Recognition Phase Research Articles

The Effect of Contextual Cueing Effect for Configuration of Numbers on Users’s Search Time of Target Number : Based on PIN Authentication

모바일 환경에서 PIN(personal identification number) 인증 방식을 사용할 경우, 보안에서 발생할 수 있는 여러 취약점이 있다. 이 취약점을 보완하기 위해 PIN 입력 시 나타나는 숫자 배열에 맥락단서효과(contextual cueing effect)를 활용하여 사용자 인증 방법으로 사용될 수 있는지 알아보았다. 연구 1에서는 8×4 행렬에 0~9까지의 10개의 숫자가 무선적으로 배열될 때 맥락단서효과가 나타날 수 있는지 확인하였다. 연구 2에서는 훈련 단계를 통해 숫자 배열의 맥락을 학습한 후, 검사 단계에서 반복 화면과 새로운 화면이 무선적으로 제시될 때, 표적숫자를 찾을 때 탐색 시간의 차이가 있는지 살펴보았다. 그 후 재인 단계에서는 참가자들이 표적숫자의 위치에 의식적으로 접근할 수 있는지 알아보았다. 그 결과, 연구 1과 2를 통해 숫자 배열에 대한 맥락단서효과가 나타날 수 있음을 확인하였다. 또한, 연구 2를 통해 반복 화면과 새로운 화면이 무선적으로 제시되는 경우에도 탐색 시간의 차이가 확인되었으며, 참가자들이 반복 화면의 표적숫자의 위치에 외현적으로 접근이 가능한 것으로 나타났다. 이에 본 연구는, 맥락단서효과를 적용한 PIN입력이 사용자를 인증하는 추가 요인으로 사용될 가능성이 있음을 제안한다.When using PIN (personal identification number) authentication method in a mobile environment, several security vulnerabilities might occur. To remedy this vulnerabilities, we investigated whether users can be authenticated by PIN authentication adopting contextual cueing effect. In the study 1, we tested whether the contextual cueing effect would takes place when numbers from 0 to 9 randomly appeared in the 8 x 4 matrix. In the study 2, after the training phase, we compared search reaction times on repeated display with that on nonrepeated displays when repeated and nonrepeated displays randomly appeared in the test phase. The results showed that contextual cueing effect for configuration of numbers was found in the study 1,2. Further, participants found the target numbers more faster on repeated displays than on the nonrepeated displays when repeated and nonrepeated displays randomly appeared in test phase. In recognition phase, participants were able to access the location of target numbers on repeated displays explicitly. The present results suggest that the contextual cueing effect can be found in the PIN input display where the numbers were randomly presented.

Collective Action for Sustainable Rural Livelihoods: The Case of Korchi Mahagramsabha

Community Forest Resource Rights (CFR rights) under The Scheduled Tribes and Other Traditional Forest Dwellers (Recognition of Forest Rights) Act, 2006 give forest-dwelling communities a unique opportunity to extract, manage, and sell Nontimber Forest Products (NTFPs) from their forests. This recognition of CFR rights has enabled forest dwelling communities to develop diverse mechanisms for transparent, equitable, and sustainable processes of NTFP procurement and marketing. The Korchi Mahagramsabha, a coalition of local-level institutions, is an example of one such mechanism that emerged in the post-CFR recognition phase in ‘Gadchiroli’, Maharashtra. The study details the mechanisms for collective action and also the factors that facilitate and impede the functioning of such mechanisms. Given the immediate positive socioeconomic impact, such coalitions/collective action mechanisms and their activities need to be supported in order to make them fully functional and empowered.

Negative expectations influence behavioral and ERP responses in the subsequent recognition of expectancy-incongruent neutral events.

Previous studies have shown that expectancy incongruence in emotional stimuli influences the encoding (i.e., the first stage of memory processing) of the stimuli. However, it is unknown about whether expectancy incongruence influences later stages of memory processing, such as recognition. To this end, expectancy cues were presented prior to emotional pictures. Most often, the cues accurately indicated the emotional consequences of the pictures, but in some cases the consequence was incongruent with the expectations, and a picture from another emotional category was presented. Afterward, participants completed an unexpected recognition task in which old and novel pictures were not preceded by expectancy cues. The results showed that, in the encoding phase, expectancy incongruence reduced response accuracy when categorizing pictorial emotions, and the effect was smaller for neutral pictures than for negative pictures. ERP results showed stronger and weaker responses to expectancy incongruent pictures compared to congruent pictures in time ranges related to the encoding-related early and middle late positive potential (LPP), respectively. In the subsequent recognition phase, d' scores were higher for incongruent neutral pictures than for congruent ones. Expectancy incongruence enlarged the P2 response but reduced the recognition-related early LPP response for neutral pictures. However, effects of expectancy incongruence were not seen for negative pictures. Therefore, the findings in the present study indicate that negative expectations influence the later recognition of expectancy incongruent neutral events, whereas negative events are more resistant to the effects of expectation incongruence.

Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction.

Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed malignant or benign - depending upon the severity of the infection and the current stage. The conventional methods require a detailed physical inspection by an expert dermatologist, which is time-consuming and imprecise. Therefore, several computer vision methods are introduced lately, which are cost-effective and somewhat accurate. In this work, we propose a new automated approach for skin lesion detection and recognition using a deep convolutional neural network (DCNN). The proposed cascaded design incorporates three fundamental steps including; a) contrast enhancement through fast local Laplacian filtering (FlLpF) along HSV color transformation; b) lesion boundary extraction using color CNN approach by following XOR operation; c) in-depth features extraction by applying transfer learning using Inception V3 model prior to feature fusion using hamming distance (HD) approach. An entropy controlled feature selection method is also introduced for the selection of the most discriminant features. The proposed method is tested on PH2 and ISIC 2017 datasets, whereas the recognition phase is validated on PH2, ISBI 2016, and ISBI 2017 datasets. From the results, it is concluded that the proposed method outperforms several existing methods and attained accuracy 98.4% on PH2 dataset, 95.1% on ISBI dataset and 94.8% on ISBI 2017 dataset.

Contextual effects of angry vocal expressions on the encoding and recognition of emotional faces: An event-related potential (ERP) study

It has been shown that stimulus memory (e.g., encoding and recognition) is influenced by emotion. In terms of face memory, event-related potential (ERP) studies have shown that the encoding of emotional faces is influenced by the emotion of concomitant context, when contextual stimuli were input from a visual modality. Behavioral studies also investigated the effect of contextual emotion on subsequent recognition of neutral faces. However, there might be no studies ever investigating the context effect on face encoding and recognition, when contextual stimuli were input from other sensory modalities (e.g., an auditory modality). Additionally, it may be unknown about the neural mechanisms underlying context effects on recognition of emotional faces. Therefore, the present study aimed to use vocal expressions as contexts to investigate whether contextual emotion influences ERP responses during face encoding and recognition. To this end, participants in the present study were asked to memorize angry and neutral faces. The faces were presented concomitant with either angry or neutral vocal expressions. Subsequently, participants were asked to perform an old/new recognition task, in which only faces were presented. In the encoding phase, ERP results showed that compared to neutral vocal expression, angry vocal expressions led to smaller P1 and N170 responses to both angry and neutral faces. For angry faces, however, late positive potential (LPP) responses were increased in the angry voice condition. In the later recognition phase, N170 responses were larger for neutral-encoded faces that had been presented with angry compared to neutral vocal expressions. Preceding angry vocal expression increased FN400 and LPP responses to both neutral-encoded and angry-encoded faces, when the faces showed the encoded expression. Therefore, the present study indicates that contextual emotion with regard to vocal expression influences neural responses during face encoding and subsequent recognition.

Impaired emotional memory and decision-making following primary insomnia.

Previous studies have indicated that sleep plays an important role in emotional memory and decision-making. However, very little attention has been given to emotional memory and decision-making in patients with primary insomnia (PI). We investigated whether PI influences the accuracy of emotional memory and social decision-making.We examined 25 patients with PI and 20 healthy controls (HC) using an emotional picture memory task and the Iowa Gambling Task (IGT). In the emotional picture memory task, participants completed two testing sessions: an emotional picture evaluation and a delayed recognition phase. During the emotional picture evaluation phase, participants were presented with 48 pictures with different valence (16 positive, 16 neutral, and 16 negative), which they had to evaluate for emotional valence and arousal. During the recognition phase, participants were asked to make a yes/no memory assessment of a set of pictures, which contained the 48 target pictures intermingled with 48 non-target pictures.The performance of the participants with PI was the same as that of the HC in the emotional picture evaluation task. However, the PI group showed worse recognition of the positive and neutral pictures than did the HC group, although recognition of negative pictures was similar in the 2 groups. In the IGT, participants in the PI group more frequently selected cards from the risky decks as the game progressed and selected more disadvantageous cards than did participants in the HC group after the first block.Our findings suggest that insomnia had different effects on memory, depending on the valence of the memory. Specifically, memory performance was impaired for positive and neutral items, but the recognition of negative stimuli seemed to be more resistant to the effects of insomnia. Our results also suggest that decision-making, which is known to be mediated by the ventromedial prefrontal cortex, including decision-making under conditions of uncertainty, may be vulnerable in PI.

A multifaceted intervention to improve the quality of care for patients undergoing total joint arthroplasty

Knee and hip arthroplasties vary in cost, quality and outcomes. We developed a Lean quality improvement intervention for knee and hip arthroplasty patients encompassing the recognition, readiness, restoration and recovery...

An Extended Agent Communication Framework for Rapid Reconfiguration of Distributed Manufacturing Systems

For proactive responses to customer requirements and rapidly advancing technologies, current manufacturing systems are required to be increasingly adaptable and reconfigurable. We present a communication framework between the coordinator, workstation agents, and executors to facilitate the reconfiguration process of manufacturing systems. The message structure of interagent information exchange is extended for the initiation, self-layout recognition, and control program update phases of reconfiguration. To provide more autonomy to a manufacturing system and to synchronize the updated shop floor condition, a self-layout recognition mechanism was developed based on infrared communication that enables each workstation to identify directly linked workstations and share the identified information with other shop floor entities for automatic layout change detection. Furthermore, we propose a message-based reprogramming approach to quickly update, insert, or delete command lines in the existing control program for a specific device in a workstation. We demonstrate a reconfiguration process via the communication framework in a factory testbed consisting of nine main workstations in a laboratory environment.

New procognitive enhancers acting at the histamine H3 and AMPA receptors reverse natural forgetting in mice: comparisons with donepezil and memantine in the object recognition task.

This study evaluated the procognitive effects of S 38093 (a new inverse agonist of the histaminergic H3 receptor) and S 47445 (a new α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) in 2-3-month-old Swiss mice as compared with donepezil and memantine, two main reference compounds in the treatment of Alzheimer's disease. The object recognition task allows the study of natural forgetting and is classically used in assessing drug effects on memory. Here, we show that mice exhibit significant object recognition at short (15 min) but not long (24 h) retention intervals separating the familiarization and recognition phases. S 47445 (1.0, 3.0, and 10.0 mg/kg) and S 38093 (0.3, 1.0, and 3.0 mg/kg), both administered postoperatively, 1 h before familiarization and recognition sessions, rescued memory at the long retention interval; their memory-enhancing effects were as powerful as those obtained with donepezil or memantine (1.0 and 3.0 mg/kg for both compounds). Thus, S 38093 and S 47445, detected as positive controls in the object recognition task, are promising compounds for the treatment of amnesic syndromes.

Influence of Chewing Cycles on Flavor Recognition Time in Healthy Adults.

Flavor recognition times were measured in 23 young, healthy participants of both sexes using an electromyography-based system. The participants were instructed to chew a gummy candy, which was randomly selected among six commercially available types, and to press a button immediately on flavor recognition. A total of 107 normally distributed, flavor recognition times were analyzed, with an average time of 7.5 seconds (± 2.34 seconds, standard deviation). No significant differences were found among the six types of candies in terms of recognition time. Analysis of the association between flavor recognition and chewing phase showed that 70 (65%) of the analyzed 107 recognition signals occurred between 0.2 seconds before and 0.4 seconds after the end of jaw closing. Recognition signals occurring during the jaw-opening phase prolonged its duration by an average of 21%, whereas those occurring during the jaw-closing phase did not influence it.

Devanagari ancient documents recognition using statistical feature extraction techniques

Devanagari ancient document recognition process is drawing a lot of consideration from researchers nowadays. These ancient documents contain a wealth of knowledge. However, these documents are not available to all because of their fragile condition. A Devanagari ancient manuscript recognition system is designed for digital archiving. This system includes image binarization, character segmentation and recognition phases. It incorporates automatic recognition of scanned and segmented characters. Segmented characters may include basic characters (vowels and consonants), modifiers (matras) and various compound characters (characters formed by joining more than one basic characters). In this paper, handwritten Devanagari ancient manuscripts recognition system has been presented using statistical features extraction techniques. In feature extraction phase, intersection points, open endpoints, centroid, horizontal peak extent and vertical peak extent features are extracted. For classification, Convolutional Neural Network, Neural Network, Multilayer Perceptron, RBF-SVM and random forest techniques are considered in this work. Various feature extraction and classification techniques are considered and compared to the recognition of basic characters segmented from Devanagari ancient manuscripts. A data set, of 6152 pre-segmented samples of Devanagari ancient documents, is considered for experimental work. Authors have achieved 88.95% recognition accuracy using a combination of all features and a combination of all classifiers considered in this work by a simple majority voting scheme.

Community Responses to Nature-based Tourism Promotion Materials in Indonesia

The growth of nature-based tourism has triggered further development of outdoor tourism activities by utilizing natural areas for commercial purposes. This trend is likely related to the role of tourism promotion. However, promotion is still interpreted partially as an effort to convey information persuasively to reach the phase of purchasing. In this study we used more comprehensive and integrated approach. We interpret promotion in seven phases from information sharing up until becoming a tourism promotion agent so that promotion of nature-based tourism destinations is more effective and optimal. This study is aimed at determining the role of promotion on visitor decisions to visit nature-based tourism destinations. A set of questionnaire was distributed to the community by considering the different income level, education, and occupation divided into three categories, namely, the upper, middle, and lower classes. In this study we used 100 respondents to assess printed promotional and audio-visual materials. Data were analyzed using One Score One Indicator System, Kruskal Wallis, and Dunn test. The results of the study showed that out of the seven phases that we examined, differences of opinion occurred in three phases of promotion including recognition and consideration phase, the motivation to enjoy natural tourism services, and willingness to be tourism promotion agent. This indicates differences of opinion over the factors that influence the three categories of respondents regarding the decision to visit a promoted nature-based tourism destination. Higher income shows a better response to the seven phases of the promotion definition of natural tourist destinations. We identified that internal factors including income characteristics, education, and occupation in addition to external factors of promotional materials (such as design and substance) together with attraction (destination attributes) can increase the the decision to visit nature-based tourism destinations. The management implications of this study were relate to consumer behavior on visit decisions as the important factor for the success of the destinations. Also, designing effective promotional activities that are tailored to the perceptions and motivations of consumers in nature-based tourism destinations is important.

ACTION RECOGNITION USING UNDECIMATED DUAL TREE COMPLEX WAVELET TRANSFORM FROM DEPTH MOTION MAPS / DEPTH SEQUENCES

Abstract. Accumulating the motion information from a video sequence is one of the highly challenging and significant phase in Human Action Recognition. To achieve this, several classical and compact representations are proposed by the research community with proven applicability. In this paper, we propose a compact Depth Motion Map based representation methodology with hastey striding, consisely accumulating the motion information. We extract Undecimated Dual Tree Complex Wavelet Transform features from the proposed DMM, to form an efficient feature descriptor. We designate a Sequential Extreme Learning Machine for classifying the human action secquences on benchmark datasets, MSR Action 3D dataset and DHA Dataset. We empirically prove the feasability of our method under standard protocols, achieving proven results.

Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning

Automatic License Plate Recognition (ALPR) is an important research topic in the intelligent transportation system and image recognition fields. In this work, we address the problem of car license plate detection using a You Only Look Once (YOLO)-darknet deep learning framework. In this paper, we use YOLO's 7 convolutional layers to detect a single class. The detection method is a sliding-window process. The object is to recognize Taiwan's car license plates. We use an AOLP dataset which contained 6 digit car license plates. The sliding window detects each digit of the license plate, and each window is then detected by a single YOLO framework. The system achieves approximately 98.22% accuracy on license plate detection and 78% accuracy on license plate recognition. The system executes a single detection recognition phase, which needs around 800 ms to 1 s for each input image. The system is also tested with different condition complexities, such as rainy background, darkness and dimness, and different hues and saturation of images.

(Invited) DNA Library Evolution of Carbon Nanotube Molecular Recognition

Molecular recognition is fundamental to the design of therapeutics, diagnostics, and sensors. Many of these technologies rely on natural molecular recognition elements such as antibodies, which have evolved to be highly selective for their molecular targets. However, despite their exquisite selectivity, antibody-based molecular recognition suffers from limited stability outside narrow ranges of temperature, pH, and salt concentration, and have limited ex-vivo stability. Conversely, synthetic molecular recognition has enabled detection of molecular targets over a broad range of extraphysiological conditions but with varying success in selective and sensitive detection of target analytes. Recent developments in synthetic molecular recognition include synthetic antibodies based on electrostatic pinning of bio-mimetic polymers to the surface of single-walled carbon nanotubes (SWNT), whereby the polymer serves as the molecular recognition element, and the SWNT provides a near-infrared fluorescent signal in response to analyte binding. In this and other embodiments of nanosensor platforms, the principal challenge lies in the process of identifying synthetically-produced selective and sensitive molecular recognition elements. Herein, we describe a polymer evolution-based platform, in which 10^12 unique SWNT-pinned polynucleotide polymers can be screened for their ability to bind a target analyte and provide a selective near-infrared fluorescence signal. Iterative selection of analyte-binding polymers that form a SWNT-surface-adsorbed phase for target recognition are identified through ionic desorption of sub-optimal polymers, and exponential amplification of synthetic polymers that recognize the target analyte. Next-generation sequencing identifies SWNT-polynucleotide conjugates selective and sensitive for the desired analyte. We demonstrate the utility of this platform for the evolution of a SWNT-polymer conjugate for neuromodulator serotonin, which we show to be reversible, to have a detection limit of 100 nM, and to have a dynamic range appropriate for in vivo imaging of serotonergic neuromodulation. Our platform is fundamentally generic in enabling the evolution of molecular nanosensors from synthetic polymer-nanoparticle conjugates for any desired analyte.

Recognition of isolated digits using DNN–HMM and harmonic noise model

Speech recognition is an area that is constantly developing. In this study, the authors present a new system of speech recognition applied to the Arabic language. The system proposed here is based on the harmonic plus noise model (HNM). This model is rather used in speech synthesis tasks and is known for providing excellent speech production quality. Thus, their contribution lies in replacing the conventional mel-frequency cepstrum coefficients (MFCC) parameters with a set of acoustic parameters, extracted through the HNM estimation process. The HNM model allows development of a more adapted processing by distinguishing voiced and unvoiced speech frames and by characterising the harmonic property of speech. As common, their system consists of both training and recognition phases. Deep neural networks and hidden Markov models (DNN–HMM) are used for modelling the voiced frames corresponding to the harmonic part. The DNN model is estimated with static and dynamic parameters. Moreover, the unvoiced frames, which represent the noise part of the HNM, are clustered with an HMM model. The spoken Arabic digits are used to measure the performance of the proposed recognition system and a comparison with the MFCC-based approach is performed.

Robust multi‐view videos face recognition based on particle filter with immune genetic algorithm

The task of face recognition in real-world scenarios is still a challenging one. There exist many techniques for the recognition of faces in videos. The recognition task may be computationally easier, but is susceptible to pose variations, lighting conditions etc. This paper focuses on recognition of faces from multi-view videos using the combination of particle filtering with Immune Genetic Algorithm (IGA) and HSH, which is insensitive to pose variations. Particle filtering along with the IGA efficiently track the target using immune system mechanism and then the recognition phases are carried out using HSH. For recognition of video, the ensemble feature similarity calculated which can be measured with the limiting Bhattacharyya distance of features in the Reproducing Kernel Hilbert space. The proposed system with HSH provides better performance than using Spherical Harmonics (SH) for recognising the face of the target and the performance are analysed with existing techniques for recognition of face.

Enrolment and Matching of Fingerprints using Minutiae Tree

Automated fingerprint matching is considered as the most challenging phase of fingerprint recognition since it can be affected by a variety of factors such as noise, skin condition, rotation, distortions and displacements. When there is a large database, the search time to get a matching fingerprint will be relatively high. To reduce the searching time in the database, we have proposed a minutiae tree based indexing method in this paper. The database is represented in the form of a minutiae tree and the fingerprint matching is done by visiting the nodes in the tree where the local configuration of the minutiae is stored. By using this tree structure it is found that the search time or matching time can be considerably reduced and the matching time is independent of the number of fingerprints enrolled in the database. Our framework is scalable and the experiments conducted explores its ability to find correct matches with minimal search time.

Pattern-based NN control for uncertain pure-feedback nonlinear systems

Implementing human-like learning and control for nonlinear dynamical systems operating in different control situations is an important and challenging issue. This paper presents a pattern-based neural network (NN) control strategy for nonlinear pure-feedback systems via deterministic learning (DL). Firstly, an appropriately designed adaptive neural dynamic surface controller is proposed to achieve the finite time tracking control. By analyzing the recurrent property of NN input signals, a partial persistent excitation (PE) condition for radial basis function (RBF) network is established, the implicit desired control dynamics under different control situations are accurately identified via DL in the case that the dimension of NN input is reduced. And a set of pattern-based experienced actual and virtual controllers is constructed using the learned knowledge. Secondly, to classify different control situations, when the system is operating in different control situations but controlled by current normal experienced controller, the dynamics of each subsystem are accurately identified via DL, n sets of dynamical estimators are constructed using the learned knowledge. Thirdly, in the recognition phase, n sets of residuals are achieved by comparing each set of estimators with the monitored system, sudden change in the control situation is rapidly recognized based on the principle of the earliest occurrence of the minimum residual. Finally, in the control phase, according to the recognition result, the correct experienced actual and virtual controllers will be selected to control the plant, guaranteed stability and superior control performance are achieved without any further re-adaptation online. Simulation studies are given to verify the proposed scheme can not only acquire and memorize knowledge like humans, but also reuse the learned knowledge to achieve rapid recognition and control of current control situation.

Eye Movement Dynamics Differ between Encoding and Recognition of Faces.

Facial recognition is widely thought to involve a holistic perceptual process, and optimal recognition performance can be rapidly achieved within two fixations. However, is facial identity encoding likewise holistic and rapid, and how do gaze dynamics during encoding relate to recognition? While having eye movements tracked, participants completed an encoding (“study”) phase and subsequent recognition (“test”) phase, each divided into blocks of one- or five-second stimulus presentation time conditions to distinguish the influences of experimental phase (encoding/recognition) and stimulus presentation time (short/long). Within the first two fixations, several differences between encoding and recognition were evident in the temporal and spatial dynamics of the eye-movements. Most importantly, in behavior, the long study phase presentation time alone caused improved recognition performance (i.e., longer time at recognition did not improve performance), revealing that encoding is not as rapid as recognition, since longer sequences of eye-movements are functionally required to achieve optimal encoding than to achieve optimal recognition. Together, these results are inconsistent with a scan path replay hypothesis. Rather, feature information seems to have been gradually integrated over many fixations during encoding, enabling recognition that could subsequently occur rapidly and holistically within a small number of fixations.