nils-herrmann/clean_scirepeval huggingface dataset:
PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup

This paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo. Our unsupervised learning approach relies on a new framework of cycle-consistent generative adversarial networks. Different from the image domain transfer problem, our style transfer problem involves two asymmetric functions: a forward function encodes example-based style transfer, whereas a backward function removes the style. We construct two coupled networks to implement these functions - one that transfers makeup style and a second that can remove makeup - such that the output of their successive application to an input photo will match the input. The learned style network can then quickly apply an arbitrary makeup style to an arbitrary photo. We demonstrate the effectiveness on a broad range of portraits and styles.
Risk-based thinking of ISO 9001:2015 — The new methods, approaches and tools of risk management

This article discusses aspects of the new thinking founded on a risk-based ISO 9001:2015. It is noted that risk-based thinking is an effective tool for creating, auditing and improving quality management systems. Shows several practical examples of the implementation of the risk management process for the phase “Plan” PDCA-cycle, proposed a new model of the ISM, which contains all the basic entity for performance audits (criteria, object, audit observation) and allows to generate the level of security assessment. Additionally an approach is presented to determine the characteristics of technological devices for the healthcare industry.
An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process

Manufacturing of powder-based products is a focus of increasing research in the recent years. The main reason is the lack of predictive process models connecting process parameters and material properties to product quality attributes. Moreover, the trend towards continuous manufacturing for the production of multiple pharmaceutical products increases the need for model-based process and product design. This work aims to identify the challenges in flowsheet model development and simulation for solid-based pharmaceutical processes and show its application and advantages for the integrated simulation and sensitivity analysis of two tablet manufacturing case studies: direct compaction and dry granulation. harmaceutical manufacturing
Confined lateral selective epitaxial growth of silicon for device fabrication

An epitaxy technique, confined lateral selective epitaxial growth (CLSEG), which produces wide, thin slabs of single-crystal silicon over insulator, using only conventional processing, is discussed. As-grown films of CLSEG 0.9 mu m thick, 8.0 mu m wide, and 500 mu m long were produced at 1000 degrees C at reduced pressure. Junction diodes fabricated in CLSEG material show ideality factors of 1.05 with reverse leakage currents comparable to those of diodes built in SEG homoepitaxial material. Metal-gate p-channel MOSFETs in CLSEG with channel dopings of 2*10/sup 16/ cm/sup -3/ exhibit average mobilities of 283 cm/sup 2//V-s and subthreshold slopes of 223 mV/decade.<<ETX>>
A Fully-Convolutional Framework for Semantic Segmentation

In this paper we propose a deep learning technique to improve the performance of semantic segmentation tasks. Previously proposed algorithms generally suffer from the over-dependence on a single modality as well as a lack of training data. We made three contributions to improve the performance. Firstly, we adopt two models which are complementary in our framework to enrich field-of-views and features to make segmentation more reliable. Secondly, we repurpose the datasets form other tasks to the segmentation task by training the two models in our framework on different datasets. This brings the benefits of data augmentation while saving the cost of image annotation. Thirdly, the number of parameters in our framework is minimized to reduce the complexity of the framework and to avoid over- fitting. Experimental results show that our framework significantly outperforms the current state-of-the-art methods with a smaller number of parameters and better generalization ability.
Textual Analysis of Stock Market Prediction Using Financial News Articles

This paper examines the role of financial news articles on three different textual representations; Bag of Words, Noun Phrases, and Named Entities and their ability to predict discrete number stock prices twenty minutes after an article release. Using a Support Vector Machine (SVM) derivative, we show that our model had a statistically significant impact on predicting future stock prices compared to linear regression. We further demonstrate that using a Noun Phrase representation scheme performs better than the de facto standard of Bag of Words.
Improved MPPT Algorithms for Rapidly Changing Environmental Conditions

The first part of this paper intends to give an overview of the maximum power point tracking methods for photovoltaic (PV) inverters presently reported in the literature. The most well-known and popular methods, like the perturb and observe (P&O), the incremental conductance (INC) and the constant voltage (CV), are presented. These methods, especially the P&O, have been treated by many works, which aim to overcome their shortcomings, either by optimizing the methods, or by combining them. In the second part of the paper an improvement for the P&O and INC method is proposed, which prevents these algorithms to get confused during rapidly changing irradiation conditions, and it considerably increases the efficiency of the MPPT
Salience-Based Content Characterisation Of Text Documents

Traditionally, the document summarisation task has been tackled either as a natural language processing problem, with an instantiated meaning template being rendered into coherent prose, or as a passage extraction problem, where certain fragments (typically sentences) of the source document are deemed to be highly representative of its content, and thus delivered as meaningful “approximations” of it. Balancing the conflicting requirements of depth and accuracy of a summary, on the one hand, and document and domain independence, on the other, has proven a very hard problem. This paper describes a novel approach to content characterisation of text documents. It is domainand genre-independent, by virtue of not requiring an in-depth analysis of the full meaning. At the same time, it remains closer to the core meaning by choosing a different granularity of its representations (phrasal expressions rather than sentences or paragraphs), by exploiting a notion of discourse contiguity and coherence for the purposes of uniform coverage and context maintenance, and by utilising a strong linguistic notion of salience, as a more appropriate and representative measure of a document’s “aboutness”.
A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing

Text-level discourse parsing remains a challenge. The current state-of-the-art overall accuracy in relation assignment is 55.73%, achieved by Joty et al. (2013). However, their model has a high order of time complexity, and thus cannot be applied in practice. In this work, we develop a much faster model whose time complexity is linear in the number of sentences. Our model adopts a greedy bottom-up approach, with two linear-chain CRFs applied in cascade as local classifiers. To enhance the accuracy of the pipeline, we add additional constraints in the Viterbi decoding of the first CRF. In addition to efficiency, our parser also significantly outperforms the state of the art. Moreover, our novel approach of post-editing, which modifies a fully-built tree by considering information from constituents on upper levels, can further improve the accuracy.
FPDetective: dusting the web for fingerprinters

In the modern web, the browser has emerged as the vehicle of choice, which users are to trust, customize, and use, to access a wealth of information and online services. However, recent studies show that the browser can also be used to invisibly fingerprint the user: a practice that may have serious privacy and security implications.
 In this paper, we report on the design, implementation and deployment of FPDetective, a framework for the detection and analysis of web-based fingerprinters. Instead of relying on information about known fingerprinters or third-party-tracking blacklists, FPDetective focuses on the detection of the fingerprinting itself. By applying our framework with a focus on font detection practices, we were able to conduct a large scale analysis of the million most popular websites of the Internet, and discovered that the adoption of fingerprinting is much higher than previous studies had estimated. Moreover, we analyze two countermeasures that have been proposed to defend against fingerprinting and find weaknesses in them that might be exploited to bypass their protection. Finally, based on our findings, we discuss the current understanding of fingerprinting and how it is related to Personally Identifiable Information, showing that there needs to be a change in the way users, companies and legislators engage with fingerprinting.
Distilled Binary Neural Network for Monaural Speech Separation

Monaural speech separation, aiming at solving the cocktail party problem, has many important application scenarios, most of which ask for the real-time response, high energy efficiency and efficient storage. However, the state-of-the-art Deep Neural Network based separation models usually require huge memory and computation for the 32-bit floating point multiply accumulations, hence most of them cannot meet those requirements. Recently, there are many methods proposed to solve the problem, and binary neural networks have drawn many attentions for they compress and speed up its counterparts at the cost of some performance. Hence, in this paper, we binarize Deep Neural Network based separation models, aiming to deploy them on embedded devices for real-time applications. Furthermore, we improve the separation performance by integrating knowledge distillation into the training phase of binary neural network based models, which is referred as Distilled Binary Neural Network (DBNN). To the best of our knowledge, DBNN is the first attempt to integrate two types of model compression. In the experiments, we demonstrate the effectiveness of our proposed method, which successfully binarizes the Deep Neural Network based separation models with a comparable performance.
THE KNOWER AND THE KNOWN : THE NATURE OF KNOWLEDGE IN RESEARCH ON TEACHING

This chapter is a review of conceptions of knowledge as they appear in selected bodies of research on teaching. Writing as a philosopher of education, my interest is in how notions of knowledge are used and analyzed in a number of research programs that study teachers and their teaching. Of particular interest is the growing research literature on the knowledge that teachers generate as a result of their experience as teachers, in contrast to the knowledge of teaching that is generated by those who specialize in research on teaching. This distinction, as will become apparent, is one that divides more conventional scientific approaches to the study of teaching from what might be thought of as alternative approaches.
A Power Factor-Oriented Railway Power Flow Controller for Power Quality Improvement in Electrical Railway Power System

Focusing on the freight-train dominant electrical railway power system (ERPS) mixed with ac-dc and ac-dc-ac locomotives (its power factorε[0.70,0.84]), this paper proposes a power factor-oriented railway power flow controller (RPFC) for the power quality improvement of ERPS. The comprehensive relationship of the primary power factor, converter capacity, and the two-phase load currents is built in this paper. Besides, as the main contribution of this paper, the optimal compensating strategy that suited the random fluctuated two-phase loads is analyzed and designed based on a real traction substation, for the purposes of satisfying the power quality standard, enhancing RPFC's control flexibility, and decreasing converter's capacity. Finally, both the simulation and the experiment are used to validate the proposed conceive.
Botnet detection via mining of traffic flow characteristics

Botnet is one of the most serious threats to cyber security as it provides a distributed platform for several illegal activities. Regardless of the availability of numerous methods proposed to detect botnets, still it is a challenging issue as botmasters are continuously improving bots to make them stealthier and evade detection. Most of the existing detection techniques cannot detect modern botnets in an early stage, or they are specific to command and control protocol and structures. In this paper, we propose a novel approach to detect botnets irrespective of their structures, based on network traffic flow behavior analysis and machine learning techniques. The experimental evaluation of the proposed method with real-world benchmark datasets shows the efficiency of the method. Also, the system is able to identify the new botnets with high detection accuracy and low false positive rate.
A deep transfer learning approach to fine-tuning facial recognition models

The challenge of developing facial recognition systems has been the focus of many research efforts in recent years and has numerous applications in areas such as security, entertainment, and biometrics. Recently, most progress in this field has come from training very deep neural networks on massive datasets which is computationally intensive and time consuming. Here, we propose a deep transfer learning (DTL) approach that integrates transfer learning techniques and convolutional neural networks and apply it to the problem of facial recognition to fine-tune facial recognition models. Transfer learning can allow for the training of robust, high-performance machine learning models that require much less time and resources to produce than similarly performing models that have been trained from scratch. Using a pre-trained face recognition model, we were able to perform transfer learning to produce a network that is capable of making accurate predictions on much smaller datasets. We also compare our results with results produced by a selection of classical algorithms on the same datasets to demonstrate the effectiveness of the proposed DTL approach.
Question Answering in TREC

Traditional text retrieval systems return a ranked list of documents in response to a user's request. While a ranked list of documents can be an appropriate response for the user, frequently it is not. Usually it would be better for the system to provide the answer itself instead of requiring the user to search for the answer in a set of documents. The Text REtrieval Conference (TREC) is sponsoring a question answering "track" to foster research on the problem of retrieving answers rather than document lists.TREC is a workshop series sponsored by the National Institute of Standards and Technology and the U.S. Department of Defense [7]. The purpose of the conference series is to encourage research on text retrieval for realistic applications by providing large test collections, uniform scoring procedures, and a forum for organizations interested in comparing results. The conference has focused primarily on the traditional IR problem of retrieving a ranked list of documents in response to a statement of information need, but has also included other tasks, called tracks, that focus on new areas or particularly difficult aspects of information retrieval. A question answering track was introduced in TREC-8 1999. The track has generated wide-spread interest in the QA problem [2, 3, 4], and has documented significant improvements in question answering system effectiveness in its two-year history.This paper provides a brief summary of the findings of the TREC question answering track to date and discusses the future directions of the track. The paper is extracted from a fuller description of the track given in "The TREC Question Answering Track" . Complete details about the TREC question answering track can be found in the TREC proceedings.
Generating Natural Language Adversarial Examples

Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. In the image domain, these perturbations are often virtually indistinguishable to human perception, causing humans and state-of-the-art models to disagree. However, in the natural language domain, small perturbations are clearly perceptible, and the replacement of a single word can drastically alter the semantics of the document. Given these challenges, we use a black-box population-based optimization algorithm to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. We additionally demonstrate that 92.3% of the successful sentiment analysis adversarial examples are classified to their original label by 20 human annotators, and that the examples are perceptibly quite similar. Finally, we discuss an attempt to use adversarial training as a defense, but fail to yield improvement, demonstrating the strength and diversity of our adversarial examples. We hope our findings encourage researchers to pursue improving the robustness of DNNs in the natural language domain.
Achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing

Cloud computing is an emerging computing paradigm in which resources of the computing infrastructure are provided as services over the Internet. As promising as it is, this paradigm also brings forth many new challenges for data security and access control when users outsource sensitive data for sharing on cloud servers, which are not within the same trusted domain as data owners. To keep sensitive user data confidential against untrusted servers, existing solutions usually apply cryptographic methods by disclosing data decryption keys only to authorized users. However, in doing so, these solutions inevitably introduce a heavy computation overhead on the data owner for key distribution and data management when fine-grained data access control is desired, and thus do not scale well. The problem of simultaneously achieving fine-grainedness, scalability, and data confidentiality of access control actually still remains unresolved. This paper addresses this challenging open issue by, on one hand, defining and enforcing access policies based on data attributes, and, on the other hand, allowing the data owner to delegate most of the computation tasks involved in fine-grained data access control to untrusted cloud servers without disclosing the underlying data contents. We achieve this goal by exploiting and uniquely combining techniques of attribute-based encryption (ABE), proxy re-encryption, and lazy re-encryption. Our proposed scheme also has salient properties of user access privilege confidentiality and user secret key accountability. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security models.
Continual Learning with Deep Generative Replay

Attempts to train a comprehensive artificial intelligence capable of solving multiple tasks have been impeded by a chronic problem called catastrophic forgetting. Although simply replaying all previous data alleviates the problem, it requires large memory and even worse, often infeasible in real world applications where the access to past data is limited. Inspired by the generative nature of the hippocampus as a short-term memory system in primate brain, we propose the Deep Generative Replay, a novel framework with a cooperative dual model architecture consisting of a deep generative model (“generator”) and a task solving model (“solver”). With only these two models, training data for previous tasks can easily be sampled and interleaved with those for a new task. We test our methods in several sequential learning settings involving image classification tasks.
Anomaly detection for symbolic sequences and time series data

This thesis deals with the problem of anomaly detection for sequence data. Anomaly detection has been a widely researched problem in several application domains such as system health management, intrusion detection, healthcare, bioinformatics, fraud detection, and mechanical fault detection. Traditional anomaly detection techniques analyze each data instance (as a univariate or multivariate record) independently, and ignore the sequential aspect of the data. Often, anomalies in sequences can be detected only by analyzing data instances together as a sequence, and hence cannot detected by traditional anomaly detection techniques. The problem of anomaly detection for sequence data is a rich area of research because of two main reasons. First, sequences can be of different types, e.g., symbolic sequences, time series data, etc., and each type of sequence poses unique set of problems. Second, anomalies in sequences can be defined in multiple ways and hence there are different problem formulations. In this thesis we focus on solving one particular problem formulation called semi-supervised anomaly detection. We study the problem separately for symbolic sequences, univariate time series data, and multivariate time series data. The state of art on anomaly detection for sequences is limited and fragmented across application domains. For symbolic sequences, several techniques have been proposed within specific domains, but it is not well-understood as to how a technique developed for one domain would perform in a completely different domain. For univariate time series data, limited techniques exist, and are only evaluated for specific domains, while for multivariate time series data, anomaly detection research is relatively untouched. This thesis has two key goals. First goal is to develop novel anomaly detection techniques for different types of sequences which perform better than existing techniques across a variety of application domains. The second goal is to identify the best anomaly detection technique for a given application domain. By realizing the first goal we develop
Learning to Diversify Search Results via Subtopic Attention

Search result diversification aims to retrieve diverse results to satisfy as many different information needs as possible. Supervised methods have been proposed recently to learn ranking functions and they have been shown to produce superior results to unsupervised methods. However, these methods use implicit approaches based on the principle of Maximal Marginal Relevance (MMR). In this paper, we propose a learning framework for explicit result diversification where subtopics are explicitly modeled. Based on the information contained in the sequence of selected documents, we use attention mechanism to capture the subtopics to be focused on while selecting the next document, which naturally fits our task of document selection for diversification. The framework is implemented using recurrent neural networks and max-pooling which combine distributed representations and traditional relevance features. Our experiments show that the proposed method significantly outperforms all the existing methods.