nils-herrmann/clean_scirepeval huggingface dataset:
Xen and the Art of Repeated Research

Xen is an x86 virtual machine monitor produced by the University of Cambridge Computer Laboratory and released under the GNU General Public License. Performance results comparing XenoLinux (Linux running in a Xen virtual machine) to native Linux as well as to other virtualization tools such as User Mode Linux (UML) were recently published in the paper “Xen and the Art of Virtualization” at the Symposium on Operating Systems Principles (October 2003). In this study, we repeat this performance analysis of Xen. We also extend the analysis in several ways, including comparing XenoLinux on x86 to an IBM zServer. We use this study as an example of repeated research. We argue that this model of research, which is enabled by open source software, is an important step in transferring the results of computer science research into production environments.
Using Movie Subtitles for Creating a Large-Scale Bilingual Corpora

This paper presents a method for compiling a large-scale bilingual corpus from a database of movie subtitles. To create the corpus, we propose an algorithm based on Gale and Church’s sentence alignment algorithm(1993). However, our algorithm not only relies on character length information, but also uses subtitle-timing information, which is encoded in the subtitle files. Timing is highly correlated between subtitles in different versions (for the same movie), since subtitles that match should be displayed at the same time. However, the absolute time values can’t be used for alignment, since the timing is usually specified by frame numbers and not by real time, and converting it to real time values is not always possible, hence we use normalized subtitle duration instead. This results in a significant reduction in the alignment error rate.
Large Vocabulary Speech Recognition Using Deep Tensor Neural Networks

Recently, we proposed and developed the context-dependent deep neural network hidden Markov models (CD-DNN-HMMs) for large vocabulary speech recognition and achieved highly promising recognition results including over one third fewer word errors than the discriminatively trained, conventional HMM-based systems on the 300hr Switchboard benchmark task. In this paper, we extend DNNs to deep tensor neural networks (DTNNs) in which one or more layers are double-projection and tensor layers. The basic idea of the DTNN comes from our realization that many factors interact with each other to predict the output. To represent these interactions, we project the input to two nonlinear subspaces through the double-projection layer and model the interactions between these two subspaces and the output neurons through a tensor with three-way connections. Evaluation on 30hr Switchboard task indicates that DTNNs can outperform DNNs with similar number of parameters with 5% relative word error reduction.
STN-OCR: A single Neural Network for Text Detection and Text Recognition

Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present STN-OCR, a step towards semi-supervised neural networks for scene text recognition that can be optimized end-to-end. In contrast to most existing works that consist of multiple deep neural networks and several pre-processing steps we propose to use a single deep neural network that learns to detect and recognize text from natural images in a semi-supervised way. STN-OCR is a network that integrates and jointly learns a spatial transformer network [16], that can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We investigate how our model behaves on a range of different tasks (detection and recognition of characters, and lines of text). Experimental results on public benchmark datasets show the ability of our model to handle a variety of different tasks, without substantial changes in its overall network structure.
Learning Invariant Feature Hierarchies

Fast visual recognition in the mammalian cortex seems to be a hierarchical process by which the representation of the visual world is transformed in multiple stages from low-level retinotopic features to high-level, global and invariant features, and to object categories. Every single step in this hierarchy seems to be subject to learning. How does the visual cortex learn such hierarchical representations by just looking at the world? How could computers learn such representations from data? Computer vision models that are weakly inspired by the visual cortex will be described. A number of unsupervised learning algorithms to train these models will be presented, which are based on the sparse auto-encoder concept. The effectiveness of these algorithms for learning invariant feature hierarchies will be demonstrated with a number of practical tasks such as scene parsing, pedestrian detection, and object classification.
Situation and development tendency of indoor positioning

This paper introduces the significance of indoor positioning and analyzes the related problems. The latest research on indoor positioning is introduced. Further, the positioning accuracy and the cost of typical local and wide area indoor positioning systems are compared. The results of the comparison show that Time & Code Division-Orthogonal Frequency Division Multiplexing (TC-OFDM) is a system that can achieve real-time meter-accuracy of indoor positioning in a wide area. Finally, in this paper, we indicate that the seamless high-accuracy indoor positioning in a wide area is the development trend of indoor positioning. The seamless Location Based Services (LBS) architecture based on a heterogeneous network, key technologies in indoor positioning for decimeter-accuracy and seamless outdoor and indoor Geographic Information System (GIS) are elaborated as the most important research fields of future indoor positioning.
Human Resource Management System

The paper titled “HUMAN RESOURCE MANAGEMENT SYSTEM” is basically concerned with managing the Administrator of HUMAN RESOURCE Department in a company. A Human Resource Management System (HRMS), refers to the systems and processes at the intersection between human resource management (HRM) and information technology. It merges HRM as a discipline and in particular its basic HR activities and processes with the information technology field, whereas the programming of data processing systems evolved into standardized routines and packages of enterprise resource planning (ERP) software[1]. The main objective of this paper is to reduce the effort of Administrator to keep the daily events such as attendance, projects, works, appointments, etc. This paper deals with the process of identifying the employees, recording their attendance hourly and calculating their effective payable hours or days. This paper should maintain the records of each and every employee and their time spend in to company, which can be used for performance appraisal. Based on that transfer, removal, promotion can be done.  Human Resource, Administrator, Employee
Indoor Robot Localization System Using WiFi Signal Measure and Minimizing Calibration Effort

This paper presents a robot localization system for indoor environments using WiFi signal strength measure. We analyse the main causes of the WiFi signal strength variation and we experimentally demonstrate that a localization technique based on a propagation model doesn’t work properly in our test-bed. We have carried out a localization system based on a priori radio-map obtained automatically from a robot navigation in the environment in a semi-autonomous way. We analyse the effect of reducing calibration effort in order to diminish practical barriers to wider adoption of this type of location measurement technique. Experimental results using a real robot moving are shown. Finally, the conclusions and future works are
An Introduction to Dimensionality Reduction Using Matlab

Dimensionality reduction is an important task in machine learning, for it facilitates classification, compression, and visualization of high-dimensional data by mitigating undesired properties of high-dimensional spaces. Over the last decade, a large number of new (nonlinear) techniques for dimensionality reduction have been proposed. Most of these techniques are based on the intuition that data lies on or near a complex low-dimensional manifold that is embedded in the high-dimensional space. New techniques for dimensionality reduction aim at identifying and extracting the manifold from the high-dimensional space. A systematic empirical evaluation of a large number of dimensionality reduction techniques has been presented in . This work has led to the development of the Matlab Toolbox for Dimensionality Reduction, which contains implementations of 27 techniques for dimensionality reduction. In addition, the toolbox contains implementation of 6 intrinsic dimensionality estimators and functions for out-of-sample extension, data generation, and data prewhitening. The report describes the techniques that are implemented in the toolbox in detail. Furthermore, it presents a number of examples that illustrate the functionality of the toolbox, and provide insight in the capabilities of state-of-the-art techniques for dimensionality reduction.
Distributed and Collaborative Visualization

Visualization is a powerful tool for analysing data and presenting results in science, engineering and medicine. This paper reviews ways in which it can be used in distributed and/or collaborative enviroments. Distributed visualization addresses a number of resource allocation problems, including the location of processing close to data for the minimization of data traffic. The advent of the Grid Computing paradigm and the link to Web Services provides fresh challenges and opportunities for distributed visualization—including the close coupling of simulations and visualizations in a steering environment. Recent developments in collaboration have seen the growth of specialized facilities (such as Access Grid) which have supplemented traditional desktop video conferencing using the Internet and multicast communications. Collaboration allows multiple users—possibly at remote sites—to take part in the visualization process at levels which range from the viewing of images to the shared control of the visualization methods. In this review, we present a model framework for distributed and collaborative visualization and assess a selection of visualization systems and frameworks for their use in a distributed or collaborative environment. We also discuss some examples of enabling technology and review recent work from research projects in this field.
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation

In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages our finding that entropy minimization is induced by the optimal alignment of second order statistics between source and target domains. We formally demonstrate this hypothesis and, aiming at achieving an optimal alignment in practical cases, we adopt a more principled strategy which, differently from the current Euclidean approaches, deploys alignment along geodesics. Our pipeline can be implemented by adding to the standard classification loss (on the labeled source domain), a source-to-target regularizer that is weighted in an unsupervised and data-driven fashion. We provide extensive experiments to assess the superiority of our framework on standard domain and modality adaptation benchmarks.
Grasping without squeezing: Shear adhesion gripper with fibrillar thin film

Nearly all robotic grippers have one trait in common: they grasp objects with normal forces, either directly, or indirectly through friction. This method of grasping is effective for objects small enough for a given gripper to partially encompass. However, to grasp larger objects, significant grip forces and a high coefficient of friction are required. We present a new grasping method for convex objects, using almost exclusively shear forces. We achieve shear grasping with a gripper that utilizes thin film gecko-inspired fibrillar adhesives that conform to the curvature of the object. We present a verified model for grasping a range of curvatures and results that demonstrate the thin film fibrillar adhesives' increased contact area on textured surfaces when loaded in shear. Finally, the gripper is implemented on a robotic arm and grasps a variety of convex objects (at rest and ballistic).
Predictive Entropy Search for Multi-objective Bayesian Optimization

This work presents PESMOC, Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based strategy for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints. PESMOC can hence be used to solve a wide range of optimization problems. Iteratively, PESMOC chooses an input location on which to evaluate the objective functions and the constraints so as to maximally reduce the entropy of the Pareto set of the corresponding optimization problem. The constraints considered in PESMOC are assumed to have similar properties to those of the objective functions in typical Bayesian optimization problems. That is, they do not have a known expression (which prevents gradient computation), their evaluation is considered to be very expensive, and the resulting observations may be corrupted by noise. These constraints arise in a plethora of expensive black-box optimization problems. We carry out synthetic experiments to illustrate the effectiveness of PESMOC, where we sample both the objectives and the constraints from a Gaussian process prior. The results obtained show that PESMOC is able to provide better recommendations with a smaller number of evaluations than a strategy based on random search.
A Self-Timed Ring Based True Random Number Generator

Self-timed rings are oscillators in which several events can evolve evenly-spaced in time thanks to analog effects inherent to the ring stage structure. One of their interesting features is that they provide precise high-speed multiphase signals. This paper presents a true random number generator that exploits the jitter of events propagating in a self-timed ring with a high entropy. Designs implemented in Alter a Cyclone III and Xilinx Virtex 5 devices provide high quality random bit sequNIST SP 800-22 statistical testsences passing FIPS 140-1 and NIST SP 800-22 statistical tests at a high bit rate.
Hot-hardening: getting more out of your security settings

Applying optimized security settings to applications is a difficult and laborious task. Especially in cloud computing, where virtual servers with various pre-installed software packages are leased, selecting optimized security settings is very difficult. In particular, optimized security settings are not identical in every setup. They depend on characteristics of the setup, on the ways an application is used or on other applications running on the same system. Configuring optimized settings given these interdependencies is a complex and time-consuming task. In this work, we present an autonomous agent which improves security settings of applications which run in virtual servers. The agent retrieves custom-made security settings for a target application by investigating its specific setup, it tests and transparently changes settings via introspection techniques unbeknownst from the perspective of the virtual server. During setting selection, the application's operation is not disturbed nor any user interaction is needed. Since optimal settings can change over time or they can change depending on different tasks the application handles, the agent can continuously adapt settings as well as improve them periodically. We call this approach hot-hardening and present results of an implementation that can hot-harden popular networking applications such as Apache2 and OpenSSH.
Docking and scoring in virtual screening for drug discovery: methods and applications

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule–protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.
The FLDA model for aspect-based opinion mining: addressing the cold start problem

Aspect-based opinion mining from online reviews has attracted a lot of attention recently. The main goal of all of the proposed methods is extracting aspects and/or estimating aspect ratings. Recent works, which are often based on Latent Dirichlet Allocation (LDA), consider both tasks simultaneously. These models are normally trained at the item level, i.e., a model is learned for each item separately. Learning a model per item is fine when the item has been reviewed extensively and has enough training data. However, in real-life data sets such as those from Epinions.com and Amazon.com more than 90% of items have less than 10 reviews, so-called cold start items. State-of-the-art LDA models for aspect-based opinion mining are trained at the item level and therefore perform poorly for cold start items due to the lack of sufficient training data. In this paper, we propose a probabilistic graphical model based on LDA, called Factorized LDA (FLDA), to address the cold start problem. The underlying assumption of FLDA is that aspects and ratings of a review are influenced not only by the item but also by the reviewer. It further assumes that both items and reviewers can be modeled by a set of latent factors which represent their aspect and rating distributions. Different from state-of-the-art LDA models, FLDA is trained at the category level and learns the latent factors using the reviews of all the items of a category, in particular the non cold start items, and uses them as prior for cold start items. Our experiments on three real-life data sets demonstrate the improved effectiveness of the FLDA model in terms of likelihood of the held-out test set. We also evaluate the accuracy of FLDA based on two application-oriented measures.
Induction Heating Technology and Its Applications: Past Developments, Current Technology, and Future Challenges

Induction heating (IH) technology is nowadays the heating technology of choice in many industrial, domestic, and medical applications due to its advantages regarding efficiency, fast heating, safety, cleanness, and accurate control. Advances in key technologies, i.e., power electronics, control techniques, and magnetic component design, have allowed the development of highly reliable and cost-effective systems, making this technology readily available and ubiquitous. This paper reviews IH technology summarizing the main milestones in its development and analyzing the current state of art of IH systems in industrial, domestic, and medical applications, paying special attention to the key enabling technologies involved. Finally, an overview of future research trends and challenges is given, highlighting the promising future of IH technology.
Measuring the Information Content of Financial News

Measuring the information content of news text is useful for decision makers in their investments since news information can influence the intrinsic values of companies. We propose a model to automatically measure the information content given news text, trained using news and corresponding cumulative abnormal returns of listed companies. Existing methods in finance literature exploit sentiment signal features, which are limited by not considering factors such as events. We address this issue by leveraging deep neural models to extract rich semantic features from news text. In particular, a novel tree-structured LSTM is used to find target-specific representations of news text given syntax structures. Empirical results show that the neural models can outperform sentiment-based models, demonstrating the effectiveness of recent NLP technology advances for computational finance.
Intent-aware query similarity

Query similarity calculation is an important problem and has a wide range of applications in IR, including query recommendation, query expansion, and even advertisement matching. Existing work on query similarity aims to provide a single similarity measure without considering the fact that queries are ambiguous and usually have multiple search intents. In this paper, we argue that query similarity should be defined upon search intents, so-called intent-aware query similarity. By introducing search intents into the calculation of query similarity, we can obtain more accurate and also informative similarity measures on queries and thus help a variety of applications, especially those related to diversification. Specifically, we first identify the potential search intents of queries, and then measure query similarity under different intents using intent-aware representations. A regularized topic model is employed to automatically learn the potential intents of queries by using both the words from search result snippets and the regularization from query co-clicks. Experimental results confirm the effectiveness of intent-aware query similarity on ambiguous queries which can provide significantly better similarity scores over the traditional approaches. We also experimentally verified the utility of intent-aware similarity in the application of query recommendation, which can suggest diverse queries in a structured way to search users.
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse

This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models. We proposed a method for on-the-fly construction and exploitation of word embeddings in both the input and output layers of a neural model by tracking contexts. This extends the dynamic entity representation used in Kobayashi et al. (2016) and incorporates a copy mechanism proposed independently by Gu et al. (2016) and Gulcehre et al. (2016). In addition, we construct a new task and dataset called Anonymized Language Modeling for evaluating the ability to capture word meanings while reading. Experiments conducted using our novel dataset show that the proposed variant of RNN language model outperformed the baseline model. Furthermore, the experiments also demonstrate that dynamic updates of an output layer help a model predict reappearing entities, whereas those of an input layer are effective to predict words following reappearing entities.