nils-herrmann/clean_scirepeval huggingface dataset:
A Program for Aligning Sentences in Bilingual Corpora

Researchers in both machine translation (e.g., Brown et al., 1990) and bilingual lexicography (e.g., Klavans and Tzoukermann, 1990) have recently become interested in studying bilingual corpora, bodies of text such as the Canadian Hansards (parliamentary proceedings) which are available in multiple languages (such as French and English). One useful step is to align the sentences, that is, to identify correspondences between sentences in one language and sentences in the other language. This paper will describe a method and a program (align) for aligning sentences based on a simple statistical model of character lengths. The program uses the fact that longer sentences in one language tend to be translated into longer sentences in the other language, and that shorter sentences tend to be translated into shorter sentences. A probabilistic score is assigned to each proposed correspondence of sentences, based on the scaled difference of lengths of the two sentences (in characters) and the variance of this difference. This probabilistic score is used in a dynamic programming framework to find the maximum likelihood alignment of sentences. It is remarkable that such a simple approach works as well as it does. An evaluation was performed based on a trilingual corpus of economic reports issued by the Union Bank of Switzerland (UBS) in English, French and German. The method correctly aligned all but 4% of the sentences. Moreover, it is possible to extract a large subcorpus which has a much smaller error rate. By selecting the best scoring 80% of the alignments, the error rate is reduced from 4% to 0.7%. There were more errors on the English-French subcorpus than on the English-German subcorpus, showing that error rates will depend on the corpus considered, however, both were small enough to hope that the method will be useful for many language pairs. To further research on bilingual corpora, a much larger sample of Canadian Hansards (approximately 90 million words, half in English and and half in French) has been aligned with the align program and will be available through the Data Collection Initiative of the Association for Computational Linguistics (ACL/DCI). In addition, in order to facilitate replication of the align program, an appendix is provided with detailed c-code of the more difficult core of the align program.
Causal Inference by Compression

Causal inference is one of the fundamental problems in science. In recent years, several methods have been proposed for discovering causal structure from observational data. These methods, however, focus specifically on numeric data, and are not applicable on nominal or binary data. In this work, we focus on causal inference for binary data. Simply put, we propose causal inference by compression. To this end we propose an inference framework based on solid information theoretic foundations, i.e. Kolmogorov complexity. However, Kolmogorov complexity is not computable, and hence we propose a practical and computable instantiation based on the Minimum Description Length (MDL) principle. To apply the framework in practice, we propose ORIGO, an efficient method for inferring the causal direction from binary data. ORIGO employs the lossless PACK compressor, works directly on the data and does not require assumptions about neither distributions nor the type of causal relations. Extensive evaluation on synthetic, benchmark, and real-world data shows that ORIGO discovers meaningful causal relations, and outperforms state-of-the-art methods by a wide margin.
Design of a D-PHY chip for mobile display interface supporting MIPI standard

This paper presents a D-PHY chip design for MIPI (Mobile Industry Processor Interface) standard. The MIPI is a flexible, source-synchronous serial interface standard connecting a host processor to a display and camera modules as used in mobile devices. The D-PHY consists of LP (low-power) mode block, HS (high-speed) mode block and control blocks. We implemented D-PHY chip using 0.13-um CMOS process under 1.2V supply. As a result, HS mode shows 1Gbps with jitter 5% and 0.74mW power consumption.
Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets.

When the number of projections does not satisfy the Shannon/Nyquist sampling requirement, streaking artifacts are inevitable in x-ray computed tomography (CT) images reconstructed using filtered backprojection algorithms. In this letter, the spatial-temporal correlations in dynamic CT imaging have been exploited to sparsify dynamic CT image sequences and the newly proposed compressed sensing (CS) reconstruction method is applied to reconstruct the target image sequences. A prior image reconstructed from the union of interleaved dynamical data sets is utilized to constrain the CS image reconstruction for the individual time frames. This method is referred to as prior image constrained compressed sensing (PICCS). In vivo experimental animal studies were conducted to validate the PICCS algorithm, and the results indicate that PICCS enables accurate reconstruction of dynamic CT images using about 20 view angles, which corresponds to an under-sampling factor of 32. This undersampling factor implies a potential radiation dose reduction by a factor of 32 in myocardial CT perfusion imaging.
Mathematical modelling of non-invasive oscillometric finger mean blood pressure measurement by maximum oscillation criterion

A mathematical study is performed to assess how the arterial pressure-volume (P-V) relationship, blood pressure pulse amplitude and shape affect the results of non-invasive oscillometric finger mean blood pressure estimation by the maximum oscillation criterion (MOC). The exponential models for a relaxed finger artery and for a partly contracted artery are studied. A new modification of the error equation is suggested. This equation and the results of simulation demonstrate that the value of pressure estimated by the MOC does not exactly agree with the value of the true mean blood pressure (the latter being defined as pressure corresponding to maximum arterial compliance). The error depends on the arterial pressure pulse amplitude, as well as on the difference between the arterial pressure pulse shape index and the arterial P-V curve shape index. In the case of contracted finger arteries, the MOC can give an overestimation of up to 19 mmHg, the pressure pulse shape index being 0.21 and the pulse amplitude 60 mmHg. In the case of relaxed arteries, the error is less evident.
Speech Recognition using SVMs

An important issue in applying SVMs to speech recognition is the ability to classify variable length sequences. This paper presents extensions to a standard scheme for handling this variable length data, the Fisher score. A more useful mapping is introduced based on the likelihood-ratio. The score-space defined by this mapping avoids some limitations of the Fisher score. Class-conditional generative models are directly incorporated into the definition of the score-space. The mapping, and appropriate normalisation schemes, are evaluated on a speaker-independent isolated letter task where the new mapping outperforms both the Fisher score and HMMs trained to maximise likelihood.
Robust PCA Via Nonconvex Rank Approximation

Numerous applications in data mining and machine learning require recovering a matrix of minimal rank. Robust principal component analysis (RPCA) is a general framework for handling this kind of problems. Nuclear norm based convex surrogate of the rank function in RPCA is widely investigated. Under certain assumptions, it can recover the underlying true low rank matrix with high probability. However, those assumptions may not hold in real-world applications. Since the nuclear norm approximates the rank by adding all singular values together, which is essentially a l1-norm of the singular values, the resulting approximation erroris not trivial and thus the resulting matrix estimator can be significantly biased. To seek a closer approximation and to alleviate the above-mentioned limitations of the nuclear norm, we propose a nonconvex rank approximation. This approximation to the matrix rank is tighter than the nuclear norm. To solve the associated nonconvex minimization problem, we develop an efficient augmented Lagrange multiplier based optimization algorithm. Experimental results demonstrate that our method outperforms current state-of-the-art algorithms in both accuracy and efficiency.
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS). Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. However, a drawback of neural networks is the amount of resources needed to train them. Many network gateways and routers devices, which could potentially host an NIDS, simply do not have the memory or processing power to train and sometimes even execute such models. More importantly, the existing neural network solutions are trained in a supervised manner. Meaning that an expert must label the network traffic and update the model manually from time to time. In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner. Kitsune’s core algorithm (KitNET) uses an ensemble of neural networks called autoencoders to collectively differentiate between normal and abnormal traffic patterns. KitNET is supported by a feature extraction framework which efficiently tracks the patterns of every network channel. Our evaluations show that Kitsune can detect various attacks with a performance comparable to offline anomaly detectors, even on a Raspberry PI. This demonstrates that Kitsune can be a practical and economic NIDS. Anomaly detection, network intrusion detection, online algorithms, autoencoders, ensemble learning.
First Demonstration of Panel Glass Fan-Out (GFO) Packages for High I/O Density and High Frequency Multi-chip Integration

Ultra-thin, panel-level glass fan-out packages (GFO) were demonstrated for next-generation fan-out packaging with high-density high-performance digital, analog, power, RF and mm-wave applications. The key advances with GFO include: 1) large area panel-scalable glass substrate processes with lower cost, 2) silicon-like RDL on large panels with 1-2 µm critical dimensions (CD), 3) lower interconnect loss and 4) improved board-level reliability enabled by the tailorability of the CTE of the glass panels and compliant interconnections. Daisy-chain test dies were used to emulate an embedded device with the size of 6.469 mm × 5.902 mm, thickness of 75 µm and pad pitch of 65 µm. Glass panels with 70 µm thickness and through-glass cavities were first fabricated, and then bonded onto a 50 µm thick glass panel carrier using adhesives. After glass-to-glass bonding, the test dies were assembled into the glass cavities using a high-speed placement tool. RDL polymers were then laminated onto both sides and cured to minimize the warpage of the ultra-thin package. A surface planar tool was then used to planarize the surface of the panel to expose the copper microbumps on the die, followed by a standard semi-additive process (SAP) for the fan-out RDL layer. The shift and warpage of the die were characterized during the multiple process steps. Initial modeling and measured results indicate the potential for less than 5 µm die shift and less than 10-15 µm warpage across a 300 mm × 300 mm panel size.
Active Learning of an Action Detector from Untrimmed Videos

Collecting and annotating videos of realistic human actions is tedious, yet critical for training action recognition systems. We propose a method to actively request the most useful video annotations among a large set of unlabeled videos. Predicting the utility of annotating unlabeled video is not trivial, since any given clip may contain multiple actions of interest, and it need not be trimmed to temporal regions of interest. To deal with this problem, we propose a detection-based active learner to train action category models. We develop a voting-based framework to localize likely intervals of interest in an unlabeled clip, and use them to estimate the total reduction in uncertainty that annotating that clip would yield. On three datasets, we show our approach can learn accurate action detectors more efficiently than alternative active learning strategies that fail to accommodate the "untrimmed" nature of real video data.
Survey of Large-Scale Data Management Systems for Big Data Applications

Today, data is flowing into various organizations at an unprecedented scale. The ability to scale out for processing an enhanced workload has become an important factor for the proliferation and popularization of database systems. Big data applications demand and consequently lead to the developments of diverse large-scale data management systems in different organizations, ranging from traditional database vendors to new emerging Internet-based enterprises. In this survey, we investigate, characterize, and analyze the large-scale data management systems in depth and develop comprehensive taxonomies for various critical aspects covering the data model, the system architecture, and the consistency model. We map the prevailing highly scalable data management systems to the proposed taxonomies, not only to classify the common techniques but also to provide a basis for analyzing current system scalability limitations. To overcome these limitations, we predicate and highlight the possible principles that future efforts need to be undertaken for the next generation large-scale data management systems.
7th Open Challenge on Question Answering over Linked Data (QALD-7)

The past years have seen a growing amount of research on question answering (QA) over Semantic Web data, shaping an interaction paradigm that allows end users to profit from the expressive power of Semantic Web standards while, at the same time, hiding their complexity behind an intuitive and easy-to-use interface. On the other hand, the growing amount of data has led to a heterogeneous data landscape where QA systems struggle to keep up with the volume, variety and veracity of the underlying knowledge. The Question Answering over Linked Data (QALD) challenge aims at providing an up-to-date benchmark for assessing and comparing state-of-the-artsystems that mediate between a user, expressing his or her information need in natural language, and RDF data. It thus targets all researchers and practitioners working on querying Linked Data, natural language processing for question answering, multilingual information retrieval and related topics. The main goal is to gain insights into the strengths and shortcomings of different approaches and into possible solutions for coping with the large, heterogeneous and distributed nature of Semantic Web data. QALD has a 6-year history of developing a benchmark that is increasingly being used as standard evaluation tool for question answering over Linked Data. Overviews of the past instantiations of the challenge are available from the CLEF Working Notes as well as ESWC proceedings:
Active learning for relation type extension with local and global data views

Relation extraction is the process of identifying instances of specified types of semantic relations in text; relation type extension involves extending a relation extraction system to recognize a new type of relation. We present LGCo-Testing, an active learning system for relation type extension based on local and global views of relation instances. Locally, we extract features from the sentence that contains the instance. Globally, we measure the distributional similarity between instances from a 2 billion token corpus. Evaluation on the ACE 2004 corpus shows that LGCo-Testing can reduce annotation cost by 97% while maintaining the performance level of supervised learning.
Verifying Properties of Binarized Deep Neural Networks

Understanding properties of deep neural networks is an important challenge in deep learning. In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks, Binarized Neural Networks, using the well-developed means of Boolean satisfiability. Our main contribution is a construction that creates a representation of a binarized neural network as a Boolean formula. Our encoding is the first exact Boolean representation of a deep neural network. Using this encoding, we leverage the power of modern SAT solvers along with a proposed counterexample-guided search procedure to verify various properties of these networks. A particular focus will be on the critical property of robustness to adversarial perturbations. For this property, our experimental results demonstrate that our approach scales to medium-size deep neural networks used in image classification tasks. To the best of our knowledge, this is the first work on verifying properties of deep neural networks using an exact Boolean encoding of the network.
Performance comparison of the RPL and LOADng routing protocols in a Home Automation scenario

RPL, the routing protocol proposed by IETF for IPv6/6LoWPAN Low Power and Lossy Networks has significant complexity. Another protocol called LOADng, a lightweight variant of AODV, emerges as an alternative solution. In this paper, we compare the performance of the two protocols in a Home Automation scenario with heterogenous traffic patterns including a mix of multipoint-to-point and point-to-multipoint routes in realistic dense non-uniform network topologies. We use Contiki OS and Cooja simulator to evaluate the behavior of the ContikiRPL implementation and a basic non-optimized implementation of LOADng. Unlike previous studies, our results show that RPL provides shorter delays, less control overhead, and requires less memory than LOADng. Nevertheless, enhancing LOADng with more efficient flooding and a better route storage algorithm may improve its performance.
Acetic acid used for the elimination of Pseudomonas aeruginosa from burn and soft tissue wounds.

Acetic acid was used topically at concentrations of between 0.5% and 5% to eliminate Pseudomonas aeruginosa from the burn wounds or soft tissue wounds of 16 patients. In-vitro studies indicated the susceptibility of P. aeruginosa to acetic acid; all strains exhibited a minimum inhibitory concentration of 2 per cent. P. aeruginosa was eliminated from the wounds of 14 of the 16 patients within two weeks of treatment. Acetic acid was shown to be an inexpensive and efficient agent for the elimination of P. aeruginosa from burn and soft tissue wounds.
Learning Context-free Grammars : Capabilities andLimitations of a Recurrent Neural Network with

This work describes an approach for inferring De-terministic Context-free (DCF) Grammars in a Connectionist paradigm using a Recurrent Neu-ral Network Pushdown Automaton (NNPDA). The NNPDA consists of a recurrent neural network connected to an external stack memory through a common error function. We show that the NNPDA is able to learn the dynamics of an underlying push-down automaton from examples of grammatical and non-grammatical strings. Not only does the network learn the state transitions in the automaton , it also learns the actions required to control the stack. In order to use continuous optimization methods, we develop an analog stack which reverts to a discrete stack by quantization of all activations, after the network has learned the transition rules and stack actions. We further show an enhancement of the network's learning capabilities by providing hints. In addition, an initial comparative study of simulations with rst, second and third order recurrent networks has shown that the increased degree of freedom in a higher order networks improve generalization but not necessarily learning speed.
Effects of changing shift schedules from a full-day to a half-day shift before a night shift on physical activities and sleep patterns of single nurses and married nurses with children.

To clarify the effects of changing shift schedules from a full-day to a half-day before a night shift, 12 single nurses and 18 married nurses with children that engaged in night shift work in a Japanese hospital were investigated. Subjects worked 2 different shift patterns consisting of a night shift after a half-day shift (HF-N) and a night shift after a day shift (D-N). Physical activity levels were recorded with a physical activity volume meter to measure sleep/wake time more precisely without restricting subjects' activities. The duration of sleep before a night shift of married nurses was significantly shorter than that of single nurses for both shift schedules. Changing shift from the D-N to the HF-N increased the duration of sleep before a night shift for both groups, and made wake-up time earlier for single nurses only. Repeated ANCOVA of the series of physical activities showed significant differences with shift (p < 0.01) and marriage (p < 0.01) for variances, and age (p < 0.05) for a covariance. The paired t-test to compare the effects of changing shift patterns in each subject group and ANCOVA for examining the hourly activity differences between single and married nurses showed that the effects of a change in shift schedules seemed to have less effect on married nurses than single nurses. These differences might due to the differences of their family/home responsibilities.
Quantifying Creativity in Art Networks

Can we develop a computer algorithm that assesses the creativity of a painting given its context within art history? This paper proposes a novel computational framework for assessing the creativity of creative products, such as paintings, sculptures, poetry, etc. We use the most common definition of creativity, which emphasizes the originality of the product and its influential value. The proposed computational framework is based on constructing a network between creative products and using this network to infer about the originality and influence of its nodes. Through a series of transformations, we construct a Creativity Implication Network. We show that inference about creativity in this network reduces to a variant of network centrality problems which can be solved efficiently. We apply the proposed framework to the task of quantifying creativity of paintings (and sculptures). We experimented on two datasets with over 62K paintings to illustrate the behavior of the proposed framework. We also propose a methodology for quantitatively validating the results of the proposed algorithm, which we call the “time machine experiment”.