these measures also forecast stock returns-indicating that investors do not appreciate how conservatism and changes in investment combine to raise questions about the quality of reported earnings.
Drawing Graphs Nicely Using Simulated Annealing

The paradigm of simulated annealing is applied to the problem of drawing graphs “nicely.” Our algorithm deals with general undirected graphs with straight-line edges, and employs several simple criteria for the aesthetic quality of the result. The algorithm is flexible, in that the relative weights of the criteria can be changed. For graphs of modest size it produces good results, competitive with those produced by other methods, notably, the “spring method” and its variants.
LATCH: Learned arrangements of three patch codes

We present a novel means of describing local image appearances using binary strings. Binary descriptors have drawn increasing interest in recent years due to their speed and low memory footprint. A known shortcoming of these representations is their inferior performance compared to larger, histogram based descriptors such as the SIFT. Our goal is to close this performance gap while maintaining the benefits attributed to binary representations. To this end we propose the Learned Arrangements of Three Patch Codes descriptors, or LATCH. Our key observation is that existing binary descriptors are at an increased risk from noise and local appearance variations. This, as they compare the values of pixel pairs: changes to either of the pixels can easily lead to changes in descriptor values and compromise their performance. In order to provide more robustness, we instead propose a novel means of comparing pixel patches. This ostensibly small change, requires a substantial redesign of the descriptors themselves and how they are produced. Our resulting LATCH representation is rigorously compared to state-of-the-art binary descriptors and shown to provide far better performance for similar computation and space requirements.
Transfer Learning Based Cross-lingual Knowledge Extraction for Wikipedia

Wikipedia infoboxes are a valuable source of structured knowledge for global knowledge sharing. However, infobox information is very incomplete and imbalanced among the Wikipedias in different languages. It is a promising but challenging problem to utilize the rich structured knowledge from a source language Wikipedia to help complete the missing infoboxes for a target language. In this paper, we formulate the problem of cross-lingual knowledge extraction from multilingual Wikipedia sources, and present a novel framework, called WikiCiKE, to solve this problem. An instancebased transfer learning method is utilized to overcome the problems of topic drift and translation errors. Our experimental results demonstrate that WikiCiKE outperforms the monolingual knowledge extraction method and the translation-based method.
38 GHz and 60 GHz angle-dependent propagation for cellular & peer-to-peer wireless communications

As the cost of massively broadband® semiconductors continue to be driven down at millimeter wave (mm-wave) frequencies, there is great potential to use LMDS spectrum (in the 28-38 GHz bands) and the 60 GHz band for cellular/mobile and peer-to-peer wireless networks. This work presents urban cellular and peer-to-peer RF wideband channel measurements using a broadband sliding correlator channel sounder and steerable antennas at carrier frequencies of 38 GHz and 60 GHz, and presents measurements showing the propagation time delay spread and path loss as a function of separation distance and antenna pointing angles for many types of real-world environments. The data presented here show that at 38 GHz, unobstructed Line of Site (LOS) channels obey free space propagation path loss while non-LOS (NLOS) channels have large multipath delay spreads and can exploit many different pointing angles to provide propagation links. At 60 GHz, there is notably more path loss, smaller delay spreads, and fewer unique antenna angles for creating a link. For both 38 GHz and 60 GHz, we demonstrate empirical relationships between the RMS delay spread and antenna pointing angles, and observe that excess path loss (above free space) has an inverse relationship with transmitter-to-receiver separation distance.
The enactive mind, or from actions to cognition: lessons from autism.

Normative-IQ individuals with autism are capable of solving explicit social cognitive problems at a level that is not matched by their ability to meet the demands of everyday social situations. The magnitude of this discrepancy is now being documented through newer techniques such as eye tracking, which allows us to see and measure how individuals with autism search for meaning when presented with naturalistic social scenes. This paper offers an approach to social cognitive development intended to address the above discrepancy, which is considered a key element for any understanding of the pathophysiology of autism. This approach, called the enactive mind (EM), originates from the emerging work on 'embodied cognitive science', a neuroscience framework that views cognition as bodily experiences accrued as a result of an organism's adaptive actions upon salient aspects of the surrounding environment. The EM approach offers a developmental hypothesis of autism in which the process of acquisition of embodied social cognition is derailed early on, as a result of reduced salience of social stimuli and concomitant enactment of socially irrelevant aspects of the environment.
Real time dynamic fracture with volumetric approximate convex decompositions

We propose a new fast, robust and controllable method to simulate the dynamic destruction of large and complex objects in real time. The common method for fracture simulation in computer games is to pre-fracture models and replace objects by their pre-computed parts at run-time. This popular method is computationally cheap but has the disadvantages that the fracture pattern does not align with the impact location and that the number of hierarchical fracture levels is fixed. Our method allows dynamic fracturing of large objects into an unlimited number of pieces fast enough to be used in computer games. We represent visual meshes by volumetric approximate convex decompositions (VACD) and apply user-defined fracture patterns dependent on the impact location. The method supports partial fracturing meaning that fracture patterns can be applied locally at multiple locations of an object. We propose new methods for computing a VACD, for approximate convex hull construction and for detecting islands in the convex decomposition after partial destruction in order to determine support structures.
On the Semantics and Pragmatics of Linguistic Feedback

This paper is an exploration in the semantics and pragmatics of linguistic feedback, i.e., linguistic mechanisms which enable the participants in spoken interaction to exchange information about basic communicative functions, such as contact, perception, understanding, and attitudinal reactions to the communicated content. Special attention is given to the type of reaction conveyed by feedback utterances, the communicative status of the information conveyed (i. e., the level of awareness and intentionality of the communicating sender), and the context sensitivity of feedback expressions. With regard to context sensitivity, which is one of the most characteristic features of feedback expressions, the discussion focuses on the way in which the type of speech act (mood), the factual polarity and the information status of the preceding utterance influence the interpretation of feedback utterances. The different content dimensions are exemplified by data from recorded dialogues and by data given through linguistic intuition. Finally, two different ways of formalizing the analysis are examined, one using attribute-value matrices and one based on the theory of situation semantics. ___________________________________________________________________ Authors' address: Jens Allwood, Joakim Nivre, and Elisabeth Ahlsén Department of Lingustics University of Göteborg Box 200 S-405 30 Göteborg Sweden
Gaussian Processes for Regression

The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions . In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.
Applying QR code and mobile application to improve service process in Thai hospital

Hospital overcrowding has been a problem in Thai public healthcare system. The main cause of this problem is the limited available resources, including a limited number of doctors, nurses, and limited capacity and availability of medical devices. There have been attempts to alleviate the problem through various strategies. In this paper, a low-cost system was developed and tested in a public hospital with limited budget. The system utilized QR code and smartphone application to capture as-is hospital processes and the time spent on individual activities. With the available activity data, two algorithms were developed to identify two quantities that are valuable to conduct process improvement: the most congested time and bottleneck activities. The system was implemented in a public hospital and results were presented.
Dropout with Expectation-linear Regularization

Dropout, a simple and effective way to train deep neural networks, has led to a number of impressive empirical successes and spawned many recent theoretical investigations. However, the gap between dropout’s training and inference phases, introduced due to tractability considerations, has largely remained under-appreciated. In this work, we first formulate dropout as a tractable approximation of some latent variable model, leading to a clean view of parameter sharing and enabling further theoretical analysis. Then, we introduce (approximate) expectation-linear dropout neural networks, whose inference gap we are able to formally characterize. Algorithmically, we show that our proposed measure of the inference gap can be used to regularize the standard dropout training objective, resulting in an explicit control of the gap. Our method is as simple and efficient as standard dropout. We further prove the upper bounds on the loss in accuracy due to expectation-linearization, describe classes of input distributions that expectation-linearize easily. Experiments on three image classification benchmark datasets demonstrate that reducing the inference gap can indeed improve the performance consistently.
Incremental learning of event definitions with Inductive Logic Programming

Event recognition systems rely on knowledge bases of event definitions to infer occurrences of events in time. Using a logical framework for representing and reasoning about events offers direct connections to machine learning, via Inductive Logic Programming (ILP), thus allowing to avoid the tedious and error-prone task of manual knowledge construction. However, learning temporal logical formalisms, which are typically utilized by logic-based event recognition systems is a challenging task, which most ILP systems cannot fully undertake. In addition, event-based data is usually massive and collected at different times and under various circumstances. Ideally, systems that learn from temporal data should be able to operate in an incremental mode, that is, revise prior constructed knowledge in the face of new evidence. In this work we present an incremental method for learning and revising event-based knowledge, in the form of Event Calculus programs. The proposed algorithm relies on abductive–inductive learning and comprises a scalable clause refinement methodology, based on a compressive summarization of clause coverage in a stream of examples. We present an empirical evaluation of our approach on real and synthetic data from activity recognition and city transport applications.
Control of a hydraulically-actuated quadruped robot leg

This paper is focussed on the modelling and control of a hydraulically-driven biologically-inspired robotic leg. The study is part of a larger project aiming at the development of an autonomous quadruped robot (hyQ) for outdoor operations. The leg has two hydraulically-actuated degrees of freedom (DOF), the hip and knee joints. The actuation system is composed of proportional valves and asymmetric cylinders. After a brief description of the prototype leg, the paper shows the development of a comprehensive model of the leg where critical parameters have been experimentally identified. Subsequently the leg control design is presented. The core of this work is the experimental assessment of the pros and cons of single-input single-output (SISO) vs. multiple-input multiple-output (MIMO) and linear vs. nonlinear control algorithms in this application (the leg is a coupled multivariable system driven by nonlinear actuators). The control schemes developed are a conventional PID (linear SISO), a Linear Quadratic Regulator (LQR) controller (linear MIMO) and a Feedback Linearisation (FL) controller (nonlinear MIMO). LQR performs well at low frequency but its behaviour worsens at higher frequencies. FL produces the fastest response in simulation, but when implemented is sensitive to parameters uncertainty and needs to be properly modified to achieve equally good performance also in the practical implementation.