nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)

1Motivation, diagnosis of features inside CNNs: In recent years, real applications usually propose new demands for deep learning beyond the accuracy. The CNN needs to earn trust from people for safety issues, because a high accuracy on testing images cannot always ensure that the CNN encodes correct features. Instead, the CNN sometimes uses unreliable reasons for prediction. Therefore, this study aim to provide a generic tool to examine middle-layer features of a CNN to ensure the safety in critical applications. Unlike previous visualization (Zeiler and Fergus 2014) and diagnosis (Bau et al. 2017; Ribeiro, Singh, and Guestrin 2016) of CNN representations, we focus on the following two new issues, which are of special values in feature diagnosis. • Disentanglement of interpretable and uninterpretable feature information is necessary for a rigorous and trustworthy examination of CNN features. Each filter of a conv-layer usually encodes a mixture of various semantics and noises . As discussed in (Bau et al. 2017), filters in high conv-layers mainly represent “object parts”2, and “material” and “color” information in high layers is not salient enough for trustworthy analysis. In particular, part features are usually more localized and thus is more helpful in feature diagnosis. Therefore, in this paper, we propose to disentangle part features from another signals and noises. For example, we may quantitatively disentangle 90% information of CNN features as object parts and interpret the rest 10% as textures and noises. • Semantic explanations: Given an input image, we aim to use clear visual concepts (here, object parts) to interpret chaotic CNN features. In comparisons, network visualization and diagnosis mainly illustrate the appearance corresponding to a network output/filter, without physically
A Deep Generative Deconvolutional Image Model

A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic unpooling is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the toplayer features, yielding max-margin discrimination. Deep deconvolutional inference is employed when testing, to infer the latent features, and the top-layer features are connected with the max-margin classifier for discrimination tasks. The model is efficiently trained using a Monte Carlo expectation-maximization (MCEM) algorithm; the algorithm is implemented on graphical processor units (GPU) to enable large-scale learning, and fast testing. Excellent results are obtained on several benchmark datasets, including ImageNet, demonstrating that the proposed model achieves results that are highly competitive with similarly sized convolutional neural networks.
Chapter 11 AAC AND CHILDREN WITH DEVELOPMENTAL DISABILITIES

Children with developmental language or speech disorders frequently benefit from augmentative and alternative communication (AAC) strategies. These children have severe expressive or receptive communication disorders or both which sometimes occur in isolation, or as part of a global developmental disability. Children with specific language impairment, pervasive developmental disorder, developmental apraxia of speech, autism, Down syndrome, or other types of developmental disabilities may need to use AAC strategies to supplement or enhance their language development. These children offer challenges to professionals, especially during the early years of language development. In the very young child, it is often difficult to determine the nature and degree of language impairment, to accurately diagnose the presence of other factors such as cognitive disabilities, and to predict the child's future prognosis for language or speech development. In the past, young children diagnosed with severe language and speech disorders would have eceived years of traditional speech therapy focused on developing spoken communication skills (Silverman, 1995). AAC would have been recommended only after traditional therapy techniques had failed. Today, professionals realize that AAC strategies can provide children who have developmental delays with an immediate means of communication; can facilitate expressive and receptive language development until other communication modalities improve (i.e., speech); and can serve as a bridge to future spoken language development (Kangas & Lloyd, 1988; Silverman 1995). AAC provides an expressive method of communication to facilitate language development in children who, in all likelihood, will eventually use speech to communicate. The focus of this chapter is on children with developmental disabilities who have the potential to use AAC strategies to develop receptive and expressive language skills, including speech. The chapter begins by presenting an overview of using AAC as a tool to enhance language and speech development. Specific AAC assessment and intervention issues are then presented in two separate sections for children with developmental apraxia of speech and for children with pervasive developmental disorders, including autism.
End-To-End Face Detection and Recognition

Plenty of face detection and recognition methods have been proposed and got delightful results in decades. Common face recognition pipeline consists of: 1) face detection, 2) face alignment, 3) feature extraction, 4) similarity calculation, which are separated and independent from each other. The separated face analyzing stages lead the model redundant calculation and are hard for end-to-end training. In this paper, we proposed a novel end-to-end trainable convolutional network framework for face detection and recognition, in which a geometric transformation matrix was directly learned to align the faces, instead of predicting the facial landmarks. In training stage, our single CNN model is supervised only by face bounding boxes and personal identities, which are publicly available from WIDER FACE [36] dataset and CASIA-WebFace  dataset. Tested on Face Detection Dataset and Benchmark (FDDB) [11] dataset and Labeled Face in the Wild (LFW)  dataset, we have achieved 89.24% recall for face detection task and 98.63% verification accuracy for face recognition task simultaneously, which are comparable to state-of-the-art results.
Critical Discourse Analysis TEUN A . VAN DIJK 0 Introduction : What Is Critical Discourse Analysis ?

Critical Discourse Analysis (CDA) is discourse analytical research that primarily studies the way social-power abuse and inequality are enacted, reproduced, legitimated, and resisted by text and talk in the social and political context. With such dissident research, critical discourse analysts take an explicit position and thus want to understand, expose, and ultimately challenge social inequality. This is also why CDA may be characterized as a social movement of politically committed discourse analysts. One widespread misunderstanding of CDA is that it is a special method of doing discourse analysis. There is no such method: in CDA all methods of the cross-discipline of discourse studies, as well as other relevant methods in the humanities and social sciences, may be used (Wodak and Meyer 2008; Titscher et al. 2000). To avoid this misunderstanding and to emphasize that many methods and approaches may be used in the critical study of text and talk, we now prefer the more general term critical discourse studies (CDS) for the field of research (van Dijk 2008b). However, since most studies continue to use the well-known abbreviation CDA, this chapter will also continue to use it. As an analytical practice, CDA is not one direction of research among many others in the study of discourse. Rather, it is a critical perspective that may be found in all areas of discourse studies, such as discourse grammar, Conversation Analysis, discourse pragmatics, rhetoric, stylistics, narrative analysis, argumentation analysis, multimodal discourse analysis and social semiotics, sociolinguistics, and ethnography of communication or the psychology of discourse-processing, among others. In other words, CDA is discourse study with an attitude. Some of the tenets of CDA could already be found in the critical theory of the Frankfurt School before World War II (Agger 1992b; Drake 2009; Rasmussen and Swindal 2004). Its current focus on language and discourse was initiated with the
Robotics in my work and life

Margaret Atwood is a giant of modern literature who refuses to rest on her laurels. She has anticipated, satirized, and even changed the popular pre-conceptions of our time, and is the rare writer whose work is adored by the public, acclaimed by the critics, and read on university campuses. On stage, Atwood is both serious minded and wickedly funny. A winner of many international literary awards, including the prestigious Booker Prize, Margaret Atwood is the author of more than thirty volumes of poetry, children's literature, fiction, and non-fiction. She is perhaps best known for her novels, which include The Edible Woman, The Handmaid's Tale, The Robber Bride, Alias Grace, The Blind Assassin, Oryx and Crake, and The Year of the Flood. Her non-fiction book Payback: Debt and the Shadow Side of Wealth, part of the Massey Lecture series, was recently made into a documentary. Her new book, Madaddam (the third novel in the Oryx and Crake trilogy), has received rave reviews: "An extraordinary achievement" (The Independent); "A fitting and joyous conclusion" (The New York Times). 
 Atwood's work has been published in more than forty languages, including Farsi, Japanese, Turkish, Finnish, Korean, Icelandic and Estonian. In 2004, she co-invented the LongPen, a remote signing device that allows someone to write in ink anywhere in the world via tablet PC and the internet. She is also a popular personality on Twitter, with over 300,000 followers.
 Atwood was born in 1939 in Ottawa and grew up in northern Ontario, Quebec, and Toronto. She received her undergraduate degree from Victoria College at the University of Toronto and her master's degree from Radcliffe College.
Comparative analysis of features based machine learning approaches for phishing detection

Machine learning based anti-phishing techniques are based on various features extracted from different sources. These features differentiate a phishing website from a legitimate one. Features are taken from various sources like URL, page content, search engine, digital certificate, website traffic, etc, of a website to detect it as a phishing or non-phishing. The websites are declared as phishing sites if the heuristic design of the websites matches with the predefined rules. The accuracy of the anti-phishing solution depends on features set, training data and machine learning algorithm. This paper presents a comprehensive analysis of Phishing attacks, their exploitation, some of the recent machine learning based approaches for phishing detection and their comparative study. It provides a better understanding of the phishing problem, current solution space in machine learning domain, and scope of future research to deal with Phishing attacks efficiently using machine learning based approaches.
C V ] 2 2 Ju n 20 18 Constructing Fast Network through Deconstruction of Convolution

Convolutional neural networks have achieved great success in various vision tasks; however, they incur heavy resource costs. By using deeper and wider networks, network accuracy can be improved rapidly. However, in an environment with limited resources (e.g., mobile applications), heavy networks may not be usable. This study deconstructs naive convolution into a shift operation and pointwise convolution. To cope with various convolutions, we propose a new shift operation called active shift layer (ASL) that formulates the amount of shift as a learnable function with shift parameters. This new layer can be optimized end-to-end through backpropagation and provide optimal shift values. Finally, we apply this layer to a light and fast network that surpasses existing state-of-the-art networks.
Combining the benefits of function approximation and trajectory optimization

Neural networks have recently solved many hard problems in Machine Learning, but their impact in control remains limited. Trajectory optimization has recently solved many hard problems in robotic control, but using it online remains challenging. Here we leverage the high-fidelity solutions obtained by trajectory optimization to speed up the training of neural network controllers. The two learning problems are coupled using the Alternating Direction Method of Multipliers (ADMM). This coupling enables the trajectory optimizer to act as a teacher, gradually guiding the network towards better solutions. We develop a new trajectory optimizer based on inverse contact dynamics, and provide not only the trajectories but also the feedback gains as training data to the network. The method is illustrated on rolling, reaching, swimming and walking tasks.
Regularization Theory and Neural Networks

We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smoothness functionals lead to a subclass of regularization networks, the well known Radial Basis Functions approximation schemes. This paper shows that regularization networks encompass a much broader range of approximation schemes, including many of the popular general additive models and some of the neural networks. In particular, we introduce new classes of smoothness functionals that lead to diierent classes of basis functions. Additive splines as well as some tensor product splines can be obtained from appropriate classes of smoothness functionals. Furthermore, the same generalization that extends Radial Basis Functions (RBF) to Hyper Basis Functions (HBF) also leads from additive models to ridge approximation models, containing as special cases Breiman's hinge functions, some forms of Projection Pursuit Regression and several types of neural networks. We propose to use the term Generalized Regularization Networks for this broad class of approximation schemes that follow from an extension of regularization. In the probabilistic interpretation of regularization, the diierent classes of basis functions correspond to diierent classes of prior probabilities on the approximating function spaces, and therefore to diierent types of smoothness assumptions. In summary, diierent multilayer networks with one hidden layer, which we collectively call Generalized Regularization Networks, correspond to diierent classes of priors and associated smoothness functionals in a classical regularization principle. Three broad classes are a) Radial Basis Functions that can be generalized to Hyper Basis Functions, b) some tensor product splines, and c) additive splines that can be generalized to schemes of the type of ridge approximation, hinge functions and several perceptron-like neural networks with one-hidden layer.
Anticancer activity of essential oils and their chemical components - a review.

Essential oils are widely used in pharmaceutical, sanitary, cosmetic, agriculture and food industries for their bactericidal, virucidal, fungicidal, antiparasitical and insecticidal properties. Their anticancer activity is well documented. Over a hundred essential oils from more than twenty plant families have been tested on more than twenty types of cancers in last past ten years. This review is focused on the activity of essential oils and their components on various types of cancers. For some of them the mechanisms involved in their anticancer activities have been carried out.
Energy Storage Systems for Automotive Applications

The fuel efficiency and performance of novel vehicles with electric propulsion capability are largely limited by the performance of the energy storage system (ESS). This paper reviews state-of-the-art ESSs in automotive applications. Battery technology options are considered in detail, with emphasis on methods of battery monitoring, managing, protecting, and balancing. Furthermore, other ESS candidates such as ultracapacitors, flywheels and fuel cells are also discussed. Finally, hybrid power sources are considered as a method of combining two or more energy storage devices to create a superior power source.
Cross-Entropy for Monte-Carlo Tree Search

Recently, Monte-Carlo Tree Search (MCTS) has become a popular approach for intelligent play in games. Amongst others, it is successfully used in most state-of-the-art Go programs. To improve the playing strength of these Go programs any further, many parameters dealing with MCTS should be fine-tuned. In this paper, we propose to apply the Cross-Entropy Method (CEM) for this task. The method is comparable to Estimation-of-Distribution Algorithms (EDAs), a new area of evolutionary computation. We tested CEM by tuning various types of parameters in our Go program MANGO. The experiments were performed in matches against the open-source program GNU GO. They revealed that a program with the CEM-tuned parameters played better than without. Moreover, MANGO plus CEM outperformed the regular MANGO for various time settings and board sizes. From the results we may conclude that parameter tuning by CEM genuinely improved the playing strength of MANGO, for various time settings. This result may be generalized to other game engines using MCTS.
Plantar fascia-specific stretching exercise improves outcomes in patients with chronic plantar fasciitis. A prospective clinical trial with two-year follow-up.

In a previous investigation, eighty-two patients with chronic proximal plantar fasciitis for a duration of more than ten months completed a randomized, prospective clinical trial. The patients received instructions for either a plantar fascia-stretching protocol or an Achilles tendon-stretching protocol and were evaluated after eight weeks. Substantial differences were noted in favor of the group managed with the plantar fascia-stretching program. The goal of this two-year follow-up study was to evaluate the long-term outcomes of the plantar fascia-stretching protocol in patients with chronic plantar fasciitis.



Phase one of the clinical trial concluded at eight weeks. At the eight-week follow-up evaluation, all patients were instructed in the plantar fascia-stretching protocol. At the two-year follow-up evaluation, a questionnaire consisting of the pain subscale of the Foot Function Index and an outcome survey related to pain, function, and satisfaction with treatment was mailed to the eighty-two subjects who had completed the initial clinical trial. Data were analyzed with use of a mixed-model analysis of covariance for each outcome of interest.



Complete data sets were obtained from sixty-six patients. The two-year follow-up results showed marked improvement for all patients after implementation of the plantar fascia-stretching exercises, with an especially high rate of improvement for those in the original group treated with the Achilles tendon-stretching program. In contrast to the eight-week results, the two-year results showed no significant differences between the groups with regard to the worst pain or pain with first steps in the morning. Descriptive analysis of the data showed that 92% (sixty-one) of the sixty-six patients reported total satisfaction or satisfaction with minor reservations. Fifty-one patients (77%) reported no limitation in recreational activities, and sixty-two (94%) reported a decrease in pain. Only sixteen of the sixty-six patients reported the need to seek treatment by a clinician.



This study supports the use of the tissue-specific plantar fascia-stretching protocol as the key component of treatment for chronic plantar fasciitis. Long-term benefits of the stretch include a marked decrease in pain and functional limitations and a high rate of satisfaction. This approach can provide the health-care practitioner with an effective, inexpensive, and straightforward treatment protocol.
Spatio-Temporal Transformer Network for Video Restoration

State-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information. However, these networks typically consider only a pair of consecutive frames and hence are not capable of capturing long-range temporal dependencies and fall short of establishing correspondences across several timesteps. To alleviate these problems, we propose a novel Spatio-temporal Transformer Network (STTN) which handles multiple frames at once and thereby manages to mitigate the common nuisance of occlusions in optical flow estimation. Our proposed STTN comprises a module that estimates optical flow in both space and time and a resampling layer that selectively warps target frames using the estimated flow. In our experiments, we demonstrate the efficiency of the proposed network and show state-ofthe-art restoration results in video super-resolution and video deblurring.
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings

The number of word embedding models is growing every year. Most of them are based on the co-occurrence information of words and their contexts. However, it is still an open question what is the best definition of context. We provide a systematical investigation of 4 different syntactic context types and context representations for learning word embeddings. Comprehensive experiments are conducted to evaluate their effectiveness on 6 extrinsic and intrinsic tasks. We hope that this paper, along with the published code, would be helpful for choosing the best context type and representation for a given task.
Midface: Clinical Anatomy and Regional Approaches with Injectable Fillers.

The clinical approach towards the midface is one of the most important interventions for practitioners when treating age-related changes of the face. Currently a plethora of procedures are used and presented. However, few of these approaches have been validated or passed review board assigned evaluations. Therefore, it is the aim of this work to establish a guideline manual for practitioners for a safe and effective mid-face treatment based on the most current concepts of facial anatomy. The latter is based on the 5-layered structural arrangement and its understanding is the key towards the favoured outcome and for minimizing complications.
Classifications of Recommender Systems : A review

This paper presents the state of art techniques in recommender systems (RS). The various techniques are diagrammatically illustrated which on one hand helps a naïve researcher in this field to accommodate the on-going researches and establish a strong base, on the other hand it focuses on different categories of the recommender systems with deep technical discussions. The review studies on RS are highlighted which helps in understanding the previous review works and their directions. 8 different main categories of recommender techniques and 19 sub categories have been identified and stated. Further, soft computing approach for recommendation is emphasized which have not been well studied earlier. The major problems of the existing area is reviewed and presented from different perspectives. However, solutions to these issues are rarely discussed in the previous works, in this study future direction for possible solutions are also addressed.