nils-herrmann/clean_scirepeval huggingface dataset:
Deep code comment generation

During software maintenance, code comments help developers comprehend programs and reduce additional time spent on reading and navigating source code. Unfortunately, these comments are often mismatched, missing or outdated in the software projects. Developers have to infer the functionality from the source code. This paper proposes a new approach named DeepCom to automatically generate code comments for Java methods. The generated comments aim to help developers understand the functionality of Java methods. DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features. We use a deep neural network that analyzes structural information of Java methods for better comments generation. We conduct experiments on a large-scale Java corpus built from 9,714 open source projects from GitHub. We evaluate the experimental results on a machine translation metric. Experimental results demonstrate that our method DeepCom outperforms the state-of-the-art by a substantial margin.
Closure and Convergence: A Foundation of Fault-Tolerant Computing

We give a formal de nition of what it means for a system to \tolerate" a class of \faults". The de nition consists of two conditions: One, if a fault occurs when the system state is within a set of \legal" states, the resulting state is within some larger set and, if faults continue occurring, the system state remains within that larger set (Closure). And two, if faults stop occurring, the system eventually reaches a state within the legal set (Convergence). We demonstrate the applicability of our de nition for specifying and verifying the fault-tolerance properties of a variety of digital and computer systems. Further, using the de nition, we obtain a simple classi cation of fault-tolerant systems and discuss methods for their systematic design.
Doppelgänger Finder: Taking Stylometry to the Underground

Stylometry is a method for identifying anonymous authors of anonymous texts by analyzing their writing style. While stylometric methods have produced impressive results in previous experiments, we wanted to explore their performance on a challenging dataset of particular interest to the security research community. Analysis of underground forums can provide key information about who controls a given bot network or sells a service, and the size and scope of the cybercrime underworld. Previous analyses have been accomplished primarily through analysis of limited structured metadata and painstaking manual analysis. However, the key challenge is to automate this process, since this labor intensive manual approach clearly does not scale. We consider two scenarios. The first involves text written by an unknown cybercriminal and a set of potential suspects. This is standard, supervised stylometry problem made more difficult by multilingual forums that mix l33t-speak conversations with data dumps. In the second scenario, you want to feed a forum into an analysis engine and have it output possible doppelgangers, or users with multiple accounts. While other researchers have explored this problem, we propose a method that produces good results on actual separate accounts, as opposed to data sets created by artificially splitting authors into multiple identities. For scenario 1, we achieve 77% to 84% accuracy on private messages. For scenario 2, we achieve 94% recall with 90% precision on blogs and 85.18% precision with 82.14% recall for underground forum users. We demonstrate the utility of our approach with a case study that includes applying our technique to the Carders forum and manual analysis to validate the results, enabling the discovery of previously undetected doppelganger accounts.
A simulation study of the number of events per variable in logistic regression analysis.

We performed a Monte Carlo study to evaluate the effect of the number of events per variable (EPV) analyzed in logistic regression analysis. The simulations were based on data from a cardiac trial of 673 patients in which 252 deaths occurred and seven variables were cogent predictors of mortality; the number of events per predictive variable was (252/7 =) 36 for the full sample. For the simulations, at values of EPV = 2, 5, 10, 15, 20, and 25, we randomly generated 500 samples of the 673 patients, chosen with replacement, according to a logistic model derived from the full sample. Simulation results for the regression coefficients for each variable in each group of 500 samples were compared for bias, precision, and significance testing against the results of the model fitted to the original sample. For EPV values of 10 or greater, no major problems occurred. For EPV values less than 10, however, the regression coefficients were biased in both positive and negative directions; the large sample variance estimates from the logistic model both overestimated and underestimated the sample variance of the regression coefficients; the 90% confidence limits about the estimated values did not have proper coverage; the Wald statistic was conservative under the null hypothesis; and paradoxical associations (significance in the wrong direction) were increased. Although other factors (such as the total number of events, or sample size) may influence the validity of the logistic model, our findings indicate that low EPV can lead to major problems.
Inferring international and internal migration patterns from Twitter data

Data about migration flows are largely inconsistent across countries, typically outdated, and often inexistent. Despite the importance of migration as a driver of demographic change, there is limited availability of migration statistics. Generally, researchers rely on census data to indirectly estimate flows. However, little can be inferred for specific years between censuses and for recent trends. The increasing availability of geolocated data from online sources has opened up new opportunities to track recent trends in migration patterns and to improve our understanding of the relationships between internal and international migration. In this paper, we use geolocated data for about 500,000 users of the social network website "Twitter". The data are for users in OECD countries during the period May 2011- April 2013. We evaluated, for the subsample of users who have posted geolocated tweets regularly, the geographic movements within and between countries for independent periods of four months, respectively. Since Twitter users are not representative of the OECD population, we cannot infer migration rates at a single point in time. However, we proposed a difference-in-differences approach to reduce selection bias when we infer trends in out-migration rates for single countries. Our results indicate that our approach is relevant to address two longstanding questions in the migration literature. First, our methods can be used to predict turning points in migration trends, which are particularly relevant for migration forecasting. Second, geolocated Twitter data can substantially improve our understanding of the relationships between internal and international migration. Our analysis relies uniquely on publicly available data that could be potentially available in real time and that could be used to monitor migration trends. The Web Science community is well-positioned to address, in future work, a number of methodological and substantive questions that we discuss in this article.
Short Group Signatures

We construct a short group signature scheme. Signatures in our scheme are approximately the size of a standard RSA signature with the same security. Security of our group signature is based on the Strong Diffie-Hellman assumption and a new assumption in bilinear groups called the Decision Linear assumption. We prove security of our system, in the random oracle model, using a variant of the security definition for group signatures recently given by Bellare, Micciancio, and Warinschi.
On the control of planar cable-driven parallel robot via classic controllers and tuning with intelligent algorithms

This paper presents different classical control approaches for planar cable-driven parallel robots. For the proposed robot, PD and PID controllers are designed based on the concept of pole placement method. In order to optimize and tune the controller parameters of planar cable-driven parallel robot, Differential Evaluation, Particle Swarm Optimization and Genetic algorithms are applied as optimization techniques. The simulation results of Genetic algorithm, Particle Swarm Optimization and Differential Evaluation algorithms reveal that the output results of tunes controllers with Particle Swarm Optimization and Differential Evaluation algorithms are more similar than Genetic algorithm and the processing time of Differential Evaluation is less than Genetic algorithm and Particle Swarm Optimization. Moreover, performance of the Particle Swarm Optimization and Differential Evaluation algorithms are better than Genetic algorithm for tuning the controllers parameters.
Learning Robust Deep Face Representation

With the development of convolution neural network, more and more researchers focus their attention on the advantage of CNN for face recognition task. In this paper, we propose a deep convolution network for learning a robust face representation. The deep convolution net is constructed by 4 convolution layers, 4 max pooling layers and 2 fully connected layers, which totally contains about 4M parameters. The Max-Feature-Map activation function is used instead of ReLU because the ReLU might lead to the loss of information due to the sparsity while the Max-Feature-Map can get the compact and discriminative feature vectors. The model is trained on CASIA-WebFace dataset and evaluated on LFW dataset. The result on LFW achieves 97.77% on unsupervised setting for single net.
Dissociation between face perception and face memory in adults, but not children, with developmental prosopagnosia

Cognitive models propose that face recognition is accomplished through a series of discrete stages, including perceptual representation of facial structure, and encoding and retrieval of facial information. This implies that impaired face recognition can result from failures of face perception, face memory, or both. Studies of acquired prosopagnosia, autism spectrum disorders, and the development of normal face recognition support the idea that face perception and face memory are distinct processes, yet this distinction has received little attention in developmental prosopagnosia (DP). To address this issue, we tested the face perception and face memory of children and adults with DP. By definition, face memory is impaired in DP, so memory deficits were present in all participants. However, we found that all children, but only half of the adults had impaired face perception. Thus, results from adults indicate that face perception and face memory are dissociable, while the results from children provide no evidence for this division. Importantly, our findings raise the possibility that DP is qualitatively different in childhood versus adulthood. We discuss theoretical explanations for this developmental pattern and conclude that longitudinal studies are necessary to better understand the developmental trajectory of face perception and face memory deficits in DP.
Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature

Object detection in very high resolution (VHR) optical remote sensing images is one of the most fundamental but challenging problems in the field of remote sensing image analysis. As object detection is usually carried out in feature space, effective feature representation is very important to construct a high-performance object detection system. During the last decades, a great deal of effort has been made to develop various feature representations for the detection of different types of objects. Among various features developed for visual object detection, the histogram of oriented gradients (HOG) feature is maybe one of the most popular features that has been successfully applied to computer vision community. However, although the HOG feature has achieved great success in nature scene images, it is problematic to directly use it for object detection in optical remote sensing images because it is difficult to effectively handle the problem of object rotation variations. To explore a possible solution to the problem, this paper proposes a novel method to learn rotation-invariant HOG (RIHOG) features for object detection in optical remote sensing images. This is achieved by learning a rotation-invariant transformation model via optimizing a new objective function, which constrains the training samples before and after rotation to share the similar features to achieve rotation-invariance. In the experiments, we evaluate the proposed method on a publicly available 10-class VHR geospatial object detection dataset and comprehensive comparisons with state-of-the-arts demonstrate the effectiveness the proposed method.
A high-efficient LLCC series-parallel resonant converter

A high efficient LLCC-type resonant dc-dc converter is discussed in this paper for a low-power photovoltaic application. Emphasis is put on the different design mechanisms of the resonant tank. At the same time soft switching of the inverter as well as the rectifier bridge are regarded. Concerning the design rules, a new challenge is solved in designing a LLCC-converter with voltage-source output. Instead of the resonant elements, ratios of them, e.g. the ratio of inductances Ls/Lp is considered as design parameters first. Furthermore, the derived design rule for the transformer-inductor device fits directly into the overall LLCC-design. Due to the nature of transformers, i.e. the relation of the inductances Ls/Lp is only a function of geometry, this design parameter is directly considered by geometry. Experimental results demonstrate the high efficiency.
Modeling and Signal Processing of Acoustic Gunshot Recordings

Audio recordings of gunshots can provide information about the gun location with respect to the microphone(s), the speed and trajectory of the projectile, and in some cases the type of firearm and ammunition. Recordings obtained under carefully controlled conditions can be well-modeled by geometrical acoustics. Special acoustic processing systems for real time gunshot detection and localization are used by the military and law enforcement agencies for sniper detection. Forensic analysis of audio recordings is also used to provide evidence in criminal and civil cases. This paper reviews the distinctive features and limitations of acoustic gunshot analysis using DSP techniques
Real-time nonintrusive monitoring and prediction of driver fatigue

This paper describes a real-time online prototype driver-fatigue monitor. It uses remotely located charge-coupled-device cameras equipped with active infrared illuminators to acquire video images of the driver. Various visual cues that typically characterize the level of alertness of a person are extracted in real time and systematically combined to infer the fatigue level of the driver. The visual cues employed characterize eyelid movement, gaze movement, head movement, and facial expression. A probabilistic model is developed to model human fatigue and to predict fatigue based on the visual cues obtained. The simultaneous use of multiple visual cues and their systematic combination yields a much more robust and accurate fatigue characterization than using a single visual cue. This system was validated under real-life fatigue conditions with human subjects of different ethnic backgrounds, genders, and ages; with/without glasses; and under different illumination conditions. It was found to be reasonably robust, reliable, and accurate in fatigue characterization.
Validation of LiDAR point clouds for classification of high-value crops using geometric-and reflectance-based extraction algorithm

This study provides an experimental analysis of high-value crop classification using geometric and reflectance-based feature extraction algorithms which can be used to validate extensive classification. The main goal of this study is to identify the class based on the 3D reconstruction of small scale LiDAR scanner and RGB images. The reference used in this study was obtained from the initially classified classes of Mapua-Phil LiDAR2 Project. In validating high-value crops, the classification results were compared to the results obtained using the reference data. The validation procedures involve comparing the locations of previously classified crops and analyzing the crop cycle in the tested fields. The proposed methodology used to identify specific class based on the geometric feature alone are found to be acceptable with an average accuracy of 92.5% while the reflectance based classification alone provides an average accuracy of 90%.
A lifelong learning perspective for mobile robot control

Abstmct-Designing robots that learn by themselves to perform complex real-world tasks is a still-open challenge for the field of Robotics and Artificial Intelligence. In this paper we present the robot learning problem as a lifelong problem, in which a robot faces a collection of tasks over its entire lifetime. Such a scenario provides the opportunity to gather general-purpose knowledge that transfers across tasks. We illustrate a particular learning mechanism, explanation-based neural network learning, that transfers knowledge between related tasks via neural network action models. The learning approach is illustrated using a mobile robot, equipped with visual, ultrasonic and laser sensors. In less than 10 minutes operation time, the robot is able to learn to navigate to a marked target object in a natural office environment.
Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon's entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.
A case for end system multicast

The conventional wisdom has been that IP is the natural protocol layer for implementing multicast related functionality. However, ten years after its initial proposal, IP Multicast is still plagued with concerns pertaining to scalability, network management, deployment and support for higher layer functionality such as error, flow and congestion control. In this paper, we explore an alternative architecture for small and sparse groups, where end systems implement all multicast related functionality including membership management and packet replication. We call such a scheme End System Multicast. This shifting of multicast support from routers to end systems has the potential to address most problems associated with IP Multicast. However, the key concern is the performance penalty associated with such a model. In particular, End System Multicast introduces duplicate packets on physical links and incurs larger end-to-end delay than IP Multicast. In this paper, we study this question in the context of the Narada protocol. In Narada, end systems self-organize into an overlay structure using a fully distributed protocol. In addition, Narada attempts to optimize the efficiency of the overlay based on end-to-end measurements. We present details of Narada and evaluate it using both simulation and Internet experiments. Preliminary results are encouraging. In most simulations and Internet experiments, the delay and bandwidth penalty are low. We believe the potential benefits of repartitioning multicast functionality between end systems and routers significantly outweigh the performance penalty incurred.
A word at a time: computing word relatedness using temporal semantic analysis

Computing the degree of semantic relatedness of words is a key functionality of many language applications such as search, clustering, and disambiguation. Previous approaches to computing semantic relatedness mostly used static language resources, while essentially ignoring their temporal aspects. We believe that a considerable amount of relatedness information can also be found in studying patterns of word usage over time. Consider, for instance, a newspaper archive spanning many years. Two words such as "war" and "peace" might rarely co-occur in the same articles, yet their patterns of use over time might be similar. In this paper, we propose a new semantic relatedness model, Temporal Semantic Analysis (TSA), which captures this temporal information. The previous state of the art method, Explicit Semantic Analysis (ESA), represented word semantics as a vector of concepts. TSA uses a more refined representation, where each concept is no longer scalar, but is instead represented as time series over a corpus of temporally-ordered documents. To the best of our knowledge, this is the first attempt to incorporate temporal evidence into models of semantic relatedness. Empirical evaluation shows that TSA provides consistent improvements over the state of the art ESA results on multiple benchmarks.
Semantic Annotation of the ACL Anthology Corpus for the Automatic Analysis of Scientific Literature

This paper describes the process of creating a corpus annotated for concepts and semantic relations in the scientific domain. A part of the ACL Anthology Corpus was selected for annotation, but the annotation process itself is not specific to the computational linguistics domain and could be applied to any scientific corpus. Concepts were identified and annotated fully automatically, based on a combination of terminology extraction and available ontological resources. A typology of semantic relations between concepts is also proposed. This typology, consisting of 18 domain-specific and 3 generic relations, is the result of a corpus-based investigation of the text sequences occurring between concepts in sentences. A sample of 500 abstracts from the corpus is currently being manually annotated with these semantic relations. Only explicit relations are taken into account, so that the data could serve to train or evaluate pattern-based semantic relation classification systems.