Recognition Stage Research Articles

Perilaku Konsumen dalam Membeli Kopi di Masa Pandemi Covid-19 pada Coffee Shop Kota Padang

The Covid-19 pandemic has an impact on coffee consumer behavior, especially consumers who make purchases at coffee shops. This study aims to determine the motivation of consumers to buy coffee and identify the decision-making process of consumers buying coffee at coffee shops in Padang City during a pandemic. The method used is a survey method on 100 consumers in 10 coffee shops (Coffee Shops Janji Jiwa, Rasa Coffee, Kopi dari Hari, Kupi Batigo, Rimbun Espresso & Brew Bar, Kubik Koffie, Bacarito Kopi, Dua Pintu Coffee, Lalito Coffee Bar, V Coffee) in Padang City. These variables are the motivation to buy and stage of the consumer decision proses (need recognition, Information Search, evaluation of alternatives, purchase decision and post-purchase evaluasion). Data were analyzed using descriptive statistics from the frequency distribution table. The results showed that the motivation of consumers to buy coffee in coffee shops was due to the comfortable atmosphere, eliminating boredom and the taste of coffee with the most purchased types of coffee, namely espresso based lattes (47%) and manual brew cold brew. (29%). In the process of making purchasing decisions at the need recognition stage, the benefit that consumers want to seek is good taste (60%). At the information search stage, most coffee purchases are based on information from friends (63%) and social media influence (67%). The alternative evaluation stage, consumers choose the comfort of the place (53%). At the stage of the purchasing process, consumers choose a good taste of the product, a comfortable place with attention to health regulations. The post-purchase stage is mostly satisfied with the coffee purchased and 92% of respondents will buy again

Kinetic Analysis of the Interaction of Nicking Endonuclease BspD6I with DNA.

Nicking endonucleases (NEs) are enzymes that incise only one strand of the duplex to produce a DNA molecule that is ‘nicked’ rather than cleaved in two. Since these precision tools are used in genetic engineering and genome editing, information about their mechanism of action at all stages of DNA recognition and phosphodiester bond hydrolysis is essential. For the first time, fast kinetics of the Nt.BspD6I interaction with DNA were studied by the stopped-flow technique, and changes of optical characteristics were registered for the enzyme or DNA molecules. The role of divalent metal cations was estimated at all steps of Nt.BspD6I–DNA complex formation. It was demonstrated that divalent metal ions are not required for the formation of a non-specific complex of the protein with DNA. Nt.BspD6I bound five-fold more efficiently to its recognition site in DNA than to a random DNA. DNA bending was confirmed during the specific binding of Nt.BspD6I to a substrate. The optimal size of Nt.BspD6I’s binding site in DNA as determined in this work should be taken into account in methods of detection of nucleic acid sequences and/or even various base modifications by means of NEs.

Macrocyclic Receptors for Identification and Selective Binding of Substrates of Different Nature.

Molecular recognition of host/guest molecules represents the basis of many biological processes and phenomena. Enzymatic catalysis and inhibition, immunological response, reproduction of genetic information, biological regulatory functions, the effects of drugs, and ion transfer—all these processes include the stage of structure recognition during complexation. The goal of this review is to solicit and publish the latest advances in the design and sensing and binding abilities of porphyrin-based heterotopic receptors with well-defined geometries, the recognition ability of which is realized due to ionic, H-bridge, charge transfer, hydrophobic, and hydrophilic interactions. The dissection of the considered low-energy processes at the molecular scale expands our capabilities in the development of effective systems for controlled recognition, selective delivery, and prolonged release of substrates of different natures (including drugs) to their sites of functioning.

Elements of reservoir simulation for tight gas reservoirs with water influx

At present, gas obtained from unconventional deposits plays an important role in the global economy as an energy factor. The simulation of the exploitation of this type of deposits is very complex and requires an individual approach for each case, which is extremely inspiring and interesting, therefore this article attempts to deal with the problem of modeling the extraction of natural gas from tight unconventional deposits. Extraction of tight gas requires the use of measures that stimulate this process and requires an unusual approach both at the stage of deposit recognition, its drilling and exploitation. Using computer programs, more and more accurate models are developed taking into account almost all known processes occurring in the deposits during exploitation, which significantly influences the better selection of parameters of wells and operations that intensify the production, and thus improves the results of exploitation.

Analisis Perilaku Konsumen Dalam Keputusan Pembelian Produk Celebrity Endorser

The use of celebrity as an endorser influences the consumption psychology of consumer. The purpose of this research was to analyze the factors considered by the consumers, the steps of purchase decision made by the consumers and consumer behavior in making decision after purchasing of Semarang Roru Cake. The data collection techniques used were observation and interview. The validity of the data using regular observation, triangulation, peer review and detailed description. Data reduction, presentation, and conclusion were the data analysis techniques. The result showed that consumer behavior in making purchase decision on Celebrity Endorser Products (A Case Study: The consumer of Roru Cake Outlet in Semarang)”. Consisted of seven factors that influenced the consumers before buying the cake, they were Product Factor, Price Factor, Promotion Factor, Personal Factor, Social Factor, Cultural Factor and Environmental and Situation Factor. There were several stages of purchase decision made by Semarang Roru Cake consumers, they included the stages of need recognition, information searching, alternative evaluation, purchasing decision and post-purchase behavior. In purchase behavior, every consumer was different in deciding what to buy, complex buying behavior, purchasing behavior that reduced the inconvenience, purchasing behavior due to purchasing habit and behavior which seeked for the variations.

Efficient Face Recognition System for Operating in Unconstrained Environments.

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. Recently, several deep neural networks algorithms have been developed to achieve state-of-the-art performance on this task. The present work was conceived due to the need for an efficient and low-cost processing system, so a real-time facial recognition system was proposed using a combination of deep learning algorithms like FaceNet and some traditional classifiers like SVM, KNN, and RF using moderate hardware to operate in an unconstrained environment. Generally, a facial recognition system involves two main tasks: face detection and recognition. The proposed scheme uses the YOLO-Face method for the face detection task which is a high-speed real-time detector based on YOLOv3, while, for the recognition stage, a combination of FaceNet with a supervised learning algorithm, such as the support vector machine (SVM), is proposed for classification. Extensive experiments on unconstrained datasets demonstrate that YOLO-Face provides better performance when the face under an analysis presents partial occlusion and pose variations; besides that, it can detect small faces. The face detector was able to achieve an accuracy of over 89.6% using the Honda/UCSD dataset which runs at 26 FPS with darknet-53 to VGA-resolution images for classification tasks. The experimental results have demonstrated that the FaceNet+SVM model was able to achieve an accuracy of 99.7% using the LFW dataset. On the same dataset, FaceNet+KNN and FaceNet+RF achieve 99.5% and 85.1%, respectively; on the other hand, the FaceNet was able to achieve 99.6%. Finally, the proposed system provides a recognition accuracy of 99.1% and 49 ms runtime when both the face detection and classifications stages operate together.

Jusnaturalism as the foundation of the concept of international protection of human rights in H. Lauterpacht’s works

Prominent international law scholar H. Lauterpacht devoted most of his exploratory work to the issue of human rights in international law.This article reveals H. Lauterpacht’s views on the role of international law in the recognition and consolidation of human rights and the role of jusnaturalism as the basis of international human rights law. Analyzing the works by H. Lauterpacht, we can say that the scholar was the founder of international human rights law. Natural law and natural human rights, according to H. Lauterpacht, have been the unchanging basis of human rights of all times.The origins and periodization of jusnaturalism in the works of leading international law scholar are considered. The main statements of the representatives of the natural law concept of different times, in particular, the basic ideas in the works of Socrates, Aulis Aarnio, Francisco de Vitoria, Francisco Suarez, Alberico Gentili, Thomas Hobbes, Samuel von Pufendorf, Hugo Grotius are outlined.The views of prominent philosophers are the foundation of the concept of jusnaturalism. 
 Numerous supporters of the concept of natural law in different periods of history testify to its importance at every stage of human rights development.International law in this matter is a kind of second stage of recognition and protection of human rights, after recognition in the national law of states.International law is designed to consolidate the rights granted by nature to the human in the international arena.H. Lauterpacht saw the real recognition and protection of human rights by enshrining them in an international document signed by all countries of the world.The scientist proposed a draft international document on the recognition of human rights at the international level called International Bill of the Rights of Man. The provisions proposed in this document were later enshrined in international instruments such as the Universal Declaration of Human Rights of 1948, the International Covenant on Economic, Social and Cultural Rights of 1966 and the International Covenant on Civil and Political Rights of 1966.

PROFILE OF STUDENTS’ COMPUTATIONAL THINKING BASED ON SELF-REGULATED LEARNING IN COMPLETING BEBRAS TASKS

Bebras task is a problem solving problem that integrates computational thinking in it, which the stages in computational thinking consist of: decomposition, abstraction, algorithm, and pattern recognition. This study aims to describe the profile of student’s computational thinking based on the level of self-regulated learning in completing bebras task. This study is a qualitative-descriptive study with three research subjects based on the level of students’ self-regulated learning, namely high self-regulated learning, medium self-regulated learning, and low self-regulated learning. The results of this study indicate that students with different levels of self-regulated learning have different computational thinking ability in completing bebras task. Student with high level of self-regulated learning can reach the stages of decomposition, abstraction, algorithm, and pattern recognition. Student with medium level of self-regulated learning can reach the stages of decomposition, absraction, and algorithm. Student with low level of self-regulated learning can reach the stage of decomposition only. Student with low level of self-regulated learning do not yet reflect independence in learning.

Pattern Recognition of Human Face With Photos Using KNN Algorithm

A Facial photos in today's era are widely used as a media for identity recognition, but not many computer applications provide identity recognition of face photos that contain the names of the photo owners, to make an a prototype the sistems use a KNN algorithm, this algorithm work is by classifying the closest object and grouping it on predetermined objects. In this paper, the object is a face photo where the KNN algorithm will be used to classify the facial patterns contained in the photo. The stages in pattern recognition, starting from preprocessing, feature extraction and then classification. In addition to using the KNN algorithm for data classification, photo of faces will be detected and stored the T-Zone area and frontal face. In this paper 11 images used for data testing and the accuracy will be calculated using a recognition algorithm. The results of this paper are a facial recognition program using python that can display faces with a validity level of 82%.

Effect of voice alarms on temporal characteristics of the evacuation process inside metro train carriages: An experiment study

Controlled experiments involved more than 700 participants inside a metro train model were conducted to investigate effects of voice alarms on the evacuation process. The process was divided into 4 stages from the perspective of collective behavior: the recognition stage (from the emergency happens to the first person standing up), the response stage (from the first person standing up to the last person standing up), the transfer stage (from the last person standing up to the first person leaving the carriage), and the queue stage (from the first person leaving the carriage to the last person leaving the carriage). Results showed that the mean evacuation time of all experiments was 77.61 s, and mean percentages of the four stages were 16.89%, 12.80%, 30.67% and 30.76%, respectively. When focusing on a specific stage, a shorter voice alarm cycle, a closer voice source and a more rapid tone resulted in a shorter recognition time. In the response stage, the response order was mainly affected by physical distance. A closer and more directive voice guided evacuees to find the exit more quickly, and further reduced the transfer time. Opening doors by using emergency devices took about 75% of the transfer time on average. Voice alarm didn’t play a key role in the queue stage, and the queue time was mainly affected by the number and the width of available doors. The results provide references for developing models and designing Public Announcement system of a metro train.

Pembelajaran Al-Qur’an dengan Metode An-Nahdliyah pada Era Pandemi Covid-19 (Studi Kasus di TPQ Al-Mubarok Dusun Sri Lestari Kampung Sriwijaya Mataram)

The method is an important part of the learning component, including learning the Qur'an. Uniquely, in the Bandar Mataram District area, they began to apply a "new" method in teaching the Koran to children, namely the an-Nahdhiyah method. This study seeks to describe the application of the an-Nahdliyah method in learning the Koran in the pandemic era, with the research background at TPQ Al-Mubarok, Sri Lestari Hamlet, Sriwijaya Village, Mataram. This empirical research uses a qualitative approach with a case study method. Direct observation techniques and structured interviews were used to obtain research data. Furthermore, the data were analyzed using data reduction techniques, data presentation and conclusion drawing. Finally, test the validity of the data through triangulation techniques. The results of this study indicate that (1) the use of the an-Nahdliyah method is quite effective in providing guidance and learning of the Qur'an to children, because the method is fun, not boring, and uses the beat of a classical stick; and (2) the application of the an-Nahdliyah method is carried out in 3 (three) stages, namely the letter recognition stage, the stage of understanding the beat as murottal reading, and the stage of reading together.

Using intentional and incidental encoding instructions to test the transfer inappropriate processing shift account of verbal overshadowing

ABSTRACT A major assumption of the transfer inappropriate processing shift account of verbal overshadowing is that poor recognition performance is due to competing and inappropriate processes used during the encoding, recall, and recognition stages. The present study tested the TIPS account. Participants watched a video of a burglary during an encoding task and attempted to identify the burglar from a photo lineup during a recognition task. Encoding and recall instructions were varied between participants to encourage featural or configural processes at the encoding and recall stages. Prior to the encoding task, some participants were given intentional encoding instructions to focus on the face of the person in the video. Other participants were given incidental encoding instructions or not told anything. Prior to the recall task, some participants were given instructions to recall the description of the burglar, and other participants were not. Results revealed an interaction between encoding and recall instructions on recognition performance.

Recognition of Human Facial Expressions Using DCT-DWT and Artificial Neural Network

Facial expressions are a term that expresses a group of movements of the facial fore muscles that is related to one's own human emotions. Human–computer interaction (HCI) has been considered as one of the most attractive and fastest-growing fields. Adding emotional expression’s recognition to expect the users’ feelings and emotional state can drastically improves HCI. This paper aims to demonstrate the three most important facial expressions (happiness, sadness, and surprise). It contains three stages; first, the preprocessing stage was performed to enhance the facial images. Second, the feature extraction stage depended on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) methods. Third, the recognition stage was applied using an artificial neural network, known as Back Propagation Neural Network (BPNN), on database images from Cohen-Kanade. The method was shown to be very efficient, where the total rate of recognition of the three facial expressions was 92.9%.

Real-Time Attendance and Feedback System

Face is the representation of one’s identity. Hence, we have proposed an automated student attendance system based on face recognition. Face recognition system is very useful in life applications especially for attendance system. In our proposed approach, firstly, video framing is performed by activating the camera through a user-friendly interface. In the pre-processing stage, scaling of the size of images is performed, if necessary, in order to prevent loss of information. In face recognition stage, enhanced local binary pattern (LBP) and principal component analysis (PCA) is applied correspondingly in order to extract the features from facial images. Another way of marking the attendance is fingerprint recognition. To mark the attendance students simply have to give the fingerprint impression in fingerprint scanner module. Finally, the attendance of the recognized student will be marked and saved in the excel file. The student who is not registered will also be able to register on the spot and notification will be given if students sign in more than once. Whenever seminar is completed then a link is sent on email. It includes the information in terms of feedback. When student fills the feedback form then analysis of overall session is done.

First and Second Language at Hand: A Chronometric Transcranial-Magnetic Stimulation Study on Semantic and Motor Resonance

According to embodied theories, motor and language processing bidirectionally interact: Motor activation modulates behavior in lexico-semantic tasks (semantic resonance), and understanding motor-related words entails activation of the corresponding motor brain areas (motor resonance). Whereas many studies investigated such interaction in the first language (L1), only few did so in a second language (L2), focusing on motor resonance. Here, we directly compared L1 and a late L2, for the first time both in terms of semantic and motor resonance and both in terms of magnitude and timing, by taking advantage of single-pulse TMS. Twenty-five bilinguals judged, in each language, whether hand motor-related ("grasp") and non-motor-related verbs ("believe"), were physical or mental. Meanwhile, we applied TMS on the hand motor cortex at 125, 275, 350, and 500 msec post verb onset, and recorded behavioral responses and TMS-induced motor evoked potentials. TMS induced faster responses for L1 versus L2 motor and nonmotor verbs at 125 msec (three-way interaction β = -0.0442, 95% CI [0.0814, -0.0070]), showing a semantic resonance effect at an early stage of word processing in L1 but not in L2. Concerning motor resonance, TMS-induced motor evoked potentials at 275 msec revealed higher motor cortex excitability for L2 versus L1 processing (two-way interaction β = 0.095, 95% CI [0.017, 0.173]). These findings confirm action-language interaction at early stages of word recognition, provide further evidence that L1 and L2 are differently embodied, and call for an update of existing models of bilingualism and embodiment, concerning both language representations and processing.

Research on identification algorithm of railway freight train number based on GSO-BP algorithm

In the stage of character recognition, it is necessary to select the algorithm that matches the character features of train number. The train number characters of railway freight trains are all printed in straight style. Compared with handwritten characters, each character has relatively fixed features. However, due to the complex operation environment of railway freight trains, the character regions are cracked, tilted and partially missing. Therefore, it is very important to select an algorithm with good robustness. In view of the above analysis, this paper proposes a research on railway freight car number recognition algorithm based on GSO-BP algorithm, the experimental results show that the proposed method is effective in character recognition.

Recognition and Classification of Glomerular Pathological Images Based on Deep Learning

<p indent=0mm>The identification and classification of glomeruli in pathological sections is the key to diagnosing the degree and type of renal lesions. In order to solve the problem of glomerular recognition and classification, a complete glomerular detection and classification framework based on deep learning is designed. Glomeruli are detected and classified in the entire slice image. The framework includes four stages of glomerular recognition. In the first stage of scanning window generation, a new network framework, RGNet, is designed to initially determine the possible location of glomeruli. In the second stage of detection and coarse classification, Faster R-CNN is improved for glomerular data. In the third stage, the NMS-Lite algorithm is designed based on the NMS algorithm to merge the detected glomeruli. In the fourth stage of fine classification, two neural networks are trained using data augmentation to classify the degree of glomerular lesions. The experimental results has show that the glomerulus detection method proposed in this paper has achieved comparable accuracy on the test set with similar methods, and to a certain extent solves the problem that similar types of glomeruli are difficult to distinguish.

Proposition of an Innovation Center in Santa Rosa, RS, Brazil, by means of the Via Cycle Methodology

A successful Innovation Center (IC) needs to be closely related to the territory in which it is inserted and have its functions aligned with regional development, the supply of facilities, services and resources and the promotion of culture and the innovation and entrepreneurship connection. This study aimed to develop stages of recognition and mapping of elements that underlie the implementation of an IC in the city of Santa Rosa, state of Rio Grande do Sul, Brazil, among them the challenges related to innovation faced in the city; the personas and actors involved; the identification of solutions; the potential stakeholders partners; the proposition of an action plan; and the presentation of this plan to those involved in the process. National and international ICs were analyzed as references for the study, the main ICs of Rio Grande do Sul, the state where Santa Rosa is located were mapped, and the innovation pathway of the city was addressed. Using the Via Cycle Methodology, the interactions between the actors and stakeholders that deal with innovation were studied, the challenges faced were identified, and a solution was proposed to face the perceived challenges. For this result, strategies capable of encompassing the various actors mapped and involved in the structuring pillars of the territory (education, governance, public power, and culture) were suggested, with the intention of proposing the implementation of the IC. The breakdown of these pillars into detailed actions resulted in a significant educational, social, cultural, and economic potential, whose details can serve as references to boost innovation-oriented experiences, meet technological needs, and strengthen various dimensions.

Early Top-Down Modulation in Visual Word Form Processing: Evidence From an Intracranial SEEG Study.

Visual word recognition, at a minimum, involves the processing of word form and lexical information. Opinions diverge on the spatiotemporal distribution of and interaction between the two types of information. Feedforward theory argues that they are processed sequentially, whereas interactive theory advocates that lexical information is processed fast and modulates early word form processing. To distinguish between the two theories, we applied stereoelectroencephalography (SEEG) to 33 human adults with epilepsy (25 males and eight females) during visual lexical decisions. The stimuli included real words (RWs), pseudowords (PWs) with legal radical positions, nonwords (NWs) with illegal radical positions, and stroked-changed words (SWs) in Chinese. Word form and lexical processing were measured by the word form effect (PW versus NW) and lexical effect (RW versus PW), respectively. Gamma-band (60 ∼ 140 Hz) SEEG activity was treated as an electrophysiological measure. A word form effect was found in eight left brain regions (i.e., the inferior parietal lobe, insula, fusiform, inferior temporal, middle temporal, middle occipital, precentral and postcentral gyri) from 50 ms poststimulus onset, whereas a lexical effect was observed in five left brain regions (i.e., the calcarine, middle temporal, superior temporal, precentral, and postcentral gyri) from 100 ms poststimulus onset. The two effects overlapped in the precentral (300 ∼ 500 ms) and postcentral (100 ∼ 200 ms and 250 ∼ 600 ms) gyri. Moreover, high-level regions provide early feedback to word form regions. These results demonstrate that lexical processing occurs early and modulates word form recognition, providing vital supportive evidence for interactive theory.SIGNIFICANCE STATEMENT A pivotal unresolved dispute in the field of word processing is whether word form recognition is obligatorily modulated by high-level lexical top-down information. To address this issue, we applied intracranial SEEG to 33 adults with epilepsy to precisely delineate the spatiotemporal dynamics between processing word form and lexical information during visual word recognition. We observed that lexical processing occurred from 100 ms poststimulus presentation and even spatiotemporally overlapped with word form processing. Moreover, the high-order regions provided feedback to the word form regions in the early stage of word recognition. These results revealed the crucial role of high-level lexical information in word form recognition, deepening our understanding of the functional coupling among brain regions in word processing networks.

Bird expertise does not increase motion sensitivity to bird flight motion.

While motion information is important for the early stages of vision, it also contributes to later stages of object recognition. For example, human observers can detect the presence of a human, judge its actions, and judge its gender and identity simply based on motion cues conveyed in a point-light display. Here we examined whether object expertise enhances the observer's sensitivity to its characteristic movement. Bird experts and novices were shown point-light displays of upright and inverted birds in flight, or upright and inverted human walkers, and asked to discriminate them from spatially scrambled point-light displays of the same stimuli. While the spatially scrambled stimuli retained the local motion of each dot of the moving objects, it disrupted the global percept of the object in motion. To estimate a detection threshold in each object domain, we systematically varied the number of noise dots in which the stimuli were embedded using an adaptive staircase approach. Contrary to our predictions, the experts did not show disproportionately higher sensitivity to bird motion, and both groups showed no inversion cost. However, consistent with previous work showing a robust inversion effect for human motion, both groups were more sensitive to upright human walkers than their inverted counterparts. Thus, the result suggests that real-world experience in the bird domain has little to no influence on the sensitivity to bird motion and that birds do not show the typical inversion effect seen with humans and other terrestrial movement.