nils-herrmann/clean_scirepeval huggingface dataset:
Information Retrieval

Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e. n-grams based models, have been extensively used on this task in real application. However, the characteristics of extremely different typing behaviors usually lead to serious sparsity problem, even n-gram with smoothing will fail. A reasonable approach to tackle this problem is to use the recently proposed neural models, such as probabilistic neural language model, recurrent neural network and word2vec. They can leverage more semantically similar words for estimating the probability. However, there is no conclusion on which approach of the two will work better in real application. In this paper, we conduct an extensive empirical study to show the differences between statistical and neural language models. The experimental results show that the two different approach have individual advantages, and a hybrid approach will bring a significant improvement.
Identity recognition with palm vein feature using local binary pattern rotation Invariant

Biometrics are being used as one of many alternatives in Recognition System. Palm vein is one of many features that could be used in biometrics. Several advantages of palm vein is that they were not easily damaged and rather hard to be duplicated because it is located deep inside skin layers. Palm vein is also cannot be seen with plain eyes or ordinary cameras. The research in this biometrics recognition system is done using Local Binary Pattern Rotation Invariant (LBPROT) as a feature extraction method on palm vein image. The matching process itself was done by using Cosine Distance to determine the nearest distance to which image. Test result shows that the optimal configuration of the method is by using 16 regions, 15 radius length, 8 neighborhood pixel, and 256×256 pixel image resolution. Another result shown by setting the threshold level in 0.085 is that the FAR score reached 0.11698 and the FRR score reached 0.1175, with 11.7% EER score and recognition accuracy rate in 96%.
PSRR of bridge-tied load PWM Class D Amps

In this paper, the effects of power supply noise, qualified by power supply rejection ratio (PSRR), on two types of bridge-tied load (BTL) pulse width modulation (PWM) class D amps (denoted as Type-I BTL and Type-II BTL respectively) are investigated and the analytical expressions for PSRR of the two designs derived. The derived analytical expressions are verified by means of HSPICE simulations. The relationships derived herein provide good insight to the design of BTL class D amps, including how various parameters may be varied/optimized to meet a given PSRR specification. Furthermore, the PSRR of the two BTL Class D amps are compared against the single-ended class D amp, and the former designs show superior PSRR compared to the latter.
Gender, Communication, and Self-Presentation in Teen Chatrooms Revisited: Have Patterns Changed?

This study evaluates empirically the proposition implicit in much recent gender and CMC research that expressions of gender distinctness among teens in online environments are becoming less frequent and less traditional. Gender preferences were analyzed in linguistic features and communication styles in synchronous text messages, along with self-presentation in user profile pictures, drawing on data from popular English-language teen chat sites collected in 2010. Significant differences were found in speech acts, message tone, and in physical stance, dress, and social distance in profile pictures that generally conform to traditional gender stereotypes. These findings are interpreted in light of previous gender and teen CMC research, adolescent development and socialization patterns, mass media representations, and trends towards media convergence in chat platforms.
LIPN at SemEval-2017 Task 10: Filtering Candidate Keyphrases from Scientific Publications with Part-of-Speech Tag Sequences to Train a Sequence Labeling Model

This paper describes the system used by the team LIPN in SemEval 2017  Extracting Keyphrases and Relations from Scientific Publications. The team participated in Scenario 1, that includes three subtasks, Identification of keyphrases (Subtask A), Classification of identified keyphrases (Subtask B) and Extraction of relationships between two identified keyphrases (Subtask C). The presented system was mainly focused on the use of part-of-speech tag sequences to filter candidate keyphrases for Subtask A. Subtasks A and B were addressed as a sequence labeling problem using Conditional Random Fields (CRFs) and even though Subtask C was out of the scope of this approach, one rule was included to identify synonyms.
Model-Based Quantitative Network Security Metrics: A Survey

Network security metrics (NSMs) based on models allow to quantitatively evaluate the overall resilience of networked systems against attacks. For that reason, such metrics are of great importance to the security-related decision-making process of organizations. Considering that over the past two decades several model-based quantitative NSMs have been proposed, this paper presents a deep survey of the state-of-the-art of these proposals. First, to distinguish the security metrics described in this survey from other types of security metrics, an overview of security metrics, in general, and their classifications is presented. Then, a detailed review of the main existing model-based quantitative NSMs is provided, along with their advantages and disadvantages. Finally, this survey is concluded with an in-depth discussion on relevant characteristics of the surveyed proposals and open research issues of the topic.
The nude image identification with adaptive skin chromatic distribution matching scheme

This paper presents a new nude image identification system. We propose an adaptive chroma-distribution matching scheme based on face detection to on-line determine the image's skin chromatic distribution such that it can tolerate the color deviation coming from special lighting without increasing false alarm. The object detection based on machine learning approach is used to locate the face region in the test image. According to the color information of face, the matched skin chromatic distribution is selected to detect the skin objects in the test image. The texture feature, namely coarseness, is used to acquire accurate skin segmentation. The low-level but reliable geometrical constraints and the mug shot exclusion procedure are employed to further examine the skin regions. Experimental results that the overall detection rate is 86.3% show our method can achieve satisfactory performance for detecting nude images under special lighting conditions.
Upright orientation of man-made objects

Humans usually associate an upright orientation with objects, placing them in a way that they are most commonly seen in our surroundings. While it is an open challenge to recover the functionality of a shape from its geometry alone, this paper shows that it is often possible to infer its upright orientation by analyzing its geometry. Our key idea is to reduce the two-dimensional (spherical) orientation space to a small set of orientation candidates using functionality-related geometric properties of the object, and then determine the best orientation using an assessment function of several functional geometric attributes defined with respect to each candidate. Specifically we focus on obtaining the upright orientation for man-made objects that typically stand on some flat surface (ground, floor, table, etc.), which include the vast majority of objects in our everyday surroundings. For these types of models orientation candidates can be defined according to static equilibrium. For each candidate, we introduce a set of discriminative attributes linking shape to function. We learn an assessment function of these attributes from a training set using a combination of Random Forest classifier and Support Vector Machine classifier. Experiments demonstrate that our method generalizes well and achieves about 90% prediction accuracy for both a 10-fold cross-validation over the training set and a validation with an independent test set.
Knowledge Based Image Enhancement Using Neural Networks

In this paper we combine the concept of adaptive filters with neural networks in order to be able to include high level knowledge about the contents of the image in the filtering process. Adaptive image enhancement algorithms often utilize low level knowledge like gradient information to guide filtering parameters. The advantage is that these filters do not need any specific knowledge and can thus be applied to a broad spectrum of images. However, for many problems this low level information is not sufficient to achieve good results. For example in medical imaging it is often very important that some features are preserved while others are suppressed. Usually these features cannot be distinguished by low level information. Therefore we propose a method to incorporate high level knowledge in the filtering process in order to adjust the parameters of any given filter thus creating a guided filter. We present a scheme for acquiring this high level knowledge which allows us to apply our method to all kinds of images using pattern recognition and special preprocessing techniques. The design of the guided filter itself is easy as for the high level knowledge only some sample pixels including their neighborhood and the desired parameters for these pixels are necessary
Sky detection and log illumination refinement for PDE-based hazy image contrast enhancement

This report presents the results of a sky detection technique used to improve the performance of a previously developed partial differential equation (PDE)-based hazy image enhancement algorithm. Additionally, a proposed alternative method utilizes a function for log illumination refinement to improve de-hazing results while avoiding over-enhancement of sky or homogeneous regions. The algorithms were tested with several benchmark and calibration images and compared with several standard algorithms from the literature. Results indicate that the algorithms yield mostly consistent results and surpasses several of the other algorithms in terms of colour and contrast enhancement in addition to improved edge visibility.
Control of lower limb exoskeleton for elderly assistance on basic mobility tasks

This paper presents the modelling, simulation and control of a lower limbs exoskeleton devices whose aim is to assist elderly people during standing-up and walking tasks. A humanoid and actuated exoskeleton frames were modelled in Solid Works and assembled in Visual Nastran 4D virtual environment. Control of exoskeletons actuators by means of PID and fuzzy controllers, together with a finite state machine, was designed. Simulations of the humanoid wearing an exoskeleton to perform the mentioned tasks, using controllers with set and adaptive orientation references as input, were performed. It is assumed that the humanoid, representing an old person, is only capable to provide 70% of the required torque, therefore the exoskeleton must supply 30% to complete the motions. With the aid of exoskeleton devices, the elderly will be able to perform motions of a physically able people. Optimization of fuzzy controller parameters for walking motion was done using SDA. The exoskeleton succeeded on assisting standing-up and walking motions but research is still needed to improve the performance and adaptability of the system.
Constrained Two-dimensional Cutting Stock Problems a Best-first Branch-and-bound Algorithm Constrained Two-dimensional Cutting Stock Problems a Best-first Branch-and-bound Algorithm

In this work, we develop a new version of the algorithm proposed in 17] for solving exactly some variants of (un)weighted constrained two-dimensional cutting stock problems. We introduce one-dimensional bounded knapsacks in order to obtain an improved initial lower bound for limiting initially the size of the search space, an improved upper bound at each internal node and a symmetry detection for neglecting some useless duplicate patterns. A new eecient data structure of the set representing the best subprob-lems is used to implement the algorithms over the BOB library. The performance of our algorithms is evaluated on some problem instances of the literature and on other randomly generated hard problem instances (a total of 27 problem instances). Les Probl emes de D ecoupe 2-Dimensions Contraints Un Algorithme de Branch-and-Bound meilleur d'abord R esum e : Dans ce travail, nous concevons une nouvelle version de l'algorithme pro-pos e dans 17] pour r esoudre de mani ere exacte des probl emes de d ecoupe en 2-dimensions contraints avec ou sans pond eration. Nous introduisons des sacs a dos unidimension-nels contraints aan d'obtenir une meilleure borne inf erieure initiale pour limiter la taille de l'espace de recherche, une meilleure borne sup erieure pour chaque nnud interne et une d etection de sym etries pour supprimer des conngurations identiques. Une nouvelle structure de donn ees eecace pour g erer les meilleurs sous-probl emes a et e employ ee pour l'impl ementation des algorithmes sur la biblioth eque BOB. La performance de nos algo-rithmes est evalu ee sur 27 instances de probl emes extraites pour certaines de la litt erature et d'autres diiciles, engendr ees al eatoirement.
On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications

The MapReduce programming model simplifies large-scale data processing on commodity cluster by exploiting parallel map tasks and reduce tasks. Although many efforts have been made to improve the performance of MapReduce jobs, they ignore the network traffic generated in the shuffle phase, which plays a critical role in performance enhancement. Traditionally, a hash function is used to partition intermediate data among reduce tasks, which, however, is not traffic-efficient because network topology and data size associated with each key are not taken into consideration. In this paper, we study to reduce network traffic cost for a MapReduce job by designing a novel intermediate data partition scheme. Furthermore, we jointly consider the aggregator placement problem, where each aggregator can reduce merged traffic from multiple map tasks. A decomposition-based distributed algorithm is proposed to deal with the large-scale optimization problem for big data application and an online algorithm is also designed to adjust data partition and aggregation in a dynamic manner. Finally, extensive simulation results demonstrate that our proposals can significantly reduce network traffic cost under both offline and online cases.
Influencing factors of adolescent internet addiction

Addiction is one of major social issues in modern life. With the rapid popularization and the widespread use of the Internet, Internet addiction, as a new form of social addiction, aroused increasing attention from society. This paper attempts to build an analytical framework with the independent variables of adolescents' individual characteristics, differences in the real society and the degree of Internet using, and the dependent variables of the determination and tendency of the addiction to analyze and card the factors influencing adolescent Internet addiction from the structure-agent perspective, and based on that, it proposes basic countermeasures on the social control of adolescent Internet addiction.
An Efficient LBP-Based Descriptor for Facial Depth Images Applied to Gender Recognition Using RGB-D Face Data

RGB-D is a powerful source of data providing the aligned depth information which has great potentials in improving the performance of various problems in image understanding, while Local Binary Patterns (LBP) have shown excellent results in representing faces. In this paper, we propose a novel efficient LBP-based descriptor, namely Gradient-LBP (G-LBP), specialized to encode the facial depth information inspired by 3DLBP, yet resolves its inherent drawbacks. The proposed descriptor is applied to gender recognition task and shows its superiority to 3DLBP in all the experimental setups on both Kinect and range scanner databases. Furthermore, a weighted combination scheme of the proposed descriptor for depth images and the state-of-the-art LBP for grayscale images applied in gender recognition is proposed and evaluated. The result reinforces the effectiveness of the proposed descriptor in complementing the source of information from the luminous intensity. All the experiments are carried out on both the high quality 3D range scanner database Texas 3DFR and images of lower quality obtained from Kinect EURECOM Kinect Face Dataset to show the consistency of the performance on different sources of RGB-D data.
Computational pan-genomics: status, promises and challenges

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.
Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale

Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of $20$ land use classes across $~300$ European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications.
Programming algorithms for multilevel phase-change memory

Phase-change memory (PCM) has emerged as one among the most promising technologies for next-generation nonvolatile solid-state memory. Multilevel storage, namely storage of non-binary information in a memory cell, is a key factor for reducing the total cost-per-bit and thus increasing the competitiveness of PCM technology in the nonvolatile memory market. In this paper, we present a family of advanced programming schemes for multilevel storage in PCM. The proposed schemes are based on iterative write-and-verify algorithms that exploit the unique programming characteristics of PCM in order to achieve significant improvements in resistance-level packing density, robustness to cell variability, programming latency, energy-per-bit and cell storage capacity. Experimental results from PCM test-arrays are presented to validate the proposed programming schemes. In addition, the reliability issues of multilevel PCM in terms of resistance drift and read noise are discussed.
A Flexibility Metric and Optimization Methods for Mixed Numerologies in 5G and Beyond

Mixed numerology-based frame structures will be a part of 5G systems in order to enhance overall flexibility and user satisfaction. However, inter-numerology interference, spectral efficiency reduction, complexity, and signaling overhead type of issues arise in such structures. It is needed to limit the number of numerologies used together. In this paper, a novel heuristic method is developed to find the efficient number of mixed numerologies. The proposed method aims to control overheads in systems using multi-numerology structures. Analysis of the trade-offs and relationships between different services and user requirements are also presented. The designed algorithm employs a new flexibility function and performance metric. Simulation results are shown for three different numerology sets, which include 5G numerologies.
Europeana Newspapers OCR Workflow Evaluation

This paper summarises the final performance evaluation results of the OCR workflow which was employed for large-scale production in the Europeana Newspapers project. It gives a detailed overview of how the involved software performed on a representative dataset of historical newspaper pages (for which ground truth was created) with regard to general text accuracy as well as layout-related factors which have an impact on how the material can be used in specific use scenarios. Specific types of errors are examined and evaluated in order to identify possible improvements related to the employed document image analysis and recognition methods. Moreover, alternatives to the standard production workflow are assessed to determine future directions and give advice on best practice related to OCR projects.
BRIEF: Computing a Local Binary Descriptor Very Fast

Binary descriptors are becoming increasingly popular as a means to compare feature points very fast while requiring comparatively small amounts of memory. The typical approach to creating them is to first compute floating-point ones, using an algorithm such as SIFT, and then to binarize them. In this paper, we show that we can directly compute a binary descriptor, which we call BRIEF, on the basis of simple intensity difference tests. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and SIFT on standard benchmarks and show that it yields comparable recognition accuracy, while running in an almost vanishing fraction of the time required by either.