Query: The WaCky wide web: a collection of very large linguistically processed web-crawled corpora

Relevant passages:
Syntactic Clustering of the Web <=SEP=> We have developed an efficient way to determine the syntactic similarity of files and have applied it to every document on the World Wide Web. Using this mechanism, we built a clustering of all the documents that are syntactically similar. Possible applications include a “Lost and Found” service, filtering the results of Web searches, updating widely distributed web-pages, and identifying violations of intellectual property rights.

Introduction to the Special Issue on the Web as Corpus <=SEP=> The Web, teeming as it is with language data, of all manner of varieties and languages, in vast quantity and freely available, is a fabulous linguists' playground. This special issue of Computational Linguistics explores ways in which this dream is being explored.

Maximum entropy models for natural language ambiguity resolution <=SEP=> MAXIMUM ENTROPY MODELS FOR NATURAL LANGUAGE AMBIGUITY RESOLUTION Adwait Ratnaparkhi Supervisor Professor Mitch Marcus This thesis demonstrates that several important kinds of natural language ambiguities can be resolved to state of the art accuracies using a single statistical modeling technique based on the principle of maximum entropy We discuss the problems of sentence boundary detection part of speech tagging prepo sitional phrase attachment natural language parsing and text categorization under the maximum entropy framework In practice we have found that maximum entropy models o er the following advantages State of the art Accuracy The probability models for all of the tasks discussed per form at or near state of the art accuracies or outperform competing learning algo rithms when trained and tested under similar conditions Methods which outperform those presented here require much more supervision in the form of additional human involvement or additional supporting resources Knowledge Poor Features The facts used to model the data or features are linguis tically very simple or knowledge poor but yet succeed in approximating complex linguistic relationships Reusable Software Technology The mathematics of the maximum entropy framework

Finding Similar Files in a Large File System <=SEP=> We present a tool, called sif, for finding all similar files in a large file system. Files are considered similar if they have significant number of common pieces, even if they are very different otherwise. For example, one file may be contained, possibly with some changes, in another file, or a file may be a reorganization of another file. The running time for finding all groups of similar files, even for as little as 25% similarity, is on the order of 500MB to 1GB an hour. The amount of similarity and several other customized parameters can be determined by the user at a post-processing stage, which is very fast. Sif can also be used to very quickly identify all similar files to a query file using a preprocessed index. Application of sif can be found in file management, information collecting (to remove duplicates), program reuse, file synchronization, data compression, and maybe even plagiarism detection.

Scaling to Very Very Large Corpora for Natural Language Disambiguation <=SEP=> The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, tested and compared after training on corpora consisting of only one million words or less. In this paper, we evaluate the performance of different learning methods on a prototypical natural language disambiguation task, confusion set disambiguation, when trained on orders of magnitude more labeled data than has previously been used. We are fortunate that for this particular application, correctly labeled training data is free. Since this will often not be the case, we examine methods for effectively exploiting very large corpora when labeled data comes at a cost.

Application of Fuzzy Graph Coloring in Traffic Light Problem <=SEP=> Let G= (V,E,σ,μ) be a simple connected undirected fuzzy graph. In this paper, we use a fuzzy graph model to represent a traffic network of a city and discuss a method to find the different type of accidental zones in a traffic flows. This paper is based on fuzzy coloring of fuzzy graphs and fuzziness of vertices. Using this approach, we can minimize the total waiting time of a traffic flow. It will help to reduce the traffic jam of a road.

Embodiment enables the spinal engine in quadruped robot locomotion <=SEP=> The biological hypothesis of spinal engine states that locomotion is mainly achieved by the spine, while the legs may serve as assistance. Inspired by this hypothesis, a compliant, multiple degree-of-freedom, biologically-inspired spine has been embedded into a quadruped robot, named Kitty, which has no actuation on the legs. In this paper, we demonstrate how versatile behaviors (bounding, trotting, and turning) can be generated exclusively by the spine's movements through dynamical interaction between the controller, the body, and the environment, known as embodiment. Moreover, we introduce information theoretic approach to quantitatively study the spine internal dynamics and its effect on the bounding gait based on three spinal morphologies. These three morphologies differ in the position of virtual spinal joint where the spine is easier to get bent. The experimental results reveal that locomotion can be enhanced by using the spine featuring a rear virtual spinal joint, which offers more freedom for the rear legs to move forward. In addition, the information theoretic analysis shows that, according to the morphological differences of the spine, the information structure changes. The relationship between the observed behavior of the robot and the corresponding information structure is discussed in detail.

Feature Mining for Localised Crowd Counting <=SEP=> This paper presents a multi-output regression model for crowd counting in public scenes. Existing counting by regression methods either learn a single model for global counting, or train a large number of separate regressors for localised density estimation. In contrast, our single regression model based approach is able to estimate people count in spatially localised regions and is more scalable without the need for training a large number of regressors proportional to the number of local regions. In particular, the proposed model automatically learns the functional mapping between interdependent low-level features and multi-dimensional structured outputs. The model is able to discover the inherent importance of different features for people counting at different spatial locations. Extensive evaluations on an existing crowd analysis benchmark dataset and a new more challenging dataset demonstrate the effectiveness of our approach.

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences <=SEP=> Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ.

Reliable Circuit Techniques for Low-Voltage Analog Design in Deep Submicron Standard CMOS: A Tutorial <=SEP=> We present in this paper an overview of circuit techniques dedicated to design reliable low-voltage (1-V and below) analog functions in deep submicron standard CMOS processes. The challenges of designing such low-voltage and reliable analog building blocks are addressed both at circuit and physical layout levels. State-ofthe-art circuit topologies and techniques (input level shifting, bulk and current driven, DTMOS), used to build main analog modules (operational amplifier, analog CMOS switches) are covered with the implementation of MOS capacitors.

PURE-LET Image Deconvolution <=SEP=> We propose a non-iterative image deconvolution algorithm for data corrupted by Poisson or mixed Poisson-Gaussian noise. Many applications involve such a problem, ranging from astronomical to biological imaging. We parameterize the deconvolution process as a linear combination of elementary functions, termed as linear expansion of thresholds. This parameterization is then optimized by minimizing a robust estimate of the true mean squared error, the Poisson unbiased risk estimate. Each elementary function consists of a Wiener filtering followed by a pointwise thresholding of undecimated Haar wavelet coefficients. In contrast to existing approaches, the proposed algorithm merely amounts to solving a linear system of equations, which has a fast and exact solution. Simulation experiments over different types of convolution kernels and various noise levels indicate that the proposed method outperforms the state-of-the-art techniques, in terms of both restoration quality and computational complexity. Finally, we present some results on real confocal fluorescence microscopy images and demonstrate the potential applicability of the proposed method for improving the quality of these images.

Motion Mode Recognition for Indoor Pedestrian Navigation Using Portable Devices <=SEP=> While indoor portable navigation using portable devices is becoming increasingly required, it faces many challenges in obtaining accurate positioning performance. One of the methods to improve navigation results is to recognize the mode of motion of the user carrying the portable device containing low-cost microelectromechanical systems (MEMS) motion sensors. Pattern recognition methodology has been employed to detect a group of motion modes that are common indoors, namely, walking, stationary, going up/down stairs, standing on an escalator, walking on an escalator, standing on a moving walkway, and walking on a moving walkway. The performance of the motion mode recognition module was examined on different types of mobile computing devices, including various brands of smartphones, tablets, smartwatches, and smartglasses, and the results obtained showed the capability of enhancing positioning performance. The module does not require satellite or wireless positioning signals, and depends only on MEMS sensors.

Stippling by Example <=SEP=> In this work, we focus on stippling as an artistic style and discuss our technique for capturing and reproducing stipple features unique to an individual artist. We employ a texture synthesis algorithm based on the gray-level co-occurrence matrix (GLCM) of a texture field. This algorithm uses a texture similarity metric to generate stipple textures that are perceptually similar to input samples, allowing us to better capture and reproduce stipple distributions. First, we extract example stipple textures representing various tones in order to create an approximate tone map used by the artist. Second, we extract the stipple marks and distributions from the extracted example textures, generating both a lookup table of stipple marks and a texture representing the stipple distribution. Third, we use the distribution of stipples to synthesize similar distributions with slight variations using a numerical measure of the error between the synthesized texture and the example texture as the basis for replication. Finally, we apply the synthesized stipple distribution to a 2D grayscale image and place stipple marks onto the distribution, thereby creating a stippled image that is statistically similar to images created by the example artist.

Ballistocardiography and Seismocardiography: A Review of Recent Advances <=SEP=> In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising results. This paper reviews the recent advances in these areas of modern BCG and SCG research.

Stochastic Video Prediction with Conditional Density Estimation <=SEP=> Frame-to-frame stochasticity is a major challenge in video prediction. The use of standard feedforward and recurrent networks for video prediction leads to averaging of future states, which can in part be attributed to the networks’ limited ability to model stochasticity. We propose the use of conditional variational autoencoders (CVAE) for video prediction. To model multi-modal densities in frame-to-frame transitions, we extend the CVAE framework by modeling the latent variable with a mixture of Gaussians in the generative network. We tested our proposed Gaussian mixture CVAE (GM-CVAE) on a simple video-prediction task involving a stochastically moving object. Our architecture demonstrates improved performance, achieving noticeably lower rates of blurring/averaging compared to a feedforward network and a Gaussian CVAE. We also describe how the CVAE framework can be applied to improve existing deterministic video prediction models.1

Body mass index reference curves for the UK, 1990. <=SEP=> Reference curves for stature and weight in British children have been available for the past 30 years, and have recently been updated. However weight by itself is a poor indicator of fatness or obesity, and there has never been a corresponding set of reference curves to assess weight for height. Body mass index (BMI) or weight/height has been popular for assessing obesity in adults for many years, but its use in children has developed only recently. Here centile curves for BMI in British children are presented, from birth to 23 years, based on the same large representative sample as used to update the stature and weight references. The charts were derived using Cole's LMS method, which adjusts the BMI distribution for skewness and allows BMI in individual subjects to be expressed as an exact centile or SD score. Use of the charts in clinical practice is aided by the provision of nine centiles, where the two extremes identify the fattest and thinnest four per 1000 of the population.

Microcomputer Playfulness: Developing a Measure With Workplace Implications <=SEP=> 

Degradation of mechanical properties of class-H winding insulation <=SEP=> Thermal ageing is one of the key factors that result in insulation degradation leading to lower dielectric strength and thereafter occurrence of partial discharge in high voltage machines. However, in low voltage high current machines, the occurrence of partial discharge is minimal and degradation of dielectric strength alone will not cause the breakdown of the insulation. This paper hypothesizes that the breakdown of insulation in low voltage/high current machines occur as a result of insulation cracking among other factors. In this paper, it is shown that thermal degradation leads to degradation of mechanical properties of Polyamide-imide insulation, particularly reduction of the ultimate tensile strength and leads to enhanced brittleness. It follows that once a crack in the insulation is formed, the breakdown strength at the point of cracking will be lower. Multiple such cracks may lead to the formation of a breakdown path thereafter leading to aggravated heating and further accelerated deterioration of the insulation. In this paper, the methodology for testing the mechanical properties of insulation material is presented. A model to account for the variation of such properties due to thermal degradation is developed.

The twisted string actuation system: Modeling and control <=SEP=> This paper describes a novel actuation system for very compact and light-weight robotic devices, like artificial hands. The actuation concept presented here allows the implementation of powerful tendon-based driving systems, using as actuators small-size DC motors characterized by high speed and low torque. After the presentation of the basic concept of this novel actuation system, the constitutive equations of the system are given, validated by means of laboratory tests. Moreover, the problem of tracking a desired actuation force profile is taken into account, considering as load a mass-spring-damper system. A control algorithm based on a second-order sliding manifold has been firstly evaluated by means of simulations, and then validated by experiments. This output-feedback controller has been chosen to guarantee a high level of robustness against disturbances, parameter variations and uncertainties while maintaining a low computational burden.

Automated Training of Deep Convolutional Neural Networks for Cell Segmentation <=SEP=> Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations.

Parsing Algebraic Word Problems into Equations <=SEP=> This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and score their likelihood by learning local and global discriminative models. These models are trained on a small set of word problems and their answers, without any manual annotation, in order to choose the equation that best matches the problem text. We refer to the overall system as Alges. We compare Alges with previous work and show that it covers the full gamut of arithmetic operations whereas Hosseini et al. (2014) only handle addition and subtraction. In addition, Alges overcomes the brittleness of the Kushman et al. (2014) approach on single-equation problems, yielding a 15% to 50% reduction in error.

Accurate Language Identification of Twitter Messages <=SEP=> We present an evaluation of “off-theshelf” language identification systems as applied to microblog messages from Twitter. A key challenge is the lack of an adequate corpus of messages annotated for language that reflects the linguistic diversity present on Twitter. We overcome this through a “mostly-automated” approach to gathering language-labeled Twitter messages for evaluating language identification. We present the method to construct this dataset, as well as empirical results over existing datasets and off-theshelf language identifiers. We also test techniques that have been proposed in the literature to boost language identification performance over Twitter messages. We find that simple voting over three specific systems consistently outperforms any specific system, and achieves state-of-the-art accuracy on the task.

Freiburger Fragebogen zur körperlichen Aktivität-Entwicklung, Prüfung und Anwendung <=SEP=> Ziel vorliegender Untersuchung war die Entwicklung und Validierung eines Fragebogens zur Erfassung gesundheitswirksamer körperlicher Aktivität, sowie die Andwendung des Instruments in einer Stichprobe der erwachsenen Freiburger Wohnbevölkerung. Die Reliabilität des Fragebogens wurde anhand von Test-Retest-Untersuchungen, die im Abstand von 2 Wochen und 6 Monaten durchgeführt wurden, überprüft. Die hohen Korrelationen zwischen den Untersuchungen weisen auf eine hohe Reliabilität unseres Instruments hin. Trends im Sinne von saisonal bedingten Schwankungen fanden wir nur beim Rad fahren und der Gartenarbeit, sowie bei der abgeleiteten Basis-und Gesamtaktivität. Die Validierung erfolgte indirekt über die maximale Sauerstoffaufnahme. Es bestand eine enge Korrelation zwischen der maximalen Sauerstoffaufnahme und dem Umfang der Sportaktivitäten (partieller Korrelationskoeffizient: r=0.422, p<0.01). Die mit dem Fragebogen erhobenen Daten waren in sich konsistent. Personen, die sich selbst als “aktiver als ihre Altersgenossen” eingeschätzt hatten, waren, bezogen auf die Sport-(r=0.334, p<0.01) und Gesamtaktivität (r=0.282, p<0.05), tatsächlich aktiver als ihre Altersgenossen. Die Befragung einer (systematischen) Stichprobe der Freiburger Wohnbevölkerung (n-612, 20–98 Jahre) ergab mittlere Gesamtaktivitätsumfänge von 9,2 h/Woche. Aktivitäten mit leichten bis moderaten Belastungsintensitäten machten den grössten Teil der Gesamtaktivität aus. Die Aktivitätsmuster waren abhängig von Alter und Geschlecht. In Anlehnung an die Empfehlungen zum Bewegungssoll waren, nach Paffenbarger (2000 kcal/Woche Gesamtaktivität), 40% der Befragten zu wenig aktiv, nach den Empfehlungen des American College of Sports Medicine (1000 kcal/Woche durch Training) waren dies 63% der Befragten. Aim of the present study was to design a questionnaire to assess health related physical activity, to validate the instrument and to apply it to a population sample. Reliability of the questionnaire was evaluated by testretest investigations with intervals of two weeks and six months. High correlations between the repeated administrations reflect a good reliability of our instrument. Only gardening and cycling, as well as the depending basic and total activity, showed typically seasonal variations. Validity was established by correlating physical activity data with maximum oxygen uptake. Maximum oxygen uptake correlated with sport activities (partial correlation coefficient: r=0.422, p<0.01). Evaluated data were consistent. People rating themselves as “more active than their coevals” were indeed more active in sport (r=0.334, p<0.01) and total activity (r=0.282, p<0.05). Studying activity patterns of a population sample of adult residents of Freiburg (systematic random sampling, n=612, 20–98 years) we found total physical activity of 9,2 hours per week (median), with activities of low to moderate intensities dominating. Age and gender are important determinants of the activity patterns. According to the recommendation of Paffenbarger (2000 kcal/week total physical activity) 40% of the residents of Freiburg did not reach the recommended energy expenditure. Compared to the recommendation of the American College of Sports Medicine (1000 kcal/week by training) 63% of the population sample were not active enough. L'objectif de la présente enquête était le développement et la validation d'un questionnaire visant a estimer les effets d'activités physiques sur la santé ainsi que l'utilisation de cet outil d'analyse sur un échantillon pris au hasard parmi la population adulte Fribourg (F.R.A.). La fiabilité du questionnaire a été verifiée sur la base de testes réeffectués à deux semaines et à six mois d'intervalle. Les hautes corrélations entre les diverses enquêtes ont indiqué une forte fiabilité de notre outil d'analyse. Les tendences dans le sens des variations saisonnières n'ont été trouvées qu'au niveau de l'activité cycliste et du jardinage ainsi qu'au niveau des activités de base et globales qui sont dérivées. La validation s'est effectuée de façon indirecte par le biais de la capacité maximale d'absorption d'oxygène. On a constaté un lien étroit entre la capacité maximale d'absorption d'oxygène et le volume d'activités sportives (coéfficient partielle de corrélation: r=0.422, p<0.01). Les données révélées par le questionnaire étaient en soi consistentes. Les personnes qui s'estiment être plus actives que leurs congénères du même âge en ce qui concerne le sport et l'activité globale étaient effectivement plus actives. Les résultats du questionnaire ont fait apparaître une quantité moyenne d'activité globale de 9,2h par semaine. Pour la plus grande partie des activités globales, il s'agissait d'activités avec un niveau d'intensité physique allant de léger à modéré. Les schemas d'activité variaient suivant l'âge et le sexe. Selon les recommendations formulées par Paffenbarger concernant le minimum d'activité physique requis (2000 kcal par semaine d'activité globale), 40% des personnes interogées etaient en dessous de ce seuil. Selon les recommandations du “American College of Sports Medicine” (1000 kcal par semaine d'activité sportive) 63% des personnes interogées se situaient en dessous de ce seuil.

Enabling ∼100fps detection on a landing unmanned aircraft for its on-ground vision-based recovery <=SEP=> In this paper, a deep learning inspired solution is proposed and developed to enable timeliness and practicality of the pre-existing ground stereo vision guidance system for flxed-wing UAVs' safe landing. Since the ground guidance prototype was restricted within applications due to its untimeliness, eventually the vision-based detection less than 15 fps (frame per second). Under such circumstances, we employ a regression based deep learning algorithm into automatic detection on the flying aircraft in the landing sequential images. The system architecture is upgraded so as to be compatible with the novel deep learning requests, and furthermore, annotated datasets are conducted to support training and testing of the regression-based learning detection algorithm. Experimental results validate that the detection attaches 100 fps or more while the localization accuracy is kept in the same level.

Computer-aided design of analog and mixed-signal integrated circuits <=SEP=> This survey presents an overview of recent advances in the state of the art for computer-aided design (CAD) tools for analog and mixed-signal integrated circuits (ICs). Analog blocks typically constitute only a small fraction of the components on mixed-signal ICs and emerging systems-on-a-chip (SoC) designs. But due to the increasing levels of integration available in silicon technology and the growing requirement for digital systems to communicate with the continuous-valued external world, there is a growing need for CAD tools that increase the design productivity and improve the quality of analog integrated circuits. This paper describes the motivation and evolution of these tools and outlines progress on the various design problems involved: simulation and modeling, symbolic analysis, synthesis and optimization, layout generation, yield analysis and design centering, and test. This paper summarizes the problems for which viable solutions are emerging and those which are still unsolved.

Effects of Small-Group Learning on Undergraduates in Science , Mathematics , Engineering , and Technology : A Meta-Analysis <=SEP=> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..I................ vii Conceptual Framework ........................................................................................................... 2 Motivational Perspective ............................................................................................. 3 Affective Perspective .................................................................................................. 3 Cognitive Perspective .................................................................................................. 3 Forms of Small-Group Learning ................................................................................. 4 Research Questions ..................................................................................................... 4 Meta-Analysis Method ............................................................................................................ 4 Literature Search Procedures ....................................................................................... 4 Inclusion Criteria ......................................................................................................... 5 Metric for Expressing Effect Sizes .............................................................................. 5 Calculations of Average Effect Sizes .......................................................................... 5 Tests for Conditional Effects ....................................................................................... 6 Study Coding ............................................................................................................... 7 Meta Analysis Results ............................................................................................................. 7 Main Effect of Small-Group Learning ........................................................................ 7 Distribution of Effect Sizes ......................................................................................... 7 Conditional Effects of Small-Group Learning .......................................................... 1 0 Discussion and Conclusions .................................................................................................. 1 7 Robust Main Effects .................................................................................................. 1 7 Conditional Effects of Small-Group Learning .......................................................... 1 8 Limitations of the Study ............................................................................................ 2 0 Implications for Theory, Research, Policy, and Practice .......................................... 2 1 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 3 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 9

IoT smart parking system for reducing green house gas emission <=SEP=> Rapid climate change results natural calamity and severe economic impact and threats to the life. Burning fossil fuels by the medium of transportation contributes 1/3 of portion in increasing greenhouse emission and leading to raise surface temperate. Commuters in and around the developed cities faces difficulties in finding parking lot due to lack of notification process and autonomous parking systems. This causes commuters to take multiple rounds trips to get the parking slot which causes burning additional fuel and ultimately producing excessive CO2 emission. This paper describes solution to smart parking system using Internet of Things (IoT) to override parking hazards and explains how does it helps to minimize emitting greenhouse gases. IoT enables smart parking system using the system of interconnected Raspberry Pi, Distance Sensor, Pi Camera devices together. This hardware reacts to one another collects data and transmits to cloud storage.

Deep Face Recognition: A Survey <=SEP=> Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief that this technique reached human performance, it still remains an open problem in unconstrained environments as demonstrated by the newly released IJB datasets. This survey aims to summarize the main advances in deep face recognition and, more in general, in learning face representations for verification and identification. The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues. The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc.); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. The survey concludes with an overview of the SOTA results at a glance along with some open issues currently overlooked by the community.

A more realistic error distance calculation for indoor positioning systems accuracy evaluation <=SEP=> The accuracy of indoor positioning systems is commonly computed as a metric based on the Euclidean distance from estimated locations to actual locations. This paper suggests that positioning error distances should be computed as the lengths of the paths that a person may follow when going from wrongly estimated positions to the real positions. The paper proposes a method that calculates the paths from floor plan and obstacles information using the visibility graphs and offsetting techniques, which are commonly used in robotics and CAD/CAM for navigation and manufacturing, respectively. Demonstration of the proposed method was done using a WiFi fingerprinting method based on kNN for pedestrian navigation. Comparisons between our proposed distance and the simple Euclidean distance have shown that the error distances are underestimated and that the differences between the two distances cannot be accurately represented by a fixed quantity in the context of an Indoor Positioning System (IPS) deployed in a library building. We consider that our proposed positioning error distance is more in line with the subjective error perceived by IPS users.

There is a blind spot in AI research <=SEP=> advances in the technical domains of AI. Alongside such efforts, designers and researchers from a range of disciplines need to conduct what we call social-systems analyses of AI. They need to assess the impact of technologies on their social, cultural and political settings. A social-systems approach could investigate, for instance, how the app AiCure — which tracks patients’ adherence to taking prescribed medication and transmits records to physicians — is changing the doctor– patient relationship. Such an approach to perform a range of complex tasks in everyday life. These ranged from the identification of skin alterations that are indicative of earlystage cancer to the reduction of energy costs for data centres. The workshops also highlighted a major blind spot in thinking about AI. Autonomous systems are already deployed in our most crucial social institutions, from hospitals to courtrooms. Yet there are no agreed methods to assess the sustained effects of such applications on human populations. Recent years have brought extraordinary There is a blind spot in AI research