nils-herrmann/clean_scirepeval huggingface dataset:
Data provenance to audit compliance with privacy policy in the Internet of Things

Managing privacy in the IoT presents a significant challenge. We make the case that information obtained by auditing the flows of data can assist in demonstrating that the systems handling personal data satisfy regulatory and user requirements. Thus, components handling personal data should be audited to demonstrate that their actions comply with all such policies and requirements. A valuable side-effect of this approach is that such an auditing process will highlight areas where technical enforcement has been incompletely or incorrectly specified. There is a clear role for technical assistance in aligning privacy policy enforcement mechanisms with data protection regulations. The first step necessary in producing technology to accomplish this alignment is to gather evidence of data flows. We describe our work producing, representing and querying audit data and discuss outstanding challenges.
Unsupervised Entity-Relation Analysis in IBM Watson

Text paraphrasing algorithms play a fundamental role in several NLP applications such as automated question answering (QA), summarization and machine translation. We propose a novel paraphrasing approach based on an entity-relation (ER) analysis of text. The algorithm uses a combination of deep linguistic analysis (part of speech, dependency parse information) and background resources (NGram, PRISMATIC KB, domain dictionaries) to detect and match entities and relations. We evaluate the ER approach in a QA setting by adding it to the suite of passage scoring algorithms in IBM Watson, a state-of-the-art question answering system. We show a statistically significant improvement in the ability of IBM Watson to identify justifying passages.
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation

We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT). We reconstruct the input from sampled translations and leverage differentiable sampling and bi-directional NMT to build a compact model that can be trained end-to-end. This approach achieves small but consistent BLEU improvements on four language pairs in both translation directions, and outperforms an alternative differentiable reconstruction strategy based on hidden states.
Revisiting the Body-Schema Concept in the Context of Whole-Body Postural-Focal Dynamics

The body-schema concept is revisited in the context of embodied cognition, further developing the theory formulated by Marc Jeannerod that the motor system is part of a simulation network related to action, whose function is not only to shape the motor system for preparing an action (either overt or covert) but also to provide the self with information on the feasibility and the meaning of potential actions. The proposed computational formulation is based on a dynamical system approach, which is linked to an extension of the equilibrium-point hypothesis, called Passive Motor Paradigm: this dynamical system generates goal-oriented, spatio-temporal, sensorimotor patterns, integrating a direct and inverse internal model in a multi-referential framework. The purpose of such computational model is to operate at the same time as a general synergy formation machinery for planning whole-body actions in humanoid robots and/or for predicting coordinated sensory-motor patterns in human movements. In order to illustrate the computational approach, the integration of simultaneous, even partially conflicting tasks will be analyzed in some detail with regard to postural-focal dynamics, which can be defined as the fusion of a focal task, namely reaching a target with the whole-body, and a postural task, namely maintaining overall stability.
A Study Into The Effects Of eLearning On Higher Education

The Internet is a technological development that has the potential to change not only the way society retains and accesses knowledge but also to transform and restructure traditional models of higher education, particularly the delivery and interaction in and with course materials and associated resources. Utilising the Internet to deliver eLearning initiatives has created expectations both in the business market and in higher education institutions. Indeed, eLearning has enabled universities to expand on their current geographical reach, to capitalise on new prospective students and to establish themselves as global educational providers. This paper examines the issues surrounding the implementation of eLearning into higher education, including the structure and delivery of higher education, the implications to both students and lecturers and the global impact on society.  Un ivers i t y Teach ing and Learn ing Prac t i ce A Study Into The Effects Of eLearning On Higher Education
The Benefits of Statistical Visualization in an Immersive Environment

We have created an immersive application for statistical graphics and have investigated what benefits it offers over more traditional data analysis tools. This paper presents a description of both the traditional data analysis tools and our virtual environment, and results of an experiment designed to determine if an immersive environment based on the XGobi desktop system provides advantages over XGobi for analysis of high-dimensional statistical data. The experiment included two aspects of each environment: three structure detection (visualization) tasks and one ease of interaction task. The subjects were given these tasks in both the C2 virtual environment and a workstation running XGobi. The experiment results showed an improvement in participants’ ability to perform structure detection tasks in the C2 to their performance in the desktop environment. However, participants were more comfortable with the interaction tools in the desktop
Deep Learning for Image-Based Localization Deep Learning für bildbasierte

“Where was this image taken?” is an intriguing question that has been posed in the literature and is of interest for many applications, from localization of smartphone images to automatic geo-tagging on an online photo platform. Traditional content-based image retrieval (CBIR) systems typically localize images with a pipeline based on hand-crafted feature descriptors such as SIFT. Convolutional neural networks (CNNs), on the other hand, can be trained end-to-end to learn appropriate features from training data. CNNs have been very successful on many computer vision tasks such as image classification, optical flow prediction, and image superresolution. Recent neural network models such as PoseNet and PlaNet have considered localization of images as regression or classification tasks. In this thesis, we investigate two different approaches for image-based localization. In the first approach, we improve upon PoseNet’s idea of performing pose regression in a specific localization area. In particular, we introduce a neural network model based on PoseNet and LSTMs for single-image regression, and extend this approach to regression from sequences of images. In a second approach, we train a Siamese network on pairs of images that have been taken at nearby locations. The resulting network has the advantage that it is not limited to a specific localization area. We evaluate our models on the Cambridge Landmarks datasets, an indoor dataset from Deutsches Museum, and the well-known Dubrovnik dataset. We obtain at least competitive, but often outperforming results on most datasets.
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces

We investigate a technique to adapt unsupervised word embeddings to specific applications, when only small and noisy labeled datasets are available. Current methods use pre-trained embeddings to initialize model parameters, and then use the labeled data to tailor them for the intended task. However, this approach is prone to overfitting when the training is performed with scarce and noisy data. To overcome this issue, we use the supervised data to find an embedding subspace that fits the task complexity. All the word representations are adapted through a projection into this task-specific subspace, even if they do not occur on the labeled dataset. This approach was recently used in the SemEval 2015 Twitter sentiment analysis challenge, attaining state-of-the-art results. Here we show results improving those of the challenge, as well as additional experiments in a Twitter Part-Of-Speech tagging task.
Maximal strength testing in healthy children.

Strength training has become an accepted method of conditioning in children. However, there is concern among some observers that maximal strength testing may be inappropriate or potentially injurious to children. The purpose of this study was to evaluate the safety and efficacy of 1 repetition maximum (1RM) strength testing in healthy children. Thirty-two girls and 64 boys between 6.2 and 12.3 years of age (mean age 9.3 +/- 1.6 years) volunteered to participate in this study. All subjects were screened for medical conditions that could worsen during maximal strength testing. Under close supervision by qualified professionals, each subject performed a 1RM test on 1 upper-body (standing chest press or seated chest press) and 1 lower-body (leg press or leg extension) exercise using child-size weight training machines. No injuries occurred during the study period, and the testing protocol was well tolerated by the subjects. No gender differences were found for any upper- or lower-body strength test. These findings demonstrate that healthy children can safely perform 1RM strength tests, provided that appropriate procedures are followed.
Application of Data Mining and Process Mining approaches for improving e-Learning Processes

The article describes the basic principles and methods of Data mining and Process mining, their similarities and differences. The authors examine the research in Educational Data Mining field, associated with the use of Data mining techniques in education, give examples of problems to be solved with the use of Data mining and Process mining techniques in the area of traditional and e-learning, describe the possibilities and limitations of different methods. Some examples of special software for Data mining and Process mining are presented. A review of major scientific conferences and journals devoted to the research in Educational Data Mining is made.
Influence of cytochrome P 450 polymorphisms on drug therapies : Pharmacogenetic , pharmacoepigenetic and clinical aspects

The polymorphic nature of the cytochrome P450 (CYP) genes affects individual drug response and adverse reactions to a great extent. This variation includes copy number variants (CNV), missense mutations, insertions and deletions, and mutations affecting gene expression and activity of mainly CYP2A6,CYP2B6, CYP2C9, CYP2C19 andCYP2D6,which have been extensively studied andwell characterized.  andCYP3A4 expression varies significantly, and the cause has been suggested to bemainly of genetic origin but the exact molecular basis remains unknown.We present a review of the major polymorphic CYP alleles and conclude that this variability is of greatest importance for treatment with several antidepressants, antipsychotics, antiulcer drugs, anti-HIV drugs, anticoagulants, antidiabetics and the anticancer drug tamoxifen. We also present tables illustrating the relative importance of specific common CYP alleles for the extent of enzyme functionality. The field of pharmacoepigenetics has just opened, and we present recent examples wherein gene methylation influences the expression of CYP. In addition microRNA (miRNA) regulation of P450 has been described. Furthermore, this review updates the fieldwith respect to regulatory initiatives and experience of predictive pharmacogenetic investigations in the clinics. It is concluded that the pharmacogenetic knowledge regarding CYP polymorphism now developed to a stage where it can be implemented in drug development and in clinical routine for specific drug treatments, thereby improving the drug response and reducing costs for drug treatment.
Hermitian Co-Attention Networks for Text Matching in Asymmetrical Domains

Co-Attentions are highly effective attention mechanisms for text matching applications. Co-Attention enables the learning of pairwise attentions, i.e., learning to attend based on computing word-level affinity scores between two documents. However, text matching problems can exist in either symmetrical or asymmetrical domains. For example, paraphrase identification is a symmetrical task while question-answer matching and entailment classification are considered asymmetrical domains. In this paper, we argue that Co-Attention models in asymmetrical domains require different treatment as opposed to symmetrical domains, i.e., a concept of word-level directionality should be incorporated while learning word-level similarity scores. Hence, the standard inner product in real space commonly adopted in co-attention is not suitable. This paper leverages attractive properties of the complex vector space and proposes a co-attention mechanism based on the complex-valued inner product (Hermitian products). Unlike the real dot product, the dot product in complex space is asymmetric because the first item is conjugated. Aside from modeling and encoding directionality, our proposed approach also enhances the representation learning process. Extensive experiments on five text matching benchmark datasets demonstrate the effectiveness of our approach.
Social entrepreneurship : the need , relevance , facets and constraints

Social entrepreneurship is gradually becoming a crucial element in the worldwide discussion on volunteerism and civic commitment. It interleaves the passion of a common cause with industrial ethics and is notable and different from the present other types of entrepreneurship models due to its quest for mission associated influence. The previous few years have noticed a striking and surprising progress in the field of social entrepreneurship and has amplified attention ranging throughout all the diverse sectors. The critical difference between social and traditional entrepreneurship can be seen in the founding mission of the venture and the market impressions. Social entrepreneurs emphasize on ways to relieve or eradicate societal pressures and produce progressive externalities or public properties. This study focuses mainly on the meaning of social entrepreneurship to different genres and where does it stand in respect to other forms of entrepreneurship in today’s times.
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning

Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. However, most previous works only consider simple or weak interactions, thereby failing to model complex correlations among three or more tasks. In this paper, we propose a multi-task learning architecture with four types of recurrent neural layers to fuse information across multiple related tasks. The architecture is structurally flexible and considers various interactions among tasks, which can be regarded as a generalized case of many previous works. Extensive experiments on five benchmark datasets for text classification show that our model can significantly improve performances of related tasks with additional information from others.
' s personal copy Recent progresses on real-time 3 D shape measurement using digital fringe projection techniques

Over the past few years, we have been developing techniques for high-speed 3D shape measurement using digital fringe projection and phase-shifting techniques: various algorithms have been developed to improve the phase computation speed, parallel programming has been employed to further increase the processing speed, and advanced hardware technologies have been adopted to boost the speed of coordinate calculations and 3D geometry rendering. We have successfully achieved simultaneous 3D absolute shape acquisition, reconstruction, and display at a speed of 30 frames/s with 300 K points per frame. This paper presents the principles of the real-time 3D shape measurement techniques that we developed, summarizes the most recent progresses that have been made in this field, and discusses the challenges for advancing this technology further.
Knowledge flow-based business process redesign: applying a knowledge map to redesign a business process

The aim of this paper is to suggest a method to redesign business processes from the viewpoint of knowledge flows using a knowledge map.  Knowledge flows and business processes cannot be separated because knowledge is inputted and outputted through business processes. Knowledge flows inherit the feature and appearance of corresponding business processes; therefore, one can identify problems within business processes by analyzing corresponding knowledge flows. The methodology is composed of the following sections: knowledge mapping, knowledge profiling, knowledge flow identification, knowledge flow optimization and TO-BE process visualization.  This paper provides a methodology for knowledge flow-based business process redesign and ten guidelines for knowledge flow optimization. The case study demonstrates that the proposed ideas constitute knowledge-intensified business processes.  A more formal validation method that is based on the statistical analysis must be provided to assert the proposed guidelines for knowledge flow optimization as the truly optimized ones.  This paper’s idea provides the practical methodology and guidelines that can be directly applicable to performing business process redesign by introducing a real case.  This paper’s ideas not only provide present companies with a practical way to enhance their business process to be more knowledge-focused, but also promote the current economy to be more knowledge-intensive.
A New Android Malware Detection Approach Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.
Encode, Review, and Decode: Reviewer Module for Caption Generation

We propose a novel module, the reviewer module, to improve the encoder-decoder learning framework. The reviewer module is generic, and can be plugged into an existing encoder-decoder model. The reviewer module performs a number of review steps with attention mechanism on the encoder hidden states, and outputs a fact vector after each review step; the fact vectors are used as the input of the attention mechanism in the decoder. We show that the conventional encoderdecoders are a special case of our framework. Empirically, we show that our framework can improve over state-of-the-art encoder-decoder systems on the tasks of image captioning and source code captioning.
Scalable Kernel TCP Design and Implementation for Short-Lived Connections

With the rapid growth of network bandwidth, increases in CPU cores on a single machine, and application API models demanding more short-lived connections, a scalable TCP stack is performance-critical. Although many clean-state designs have been proposed, production environments still call for a bottom-up parallel TCP stack design that is backward-compatible with existing applications.
 We present Fastsocket, a BSD Socket-compatible and scalable kernel socket design, which achieves table-level connection partition in TCP stack and guarantees connection locality for both passive and active connections. Fastsocket architecture is a ground up partition design, from NIC interrupts all the way up to applications, which naturally eliminates various lock contentions in the entire stack. Moreover, Fastsocket maintains the full functionality of the kernel TCP stack and BSD-socket-compatible API, and thus applications need no modifications.
 Our evaluations show that Fastsocket achieves a speedup of 20.4x on a 24-core machine under a workload of short-lived connections, outperforming the state-of-the-art Linux kernel TCP implementations. When scaling up to 24 CPU cores, Fastsocket increases the throughput of Nginx and HAProxy by 267% and 621% respectively compared with the base Linux kernel. We also demonstrate that Fastsocket can achieve scalability and preserve BSD socket API at the same time. Fastsocket is already deployed in the production environment of Sina WeiBo, serving 50 million daily active users and billions of requests per day.
Using psychophysiological techniques to measure user experience with entertainment technologies

Emerging technologies offer exciting new ways of using entertainment technology to create fantastic play experiences and foster interactions between players. Evaluating entertainment technology is challenging because success isn’ t defined in terms of productivity and performance, but in terms of enjoyment and interaction. Current subjective methods of evaluating entertainment technology aren’ t sufficiently robust. This paper describes two experiments designed to test the efficacy of physiological measures as evaluators of user experience with entertainment technologies. We found evidence that there is a different physiological response in the body when playing against a computer versus playing against a friend. These physiological results are mirrored in the subjective reports provided by the participants. In addition, we provide guidelines for collecting physiological data for user experience analysis, which were informed by our empirical investigations. This research provides an initial step towards using physiological responses to objectively evaluate a user’s experience with entertainment technology.