nils-herrmann/clean_scirepeval huggingface dataset:
A comparative study on illumination preprocessing in face recognition

Illumination preprocessing is an effective and efficient approach in handling lighting variations for face recognition. Despite much attention to face illumination preprocessing, there is seldom systemic comparative study on existing approaches that presents fascinating insights and conclusions in how to design better illumination preprocessing methods. To fill this vacancy, we provide a comparative study of 12 representative illumination preprocessing methods (HE, LT, GIC, DGD, LoG, SSR, GHP, SQI, LDCT, LTV, LN and TT) from two novel perspectives: (1) localization for holistic approach and (2) integration of large-scale and small-scale feature bands. Experiments on public face databases (YaleBExt, CMU-PIE, CAS-PEAL and FRGC V2.0) with illumination variations suggest that localization for holistic illumination preprocessing methods (HE, GIC, LTV and TT) further improves the performance. Integration of large-scale and small-scale feature bands for reflectance field estimation based illumination preprocessing approaches (SSR, GHP, SQI, LDCT, LTV and TT) is also found helpful for illumination-insensitive face recognition. & 2012 Elsevier Ltd. All rights reserved.
Bricks: Laying the Foundations for Graspable User Interfaces

We introduce the concept of Graspable User Interfaces that allow direct control of electronic or virtual objects through physical handles for control. These physical artifacts, which we call "bricks," are essentially new input devices that can be tightly coupled or "attached" to virtual objects for manipulation or for expressing action (e.g., to set parameters or for initiating processes). Our bricks operate on top of a large horizontal display surface known as the "ActiveDesk." We present four stages in the development of Graspable UIs: (1) a series of exploratory studies on hand gestures and grasping; (2) interaction simulations using mock-ups and rapid prototyping tools; (3) a working prototype and sample application called GraspDraw; and (4) the initial integrating of the Graspable UI concepts into a commercial application. Finally, we conclude by presenting a design space for Bricks which lay the foundation for further exploring and developing Graspable User Interfaces.
Reactive and effortful processes in the organization of temperament.

Self-organization can be approached in terms of developmental processes occurring within and between component systems of temperament. Within-system organization involves progressive shaping of cortical representations by subcortical motivational systems. As cortical representations develop, they feed back to provide motivational systems with enhanced detection and guidance capabilities. These reciprocal influences may amplify the underlying motivational functions and promote excessive impulsivity or anxiety. However, these processes also depend upon interactions arising between motivational and attentional systems. We discuss these between-system effects by considering the regulation of approach motivation by reactive attentional processes related to fear and by more voluntary processes related to effortful control. It is suggested than anxious and impulsive psychopathology may reflect limitations in these dual means of control, which can take the form of overregulation as well as underregulation.
Learn++: an incremental learning algorithm for supervised neural networks

We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern classifiers. The proposed algorithm enables supervised NN paradigms, such as the multilayer perceptron (MLP), to accommodate new data, including examples that correspond to previously unseen classes. Furthermore, the algorithm does not require access to previously used data during subsequent incremental learning sessions, yet at the same time, it does not forget previously acquired knowledge. Learn++ utilizes ensemble of classifiers by generating multiple hypotheses using training data sampled according to carefully tailored distributions. The outputs of the resulting classifiers are combined using a weighted majority voting procedure. We present simulation results on several benchmark datasets as well as a real-world classification task. Initial results indicate that the proposed algorithm works rather well in practice. A theoretical upper bound on the error of the classifiers constructed by Learn++ is also provided.
SCNet: A simplified encoder-decoder CNN for semantic segmentation

We present a simplified and novel fully convolutional neural network (CNN) architecture for semantic pixel-wise segmentation named as SCNet. Different from current CNN pipelines, proposed network uses only convolution layers with no pooling layer. The key objective of this model is to offer a more simplified CNN model with equal benchmark performance and results. It is an encoder-decoder based fully convolution network model. Encoder network is based on VGG 16-layer while decoder networks use upsampling and deconvolution units followed by a pixel-wise classification layer. The proposed network is simple and offers reduced search space for segmentation by using low-resolution encoder feature maps. It also offers a great deal of reduction in trainable parameters due to reusing encoder layer's sparse features maps. The proposed model offers outstanding performance and enhanced results in terms of architectural simplicity, number of trainable parameters and computational time.
Clustering Techniques: A Brief Survey of Different Clustering Algorithms

Partitioning a set of objects into homogeneous clusters is a fundamental operation in data mining. The operation is needed in a number of data mining tasks. Clustering or data grouping is the key technique of the data mining. It is an unsupervised learning task where one seeks to identify a finite set of categories termed clusters to describe the data . The grouping of data into clusters is based on the principle of maximizing the intra class similarity and minimizing the inter class similarity. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. But how to decide what constitutes a good clustering? This paper deal with the study of various clustering algorithms of data mining and it focus on the clustering basics, requirement, classification, problem and application area of the clustering algorithms.
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation

With the rapid growth of location-based social networks, Point of Interest (POI) recommendation has become an important research problem. However, the scarcity of the check-in data, a type of implicit feedback data, poses a severe challenge for existing POI recommendation methods. Moreover, different types of context information about POIs are available and how to leverage them becomes another challenge. In this paper, we propose a ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges. In the proposed model, we consider that the check-in frequency characterizes users' visiting preference and learn the factorization by ranking the POIs correctly. In our model, POIs both with and without check-ins will contribute to learning the ranking and thus the data sparsity problem can be alleviated. In addition, our model can easily incorporate different types of context information, such as the geographical influence and temporal influence. We propose a stochastic gradient descent based algorithm to learn the factorization. Experiments on publicly available datasets under both user-POI setting and user-time-POI setting have been conducted to test the effectiveness of the proposed method. Experimental results under both settings show that the proposed method outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.
Tactical Approaches for Alleviating Distance in Global Software Development

The phenomenon of global software development Only a decade ago, the number of entities engaging in global software development was small—but this has rapidly changed. Today, 203 of the US Fortune 500 engage in offshore outsourcing.1 In a recent study, we found that within the largest US firms, the median ratio of IT work outsourced—but consumed largely in the US— is 6.5 percent.2 Meanwhile, in the much smaller Netherlands, about 250 Dutch companies of various sizes engage in some kind of offshore work.3 Upwards of 50 nations are currently participating—at least minimally—in collaborative software development internationally. In India, there are now 800 IT service firms competing for work globally.3 Many American firms are in the process of a radical push to send their key software processes offshore, and critical centers of software R&D are growing outside the traditional centers (such as the US)—in Ireland, Israel, Singapore, Finland, and many other nations. Finally, the marketplace is responding to the increased demand for IT labor through the construction of new commercial mechanisms. IT business-to-business intermediaries, such as ITsquare.com and IT-radar.com, serve as exchanges between worldwide IT services vendors and smalland medium-size businesses with IT needs. There are two critical, strategic reasons for moving to offshore software developfocus
Sparsest solutions of underdetermined linear systems via q-minimization for 0 < q 1

Article history: Received 14 December 2007 Revised 9 September 2008 Accepted 11 September 2008 Available online 25 September 2008 Communicated by Naoki Saito We present a condition on the matrix of an underdetermined linear system which guarantees that the solution of the system with minimal q-quasinorm is also the sparsest one. This generalizes, and slightly improves, a similar result for the 1-norm. We then introduce a simple numerical scheme to compute solutions with minimal q-quasinorm, and we study its convergence. Finally, we display the results of some experiments which indicate that the q-method performs better than other available methods.
Perceptual Quality Assessment of Screen Content Images

Research on screen content images (SCIs) becomes important as they are increasingly used in multi-device communication applications. In this paper, we present a study on perceptual quality assessment of distorted SCIs subjectively and objectively. We construct a large-scale screen image quality assessment database (SIQAD) consisting of 20 source and 980 distorted SCIs. In order to get the subjective quality scores and investigate, which part (text or picture) contributes more to the overall visual quality, the single stimulus methodology with 11 point numerical scale is employed to obtain three kinds of subjective scores corresponding to the entire, textual, and pictorial regions, respectively. According to the analysis of subjective data, we propose a weighting strategy to account for the correlation among these three kinds of subjective scores. Furthermore, we design an objective metric to measure the visual quality of distorted SCIs by considering the visual difference of textual and pictorial regions. The experimental results demonstrate that the proposed SCI perceptual quality assessment scheme, consisting of the objective metric and the weighting strategy, can achieve better performance than 11 state-of-the-art IQA methods. To the best of our knowledge, the SIQAD is the first large-scale database published for quality evaluation of SCIs, and this research is the first attempt to explore the perceptual quality assessment of distorted SCIs.
CRISPR/Cas, the immune system of bacteria and archaea.

Microbes rely on diverse defense mechanisms that allow them to withstand viral predation and exposure to invading nucleic acid. In many Bacteria and most Archaea, clustered regularly interspaced short palindromic repeats (CRISPR) form peculiar genetic loci, which provide acquired immunity against viruses and plasmids by targeting nucleic acid in a sequence-specific manner. These hypervariable loci take up genetic material from invasive elements and build up inheritable DNA-encoded immunity over time. Conversely, viruses have devised mutational escape strategies that allow them to circumvent the CRISPR/Cas system, albeit at a cost. CRISPR features may be exploited for typing purposes, epidemiological studies, host-virus ecological surveys, building specific immunity against undesirable genetic elements, and enhancing viral resistance in domesticated microbes.
Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks

We study answer selection for question answering, in which given a question and a set of candidate answer sentences, the goal is to identify the subset that contains the answer. Unlike previous work which treats this task as a straightforward pointwise classification problem, we model this problem as a ranking task and propose a pairwise ranking approach that can directly exploit existing pointwise neural network models as base components. We extend the Noise-Contrastive Estimation approach with a triplet ranking loss function to exploit interactions in triplet inputs over the question paired with positive and negative examples. Experiments on TrecQA and WikiQA datasets show that our approach achieves state-of-the-art effectiveness without the need for external knowledge sources or feature engineering.
What could move? Finding cars, pedestrians and bicyclists in 3D laser data

This paper tackles the problem of segmenting things that could move from 3D laser scans of urban scenes. In particular, we wish to detect instances of classes of interest in autonomous driving applications - cars, pedestrians and bicyclists - amongst significant background clutter. Our aim is to provide the layout of an end-to-end pipeline which, when fed by a raw stream of 3D data, produces distinct groups of points which can be fed to downstream classifiers for categorisation. We postulate that, for the specific classes considered in this work, solving a binary classification task (i.e. separating the data into foreground and background first) outperforms approaches that tackle the multi-class problem directly. This is confirmed using custom and third-party datasets gathered of urban street scenes. While our system is agnostic to the specific clustering algorithm deployed we explore the use of a Euclidean Minimum Spanning Tree for an end-to-end segmentation pipeline and devise a RANSAC-based edge selection criterion.
Appearance-based person reidentification in camera networks: problem overview and current approaches

Recent advances in visual tracking methods allow following a given object or individual in presence of significant clutter or partial occl usions in a single or a set of overlapping camera views. The question of when person detections in different views or at different time instants can be linked to the same individual is of funda mental importance to the video analysis in large-scale network of cameras. This is the pers on reidentification problem. The paper focuses on algorithms that use the overall appearance of an individual as opposed to passive biometrics such as face and gait. Methods that effec tively address the challenges associated with changes in illumination, pose, and clothing a ppearance variation are discussed. More specifically, the development of a set of models that ca pture the overall appearance of an individual and can effectively be used for information retrieval are reviewed. Some of them provide a holistic description of a person, and some o th rs require an intermediate step where specific body parts need to be identified. Some ar e designed to extract appearance features over time, and some others can operate reliabl y also on single images. The paper discusses algorithms for speeding up the computation of signatures. In particular it describes very fast procedures for computing co-occurrenc e matrices by leveraging a generalization of the integral representation of images. The alg orithms are deployed and tested in a camera network comprising of three cameras with non-overl apping field of views, where a multi-camera multi-target tracker links the tracks in dif ferent cameras by reidentifying the same people appearing in different views.
A simultaneous X/Ka feed system for reflectors with a F/D ratio of 0.8

A simultaneous X/Ka feed system has been designed to cater for reflector antennas with a F/D ratio of 0.8. This work is an extension of the successful design of the initial X/Ka feed system that was designed for reflectors with a F/D ratio of 0.65. Although simple in concept, this move from F/D=0.65 to F/D=0.8 is not an easy task from a design point of view.
FCFraud: Fighting Click-Fraud from the User Side

Pay-Per-Click (PPC) ad networks charge advertisers for every click on their ads. Click-fraud happens when a user or an automated software clicks on an ad with a malicious intent and advertisers need to pay for those valueless clicks. Click-fraud has been proved to be a serious problem for the online advertisement industry. Although it has attracted much attention from the security community, the direct victims of click-fraud, the advertisers, still lack confidence in the click-fraud detection techniques. Among many forms of click-fraud, botnets with the automated clickers are the most severe ones. In this paper, we present a technique for detecting automated clickers from the user side. Normal internet users are victimized by malicious attackers (e.g., bot-master of a botnet) and the attackers infect and use their machines to defraud advertisers. We propose a technique to Fight Click-Fraud, FCFraud, which can be integrated into the operating system. Since most modern operating systems already provide some kind of anti-malware service, our proposed technique can be implemented with a negligible overhead. We believe that an effective protection at the operating system level can save billions of dollars of the advertisers. Experiments show that FCFraud is 99.6% accurate in classifying ad requests from all user processes and it is 100% successful in finding the fraudulent processes.
Anti-forensics techniques: An analytical review

The rapid growth and development in technology has made computer as a weapon which can cause great loss if used with wrong intentions. Computer forensics aims at collecting, and analyzing evidences from the seized devices in such ways so that they are admissible in court of law. Anti-forensics, on the other hand, is collection of tricks and techniques that are used and applied with clear aim of forestalling the forensic investigation. Crime and crime prevention go hand in hand. Once a crime surfaces, then a defense is developed, then a new crime counters the new defense. Hence along with continuous developments in forensics, a thorough study and knowledge of developments in anti-forensics is equally important. This paper focuses on understanding different techniques that can be used for anti-forensic purposes with help of open source tools.
Nonlinear phase portrait modeling of fingerprint orientation

Fingerprint orientation is crucial for automatic fingerprint identification. However, recovery of orientation is still difficult especially in noisy region. A way to aid recovery of the orientation is to provide an orientation model. In this paper, an orientation model for the entire fingerprint orientation using high order phase portrait is suggested. Proper analysis of the orientation pattern at the singular point regions is provided. Then a low-order phase portrait near each of the singular point is added as constraint to the high-order phase portrait to provide accurate orientation modeling for the entire fingerprint image. The main advantage of the proposed approach is that the nonlinear model itself is able to model all type of fingerprint orientations completely. Experiments and visual analysis show the effectiveness of the proposed model.
YouthTube: Youth Video Authorship on YouTube and Vine

What kinds of content do children and teenagers author and share on public video platforms? We approached this question through a qualitative directed content analysis of over 250 youth-authored videos filtered by crowdworkers from public videos on YouTube and Vine. We found differences between YouTube and Vine platforms in terms of the age of the youth authors, the type of collaborations witnessed in the videos, and the significantly greater amount of violent, sexual, and obscene content on Vine. We also highlight possible differences in how adults and youths approach online video sharing. Specifically, we consider that adults may view online video as an archive to keep precious memories of everyday life with their family, friends, and pets, humorous moments, and special events, while children and teenagers treat online video as a stage to perform, tell stories, and express their opinions and identities in a performative way.
Exploring the Edge of the Magic Circle : Defining Pervasive Games

Pervasive gaming is a genre of gaming systematically blurring and breaking the traditional boundaries of game. The limits of the magic circle are explored in spatial, temporal and social dimensions. These ways of expanding the game are not new, since many intentional and unintentional examples of similar expansions can be found from earlier games, but the recently emerged fashion of pervasive gaming is differentiated with the use of these expansions in new, efficient ways to produce new kinds of gameplay experiences. These new game genres include alternate reality games, reality games, trans-reality games and crossmedia games.
Statistical Machine Translation of German Compound Words

German compound words pose special problems to statistical machine translation systems: the occurence of each of the components in the training data is not sufficient for successful translation. Even if the compound itself has been seen during training, the system may not be capable of translating it properly into two or more words. If German is the target language, the system might generate only separated components or may not be capable of choosing the correct compound. In this work, we investigate and compare different strategies for the treatment of German compound words in statistical machine translation systems. For translation from German, we compare linguistic-based and corpusbased compound splitting. For translation into German, we investigate splitting and rejoining German compounds, as well as joining English potential components. Additionaly, we investigate word alignments enhanced with knowledge about the splitting points of German compounds. The translation quality is consistently improved by all methods for both translation directions.