Automatic Means Research Articles

On the Complementarity between Human Translators and Machine Translation

Many translators are fearful of the impact of Machine Translation (MT) on their profession, broadly speaking, and on their livelihoods more specifically. We contend that their concern is misplaced, as human translators have a range of skills, many of which are currently – with no signs of any imminent breakthroughs on the horizon – impossible to replicate by automatic means. Nonetheless, in this paper, we will show that MT engines have considerable potential to improve translators’ productivity and ensure that the output translations are more consistent. Furthermore, we will investigate what machines are good at, where they break down, and why the human is likely to remain the most critical component in the translation pipeline for many years to come.

Concept Clustering in Design Teams: A Comparison of Human and Machine Clustering

Concept clustering is an important element of the product development process. The process of reviewing multiple concepts provides a means of communicating concepts developed by individual team members and by the team as a whole. Clustering, however, can also require arduous iterations and the resulting clusters may not always be useful to the team. In this paper, we present a machine learning approach on natural language descriptions of concepts that enables an automatic means of clustering. Using data from over 1000 concepts generated by student teams in a graduate new product development class, we provide a comparison between the concept clustering performed manually by the student teams and the work automated by a machine learning algorithm. The goal of our machine learning tool is to support design teams in identifying possible areas of “over-clustering” and/or “under-clustering” in order to enhance divergent concept generation processes.

Automated spatio-temporal analysis of dendritic spines and related protein dynamics.

Cofilin and other Actin-regulating proteins are essential in regulating the shape of dendritic spines, which are sites of neuronal communications in the brain, and their malfunctions are implicated in neurodegeneration related to aging. The analysis of cofilin motility in dendritic spines using fluorescence video-microscopy may allow for the discovery of its effects on synaptic functions. To date, the flow of cofilin has not been analyzed by automatic means. This paper presents Dendrite Protein Analysis (DendritePA), a novel automated pattern recognition software to analyze protein trafficking in neurons. Using spatiotemporal information present in multichannel fluorescence videos, the DendritePA generates a temporal maximum intensity projection that enhances the signal-to-noise ratio of important biological structures, segments and tracks dendritic spines, estimates the density of proteins in spines, and analyzes the flux of proteins through the dendrite/spine boundary. The motion of a dendritic spine is used to generate spine energy images, which are used to automatically classify the shape of common dendritic spines such as stubby, mushroom, or thin. By tracking dendritic spines over time and using their intensity profiles, the system can analyze the flux patterns of cofilin and other fluorescently stained proteins. The cofilin flux patterns are found to correlate with the dynamic changes in dendritic spine shapes. Our results also have shown that the activation of cofilin using genetic manipulations leads to immature spines while its inhibition results in an increase in mature spines.

Ensemblator v3: Robust atom-level comparative analyses and classification of protein structure ensembles.

Ensembles of protein structures are increasingly used to represent the conformational variation of a protein as determined by experiment and/or by molecular simulations, as well as uncertainties that may be associated with structure determinations or predictions. Making the best use of such information requires the ability to quantitatively compare entire ensembles. For this reason, we recently introduced the Ensemblator (Clark et al., Protein Sci 2015; 24:1528), a novel approach to compare user-defined groups of models, in residue level detail. Here we describe Ensemblator v3, an open-source program that employs the same basic ensemble comparison strategy but includes major advances that make it more robust, powerful, and user-friendly. Ensemblator v3 carries out multiple sequence alignments to facilitate the generation of ensembles from non-identical input structures, automatically optimizes the key global overlay parameter, optionally performs "ensemble clustering" to classify the models into subgroups, and calculates a novel "discrimination index" that quantifies similarities and differences, at residue or atom level, between each pair of subgroups. The clustering and automatic options mean that no pre-knowledge about an ensemble is required for its analysis. After describing the novel features of Ensemblator v3, we demonstrate its utility using three case studies that illustrate the ease with which complex analyses are accomplished, and the kinds of insights derived from clustering into subgroups and from the detailed information that locates significant differences. The Ensemblator v3 enhances the structural biology toolbox by greatly expanding the kinds of problems to which this ensemble comparison strategy can be applied.

Le français écrit numérisé

What becomes of grammars and dictionaries when written French goes digital? The explosion of variation in the contemporary written language goes hand in hand with an unmatched strengthening of printing norms. Using a set of (semi)-automatic means gradually but thoroughly transforms previous habits concerning memorization and reasoning. Digital communication opens the door to new ways of distance teaching and learning.

Closed-loop regulation of arterial pressure after acute brain death

The purpose of this concept study was to investigate the possibility of automatic mean arterial pressure (MAP) regulation in a porcine heart-beating brain death (BD) model. Hemodynamic stability of BD donors is necessary for maintaining acceptable quality of donated organs for transplantation. Manual stabilization is challenging, due to the lack of vasomotor function in BD donors. Closed-loop stabilization therefore has the potential of increasing availability of acceptable donor organs, and serves to indicate feasibility within less demanding patient groups. A dynamic model of nitroglycerine pharmacology, suitable for controller synthesis, was identified from an experiment involving an anesthetized pig, using a gradient-based output error method. The model was used to synthesize a robust PID controller for hypertension prevention, evaluated in a second experiment, on a second, brain dead, pig. Hypotension was simultaneously prevented using closed-loop controlled infusion of noradrenaline, by means of a previously published controller. A linear model of low order, with variable (uncertain) gain, was sufficient to describe the dynamics to be controlled. The robustly tuned PID controller utilized in the second experiment kept the MAP within a user-defined range. The system was able to prevent hypertension, exceeding a reference of 100 mmHg by more than 10%, during 98% of a 12 h experiment. This early work demonstrates feasibility of the investigated modelling and control synthesis approach, for the purpose of maintaining normotension in a porcine BD model. There remains a need to characterize individual variability, in order to ensure robust performance over the expected population.

Assessment of multi-exposure HDR image deghosting methods

To avoid motion artefacts when merging multiple exposures into a high dynamic range image, a number of HDR deghosting algorithms have been proposed. However, these algorithms do not work equally well on all types of scenes, and some may even introduce additional artefacts. As the number of proposed deghosting methods is increasing rapidly, there is an immediate need to evaluate them and compare their results. Even though subjective methods of evaluation provide reliable means of testing, they are often cumbersome and need to be repeated for each new proposed method or even its slight modification. Because of that, there is a need for objective quality metrics that will provide automatic means of evaluation of HDR deghosting algorithms. In this work, we explore several computational approaches of quantitative evaluation of multi-exposure HDR deghosting algorithms and demonstrate their results on five state-of-the-art algorithms. In order to perform a comprehensive evaluation, a new dataset consisting of 36 scenes has been created, where each scene provides a different challenge for a deghosting algorithm. The quality of HDR images produced by deghosting method is measured in a subjective experiment and then evaluated using objective metrics. As this paper is an extension of our conference paper, we add one more objective quality metric, UDQM, as an additional metric in the evaluation. Furthermore, analysis of objective and subjective experiments is performed and explained more extensively in this work. By testing correlation between objective metric and subjective scores, the results show that from the tested metrics, that HDR-VDP-2 is the most reliable metric for evaluating HDR deghosting algorithms. The results also show that for most of the tested scenes, Sen et al.’s deghosting method outperforms other evaluated deghosting methods. The observations based on the obtained results can be used as a vital guide in the development of new HDR deghosting algorithms, which would be robust to a variety of scenes and could produce high quality results.

The reliability of inverse modelling for the wide scale characterization of the thermal properties of buildings

The reduction of energy use in buildings is a major component of greenhouse gas mitigation policy and requires knowledge of the fabric and the occupant behaviour. Hence there has been a longstanding desire to use automatic means to identify these. Smart metres and the internet-of-things have the potential to do this. This paper describes a study where the ability of inverse modelling to identify building parameters is evaluated for 6 monitored real and 1000 simulated buildings. It was found that low-order models provide good estimates of heat transfer coefficients and internal temperatures if heating, electricity use and CO2 concentration are measured during the winter period. This implies that the method could be used with a small number of cheap sensors and enable the accurate assessment of buildings’ thermal properties, and therefore the impact of any suggested retrofit. This has the potential to be transformative for the energy efficiency industry.

India: Science and Technology

According to UNESCO, Data may be defined as ‘facts, concepts or instructions in a formalized manner suitable for communication, interpretation or processing by human or automatic means.’ Also, CODATA (Committee on Data for Science and Technology) defines data as a “crystallized presentation of the essence of scientific knowledge in the most accurate form.” The data analysis process essentially involves inspecting, cleaning, transforming and modelling of data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. It has multiple facets and approaches, orienting versatile techniques under a variety of names, in different subject areas. An increase in the volume of a subject field escalates various characteristic features of data related to the nature of activity, quantity, quality and impact of the activities of practitioners on society. Read more. . . .

Talk of Pedophilia: The Reflexive Management of Desire

ABSTRACTSociologists have increasingly been using theories and models borrowed from cognitive science and psychology to address questions of personal motivation. One influential model is the dual-process model, a cognitive model which holds that individuals think and act either unconsciously and automatically, or consciously and deliberately. Through an analysis of conversations between individuals who are sexually attracted to minors, however, I find that these individuals’ sexual desires cannot be reduced to strictly automatic or deliberate means. Due to the taboo nature of this sexual preference in these individuals’ locales, their unconscious sexual desires are frequently called into question and made conscious.

Automated Mapping using Multilevel B-Splines

This paper describes application of multilevel B-Splines approximation (MBA) algorithm to the SIC2004 exercise as a high performance automatic mapping means in emergency situations. MBA method was compared with kriging, and various flexible MBA methods tuning controls were proposed and discussed. Outliers automatic detection and delineation techniques considered could be used to process outliers without the loss in performance. Prior data were used to adjust method parameters and discover the pattern of spatial correlation. The interpolation algorithm development did not assume any information about the phenomena in addition to values given.

Selection of effective training instances for scalable automatic image annotation

Automatic image annotation means employing learning models for describing visual contents of images by using text descriptors. With the fast growth of digital images in the web, large-scale automatic image annotation has started to deal with major challenges. The most important challenges are scalability and annotation performance. In this research, in order to solve scalability and the image annotation time challenge, the prototype selection approach is used. The assumption of the prototype selection is based on single-label instances while, in image annotation, an instance has more than one label. It means that instances are multi-label. Hence, to employ prototype selection algorithms in image annotation, focusing on the concept of multi-label is a critical task. Thus, taking an appropriate measure in these methods to compute the rate of dissimilarity between label vectors has a great importance. The proposed approach in this paper is based on multi-labeling of prototype selection methods by selecting a modifying appropriate binary dissimilarity measure, in comparison two label vectors. The effectiveness of the proposed approach in reducing the number of training instances and selecting effective ones has been shown by experiments on large-scale NUS-WIDE family image sets. The experimental results showed the effectiveness of the proposed approach in reducing the number of instances and improving annotation performance.

Automated spoof-detection for fingerprints using optical coherence tomography.

Fingerprint recognition systems are prevalent in high-security applications. As a result, the act of spoofing these systems with artificial fingerprints is of increasing concern. This research presents an automatic means for spoof-detection using optical coherence tomography (OCT). This technology is able to capture a 3D representation of the internal structure of the skin and is thus not limited to a 2D surface scan. The additional information afforded by this representation means that accurate spoof-detection can be achieved. Two features were extracted to detect the presence of (1) an additional thin layer on the surface of the skin and (2) a thicker additional layer or a complete artificial finger. An analysis of these features showed that they are highly separable, resulting in 100% accuracy regarding spoof-detection, with no false rejections of real fingers. This is the first attempt at fully automated spoof-detection using OCT.

ОСОБЕННОСТИ ОРГАНИЗАЦИИ НАТУРНЫХ ИССЛЕДОВАНИЙ ТРАНСПОРТНЫХ ПОТОКОВ С ИСПОЛЬЗОВАНИЕМ СРЕДСТВ АВТОМАТИЧЕСКОЙ ФИКСАЦИИ

The article describes the organization and carrying out traffic flow natural researches method with using automatic video fixing means on the example of individual sections of the road network of the Serpukhov district, Moscow region. Also in the article, a list of necessary equipment and software

Study on road sign recognition in LabVIEW

Road and traffic sign identification is a field of study that can be used to aid the development of in-car advisory systems. It uses computer vision and artificial intelligence to extract the road signs from outdoor images acquired by a camera in uncontrolled lighting conditions where they may be occluded by other objects, or may suffer from problems such as color fading, disorientation, variations in shape and size, etc. An automatic means of identifying traffic signs, in these conditions, can make a significant contribution to develop an Intelligent Transport Systems (ITS) that continuously monitors the driver, the vehicle, and the road. Road and traffic signs are characterized by a number of features which make them recognizable from the environment. Road signs are located in standard positions and have standard shapes, standard colors, and known pictograms. These characteristics make them suitable for image identification. Traffic sign identification covers two problems: traffic sign detection and traffic sign recognition. Traffic sign detection is meant for the accurate localization of traffic signs in the image space, while traffic sign recognition handles the labeling of such detections into specific traffic sign types or subcategories [1].

Special issue on bio-ontologies and phenotypes.

The bio-ontologies and phenotypes special issue includes eight papers selected from the 11 papers presented at the Bio-Ontologies SIG (Special Interest Group) and the Phenotype Day at ISMB (Intelligent Systems for Molecular Biology) conference in Boston in 2014. The selected papers span a wide range of topics including the automated re-use and update of ontologies, quality assessment of ontological resources, and the systematic description of phenotype variation, driven by manual, semi- and fully automatic means.

Laser-based measuring method for mean joint faulting value of concrete pavement

To realize the critical assessment of concrete joint faulting, a laser-based 3D detection system (Laser-based Joint Faulting Meter, LJFM) was designed and a calculation method was proposed. The 3D information of joint faulting was obtained based on laser triangulation method. Automatic threshold determination method and mean joint faulting value calculation method were proposed based on the double peak characteristic of joint faulting 3D height histogram. To verify the precision and accuracy of the LJFM, indoor and field experiments were designed and conducted. Repeated experiments of simulated joint faulting in indoor experiment show that measurement results of the LJFM has lower coefficients of variation, better reproducibility and higher stability than that of the Level. In indoor and field experiments of different joint faulting, the coefficient of determination between the LJFM and the Level are 0.944 and 0.958 separately while the correlation coefficients between them are 0.9761and 0.979 (both significant at p<0.05 level), respectively, indicating the good repeatability of the LJFM and the high correlation of the two methods. Experimental results indicate that measuring mean joint faulting value using LJFM has high stability and accuracy.

Iconic Meaning in Music: An Event-Related Potential Study.

Although there has been extensive research on the processing of the emotional meaning of music, little is known about other aspects of listeners’ experience of music. The present study investigated the neural correlates of the iconic meaning of music. Event-related potentials (ERP) were recorded while a group of 20 music majors and a group of 20 non-music majors performed a lexical decision task in the context of implicit musical iconic meaning priming. ERP analysis revealed a significant N400 effect of congruency in time window 260-510 ms following the onset of the target word only in the group of music majors. Time-course analysis using 50 ms windows indicated significant N400 effects both within the time window 410-460 ms and 460-510 ms for music majors, whereas only a partial N400 effect during time window 410-460 ms was observed for non-music majors. There was also a trend for the N400 effects in the music major group to be stronger than those in the non-major group in the sub-windows of 310-360ms and 410-460ms. Especially in the sub-window of 410-460 ms, the topographical map of the difference waveforms between congruent and incongruent conditions revealed different N400 distribution between groups; the effect was concentrated in bilateral frontal areas for music majors, but in central-parietal areas for non-music majors. These results imply probable neural mechanism differences underlying automatic iconic meaning priming of music. Our findings suggest that processing of the iconic meaning of music can be accomplished automatically and that musical training may facilitate the understanding of the iconic meaning of music.

Process Mining by using Event Logs

Process mining techniques have usual notable attention within the literature for their ability to help within the redesign of complex processes by mechanically discovering models that specify the events registered in some log traces provided as input. Process mining refers to the extraction of process models from event logs. Now real-life processes tend to be less structured and a lot of flexible. Traditional process mining algorithms have issues dealing with such unstructured processes and generate “spaghetti-like process models that are exhausting to understand. An approach to beat this is often to cluster process instances specified every of the ensuing clusters correspond to coherent sets of process instances which will every be adequately represented by a process model. To overcome these issues projected system aims to produce associate automatic means for code engineers to get mined models from systematic event logs specification embrace drawback finding, operating to learn others and technical challenge. This technique at first converts the Systematic Event Logs into some intermediate type like translated tokenized log file and keyword filtered log file. Then this filtered log file format is analyzed to extract the knowledge like Similarity matrix, Frequency count, Most read/write information, database queries and these event logs data measure accustomed build the clusters. Any system would generates the clusters using ActiTraC algorithm to produce refined description of generated models therefore incorrectness and additional overhead in analysis part of model development is removed to extended extent. This is supported on an repetitious, graded, refinement of the process model, where, at every step, traces sharing similar behavior patterns are clustered along and equipped with a specialized schema. The formula guarantees that every refinement results in an progressively sound model, so attaining a monotonic search.

A Novel Method to Improve Weld Uniformity by Automatic Control of Current Intensity

This study aims to develop an automatic arc welding system capable of welding irregular and moving joints which are difficult to weld manually. The motivation of this research comes from the need to produce uniforms weldings on regular and irregular surfaces by automatic means. The system consists of a personal computer equipped with A/D interface card connected to an electric current sensor and two electric motors controlled via electronic circuits. A control program receives the electric current measurements and makes the necessary calculations for the correct vertical motion of the welding electrode. A modified PID controller was used to control the process dynamics. An X-Y mechanism with two electric motors was developed to control the motion of the electrode as well as the work piece. A comparison was made between two welded specimens; one was welded by traditional manual method and, the second by using automatic control mechanism. The weld distribution through the welded joints was compared. Different irregular joints were also compared for the manual and automatic control welding methods. It was found that the control system works with an accuracy of ±28 µm for a 1.01 mm weld thickness of a flat weld by controlling the current at 60 A within a standard deviation of ±1.45 A. As a result welds with uniform thickness were obtained. The uniform heating further contributes to a uniform microstructure in the weld area. The implications of this research will be enhanced automation of the welding process in industry, with improved control over its uniformity, especially while welding around circular objects and during vibrating or unstable conditions.