nils-herrmann/clean_scirepeval huggingface dataset:
Measuring the Effect of Conversational Aspects on Machine Translation Quality

Research in statistical machine translation (SMT) is largely driven by formal translation tasks, while translating informal text is much more challenging. In this paper we focus on SMT for the informal genre of dialogues, which has rarely been addressed to date. Concretely, we investigate the effect of dialogue acts, speakers, gender, and text register on SMT quality when translating fictional dialogues. We first create and release a corpus of multilingual movie dialogues annotated with these four dialogue-specific aspects. When measuring translation performance for each of these variables, we find that BLEU fluctuations between their categories are often significantly larger than randomly expected. Following this finding, we hypothesize and show that SMT of fictional dialogues benefits from adaptation towards dialogue acts and registers. Finally, we find that male speakers are harder to translate and use more vulgar language than female speakers, and that vulgarity is often not preserved during translation.
Scalable product line configuration: A straw to break the camel's back

Software product lines are hard to configure. Techniques that work for medium sized product lines fail for much larger product lines such as the Linux kernel with 6000+ features. This paper presents simple heuristics that help the Indicator-Based Evolutionary Algorithm (IBEA) in finding sound and optimum configurations of very large variability models in the presence of competing objectives. We employ a combination of static and evolutionary learning of model structure, in addition to utilizing a pre-computed solution used as a “seed” in the midst of a randomly-generated initial population. The seed solution works like a single straw that is enough to break the camel's back -given that it is a feature-rich seed. We show promising results where we can find 30 sound solutions for configuring upward of 6000 features within 30 minutes.
A Broadband Dual-Element Folded Dipole Antenna With a Reflector

A wideband dual-element planar folded dipole antenna is proposed in this letter. It is composed of a pair of asymmetric coplanar strip folded dipoles. The dual-element folded dipole antenna is fed by a microstrip feedline. With suitable design of the asymmetric coplanar strip folded dipole and the microstrip feedline, a 10-dB return-loss operating bandwidth of 107% (1.23 ~ 4.07 GHz) is achieved. By adding a metal plate, a wideband unidirectional antenna can be obtained with a measured impedance bandwidth of 113% (1.15 ~ 4.07 GHz) with better than 10 dB return loss. Its fabricated prototype is also measured with stable unidirectional radiation patterns in working band 1.13 ~ 3.3 GHz with a gain in + z-direction higher than 8.2 dBi and a front-to-back ratio better than 11.5 dB.
Competing with giants. Survival strategies for local companies in emerging markets.

The arrival of a multinational corporation often looks like a death sentence to local companies in an emerging market. After all, how can they compete in the face of the vast financial and technological resources, the seasoned management, and the powerful brands of, say, a Compaq or a Johnson & Johnson? But local companies often have more options than they might think, say the authors. Those options vary, depending on the strength of globalization pressures in an industry and the nature of a company's competitive assets. In the worst case, when globalization pressures are strong and a company has no competitive assets that it can transfer to other countries, it needs to retreat to a locally oriented link within the value chain. But if globalization pressures are weak, the company may be able to defend its market share by leveraging the advantages it enjoys in its home market. Many companies in emerging markets have assets that can work well in other countries. Those that operate in industries where the pressures to globalize are weak may be able to extend their success to a limited number of other markets that are similar to their home base. And those operating in global markets may be able to contend head-on with multinational rivals. By better understanding the relationship between their company's assets and the industry they operate in, executives from emerging markets can gain a clearer picture of the options they really have when multinationals come to stay.
No Grouping Left Behind: From Edges to Curve Fragments

We present a framework for extracting image contours based on geometric and structural consistency among edge element locations and orientations. The paper presents two contributions. First, we observe that while the traditional edge orientation operators are based on first-order derivatives, orientation as tangent of a localized curve requires third-order derivatives. We derive a numerically stable third-order edge operator and show that it outperforms current techniques. Second, we consider all discrete n-tuples of edges in a local neighborhood (7times7) and retain those that are geometrically consistent with a third-order local curve model. This results in a number of ordered discrete combinations of edges, each represented by a bundle of curves. The resulting curve bundle map is a representation of all possible local groupings from which longer contour fragments are constructed. We validate our results and show that our framework outperforms traditional approaches to contour extraction.
A 12 bit 50 MS/s CMOS Nyquist A/D Converter With a Fully Differential Class-AB Switched Op-Amp

A 12 bit 50 MS/s 1.8 V pipelined CMOS analog-to-digital converter (ADC) based on a fully differential class-AB switched operational amplifier achieves low power consumption with a differential input voltage of 2.4 Vp-p. A global-loop dynamic common-mode feedback circuit enables fully differential class-AB operation with dynamic current switching for power reduction. The prototype ADC shows a peak signal-to-noise-and-distortion ratio of 64.0 dB and a peak spurious-free dynamic range of 76.6 dB for a 31 MHz input signal at 50 MS/s while the measured differential and integral nonlinearities are within ±0.26 LSB and ±0.72 LSB, respectively. The prototype ADC in a 0.18 ¿m 1P6M CMOS process consumes 18.4 mW at 50 MS/s and 1.8 V occupying an active die area of 0.26 mm2.
Using Machine-Learning Methods for Musical Style Modeling

T he ability to construct a musical theory from examples presents a great intellectual challenge that, if successfully met, could foster a range of new creative applications. Inspired by this challenge, we sought to apply machine-learning methods to the problem of musical style modeling. Our work so far has produced examples of musical generation and applications to a computer-aided composition system. Machine learning consists of deriving a mathematical model, such as a set of stochastic rules, from a set of musical examples. The act of musical composition involves a highly structured mental process. Although it is complex and difficult to formalize, it is clearly far from being a random activity. Our research seeks to capture some of the regularity apparent in the composition process by using statistical and information-theoretic tools to analyze musical pieces. The resulting models can be used for inference and prediction and, to a certain extent, to generate new works that imitate the style of the great masters.
POMDPs Make Better Hackers: Accounting for Uncertainty in Penetration Testing

Testing is a methodology for assessing network security, by generating and executing possible hacking attacks. Doing so automatically allows for regular and systematic testing. A key question is how to generate the attacks. This is naturally formulated as planning under uncertainty, i.e., under incomplete knowledge about the network configuration. Previous work uses classical planning, and requires costly pre-processes reducing this uncertainty by extensive application of scanning methods. By contrast, we herein model the attack planning problem in terms of partially observable Markov decision processes (POMDP). This allows to reason about the knowledge available, and to intelligently employ scanning actions as part of the attack. As one would expect, this accurate solution does not scale. We devise a method that relies on POMDPs to find good attacks on individual machines, which are then composed into an attack on the network as a whole. This decomposition exploits network structure to the extent possible, making targeted approximations (only) where needed. Evaluating this method on a suitably adapted industrial test suite, we demonstrate its effectiveness in both runtime and solution quality.
Multiview LSA: Representation Learning via Generalized CCA

Multiview LSA (MVLSA) is a generalization of Latent Semantic Analysis (LSA) that supports the fusion of arbitrary views of data and relies on Generalized Canonical Correlation Analysis (GCCA). We present an algorithm for fast approximate computation of GCCA, which when coupled with methods for handling missing values, is general enough to approximate some recent algorithms for inducing vector representations of words. Experiments across a comprehensive collection of test-sets show our approach to be competitive with the state of the art.
Software Project Management: The Manager's View

As businesses become more dependent on information technology for their operations, IS managers are under increasing pressure to deliver quality applications software on time and within budget. Thus, in addition to their technical skills, they must master the necessary management skills to lead and control software development projects. The purpose of this tutorial is to present the fundamental concepts of modern project management and show how these concepts can be applied to software development projects. The tutorial presents a broad overview of current software project management practices that evolved over the years from a variety of complex projects. The subject is presented from the manager's rather than from the developer's perspective. The focus is on large and complex projects because these projects are the most challenging and in need of an effective project management discipline.
Optical Tracking Using Projective Invariant Marker Pattern Properties

In this paper, we describe a new optical tracker algorithm for the tracking of interaction devices in virtual and augmented reality. The tracker uses invariant properties of marker patterns to efficiently identify and reconstruct the pose of these interaction devices. Since invariant properties are sensitive to noise in the 2D marker positions, an off-line training session is used to determine deviations in these properties. These deviations are taken into account when searching for the patterns once the tracker is used.
A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks

Today’s smartphone users face a security dilemma: many apps they install operate on privacy-sensitive data, although they might originate from developers whose trustworthiness is hard to judge. Researchers have addressed the problem with more and more sophisticated static and dynamic analysis tools as an aid to assess how apps use private user data. Those tools, however, rely on the manual configuration of lists of sources of sensitive data as well as sinks which might leak data to untrusted observers. Such lists are hard to come by. We thus propose SUSI, a novel machine-learning guided approach for identifying sources and sinks directly from the code of any Android API. Given a training set of hand-annotated sources and sinks, SUSI identifies other sources and sinks in the entire API. To provide more fine-grained information, SUSI further categorizes the sources (e.g., unique identifier, location information, etc.) and sinks (e.g., network, file, etc.). For Android 4.2, SUSI identifies hundreds of sources and sinks with over 92% accuracy, many of which are missed by current information-flow tracking tools. An evaluation of about 11,000 malware samples confirms that many of these sources and sinks are indeed used. We furthermore show that SUSI can reliably classify sources and sinks even in new, previously unseen Android versions and components like Google Glass or
Facial Emotion Recognition in Real Time

We have developed a convolutional neural network for classifying human emotions from dynamic facial expressions in real time. We use transfer learning on the fullyconnected layers of an existing convolutional neural network which was pretrained for human emotion classification. A variety of datasets, as well as our own unique image dataset, is used to train the model. An overall training accuracy of 90.7% and test accuracy of 57.1% is achieved. Finally, a live video stream connected to a face detector feeds images to the neural network. The network subsequently classifies an arbitrary number of faces per image simultaneously in real time, wherein appropriate emojis are superimposed over the subjects’ faces (https://youtu.be/MDHtzOdnSgA). The results demonstrate the feasibility of implementing neural networks in real time to detect human emotion.
Is it true ? ” Deep Learning for Stance Detection in News

Seismic sensors collect massive quantities of data that contain a wealth of information about processes within the earth. Seismologists are increasingly adopting data mining and machine learning techniques to identify previously unknown earthquakes in large seismic data sets. Our new earthquake detection method, Fingerprint and Similarity Thresholding (FAST), enables waveform-similarity-based earthquake detection in long duration continuous seismic data by leveraging locality sensitive hashing, a data mining technique for efficiently identifying similar items in large data sets. Ongoing research into waveform feature representations and detection over a seismic network will be presented.
An efficient image rectification method for parallel multi-camera arrangement

In this paper, we present an image rectification method for the multi-view image captured by a parallel multicamera arrangement. Since most multi-camera arrangements are manually adjusted, there exist misalignments with the camera positions and orientations. These misalignments cause geometric errors that can be a serious obstruction to three-dimensional (3D) image processing and applications. Moreover, different internal characteristics of cameras increase geometric errors. In order to minimize those geometric errors, we design an ideal multi-camera arrangement and then calculate the multi-view image rectifying transform (MIRT). Experimental results show that the proposed method reduces geometric errors efficiently without any visual distortion, and generates the rectified multi-view image in very short processing time.
Precoding for Full Duplex Multiuser MIMO Systems: Spectral and Energy Efficiency Maximization

We consider data transmissions in a full duplex (FD) multiuser multiple-input multiple-output (MU-MIMO) system, where a base station (BS) bidirectionally communicates with multiple users in the downlink (DL) and uplink (UL) channels on the same system resources. The system model of consideration has been thought to be impractical due to the self-interference (SI) between transmit and receive antennas at the BS. Interestingly, recent advanced techniques in hardware design have demonstrated that the SI can be suppressed to a degree that possibly allows for FD transmission. This paper goes one step further in exploring the potential gains in terms of the spectral efficiency (SE) and energy efficiency (EE) that can be brought by the FD MU-MIMO model. Toward this end, we propose low-complexity designs for maximizing the SE and EE, and evaluate their performance numerically. For the SE maximization problem, we present an iterative design that obtains a locally optimal solution based on a sequential convex approximation method. In this way, the nonconvex precoder design problem is approximated by a convex program at each iteration. Then, we propose a numerical algorithm to solve the resulting convex program based on the alternating and dual decomposition approaches, where analytical expressions for precoders are derived. For the EE maximization problem, using the same method, we first transform it into a concave-convex fractional program, which then can be reformulated as a convex program using the parametric approach. We will show that the resulting problem can be solved similarly to the SE maximization problem. Numerical results demonstrate that, compared to a half duplex system, the FD system of interest with the proposed designs achieves a better SE and a slightly smaller EE when the SI is small.
Integrating forensic information in a crime intelligence database.

Since 2008, intelligence units of six states of the western part of Switzerland have been sharing a common database for the analysis of high volume crimes. On a daily basis, events reported to the police are analysed, filtered and classified to detect crime repetitions and interpret the crime environment. Several forensic outcomes are integrated in the system such as matches of traces with persons, and links between scenes detected by the comparison of forensic case data. Systematic procedures have been settled to integrate links assumed mainly through DNA profiles, shoemarks patterns and images. A statistical outlook on a retrospective dataset of series from 2009 to 2011 of the database informs for instance on the number of repetition detected or confirmed and increased by forensic case data. Time needed to obtain forensic intelligence in regard with the type of marks treated, is seen as a critical issue. Furthermore, the underlying integration process of forensic intelligence into the crime intelligence database raised several difficulties in regards of the acquisition of data and the models used in the forensic databases. Solutions found and adopted operational procedures are described and discussed. This process form the basis to many other researches aimed at developing forensic intelligence models.
Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

(2012)  Pinheiro, P.O., et.  From image-level to pixel-level labeling with convolutional networks. In: CVPR (2015)  Bearman, et.  What's the point: Semantic segmentation with point supervision.  (2015)  Papandreou, G., et. al: Weaklyand semi-supervised learning of a deep convolutional network for semantic image segmentation.  (2015)  Pathak, D., et.  Constrained convolutional neural networks for weakly supervised segmentation.  ICCV(2015)  Wei, Y., et. al: Learning to segment with image-level annotations. Pattern Recognition (2016)  Wei, Y., et.  A simple to complex framework for weakly-supervised semantic segmentation.  (2015)  Alexe, B., et. al: Measuring the objectness of image windows. Pattern Analysis and Machine Intelligence (2012)  Koltun, V.: Efficient inference in fully connected crfs with gaussian edge potentials. Advances in Neural Information Processing Systems (2011)
Hype or Help? A Longitudinal Field Study of Virtual World Use for Team Collaboration

Special Issue Despite increasing organizational interest and investment in virtual worlds (VWs), there is a lack of research on the benefits of VWs. When and how does the use of VW systems engender better organizational outcomes than traditional collaborative technologies? This paper investigates the value of VWs for team collaboration. Team collaboration is particularly relevant in studying VWs given the rich interactive nature of VWs and an increasing organizational reliance on virtual teamwork. To understand the value of VW use for team collaboration, we examine the relationship between a team's disposition toward IT, their general disposition (personality) and VW use in influencing team cohesion and performance. We conducted a field study that compares two collaborative technology systems – one that is based on a traditional desktop metaphor and one that is grounded in the principles of a virtual world. We tracked the use of the systems for one year. We analyzed data at the team level and the results generally support our model, with agreeableness, conscientiousness, extraversion, openness, and computer self-efficacy interacting with time and technology type to positively influence team technology use. We also found that the use of the virtual world system positively influenced the relationship between technology use and team cohesion, which, in turn, predicts team performance. The model explains 57 percent, 21 percent, and 24 percent of the variance in team technology use, team cohesion, and team performance, respectively.
Speech Repairs, Intonational Phrases and Discourse Markers: Modeling Speakers' Utterances in Spoken Dialog

Interactive spoken dialogue provides many new challenges for natural language understanding systems. One of the most critical challenges is simply determining the speaker’s intended utterances: both segmenting a speaker’s turn into utterances and determining the intended words in each utterance. Even assuming perfect word recognition, the latter problem is complicated by the occurrence of speech repairs, which occur where speakers go back and change (or repeat) something they just said. The words that are replaced or repeated are no longer part of the intended utterance, and so need to be identified. Segmenting turns and resolving repairs are strongly intertwined with a third task: identifying discourse markers. Because of the interactions, and interactions with POS tagging and speech recognition, we need to address these tasks together and early on in the processing stream. This paper presents a statistical language model in which we redefine the speech recognition problem so that it includes the identification of POS tags, discourse markers, speech repairs and intonational phrases. By solving these simultaneously, we obtain better results on each task than addressing them separately. Our model is able to identify 72% of turn-internal intonational boundaries with a precision of 71%, 97% of discourse markers with 96% precision, and detect and correct 66% of repairs with 74% precision.
SPLATT: Efficient and Parallel Sparse Tensor-Matrix Multiplication

Multi-dimensional arrays, or tensors, are increasingly found in fields such as signal processing and recommender systems. Real-world tensors can be enormous in size and often very sparse. There is a need for efficient, high-performance tools capable of processing the massive sparse tensors of today and the future. This paper introduces SPLATT, a C library with shared-memory parallelism for three-mode tensors. SPLATT contains algorithmic improvements over competing state of the art tools for sparse tensor factorization. SPLATT has a fast, parallel method of multiplying a matricide tensor by a Khatri-Rao product, which is a key kernel in tensor factorization methods. SPLATT uses a novel data structure that exploits the sparsity patterns of tensors. This data structure has a small memory footprint similar to competing methods and allows for the computational improvements featured in our work. We also present a method of finding cache-friendly reordering and utilizing them with a novel form of cache tiling. To our knowledge, this is the first work to investigate reordering and cache tiling in this context. SPLATT averages almost 30x speedup compared to our baseline when using 16 threads and reaches over 80x speedup on NELL-2.