nils-herrmann/clean_scirepeval huggingface dataset:
Manual and Automatic Evaluation of Machine Translation between European Languages

We evaluated machine translation performance for six European language pairs that participated in a shared task: translating French, German, Spanish texts to English and back. Evaluation was done automatically using the BLEU score and manually onfluencyandadequacy . For the 2006 NAACL/HLT Workshop on Machine Translation, we organized a shared task to evaluate machine translation performance. 14 teams from 11 institutions participated, ranging from commercial companies, industrial research labs to individual graduate students. The motivation for such a competition is to establish baseline performance numbers for defined training scenarios and test sets. We assembled various forms of data and resources: a baseline MT system, language models, prepared training and test sets, resulting in actual machine translation output from several state-of-the-art systems and manual evaluations. All this is available at the workshop website 1. The shared task is a follow-up to the one we organized in the previous year, at a similar venue (Koehn and Monz, 2005). As then, we concentrated on the translation of European languages and the use of the Europarl corpus for training. Again, most systems that participated could be categorized as statistical phrase-based systems. While there is now a number of competitions — DARPA/NIST (Li, 2005), IWSLT (Eck and Hori, 2005), TC-Star — this one focuses on text translation between various European languages. This year’s shared task changed in some aspects from last year’s: • We carried out a manual evaluation in addition to the automatic scoring. Manual evaluation http://www.statmt.org/wmt06/ was done by the participants. This revealed interesting clues about the properties of automatic and manual scoring. • We evaluated translation from English, in addition to into English. English was again paired with German, French, and Spanish. We dropped, however, one of the languages, Finnish, partly to keep the number of tracks manageable, partly because we assumed that it would be hard to find enough Finnish speakers for the manual evaluation. • We included an out-of-domain test set. This allows us to compare machine translation performance in-domain and out-of-domain. 1 Evaluation Framework The evaluation framework for the shared task is similar to the one used in last year’s shared task. Training and testing is based on the Europarl corpus. Figure 1 provides some statistics about this corpus.
A survey on wireless security protocols (WEP, WPA and WPA2/802.11i)

Wireless technology has been gaining rapid popularity for some years. Adaptation of a standard depends on the ease of use and level of security it provides. In this case, contrast between wireless usage and security standards show that the security is not keeping up with the growth paste of end user's usage. Current wireless technologies in use allow hackers to monitor and even change the integrity of transmitted data. Lack of rigid security standards has caused companies to invest millions on securing their wireless networks. There are three major types of security standards in wireless. In our previous papers which registered in ICFCC 2009 Malaysia and ICCDA 2009 Singapore  , we explained the structure of WEP and WPA as first and second wireless security protocols and discussed all their versions, problems and improvements. Now, we try to explain WPA2 versions, problems and enhancements that have done solve the WPA major weakness. Finally we make a comparison among WEP and WPA and WPA2 as all wireless security protocols in Wi-Fi technology. In the next phase we hope that we will publish a complete comparison among wireless security techniques by add the WiMax security technique and make a whole comparison among all security protocols in this area.
Printed Rectangular Monopole Antenna with Slots for UWB Applications

This paper presents various slotted rectangular Monopole Antenna. The proposed antenna is designed in order to meet ultra wideband (UWB) requirements and applications. This antenna is small in size enabling it to be fixed into wireless Personal Area Network (PAN) devices. There are 3 types of proposed slots; T-slot, Π-slot and I-slot for patch. The T & I slots produce more vertical current, thus improve the matching impedance performance at higher frequencies. The Π-slot on rectangular patch has shown dual frequency performance. For all proposed antennas, their measured return loss and radiation pattern meet the UWB requirements.
Towards SMP Challenge: Stacking of Diverse Models for Social Image Popularity Prediction

Popularity prediction on social media has attracted extensive attention nowadays due to its widespread applications, such as online marketing and economical trends. In this paper, we describe a solution of our team CASIA-NLPR-MMC for Social Media Prediction (SMP) challenge. This challenge is designed to predict the popularity of social media posts. We present a stacking framework by combining a diverse set of models to predict the popularity of images on Flickr using user-centered, image content and image context features. Several individual models are employed for scoring popularity of an image at earlier stage, and then a stacking model of Support Vector Regression (SVR) is utilized to train a meta model of different individual models trained beforehand. The Spearman's Rho of this Stacking model is 0.88 and the mean absolute error is about 0.75 on our test set. On the official final-released test set, the Spearman's Rho is 0.7927 and mean absolute error is about 1.1783. The results on provided dataset demonstrate the effectiveness of our proposed approach for image popularity prediction.
Describing Videos by Exploiting Temporal Structure

Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description model. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.
Balancing Performance, Energy, and Quality in Pervasive Computing

We describe Spectra, a remote execution system for batterypowered clients used in pervasive computing. Spectra enables applications to combine the mobility of small devices with the greater processing power of static compute servers. Spectra is self-tuning: it monitors both application resource usage and the availability of resources in the environment, and dynamically determines how and where to execute application components. In making this determination, Spectra balances the competing goals of performance, energy conservation, and application quality. We have validated Spectra’s approach on the Compaq Itsy v2.2 and IBM ThinkPad 560X using a speech recognizer, a document preparation system, and a natural language translator. Our results confirm that Spectra almost always selects the best execution plan, and that its few suboptimal choices are very close to optimal.
On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals

Modern web search engines are federated --- a user query is sent to the numerous specialized search engines called verticals like web (text documents), News, Image, Video, etc. and the results returned by these engines are then aggregated and composed into a search result page (SERP) and presented to the user. For a specific query, multiple verticals could be relevant, which makes the placement of these vertical results within blocks of textual web results challenging: how do we represent, assess, and compare the relevance of these heterogeneous entities?
 In this paper we present a machine-learning framework for SERP composition in the presence of multiple relevant verticals. First, instead of using the traditional label generation method of human judgment guidelines and trained judges, we use a randomized online auditioning system that allows us to evaluate triples of the form query, web block, vertical>. We use a pairwise click preference to evaluate whether the web block or the vertical block had a better users' engagement. Next, we use a hinged feature vector that contains features from the web block to create a common reference frame and augment it with features representing the specific vertical judged by the user. A gradient boosted decision tree is then learned from the training data. For the final composition of the SERP, we place a vertical result at a slot if the score is higher than a computed threshold. The thresholds are algorithmically determined to guarantee specific coverage for verticals at each slot.
 We use correlation of clicks as our offline metric and show that click-preference target has a better correlation than human judgments based models. Furthermore, on online tests for News and Image verticals we show higher user engagement for both head and tail queries.
Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey

Research Scholar and Research guide, Research and Development, Bharathiar University, Coimbatore, Tamil Nadu, India Email: vanajasha@yahoo.com  The aim of this research paper is to study and discuss the various classification algorithms applied on different kinds of medical datasets and compares its performance. The classification algorithms with maximum accuracies on various kinds of medical datasets are taken for performance analysis. The result of the performance analysis shows the most frequently used algorithms on particular medical dataset and best classification algorithm to analyse the specific disease. This study gives the details of different classification algorithms and feature selection methodologies. The study also discusses about the data constraints such as volume and dimensionality problems. This research paper also discusses the new features of C5.0 classification algorithm over C4.5 and performance of classification algorithm on high dimensional datasets. This research paper summarizes various reviews and technical articles which focus on the current research on Medical diagnosis.
Efficient Time-Domain Image Formation with Precise Topography Accommodation for General Bistatic SAR Configurations

Due to the lack of an appropriate symmetry in the acquisition geometry, general bistatic synthetic aperture radar (SAR) cannot benefit from the two main properties of low-to-moderate resolution monostatic SAR: azimuth-invariance and topography-insensitivity. The precise accommodation of azimuth-variance and topography is a real challenge for efficent image formation algorithms working in the Fourier domain, but can be quite naturally handled by time-domain approaches. We present an efficient and practical implementation of a generalised bistatic SAR image formation algorithm with an accurate accommodation of these two effects. The algorithm has a common structure with the monostatic fast-factorised backprojection (FFBP), and is therefore based on subaperture processing. The images computed over the different subapertures are displayed in an advantageous elliptical coordinate system capable of incorporating the topographic information of the imaged scene in an analogous manner as topography-dependent monostatic SAR algorithms do. Analytical expressions for the Nyquist requirements using this coordinate system are derived. The overall discussion includes practical implementation hints and a realistic computational burden estimation. The algorithm is tested with both simulated and actual bistatic SAR data. The actual data correspond to the spaceborne-airborne experiment between TerraSAR-X and F-SAR performed in 2007 and to the DLR-ONERA airborne experiment carried out in 2003. The presented approach proves its suitability for the precise SAR focussing of the data acquired in general bistatic configurations.
An event-based data fusion algorithm for smart cities

The last decade has seen a considerable increase in the number of sensors we interact with on a daily basis. However, it is not always possible for a single sensing system to capture the complete story. While statically mounted infrastructure sensors typically capture the what, where, how much etc aspects of a detected event, e.g. (what appliance was used, how much energy did it consume), they do not always answer the who question. On the other hand, the advent of wearables has helped answer the what and who aspects - e.g. (who used the appliance). Fusing such sensor streams that observe the same event but different attributes of it, opens up many interesting applications. In this paper, we present a globally optimal data fusion algorithm for such pairs of systems, and show why traditional bipartite algorithms do not work. We evaluate our algorithm against two greedy baselines and show that our algorithm has lesser variance in the presence of time skew, false positives and false negatives.
HERB: a home exploring robotic butler

We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating doors and other constrained objects using caging grasps, grasp planning and execution in clutter, and manipulation on pose and torque constraint manifolds. S.S. Srinivasa ( ) · D. Ferguson · C.J. Helfrich Intel Research Pittsburgh, 4720 Forbes Avenue, Suite 410, Pittsburgh, PA 15213, USA e-mail: siddhartha.srinivasa@intel.com C.J. Helfrich e-mail: casey.j.helfrich@intel.com D. Berenson · A. Collet · R. Diankov · G. Gallagher · G. Hollinger · J. Kuffner · M.V. Weghe The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA D. Berenson  acollet@ri.cmu.edu R. Diankov e-mail: rdiankov@ri.cmu.edu G. Gallagher e-mail: ggallagh@ri.cmu.edu G. Hollinger e-mail: gholling@ri.cmu.edu J. Kuffner e-mail: kuffner@ri.cmu.edu M.V. Weghe  vandeweg@ri.cmu.edu We also present numerous severe real-world test results from the integration of these algorithms into a single mobile manipulator.
Experimental Analysis of Mode Switching Techniques in Touch-based User Interfaces

This paper presents the results of a 36 participant empirical comparison of touch mode-switching. Six techniques are evaluated, spanning current and future techniques: long press, non-dominant hand, two-fingers, hard press, knuckle, and thumb-on-finger. Two poses are controlled for, seated with the tablet on a desk and standing with the tablet held on the forearm. Findings indicate pose has no effect on mode switching time and little effect on error rate; using two-fingers is fastest while long press is much slower; non-preferred hand and thumb-on-finger also rate highly in subjective scores. The experiment protocol is based on Li et al.'s pen mode-switching study, enabling a comparison of touch and pen mode switching. Among the common techniques, the non-dominant hand is faster than pressure with touch, whereas no significant difference had been found for pen. Our work addresses the lack of empirical evidence comparing touch mode-switching techniques and provides guidance to practitioners when choosing techniques and to researchers when designing new mode-switching methods.
A Large Scale Ranker-Based System for Search Query Spelling Correction

This paper makes three significant extensions to a noisy channel speller designed for standard written text to target the challenging domain of search queries. First, the noisy channel model is subsumed by a more general ranker, which allows a variety of features to be easily incorporated. Second, a distributed infrastructure is proposed for training and applying Web scale n-gram language models. Third, a new phrase-based error model is presented. This model places a probability distribution over transformations between multi-word phrases, and is estimated using large amounts of query-correction pairs derived from search logs. Experiments show that each of these extensions leads to significant improvements over the stateof-the-art baseline methods.
Visualization of test information to assist fault localization

One of the most expensive and time-consuming components of the debugging process is locating the errors or faults. To locate faults, developers must identify statements involved in failures and select suspicious statements that might contain faults. This paper presents a new technique that uses visualization to assist with these tasks. The technique uses color to visually map the participation of each program statement in the outcome of the execution of the program with a test suite, consisting of both passed and failed test cases. Based on this visual mapping, a user can inspect the statements in the program, identify statements involved in failures, and locate potentially faulty statements. The paper also describes a prototype tool that implements our technique along with a set of empirical studies that use the tool for evaluation of the technique. The empirical studies show that, for the subject we studied, the technique can be effective in helping a user locate faults in a program.
Learning for Semantic Parsing

Semantic parsing is the task of mapping a natural language sentence into a complete, formal meaning representation. Over the past decade, we have developed a number of machine learning methods for inducing semantic parsers by training on a corpus of sentences paired with their meaning representations in a specified formal language. We have demonstrated these methods on the automated construction of naturallanguage interfaces to databases and robot command languages. This paper reviews our prior work on this topic and discusses directions for future research.
Colony of NPUs : Scaling the Efficiency of Neural Accelerators

Trading small amounts of output accuracy to obtain significant performance improvement or reduce power consumption is a promising research direction. Many modern applications from different domains such as image and signal processing, computer vision, data mining, machine learning and speech recognition are amenable to approximate computing. In many cases, their output is interpreted by humans, so infrequent variation in outputs are not noticeable by users. Moreover, the input of these applications is noisy which prevents strict constraints on the output of the system. Hence, these domains have significant potential for the aforementioned trade-offs, and these applications are ideal targets for approximate computing. Existing approaches for approximate computing include modifying the ISA [6], compiler , programming language [2, 4], underlying hardware [14] or the entire framework [3, 8, 10]. Approximation accelerators [5, 7, 13] use some of these methods to trade off accuracy for higher performance or energy savings. These accelerators require the programmer to annotate code sections which are amenable for approximation. Approximate parts are offloaded to the accelerator and other parts are executed by the CPU at run time. Esmaeilzadeh et. al.  proposed a Neural Processing Unit (NPU) as a programmable approximate accelerator. The key idea is to train a neural network (nn) to mimic an approximable region of original code and replace that with an efficient computation of the learned model. Although the proposed approximate accelerators such as NPU demonstrate acceptable results on the benchmarks from different domains, they have some shortcomings. Firstly, different invocations of a program might produce varying output qualities because the output quality is dependent on the input values. Consequently, using an NPU with fixed configuration for a wide range of inputs may produce outputs with poor accuracies. On the other hand, if the output quality drops below a determined threshold, one way to improve the quality is re-executing the whole program on the exact hardware. However, the overhead of this recovery process may offset the gains of approximation. Secondly, existing techniques usually measure the output quality by averaging errors in individual output elements, e.g., pixels in an image. Previous works in approximate computing [9, 11, 12] show that although most of the output elements have small errors, there exist a few output elements that have considerably large errors which may degrade the whole user experience. Therefore, some inputs might need more specialized methods to deal with the issue of output quality. Lastly, it is challenging to tune the output quality of an approximate hardware dynamically. If different NPUs with different configurations are available in the system, defining the number of active NPUs per invocation will be a reasonable knob for changing output quality based on users preferences. NPU Output Input Monolithic NPU
Multilingual Relation Extraction using Compositional Universal Schema

When building a knowledge base (KB) of entities and relations from multiple structured KBs and text, universal schema represents the union of all input schema, by jointly embedding all relation types from input KBs as well as textual patterns expressing relations. In previous work, textual patterns are parametrized as a single embedding, preventing generalization to unseen textual patterns. In this paper we employ an LSTM to compositionally capture the semantics of relational text. We dramatically demonstrate the flexibility of our approach by evaluating in a multilingual setting, in which the English training data entities overlap with the seed KB, but the Spanish text does not. Additional improvements are obtained by tying word embeddings across languages. In extensive experiments on the English and Spanish TAC KBP benchmark, our techniques provide substantial accuracy improvements. Furthermore we find that training with the additional non-overlapping Spanish also improves English relation extraction accuracy. Our approach is thus suited to broad-coverage automated knowledge base construction in low-resource languages and domains.
One-Shot Video Object Segmentation

This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Although all frames are processed independently, the results are temporally coherent and stable. We perform experiments on two annotated video segmentation databases, which show that OSVOS is fast and improves the state of the art by a significant margin (79.8% vs 68.0%).