nils-herrmann/clean_scirepeval huggingface dataset:
Perceiving an object and its context in different cultures: a cultural look at new look.

In two studies, a newly devised test (framed-line test) was used to examine the hypothesis that individuals engaging in Asian cultures are more capable of incorporating contextual information and those engaging in North American cultures are more capable of ignoring contextual information. On each trial, participants were presented with a square frame, within which was printed a vertical line. Participants were then shown another square frame of the same or different size and asked to draw a line that was identical to the first line in either absolute length (absolute task) or proportion to the height of the surrounding frame (relative task). The results supported the hypothesis: Whereas Japanese were more accurate in the relative task, Americans were more accurate in the absolute task. Moreover, when engaging in another culture, individuals tended to show the cognitive characteristic common in the host culture.
Temporal Generative Adversarial Nets

In this paper we propose a generative model, the Temporal Generative Adversarial Network (TGAN), which can learn a semantic representation of unlabelled videos, and is capable of generating consistent videos. Unlike an existing GAN that generates videos with a generator consisting of 3D deconvolutional layers, our model exploits two types of generators: a temporal generator and an image generator. The temporal generator consists of 1D deconvolutional layers and outputs a set of latent variables, each of which corresponds to a frame in the generated video, and the image generator transforms them into a video with 2D deconvolutional layers. This representation allows efficient training of the network parameters. Moreover, it can handle a wider range of applications including the generation of a long sequence, frame interpolation, and the use of pre-trained models. Experimental results demonstrate the effectiveness of our method.
Quality metric for camera-based pulse rate monitoring in fitness exercise

Camera-based remote pulse rate monitoring can be used during fitness exercise to optimize the effectiveness of a workout. However, such monitoring suffers from vigorous body motions and dynamic illumination changes due to exercise, which may lead to erroneous estimates. To better cope with this, we propose a quality metric, comprised of a front-end metric and a back-end metric, to indicate the monitoring conditions (e.g. luminance, skin property) and assess the reliability of pulse rate measurement (e.g. signal quality). The proposed quality metric has been thoroughly benchmarked on 78 videos recorded in a fitness setting. The experimental results show that (i) appropriate light source intensity variation and its angle variation in the front-end metric are critical indicators for pulse rate measurement accuracy, and (ii) the back-end metric can effectively indicate/reject unreliable estimates. The proposed method in this paper is the first quality metric for camera-based pulse rate monitoring, validated for the challenging use-case of fitness exercises.
Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets

This paper describes our attempt to build a sentiment analysis system for Indonesian tweets. With this system, we can study and identify sentiments and opinions in a text or document computationally. We used four thousand manually labeled tweets collected in February and March 2016 to build the model. Because of the variety of content in tweets, we analyze tweets into eight groups in total, including pos(itive), neg(ative), and neu(tral). Finally, we obtained 73.2% accuracy with Long Short Term Memory (LSTM) without normalizer.
CryptoDL: Deep Neural Networks over Encrypted Data

Machine learning algorithms based on deep neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. More specifically, we focus on classification of the well-known convolutional neural networks (CNN). First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train convolutional neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement convolutional neural networks over encrypted data and measure performance of the models. Our experimental results validate the soundness of our approach with several convolutional neural networks with varying number of layers and structures. When applied to the MNIST optical character recognition tasks, our approach achieves 99.52% accuracy which significantly outperforms the state-of-the-art solutions and is very close to the accuracy of the best non-private version, 99.77%. Also, it can make close to 164000 predictions per hour. We also applied our approach to CIFAR-10, which is much more complex compared to MNIST, and were able to achieve 91.5% accuracy with approximation polynomials used as activation functions. These results show that CryptoDL provides efficient, accurate and scalable privacy-preserving predictions.
Compact Noise Models for MOSFETs

A physical understanding of both intrinsic and extrinsic noise mechanisms in a MOSFET is developed. Intrinsic noise mechanisms fundamental to device operation include channel thermal noise, induced gate noise, and induced substrate noise. While the effect of channel thermal noise is observable at zero drain-to-source voltage, the induced gate and substrate noise do not manifest themselves under these conditions. However, the attendant fluctuations in the channel charge are observable by the passage of electric current through the device. Extrinsic noise mechanisms manifested due to structural evolution of the MOSFET include the distributed gate resistance noise, distributed substrate resistance noise, bulk charge effects, substrate current supershot noise, gate current noise, excess channel noise, and 1/f noise. Where available, compact noise models covering these noise mechanisms are explained. Also, where possible, methods of suppression of these mechanisms are highlighted. A survey of current public domain MOS models is presented, and a lack of comprehensive coverage of noise models is noted. Open areas of MOSFET noise research in the sub-hundred-nanometer regime are also highlighted. With suitable adaptation, noise concepts elucidated in the context of MOS transistors have a much wider applicability to the operation of HEMTs, JFETs, MESFETs, and other field-effect devices
Compact $S$ -/$Ka$-Band CMOS Quadrature Hybrids With High Phase Balance Based on Multilayer Transformer Over-Coupling Technique

This paper presents compact CMOS quadrature hybrids by using the transformer over-coupling technique to eliminate significant phase error in the presence of low-Q CMOS components. The technique includes the inductive and capacitive couplings, where the former is realized by employing a tightly inductive-coupled transformer and the latter by an additional capacitor across the transformer winding. Their phase balance effects are investigated and the design methodology is presented. The measurement results show that the designed 24-GHz CMOS quadrature hybrid has excellent phase balance within plusmn0.6deg and amplitude balance less than plusmn 0.3 dB over a 16% fractional bandwidth with extremely compact size of 0.05 mm2. For the 2.4-GHz hybrid monolithic microwave integrated circuit, it has measured phase balance of plusmn0.8deg and amplitude balance of plusmn 0.3 dB over a 10% fractional bandwidth with a chip area of 0.1 mm2 .
The stochastic knapsack problem

Considered is a knapsack with integer volume Fand which is capable of holding K different classes of objects. An object from class k has integer volume bk, k = 1, . . ., K . Objects arrive randomly to the knapsack; interarrivals are exponential with mean depending on the state of the system. The sojourn time of an object has a general classdependent distribution. An object in the knapsack from class k accrues revenue at a rate rk. The problem is to find a control policy in order to acceptlreject the arriving objects as a function of the current state in order to maximize the average revenue. Optimization is carried out over the class of coordinate convex policies. For the case of K = 2, we show for a wide range of parameters that the optimal control is of the threshold type. In the case of Poisson arrivals and of knapsack and object volumes being integer multiples of each other, it is shown that the optimal policy is always of the double-threshold type. An O(F) algorithm to determine the revenue of threshold policies is also given. For the general case of K classes, we consider the problem of finding the optimal static control where for each class a portion of the knapsack is dedicated. An efficient finite-stage dynamic programming algorithm for locating the optimal static control is presented. Furthermore, variants of the optimal static control which allow some sharing among classes are also discussed.
Bijoux: Data Generator for Evaluating ETL Process Quality

Obtaining the right set of data for evaluating the fulfillment of different quality standards in the extract-transform-load (ETL) process design is rather challenging. First, the real data might be out of reach due to different privacy constraints, while providing a synthetic set of data is known as a labor-intensive task that needs to take various combinations of process parameters into account. Additionally, having a single dataset usually does not represent the evolution of data throughout the complete process lifespan, hence missing the plethora of possible test cases. To facilitate such demanding task, in this paper we propose an automatic data generator (i.e., Bijoux). Starting from a given ETL process model, Bijoux extracts the semantics of data transformations, analyzes the constraints they imply over data, and automatically generates testing datasets. At the same time, it considers different dataset and transformation characteristics (e.g., size, distribution, selectivity, etc.) in order to cover a variety of test scenarios. We report our experimental findings showing the effectiveness and scalability of our approach.
Reinforcement Learning Neural Turing Machines

The expressive power of a machine learning model is closely r elated to the number of sequential computational steps it can learn. For exam ple, Deep Neural Networks have been more successful than shallow networks be caus they can perform a greater number of sequential computational steps (ea ch highly parallel). The Neural Turing Machine (NTM)  is a model that can compac tly express an even greater number of sequential computational steps, so i t is even more powerful than a DNN. Its memory addressing operations are designed to be differentiable; thus the NTM can be trained with backpropagation. While differentiable memory is relatively easy to implemen t a d train, it necessitates accessing the entire memory content at each computati onal step. This makes it difficult to implement a fast NTM. In this work, we use the Re inforce algorithm to learnwhere to access the memory, while using backpropagation to learn what to write to the memory. We call this model the RL-NTM. Reinfor ce allows our model to access a constant number of memory cells at each comp utational step, so its implementation can be faster. The RL-NTM is the first mo del that can, in principle, learn programs of unbounded running time. We suc cessfully trained the RL-NTM to solve a number of algorithmic tasks that are simple r than the ones solvable by the fully differentiable NTM. As the RL-NTM is a fairly intricate model, we needed a method f r verifying the correctness of our implementation. To do so, we developed a s imple technique for numerically checking arbitrary implementations of mod els that use Reinforce, which may be of independent interest.
Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge

Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05) and had an efficient implementation with a run time of 8min and 3s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/.
Assessing the contribution of venture capital to innovation

We examine the influence of venture capital on patented inventions in the United States across twenty industries over three decades. We address concerns about causality in several ways, including exploiting a 1979 policy shift that spurred venture capital fundraising. We find that increases in venture capital activity in an industry are associated with significantly higher patenting rates. While the ratio of venture capital to R&D averaged less than 3% from 1983–1992, our estimates suggest that venture capital may have accounted for 8% of industrial innovations in that period.
RQ-RDF-3X: Going beyond triplestores

Efficient storage and querying of large repositories of RDF content is important due to the widespread growth of Semantic Web and Linked Open Data initiatives. Many novel database systems that store RDF in its native form or within traditional relational storage have demonstrated their ability to scale to large volumes of RDF content. However, it is increasingly becoming obvious that the simple dyadic relationship captured through traditional triples alone is not sufficient for modelling multi-entity relationships, provenance of facts, etc. Such richer models are supported in RDF through two techniques - first, called reification which retains the triple nature of RDF and the second, a non-standard extension called N-Quads. In this paper, we explore the challenges of supporting such richer semantic data by extending the state-of-the-art RDF-3X system. We describe our implementation of RQ-RDF-3X, a reification and quad enhanced RDF-3X, which involved a significant re-engineering ranging from the set of indexes and their compression schemes to the query processing pipeline for queries over reified content. Using large RDF repositories such as YAGO2S and DBpedia, and a set of SPARQL queries that utilize reification model, we demonstrate that RQ-RDF-3X is significantly faster than RDF-3X.
High speed stress tolerant 1.6 V – 3.6 V low to high voltage CMOS level shift architecture in 40 nm

An architecture for the scalable speed and multiple supply voltage range of 1.6 V to 3.6 V, low voltage to high voltage level shifter has been proposed. The buffer containing the level shifter is fabricated in 40 nm CMOS process by thin oxide (32 Å thick) devices whose stress limit is 1.98 V (max). The technique generates a set of dynamic differential bias signals as a function of input data sequence, output state of the level shifter and the supply voltage for a given process and temperature to ensure the reliable operation of the level shift stage. The measurement results confirmed successful operation at 40 Mbps with 10 pF load on IO pad, with multiple supplies, 1.8 V - 2.7 V - 3.6 V.
Teaching Distance Relay using Matlab/Simulink Graphical user Interface

This paper describes the modeling of distance relay using Matlab/Simulink package. SimPowerSystem toolbox was used for detailed modeling of distance relay, transmission line and fault simulation. Inside the modeling, single line to ground (SLG) fault was choose to be the fault type and Mho type distance characteristic was choose to be as the protection scheme. A graphical user interface (GUI) was created using GUI package inside Matlab for the developed model. With the interactive environment of graphical user interface, the difficulties in teaching of distance relay for undergraduate students can be eliminated.  Selection and/or peer-review under responsibility of the Research Management & Innovation Centre, Universiti Malaysia Perlis.
Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection

A new approach for the detection of the road surface and obstacles is presented. The high accuracy of the method allows the detection of traffic isles as a distinct class. The 3-D data inferred from dense stereo are transformed into a rectangular digital elevation map (DEM). Two classifiers are proposed, namely, density based and road surface based. The density-based obstacle classifier marks DEM cells as road or obstacles, using the density of 3-D points as a criterion. A quadratic road surface model is initially fitted by a random sample consensus (RANSAC) approach to the region in front of the ego vehicle. A region growing-like process refines this primary solution, driven by the 3-D uncertainty model of the stereo sensor. A robust global solution for the road surface is obtained. The road surface is used for discrimination between road, traffic isle, and obstacle points. Fusion and error filtering is performed on the results of the two classifiers. The proposed real-time algorithm was evaluated in an urban scenario and can be used in complex applications from collision avoidance to path planning.
Standardizing generic cross-domain applications in Internet of Things

Domain-specific Internet of Things (IoT) applications are becoming more and more popular. Each of these applications uses their own technologies and terms to describe sensors and their measurements. This is a difficult task to help users build generic IoT applications to combine several domains. To explicitly describe sensor measurements in uniform way, we propose to enrich them with semantic web technologies. Domain knowledge is already defined in more than 200 ontology and sensor-based projects that we could reuse to build cross-domain IoT applications. There is a huge gap to reason on sensor measurements without a common nomenclature and best practices to ease the automation of generic IoT applications. We present our Machine-to-Machine Measurement (M3) framework and share lessons learned to improve existing standards such as oneM2M, ETSI M2M, W3C Web of Things and W3C Semantic Sensor Network.
Intelligent Transport Systems (its); Decentralized Congestion Control Mechanisms for Intelligent Transport Systems Operating in the 5 Ghz Range; Access Layer Part

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Tensor-Train Recurrent Neural Networks for Video Classification

The Recurrent Neural Networks and their variants have shown promising performances in sequence modeling tasks such as Natural Language Processing. These models, however, turn out to be impractical and difficult to train when exposed to very high-dimensional inputs due to the large input-to-hidden weight matrix. This may have prevented RNNs’ large-scale application in tasks that involve very high input dimensions such as video modeling; current approaches reduce the input dimensions using various feature extractors. To address this challenge, we propose a new, more general and efficient approach by factorizing the input-to-hidden weight matrix using Tensor-Train decomposition which is trained simultaneously with the weights themselves. We test our model on classification tasks using multiple real-world video datasets and achieve competitive performances with state-of-the-art models, even though our model architecture is orders of magnitude less complex. We believe that the proposed approach provides a novel and fundamental building block for modeling highdimensional sequential data with RNN architectures and opens up many possibilities to transfer the expressive and advanced architectures from other domains such as NLP to modeling highdimensional sequential data.
Logic for improving integrity checking in relational data bases⋆

When an “updating” operation occurs on the current state of a data base, one has to ensure the new state obeys the integrity constraints. So, some of them have to be evaluated on this new state. The evaluation of an integrity constraint can be time consuming, but one can improve such an evaluation by taking advantage from the fact that the integrity constraint is satisfied in the current state. Indeed, it is then possible to derive a simplified form of this integrity constraint which is sufficient to evaluate in the new state in order to determine whether the initial constraint is still satisfied in this new state. The purpose of this paper is to present a simplification method yielding such simplified forms for integrity constraints. These simplified forms depend on the nature of the updating operation which is the cause of the state change. The operations of inserting, deleting, updating a tuple in a relation as well as transactions of such operations are considered. The proposed method is based on syntactical criteria and is validated through first order logic. Examples are treated and some aspects of the method application are discussed.