nils-herrmann/clean_scirepeval huggingface dataset:
Fast Policy Learning through Imitation and Reinforcement

Imitation learning (IL) consists of a set of tools that leverage expert demonstrations to quickly learn policies. However, if the expert is suboptimal, IL can yield policies with inferior performance compared to reinforcement learning (RL). In this paper, we aim to provide an algorithm that combines the best aspects of RL and IL. We accomplish this by formulating several popular RL and IL algorithms in a common mirror descent framework, showing that these algorithms can be viewed as a variation on a single approach. We then propose LOKI, a strategy for policy learning that first performs a small but random number of IL iterations before switching to a policy gradient RL method. We show that if the switching time is properly randomized, LOKI can learn to outperform a suboptimal expert and converge faster than running policy gradient from scratch. Finally, we evaluate the performance of LOKI experimentally in several simulated environments.
D-PAGE: Diverse Paraphrase Generation

In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs. We propose a simple method Diverse Paraphrase Generation (D-PAGE), which extends neural machine translation (NMT) models to support the generation of diverse paraphrases with implicit rewriting patterns. Our experimental results on two real-world benchmark datasets demonstrate that our model generates at least one order of magnitude more diverse outputs than the baselines in terms of a new evaluation metric Jeffrey’s Divergence. We have also conducted extensive experiments to understand various properties of our model with a focus on diversity.
Digital Social Norm Enforcement: Online Firestorms in Social Media

Actors of public interest today have to fear the adverse impact that stems from social media platforms. Any controversial behavior may promptly trigger temporal, but potentially devastating storms of emotional and aggressive outrage, so called online firestorms. Popular targets of online firestorms are companies, politicians, celebrities, media, academics and many more. This article introduces social norm theory to understand online aggression in a social-political online setting, challenging the popular assumption that online anonymity is one of the principle factors that promotes aggression. We underpin this social norm view by analyzing a major social media platform concerned with public affairs over a period of three years entailing 532,197 comments on 1,612 online petitions. Results show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals. This effect is reinforced if selective incentives are present and if aggressors are intrinsically motivated.
Audio-Visual Event Localization in Unconstrained Videos

In this paper, we introduce a novel problem of audio-visual event localization in unconstrained videos. We define an audio-visual event as an event that is both visible and audible in a video segment. We collect an Audio-Visual Event (AVE) dataset to systemically investigate three temporal localization tasks: supervised and weakly-supervised audio-visual event localization, and cross-modality localization. We develop an audio-guided visual attention mechanism to explore audio-visual correlations, propose a dual multimodal residual network (DMRN) to fuse information over the two modalities, and introduce an audio-visual distance learning network to handle the cross-modality localization. Our experiments support the following findings: joint modeling of auditory and visual modalities outperforms independent modeling, the learned attention can capture semantics of sounding objects, temporal alignment is important for audio-visual fusion, the proposed DMRN is effective in fusing audio-visual features, and strong correlations between the two modalities enable cross-modality localization.
An Open Approach to Autonomous Vehicles

Autonomous vehicles are an emerging application of automotive technology. They can recognize the scene, plan the path, and control the motion by themselves while interacting with drivers. Although they receive considerable attention, components of autonomous vehicles are not accessible to the public but instead are developed as proprietary assets. To facilitate the development of autonomous vehicles, this article introduces an open platform using commodity vehicles and sensors. Specifically, the authors present algorithms, software libraries, and datasets required for scene recognition, path planning, and vehicle control. This open platform allows researchers and developers to study the basis of autonomous vehicles, design new algorithms, and test their performance using the common interface.
Segment-Level Neural Conditional Random Fields for Named Entity Recognition

We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks.
Evolution of human mate choice.

This article provides a review of evolutionary theory and empirical research on mate choices in nonhuman species and uses it as a frame for understanding the how and why of human mate choices. The basic principle is that the preferred mate choices and attendant social cognitions and behaviors of both women and men, and those of other species, have evolved to focus on and exploit the reproductive potential and reproductive investment of members of the opposite sex. Reproductive potential is defined as the genetic, material, and/or social resources an individual can invest in offspring, and reproductive investment is the actual use of these resources to enhance the physical and social well- being of offspring. Similarities and differences in the mate preferences and choices of women and men are reviewed and can be understood in terms of similarities and differences in the form of reproductive potential that women and men have to offer and their tendency to use this potential for the well-being of children.
Android Malware Detection using Large-scale Network Representation Learning

With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches. Traditional methods such as signature based ones cannot defend users from an increasing number of new types of malware or rapid malware behavior changes. In this paper, we propose a new Android malware detection approach based on deep learning and static analysis. Instead of using Application Programming Interfaces (APIs) only, we further analyze the source code of Android applications and create their higher-level graphical semantics, which makes it harder for attackers to evade detection. In particular, we use a call graph from method invocations in an Android application to represent the application, and further analyze method attributes to form a structured Program Representation Graph (PRG) with node attributes. Then, we use a graph convolutional network (GCN) to yield a graph representation of the application by embedding the entire graph into a dense vector, and classify whether it is a malware or not. To efficiently train such a graph convolutional network, we propose a batch training scheme that allows multiple heterogeneous graphs to be input as a batch. To the best of our knowledge, this is the first work to use graph representation learning for malware detection. We conduct extensive experiments from real-world sample collections and demonstrate that our developed system outperforms multiple other existing malware detection techniques.
Joint self-representation and subspace learning for unsupervised feature selection

This paper proposes a novel unsupervised feature selection method by jointing self-representation and subspace learning. In this method, we adopt the idea of self-representation and use all the features to represent each feature. A Frobenius norm regularization is used for feature selection since it can overcome the over-fitting problem. The Locality Preserving Projection (LPP) is used as a regularization term as it can maintain the local adjacent relations between data when performing feature space transformation. Further, a low-rank constraint is also introduced to find the effective low-dimensional structures of the data, which can reduce the redundancy. Experimental results on real-world datasets verify that the proposed method can select the most discriminative features and outperform the state-of-the-art unsupervised feature selection methods in terms of classification accuracy, standard deviation, and coefficient of variation.
A Question-Entailment Approach to Question Answering

One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domainspecific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is to map new questions to formerly answered questions that are “similar”. In this paper, we propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions. First, we compare machine learning and deep learning methods for RQE using different kinds of datasets, including textual inference, question similarity and entailment in both the open and clinical domains. Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. To study the end-to-end QA approach, we built the MedQuAD collection of 47,457 question-answer pairs from trusted medical sources, that we introduce and share in the scope of this paper. Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score. The evaluation results also support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts. Our findings also show that relying on a restricted set of reliable answer sources can bring a substantial improvement in medical QA.
Auto-Meta: Automated Gradient Based Meta Learner Search

Fully automating machine learning pipeline is one of the outstanding challenges of general artificial intelligence, as practical machine learning often requires costly human driven process, such as hyper-parameter tuning, algorithmic selection, and model selection. In this work, we consider the problem of executing automated, yet scalable search for finding optimal gradient based meta-learners in practice. As a solution, we apply progressive neural architecture search to proto-architectures by appealing to the model agnostic nature of general gradient based meta learners. In the presence of recent universality result of Finn et ], our search is a priori motivated in that neural network architecture search dynamics—automated or not—may be quite different from that of the classical setting with the same target tasks, due to the presence of the gradient update operator. A posteriori, our search algorithm, given appropriately designed search spaces, finds gradient based meta learners with non-intuitive proto-architectures that are narrowly deep, unlike the inception-like structures previously observed in the resulting architectures of traditional NAS algorithms. Along with these notable findings, the searched gradient based meta-learner achieves state-of-the-art results on the few shot classification problem on Mini-ImageNet with 76.29% accuracy, which is an 13.18% improvement over results reported in the original MAML paper. To our best knowledge, this work is the first successful AutoML implementation in the context of meta learning.
Down will come baby, cradle and all: diagnostic and therapeutic implications of chronic trauma on child development.

This review examines the clinical outcomes associated with exposure to chronic intrafamilial trauma and explores the treatment of the psychological, biological and cognitive sequelae.



The existing research literature on the subject was collected, using Index Medicus/MEDLINE,  The research findings were supplemented with clinical observations by the authors and other clinical writings on this topic.



Children with histories of exposure to multiple traumatic experiences within their families or in medical settings usually meet criteria for numerous clinical diagnoses, none of which capture the complexity of their biological, emotional and cognitive problems. These are expressed in a multitude of psychological, cognitive, somatic and behavioural problems, ranging from learning disabilities to aggression against self and others.



Exposure to intrafamilial violence and other chronic trauma results in pervasive psychological and biological deficits. Treatment needs to address issues of safety, stabilise impulsive aggression against self and others, promote mastery experiences, compensate for specific developmental deficits, and judiciously process both the traumatic memories and trauma-related expectations.
Application of ISO 22000 in comparison with HACCP on industrial processing of milk chocolate

Hazard analysis was conducted to identify critical control points (CCPs) during cocoa processing and milk chocolate manufacture and applied into a hazard analysis and critical control point (HACCP) plan. During the process, the different biological, physical and chemical hazards identified at each processing stage in the hazard analysis worksheet were incorporated into the HACCP plan to assess the risks associated with the processes. Physical hazards such as metals, stones, fibres, plastics and papers; chemical hazards such as pesticide residues, mycotoxins and heavy metals; and microbiological hazards such as Staphyloccous aureus, coliforms, Salmonella, Aspergillus and Penicillium were identified. ISO 22000 analysis was conducted for the determination of some pre-requisite programmes (PrPs) during the chocolate processing and compared with the HACCP system. The ISO 22000 Analysis worksheet reduced the CCPs for both cocoa processing and chocolate manufacture due to the elimination of the pre-requisite programmes (PrPs). Monitoring systems were established for the CCPs identified and these included preventive measures, critical limits, corrective actions, assignment of responsibilities and verification procedures. The incorporation of PrPs in the ISO 22000 made the system simple, more manageable and effective since a smaller number of CCPs were obtained.
Determination of Unknown Parameters of Photovoltaic Module Using Genetic Algorithm

In this paper, the unknown parameters of the photovoltaic (PV) module are determined using Genetic Algorithm (GA) method. This algorithm based on minimizing the absolute difference between the maximum powers obtained from module datasheet and the maximum power obtained from the mathematical model of the PV module, at different operating conditions. This method does not need to initial values, so these parameters of the PV module are easily obtained with high accuracy. To validate the proposed method, the results obtained from it are compared with the experimental results obtained from the PV module datasheet for different operating conditions. The results obtained from the proposed model are found to be very close compared to the results given in the datasheet of the PV module.
Efficient Knowledge Distillation from an Ensemble of Teachers

This paper describes the effectiveness of knowledge distillation using teacher student training for building accurate and compact neural networks. We show that with knowledge distillation, information from multiple acoustic models like very deep VGG networks and Long Short-Term Memory (LSTM) models can be used to train standard convolutional neural network (CNN) acoustic models for a variety of systems requiring a quick turnaround. We examine two strategies to leverage multiple teacher labels for training student models. In the first technique, the weights of the student model are updated by switching teacher labels at the minibatch level. In the second method, student models are trained on multiple streams of information from various teacher distributions via data augmentation. We show that standard CNN acoustic models can achieve comparable recognition accuracy with much smaller number of model parameters compared to teacher VGG and LSTM acoustic models. Additionally we also investigate the effectiveness of using broadband teacher labels as privileged knowledge for training better narrowband acoustic models within this framework. We show the benefit of this simple technique by training narrowband student models with broadband teacher soft labels on the Aurora 4 task.
Pattern Generation Strategies for Improving Recognition of Handwritten Mathematical Expressions

Recognition of Handwritten Mathematical Expressions (HMEs) is a challenging problem because of the ambiguity and complexity of two-dimensional handwriting. Moreover, the lack of large training data is a serious issue, especially for academic recognition systems. In this paper, we propose pattern generation strategies that generate shape and structural variations to improve the performance of recognition systems based on a small training set. For data generation, we employ the public databases: CROHME 2014 and 2016 of online HMEs. The first strategy employs local and global distortions to generate shape variations. The second strategy decomposes an online HME into sub-online HMEs to get more structural variations. The hybrid strategy combines both these strategies to maximize shape and structural variations. The generated online HMEs are converted to images for offline HME recognition. We tested our strategies in an end-to-end recognition system constructed from a recent deep learning model: Convolutional Neural Network and attention-based encoder-decoder. The results of experiments on the CROHME 2014 and 2016 databases demonstrate the superiority and effectiveness of our strategies: our hybrid strategy achieved classification rates of 48.78% and 45.60%, respectively, on these databases. These results are competitive compared to others reported in recent literature. Our generated datasets are openly available for research community and constitute a useful resource for the HME recognition research in future.
Memes : The Interaction Between Imagery and Subculture : An Analysis of Situation , Race , and Gender on the Pi Kappa Delta Social Media App

Collegiate speech and debate participants are committed to performance excellence and organizational unity. Pi Kappa Delta, a central organization for this subculture, annually hosts a national competition, during which competitors can create and post memes via the tournament phone app. While it is well-known that memes are a function of participatory culture, no analysis has yet examined memes exclusively consumed by the same subculture which created them. In this study, we examine the implicit messaging of this memetic imagery, and by doing so, gain insight into both the collegiate forensics subculture, and the function of memes in a small group.
Personalised Information Retrieval: survey and classification

Information Retrieval (IR) systems assist users in finding information from the myriad of information resources available on the Web. A traditional characteristic of IR systems is that if different users submit the same query, the system would yield the same list of results, regardless of the user. Personalised Information Retrieval (PIR) systems take a step further to better satisfy the user’s specific information needs by providing search results that are not only of relevance to the query but are also of particular relevance to the user who submitted the query. PIR has thereby attracted increasing research and commercial attention as information portals aim at achieving user loyalty by improving their performance in terms of effectiveness and user satisfaction. In order to provide a personalised service, a PIR system maintains information about the users and the history of their interactions with the system. This information is then used to adapt the users’ queries or the results so that information that is more relevant to the users is retrieved and presented. This survey paper features a critical review of PIR systems, with a focus on personalised search. The survey provides an insight into the stages involved in building and evaluating PIR systems, namely: information gathering, information representation, personalisation execution, and system evaluation. Moreover, the survey provides an analysis of PIR systems with respect to the scope of personalisation addressed. The survey proposes a classification of PIR systems into three scopes: individualised systems, community-based systems, and aggregate-level systems. Based on the conducted survey, the paper concludes by highlighting challenges and future research directions in the field of PIR.
Data quality evaluation and improvement for prognostic modeling using visual assessment based data partitioning method

When developing Prognostic and Health Management (PHM) applications for manufacturing systems, data acquired frequently comes with issues which hinder further data analysis. However, there is neither a clear definition of the data quality nor evaluation methods to quantify if acquired data is suitable for these prognostic modeling tasks such as failures detection, diagnosis and prediction. Especially, during health diagnosis modeling of engineering systems, based on data-driven method, acquired data is expected to contain clusters that can be used to differentiate multiple system health conditions. So in most cases, once data is acquired, people would like to intuitively believe that data is able to cluster into subgroups. However, this bias could lead to acceptance of false information in data. Furthermore, most of the existing metrics, such as clustering tendency in statistics and cluster-ability in data mining, only individually evaluate data characteristics without considering prognostic modeling. This paper proposes a new method to evaluate and improve data quality for system health diagnosis modeling. The clusters, as critical data characteristics for modeling multiple system conditions, are first estimated by ‘‘visualization’’ on the dissimilarity spectrum from spectral analysis and then evaluated in terms of their fitness and separation with each others. A visual assessment based outlier detection method is also proposed to recognize outliers from the data, which utilizes the graphic intermediate results from previous evaluation. Finally one group of bearing testing dataset acquired from real industrial applications is used to demonstrate how proposed methods are used to evaluate and improve the data quality.