nils-herrmann/clean_scirepeval huggingface dataset:
SciTaiL: A Textual Entailment Dataset from Science Question Answering

We present a new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem. SCITAIL is the first entailment set that is created solely from natural sentences that already exist independently “in the wild” rather than sentences authored specifically for the entailment task. Different from existing entailment datasets, we create hypotheses from science questions and the corresponding answer candidates, and premises from relevant web sentences retrieved from a large corpus. These sentences are often linguistically challenging. This, combined with the high lexical similarity of premise and hypothesis for both entailed and non-entailed pairs, makes this new entailment task particularly difficult. The resulting challenge is evidenced by state-of-the-art textual entailment systems achieving mediocre performance on SCITAIL, especially in comparison to a simple majority class baseline. As a step forward, we demonstrate that one can improve accuracy on SCITAIL by 5% using a new neural model that exploits linguistic structure.
Evidence Extraction for Machine Reading Comprehension with Deep Probabilistic Logic

In this paper, we focus on extracting evidence sentences for multiple-choice machine reading comprehension. Previous neural multiple-choice reading comprehension models take the whole context as input and seldom provide evidence to make their predictions convincing. We hypothesize that identifying evidence sentences that explain or support question-answer pairs can improve the interpretability of models and further boost their performance. The unavailability of ground truth in most existing datasets, and the fact that we usually require multiple-sentence reasoning and prior knowledge to find evidence makes this task more challenging. Our system contains two deep neural network (DNN) modules: one for evidence sentence extraction and the other for answer option selection (e.g., exsiting reading comprehension models). We apply distant supervision to train a DNN for evidence sentence extraction. To denoise imperfect labels, we treat evidence sentences as latent variables and combine all the linguistic priors over evidence sentences based on a deep probabilistic logic learning framework. We evaluate our methods on three challenging multiple-choice reading comprehension datasets: MultiRC, RACE, and SemEval2018 Task 11. We achieve state-of-the-art performance on all these datasets, indicating the effectiveness of our proposed evidence sentence extraction
How Information Visualization Novices Construct Visualizations

It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. We derived abstract models from our observations that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process.
REMOTE SENSING OF VEGETATION FROM UAV PLATFORMS USING LIGHTWEIGHT MULTISPECTRAL AND THERMAL IMAGING SENSORS

The recent interest of unmanned aerial vehicles (UAV) for vegetation monitoring has been motivated by the benefits of these platforms as compared to full size airborne operation, namely the combination of high spatial resolution and quick turnaround times together with lower operation costs and complexity. These features are of special interest in agriculture where short revisit time is required for management applications and high spatial resolution is mandatory in heterogeneous covers like woody crops. Most of these applications have been possible due to the miniaturization of commercial multispectral and thermal cameras which however require radiometric and geometric calibrations together with atmospheric correction and photogrammetric techniques in order to provide image products similar to the available from traditional airborne sensors. The radiometric quality of the images is critical in order to enable the application of quantitative remote sensing methodologies for a successful estimation of biophysical parameters from remote sensing imagery.
Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.
High boost converter using voltage multiplier

With the increasing demand for renewable energy, distributed power included in fuel cells have been studied and developed as a future energy source. For this system, a power conversion circuit is necessary to interface the generated power to the utility. In many cases, a high step-up DC/DC converter is needed to boost low input voltage to high voltage output. Conventional methods using cascade DC/DC converters cause extra complexity and higher cost. The conventional topologies to get high output voltage use flyback DC/DC converters. They have the leakage components that cause stress and loss of energy that results in low efficiency. This paper presents a high boost converter with a voltage multiplier and a coupled inductor. The secondary voltage of the coupled inductor is rectified using a voltage multiplier. High boost voltage is obtained with low duty cycle. Theoretical analysis and experimental results verify the proposed solutions using a 300 W prototype.
Effects of soft drink consumption on nutrition and health: a systematic review and meta-analysis.

In a meta-analysis of 88 studies, we examined the association between soft drink consumption and nutrition and health outcomes. We found clear associations of soft drink intake with increased energy intake and body weight. Soft drink intake also was associated with lower intakes of milk, calcium, and other nutrients and with an increased risk of several medical problems (e.g., diabetes). Study design significantly influenced results: larger effect sizes were observed in studies with stronger methods (longitudinal and experimental vs cross-sectional studies). Several other factors also moderated effect sizes (e.g., gender, age, beverage type). Finally, studies funded by the food industry reported significantly smaller effects than did non-industry-funded studies. Recommendations to reduce population soft drink consumption are strongly supported by the available science.
Impulsive Buying Behaviour : The Role of Feelings When Shopping for Online Fashion

This paper presents an in-depth study of young Swedish consumers and their impulsive online buying behaviour for clothing. The aim of the study is to develop the understanding of what factors affect impulse buying of clothing online and what feelings emerge when buying online. The study carried out was exploratory in nature, aiming to develop an understanding of impulse buying behaviour online before, under and after the actual purchase. The empirical data was collected through personal interviews. In the study, a pattern of the consumers recurrent feelings are identified through the impulse buying process; escapism, pleasure, reward, scarcity, security and anticipation. The escapism is particularly occurring since the study revealed that the consumers often carried out impulse purchases when they initially were bored, as opposed to previous studies. 1 University of Borås, Swedish Institute for Innovative Retailing, School of Business and IT, Allégatan 1, S-501 90 Borås, Sweden.
LAPAROSCOPIC TRANSABDOMINAL PRE-PERITONEAL (TAPP) PROCEDURE FOR GROIN HERNIA. HOW TO DO IT FOR BETTER OUTCOMES

LAPAROSCOPIC TRANSABDOMINAL PRE-PERITONEAL PROCEDURE (TAPP) FOR GROIN HERNIA. HOW TO DO IT FOR BETTER OUTCOMES (Abstract): The laparoscopic approach for the groin hernia repair has several advantages: decreased immediate and late postoperative pain, less numbness in inguinal aria, less mesh infection and a rapid recovery. However the good outcomes are not granted, and there are some key points to be followed for better postoperative results. The aim of this video is to highlight these TAPP (TransAbdominal Pre-Peritoneal) related key points, from the operative indication, pre operative preparation and surgical procedure, until the post operative follow up.
Document Fraud Detection with the help of Data Mining and Secure Substitution Method with Frequency Analysis Ms

Prevention of fraud and abuse has become a major concern of many organizations. The industry recognizes the problem and is just now starting to act. Although prevention is the best way to reduce frauds, fraudsters are adaptive and will usually find ways to circumvent such measures. Detecting fraud is essential once prevention mechanism has failed. Several data mining algorithms have been developed that allow one to extract relevant knowledge from a large amount of data like fraudulent financial statements to detect. In this paper we present an efficient approach for fraud detection. In our approach we first maintain a log file for data which contain the content separated by space, position and also the frequency. Then we encrypt the data by substitution method and send to the receiver end. We also send the log file to the receiver end before proceed to the encryption which is also in the form of secret message. So the receiver can match the data according to the content, position and frequency, if there is any mismatch occurs, we can detect the fraud and does not accept the file.
Predicting movie ratings from audience behaviors

We propose a method of representing audience behavior through facial and body motions from a single video stream, and use these features to predict the rating for feature-length movies. This is a very challenging problem as: i) the movie viewing environment is dark and contains views of people at different scales and viewpoints; ii) the duration of feature-length movies is long (80-120 mins) so tracking people uninterrupted for this length of time is still an unsolved problem; and iii) expressions and motions of audience members are subtle, short and sparse making labeling of activities unreliable. To circumvent these issues, we use an infrared illuminated test-bed to obtain a visually uniform input. We then utilize motion-history features which capture the subtle movements of a person within a pre-defined volume, and then form a group representation of the audience by a histogram of pair-wise correlations over a small-window of time. Using this group representation, we learn our movie rating classifier from crowd-sourced ratings collected by rottentomatoes.com and show our prediction capability on audiences from 30 movies across 250 subjects (> 50 hrs).
Gesture-Based Affective Computing on Motion Capture Data

This paper presents research using full body skeletal movements captured using video-based sensor technology developed by Vicon Motion Systems, to train a machine to identify different human emotions. The Vicon system uses a series of 6 cameras to capture lightweight markers placed on various points of the body in 3D space, and digitizes movement into x, y, and z displacement data. Gestural data from five subjects was collected depicting four emotions: sadness, joy, anger, and fear. Experimental results with different machine learning techniques show that automatic classification of this data ranges from 84% to 92% depending on how it is calculated. In order to put these automatic classification results into perspective a user study on the human perception of the same data was conducted with average classification accuracy of 93%.
Culture and the Self : Implications for Cognition , Emotion , and Motivation

People in different cultures have strikingly different construals of the self, of others, and of the interdependence of the 2. These construals can influence, and in many cases determine, the very nature of individual experience, including cognition, emotion, and motivation. Many Asian cultures have distinct conceptions of individuality that insist on the fundamental relatedness of individuals to each other. The emphasis is on attending to others, fitting in, and harmonious interdependence with them. American culture neither assumes nor values such an overt connectedness among individuals. In contrast, individuals seek to maintain their independence from others by attending to the self and by discovering and expressing their unique inner attributes. As proposed herein, these construals are even more powerful than previously imagined. Theories of the self from both psychology and anthropology are integrated to define in detail the difference between a construal of the self as independent and a construal of the self as interdependent. Each of these divergent construals should have a set of specific consequences for cognition, emotion, and motivation; these consequences are proposed and relevant empirical literature is reviewed. Focusing on differences in self-construals enables apparently inconsistent empirical findings to be reconciled, and raises questions about what have been thought to be culture-free aspects of cognition, emotion, and motivation.
The challenge of abstract concepts.

concepts ("freedom") differ from concrete ones ("cat"), as they do not have a bounded, identifiable, and clearly perceivable referent. The way in which abstract concepts are represented has recently become a topic of intense debate, especially because of the spread of the embodied approach to cognition. Within this framework concepts derive their meaning from the same perception, motor, and emotional systems that are involved in online interaction with the world. Most of the evidence in favor of this view, however, has been gathered with regard to concrete concepts. Given the relevance of abstract concepts for higher-order cognition, we argue that being able to explain how they are represented is a crucial challenge that any theory of cognition needs to address. The aim of this article is to offer a critical review of the latest theories on abstract concepts, focusing on embodied ones. Starting with theories that question the distinction between abstract and concrete concepts, we review theories claiming that abstract concepts are grounded in metaphors, in situations and introspection, and in emotion. We then introduce multiple representation theories, according to which abstract concepts evoke both sensorimotor and linguistic information. We argue that the most promising approach is given by multiple representation views that combine an embodied perspective with the recognition of the importance of linguistic and social experience. We conclude by discussing whether or not a single theoretical framework might be able to explain all different varieties of abstract concepts.
Automated Dynamic Enforcement of Synthesized Security Policies in Android

As the dominant mobile computing platform, Android has become a prime target for cyber-security attacks. Many of these attacks are manifested at the application level, and through the exploitation of vulnerabilities in apps downloaded from the popular app stores. Increasingly, sophisticated attacks exploit the vulnerabilities in multiple installed apps, making it extremely difficult to foresee such attacks, as neither the app developers nor the store operators know a priori which apps will be installed together. This paper presents an approach that allows the end-users to safeguard a given bundle of apps installed on their device from such attacks. The approach, realized in a tool, called DROIDGUARD, combines static code analysis with lightweight formal methods to automatically infer security-relevant properties from a bundle of apps. It then uses a constraint solver to synthesize possible security exploits, from which fine-grained security policies are derived and automatically enforced to protect a given device. In our experiments with over 4,000 Android apps, DROIDGUARD has proven to be highly effective at detecting previously unknown vulnerabilities as well as preventing their exploitation.
Compositional Memory for Visual Question Answering

Visual Question Answering (VQA) emerges as one of the most fascinating topics in computer vision recently. Many state of the art methods naively use holistic visual features with language features into a Long Short-Term Memory (LSTM) module, neglecting the sophisticated interaction between them. This coarse modeling also blocks the possibilities of exploring finer-grained local features that contribute to the question answering dynamically over time. This paper addresses this fundamental problem by directly modeling the temporal dynamics between language and all possible local image patches. When traversing the question words sequentially, our end-to-end approach explicitly fuses the features associated to the words and the ones available at multiple local patches in an attention mechanism, and further combines the fused information to generate dynamic messages, which we call episode. We then feed the episodes to a standard question answering module together with the contextual visual information and linguistic information. Motivated by recent practices in deep learning, we use auxiliary loss functions during training to improve the performance. Our experiments on two latest public datasets suggest that our method has a superior performance. Notably, on the DAQUAR dataset we advanced the state of the art by 6%, and we also evaluated our approach on the most recent MSCOCO-VQA dataset.
The influence of exercise on cognitive abilities.

Scientific evidence based on neuroimaging approaches over the last decade has demonstrated the efficacy of physical activity improving cognitive health across the human lifespan. Aerobic fitness spares age-related loss of brain tissue during aging, and enhances functional aspects of higher order regions involved in the control of cognition. More active or higher fit individuals are capable of allocating greater attentional resources toward the environment and are able to process information more quickly. These data are suggestive that aerobic fitness enhances cognitive strategies enabling to respond effectively to an imposed challenge with a better yield in task performance. In turn, animal studies have shown that exercise has a benevolent action on health and plasticity of the nervous system. New evidence indicates that exercise exerts its effects on cognition by affecting molecular events related to the management of energy metabolism and synaptic plasticity. An important instigator in the molecular machinery stimulated by exercise is brain-derived neurotrophic factor, which acts at the interface of metabolism and plasticity. Recent studies show that exercise collaborates with other aspects of lifestyle to influence the molecular substrates of cognition. In particular, select dietary factors share similar mechanisms with exercise, and in some cases they can complement the action of exercise. Therefore, exercise and dietary management appear as a noninvasive and effective strategy to counteract neurological and cognitive disorders.
Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training

Existing neural relation extraction (NRE) models rely on distant supervision and suffer from wrong labeling problems. In this paper, we propose a novel adversarial training mechanism over instances for relation extraction to alleviate the noise issue. As compared with previous denoising methods, our proposed method can better discriminate those informative instances from noisy ones. Our method is also efficient and flexible to be applied to various NRE architectures. As shown in the experiments on a large-scale benchmark dataset in relation extraction, our denoising method can effectively filter out noisy instances and achieve significant improvements as compared with the state-of-theart models.
Generating Sentences from a Continuous Space

The standard unsupervised recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global distributed sentence representation. In this work, we present an RNN-based variational autoencoder language model that incorporates distributed latent representations of entire sentences. This factorization allows it to explicitly model holistic properties of sentences such as style, topic, and high-level syntactic features. Samples from the prior over these sentence representations remarkably produce diverse and well-formed sentences through simple deterministic decoding. By examining paths through this latent space, we are able to generate coherent novel sentences that interpolate between known sentences. We present techniques for solving the difficult learning problem presented by this model, demonstrate strong performance in the imputation of missing tokens, and explore many interesting properties of the latent sentence space.
Dynamic Privacy Pricing: A Multi-Armed Bandit Approach With Time-Variant Rewards

Recently, the conflict between exploiting the value of personal data and protecting individuals' privacy has attracted much attention. Personal data market provides a promising solution to this conflict, while determining the price of privacy is a tough issue. In this paper, we study the pricing problem in a setting where a data collector sequentially buys data from multiple data owners whose valuations of privacy are randomly drawn from an unknown distribution. To maximize the total payoff, the collector needs to dynamically adjust the prices offered to owners. We model the sequential decision-making problem of the collector as a multi-armed bandit problem with each arm representing a candidate price. Specifically, the privacy protection technique adopted by the collector is taken into account. Protecting privacy generally causes a negative effect on the value of data, and this effect is embodied by the time-variant distributions of the rewards associated with arms. Based on the classic upper confidence bound policy, we propose two learning policies for the bandit problem. The first policy estimates the expected reward of a price by counting how many times the price has been accepted by data owners. The second policy treats the time-variant data value as a context and uses ridge regression to estimate the rewards in different contexts. Simulation results on real-world data demonstrate that by applying the proposed policies, the collector can get a payoff which is close to that he can get by setting a fixed price, which is the best in hindsight, for all data owners.