Search results for: positive behavior recognition

Authors: Prami Sengupta, Randall A. Cantrell, Tracy Johns

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A typical US household consumes more energy than households in other countries and is directly responsible for a considerable proportion of the atmospheric concentration of the greenhouse gases. This makes U.S. household a vital target group for energy conservation studies. Positive household behavior is central to residential energy conservation. However, for individuals to conserve energy they must not only know how to conserve energy but be also willing to do so. That is, a positive attitude towards residential conservation and an intention to conserve energy are two of the most important psychological determinants for energy conservation behavior. Most social science studies, to date, have studied the relationships between attitude, intention, and behavior by building upon socio-psychological theories of behavior. However, these frameworks, including the widely used Theory of Planned Behavior and Social Cognitive Theory, lack a spatial component. That is, these studies fail to capture the impact of the geographical locations of homeowners’ residences on their residential energy consumption and conservation practices. Therefore, the purpose of this study is to explore geospatial relationships between homeowners’ residential energy conservation-attitudes, conservation-intentions, and consumption behavior. The study analyzes residential conservation-attitudes and conservation-intentions of homeowners across 63 counties in Florida and compares it with quantifiable measures of residential energy consumption. Empirical findings revealed that the spatial distribution of high and/or low values of homeowners’ mean-score values of conservation-attitudes and conservation-intentions are more spatially clustered than would be expected if the underlying spatial processes were random. On the contrary, the spatial distribution of high and/or low values of households’ carbon footprints was found to be more spatially dispersed than assumed if the underlying spatial process were random. The study also examined the influence of potential spatial variables, such as urban or rural setting and presence of educational institutions and/or extension program, on the conservation-attitudes, intentions, and behaviors of homeowners.

Keywords: conservation-attitude, conservation-intention, geospatial analysis, residential energy consumption, spatial autocorrelation

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Authors: Chantel Snyman, Chrizanne van Eeden, Marita Heyns

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Autism spectrum disorder (ASD) is one of the most common disabilities in schools, with up to50% of children displaying behaviors that challenge, bringing about demanding teaching circumstances. The teachers and teaching assistants of such learners often experience a negative impact on their own quality of life. Research globally and in South Africa about the teachers of ASD learners and teaching interventions, especially positive psychology approaches aimed at supporting learners with ASD, is limited. The primary research aim of this study was to investigate the feasibility as well as the effect of a strength-based intervention for teachers on the behavior of their learners with ASD and on the wellbeing and self-efficacy of teachers and assistants over time. This quantitative study used a pre-experimental group design with a pre-test-post-test method for the proposed school-based intervention. Teachers and teaching assistants completed the Difficult Behavior Self-Efficacy Scale, the Mental Health Questionnaire, and the short Behaviors That Challenge Checklist for learners with ASD. The six-week intervention on character strengths was delivered by the researcher as part of Teacher Staff Development. Results were generally significant on a practical level (based on practical effect sizes), which indicate that the intervention had a visible effect on behaviors that challenge. Research scores over time suggested a positive effect of the intervention in the well-being of participants and an overall positive effect on behaviors that challenge of ASD learners. Results showed that the character strengths intervention shows promise as a simple but effective intervention for teachers and teaching assistants, with positive effects for learners and teaching staff in the ASD classroom. It is recommended that this intervention should be repeated over a longer period of time and with a larger sample to determine its validity.

Keywords: autism spectrum disorder (ASD), behavior that challenge, character strengths, disabilities, self-efficacy, teachers, teaching assistants, well-being

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Authors: Sung-Chun Tsai, Li-Fang Chou, Chun-Jung Tseng

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How relational identity of leader-subordinate relationship affects behavior of both parties is getting more and more attentions in recent years. Different from past studies on leader-subordinate relationship taking viewpoint of self-concept or interaction between categories, we took perspective of social cognitive schema with special focus on the cognition structure and category content of the vertical leader-subordinate relationship. This study firstly clarified the dimensions and contents of cognitive structure of vertical leader-subordinate relationship. By using two dimensions of “equal/unequal” and “close/distant”, the contents of the leader-subordinate relational identity (LSRI) are classified into four categories: communal affection RI (equal and close), instrumental exchange RI (equal but distant), care-repay RI (unequal but close), and authority-obedience RI (unequal and distant). Furthermore, according to the four dimensions of leader-subordinate relational identity, we explored: (1) how a leader’s LSRI leads to paternalistic leadership; and (2) how paternalistic leadership affects subordinate’s LSRI. Using 59 work group as sample (59 leaders and 251 subordinates), the results of HLM and regression analysis showed: (1) leader’s LSRI significantly affects leadership behavior: instrumental exchange RI is positively relates to authoritarian leadership behavior, but significantly has negative relationship with benevolent leadership; care-repay RI has significantly positive relationship with authoritative leadership; authority-obedience RI has significantly positive relationship with authoritarian leadership; (2) paternalistic leadership is significantly related to subordinates’ LSRI: benevolent leadership is positively related to subordinate’s communal affection and care-repay RI; authoritative leadership has significantly positive relationship with care-repay and authority-obedience RI; authoritarian leadership has significantly positive relationship with subordinate’s instrumental exchange RI. Finally, the main findings, contributions and limits, future research directions, and implications were also discussed.

Keywords: relational identity, leader-subordinate relational identity (LSRI), relational schema, paternalistic leadership

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Authors: John Lorenzo Bautista, Yoon-Joong Kim

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This paper presents the development of a Filipino speech recognition tool using the HTK System. The system was trained from a subset of the Filipino Speech Corpus developed by the DSP Laboratory of the University of the Philippines-Diliman. The speech corpus was both used in training and testing the system by estimating the parameters for phonetic HMM-based (Hidden-Markov Model) acoustic models. Experiments on different mixture-weights were incorporated in the study. The phoneme-level word-based recognition of a 5-state HMM resulted in an average accuracy rate of 80.13 for a single-Gaussian mixture model, 81.13 after implementing a phoneme-alignment, and 87.19 for the increased Gaussian-mixture weight model. The highest accuracy rate of 88.70% was obtained from a 5-state model with 6 Gaussian mixtures.

Keywords: Filipino language, Hidden Markov Model, HTK system, speech recognition

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Authors: Kashif Mahmood, Tehreem Fatima, Adeel Hassan

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Firms are always concerned with their performance which is directly linked to employees’ performance. In the thrive of this goal, number of researches have been conducted where Organizational Citizenship Behavior (OCB) and Counterproductive Work Behavior (CPWB) is among those studies. This study is aimed at investigating the role OCB by considering altruism and conscientiousness in an employee’s job performance with the mediating role of CPWB by considering sabotage and withdraw among the employees of hotel industry in Pakistan. A quantitative method was used by following deductive approach in positivist paradigm where survey was conducted through self-administered questionnaires and data was collected from the employees working in hotel industry of Pakistan. Top 10 hotels from the region of Lahore, Punjab was selected as population, and 500 questionnaires were distributed among their employees by using stratified random sampling technique. There is a positive impact of OCB is found on job performance of an employee whereas full mediation of CPWB is also found between OCB and job performance. The study is important for the practitioners in a way that hotel industry is growing at an enormous rate where employee behavior is always a concern specifically in emerging markets due to the exploitation of employees at the workplace, so the findings of the study can be helpful for practitioners and policy makers.

Keywords: organizational citizenship behavior, counterproductive work behavior, employee performance, altruism, conscientiousness, sabotage, withdraw, hotel industry

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Authors: Svitov David, Alyamkin Sergey

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The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax

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Authors: Yang-Keun Ahn, Kwang-Soon Choi, Young-Choong Park, Kwang-Mo Jung

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Vision based user interfaces to control TVs and PCs have the advantage of being able to perform natural control without being limited to a specific device. Accordingly, various studies on hand gesture recognition using RGB cameras or depth cameras have been conducted. However, such cameras have the disadvantage of lacking in accuracy or the construction cost being large. The proposed method uses a low cost IR camera to accurately differentiate between the hand and the background. Also, complicated learning and template matching methodologies are not used, and the correlation between the fingertips extracted through curvatures is utilized to recognize Click and Move gestures.

Keywords: recognition, hand gestures, infrared camera, RGB cameras

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Authors: Rajeshwari Biradar

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Background: The anecdotal evidences indicate that utilization of HIV services especially in Government facilities is affected by stigma and discrimination among HIV positive female sex workers (FSWs) in Karnataka. To our knowledge, there is no quantitative study on this issue. In this study an attempt is made to examine these aspects among positive FSWs exposed to prevention programs. Methods: This is a cross‐ sectional quantitative survey of HIV positive FSWs in the 3 districts of northern Karnataka using a structured questionnaire. The list of HIV Positive FSWs was organized by stratification, and 607 positive FSWs were selected using a systematic random selection. The data were analyzed using both bivariate and multivariate statistical techniques. Results: Half of the sex workers (52%) are traditional (devadasi, dedicated to the temple), 22% are widowed and the mean age is 33 years. The FSWs practice sex work on an average 13 days a month with 2.3 clients per day and was in sex work for about 13 years. Almost all of them (97%) used condom with the clients they had on the last day of sex work. About 74% were ever registered in the ART center and 47% of them reported being ever on ART, of which 6% dropped out. Multivariate results support the hypothesis that the interventions addressing stigma and discrimination enabled accessing health services in the government facilities (AOR=1.37; p=0.17). Conclusions: Based on the results of the study, programs addressing stigma, discrimination and positive prevention can be implemented in places where government health services are not utilized by HIV positive FSWs. However, the study may be limited by the fact that majority of the FSWs entered into sex work through the traditional devadasi system, which may not be the case in other parts of India.

Keywords: sex work, HIV/AIDS, female sex workers, health

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Authors: Jong Han Joo, Jung Hoon Lee, Young Sun Kim, Jae Young Kang, Seung Ho Choi

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In this study, we propose a novel technique for acoustic echo suppression (AES) during speech recognition under barge-in conditions. Conventional AES methods based on spectral subtraction apply fixed weights to the estimated echo path transfer function (EPTF) at the current signal segment and to the EPTF estimated until the previous time interval. We propose a new approach that adaptively updates weight parameters in response to abrupt changes in the acoustic environment due to background noises or double-talk. Furthermore, we devised a voice activity detector and an initial time-delay estimator for barge-in speech recognition in communication networks. The initial time delay is estimated using log-spectral distance measure, as well as cross-correlation coefficients. The experimental results show that the developed techniques can be successfully applied in barge-in speech recognition systems.

Keywords: acoustic echo suppression, barge-in, speech recognition, echo path transfer function, initial delay estimator, voice activity detector

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Authors: Bin Liu

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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

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Authors: Kahandawala Arachchige Thani Chathurika Kahandawala

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This research is attempted to analyse, Sri Lanka’s current situation regarding the recognition and enforcement of international commercial arbitration awards. Sri Lanka has been involved with commercial arbitration for a long time period. But there are good and bad legal practices in place in proceedings in Sri Lanka legal system. The common perception and reality of Sri Lanka’s arbitration law and practices regarding recognition and enforcement of international arbitral awards is far behind the international standards. Therefore arbitration as a dispute resolution method has become a time-consuming and costly method in Sri Lanka. This research is employed with the qualitative method based on both primary and secondary resources. This carried out the comparative analysis of recognition and enforcement in international arbitration laws established jurisdiction in Singapore and the United Kingdom, which are known as best counties as a seat of arbitration in Asia and Europe. International conventions, act and all the legal proceedings regarding recognition and enforcement of an international arbitral award in Sri Lanka are going to be discussed in the research. In the Jurisdiction of Sri Lanka, critically need to value an international arbitral award in the domestic legal system. Therefore an award has to be recognised in Sri Lanka. Otherwise, it doesn’t have any value. After recognizing it, court can enforce it. This research intends to provide a comparative analysis to overcome the drawbacks.

Keywords: arbitration, alternative dispute method, recognition and enforcement, foreign arbitral awards, Sri Lankan legal system, arbitral award in Singapore

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Authors: Hee Soo Kim, Ha Young Kyung

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This paper examines the role of positive emotions in human psychology. By understanding Fredrickson and Lyubomirsky et al.’s on positive emotions, one can better understand people’s intuitive understanding, mental health and well-being. Fredrickson asserts that positive emotions create positive affects and personal resources, and Lyubomirsky et al. relate such positive resources to the creation of happiness and personal development. This paper finds that positive emotions play a significant role in the learning process, and they are instrumental in creating a long-lasting repertoire of personal resources and play an essential role in the development of the intuitive understanding of life variables, resilience in coping with life challenges, and ability to build more successful lives.

Keywords: Positive emotions, positive affects, personal resources, negative emotions, development

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Authors: M. Dabirinezhad, M. Bayat Pour, A. Dabirinejad

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Recognition of the color spectrum of the teeth plays a significant role in the dental laboratories to produce dentures. Since there are various types and colours of teeth for each patient, there is a need to specify the exact and the most suitable colour to produce a denture. Usually, dentists utilize pallets to identify the color that suits a patient based on the color of the adjacent teeth. Consistent with this, there can be human errors by dentists to recognize the optimum colour for the patient, and it can be annoying for the patient. According to the statistics, there are some claims from the patients that they are not satisfied by the colour of their dentures after the installation of the denture in their mouths. This problem emanates from the lack of sufficient accuracy during the colour recognition process of denture production. The colour recognition pen (CRP) is a technology to distinguish the colour spectrum of the intended teeth with the highest accuracy. CRP is equipped with a sensor that is capable to read and analyse a wide range of spectrums. It is also connected to a database that contains all the spectrum ranges, which exist in the market. The database is editable and updatable based on market requirements. Another advantage of this invention can be mentioned as saving time for the patients since there is no need to redo the denture production in case of failure on the first try.

Keywords: colour recognition pen, colour spectrum, dental laboratory, denture

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Mohammad Ehsani, Iman Zarei, Soudabeh Moazemigoudarzi

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The aim of this study is to determine Pro-Environmental Behavioral Intention of Mountain Hikers to the Theory of Planned Behavior. According to many researchers nature-based recreation activities play a significant role in the tourism industry and have provided myriad opportunities for the protection of natural areas. It is essential to investigate individuals' behavior during such activities to avoid further damage to precious and dwindling natural resources. This study develops a robust model that provides a comprehensive understanding of the formation of pro-environmental behavioral intentions among climbers of Mount Damavand National Park in Iran. To this end, we combined the theory of planned behavior (TPB), value-belief-norm theory (VBN), and a hierarchical model of leisure constraints to predict individuals’ pro-environmental hiking behavior during outdoor recreation. It was used structural equation modeling to test the theoretical framework. A sample of 787 climbers was analyzed. Among the theory of planned behavior variables, perceived behavioral control showed the strongest association with behavioral intention (β = .57). This relationship indicates that if people feel they can have fewer negative impacts on national resources while hiking, it will result in more environmentally acceptable behavior. Subjective norms had a moderate positive impact on behavioral intention, indicating the importance of other people on the individual's behavior. Attitude had a small positive effect on intention. Ecological worldview positively influenced attitude and personal belief. Personal belief (awareness of consequences and ascribed responsibility) showed a positive association with TPB variables. Although the data showed a high average score in awareness of consequences (mean = 4.219 out of 5), evidence from Damavand Mount shows that there are many environmental issues that need addressing (e.g., vast amounts of garbage). National park managers need to make sure that their solutions result in awareness about proenvironmental behavior (PEB). Findings showed that negative relationship between constraints and all TPB predictors. Providing proper restrooms and parking spaces in campgrounds, strategies controlling limiting capacity and solutions for removing waste from high altitudes are helpful to decrease the negative impact of structural constraints. In order to address intrapersonal constraints, managers should provide opportunities to interest individuals in environmental activities, such as environmental celebrations or making documentaries about environmental issues. Moreover, promoting a culture of environmental protection in the Damavand Mount area would reduce interpersonal constraints. Overall, the proposed model improved the explanatory power of the TPB by predicting 64.7% of intention compared to the original TPB that accounted for 63.8% of the variance in intention.

Keywords: theory of planned behavior, pro-environmental behavior, national park, constraints

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Authors: Diego Adrian Cardenas Jorge, Gerardo Mirando Guisado, Alfredo Barrientos Padilla

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A system based on Affective Computing can detect and interpret human information like voice, facial expressions and body movement to detect emotions and execute a corresponding response. This data is important due to the fact that a person can communicate more effectively with emotions than can be possible with words. This information can be processed through technological components like Facial Recognition, Gait Recognition or Gesture Recognition. As of now, solutions proposed using this technology only consider one component at a given moment. This research investigation proposes two solutions based on Affective Computing taking into account more than one component for emotion detection. The proposals reflect the levels of dependency between hardware devices and software, as well as the interaction process between the system and the user which implies the development of scenarios where both proposals will be put to the test in a live environment. Both solutions are to be developed in code by software engineers to prove the feasibility. To validate the impact on society and business interest, interviews with stakeholders are conducted with an investment mind set where each solution is labeled on a scale of 1 through 5, being one a minimum possible investment and 5 the maximum.

Keywords: affective computing, emotions, emotion detection, face recognition, gait recognition

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Authors: Muhammad Imran Ahmad, Ruzelita Ngadiran, Mohd Nazrin Md Isa, Nor Ashidi Mat Isa, Mohd ZaizuIlyas, Raja Abdullah Raja Ahmad, Said Amirul Anwar Ab Hamid, Muzammil Jusoh

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This paper presents two technique, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapping on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non Gaussian in the feature space and by using combination of several Gaussian function that has different statistical properties, the best feature representation can be model using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculate GMM components. The method is tested using FERET data sets and is able to achieved 92% recognition rates.

Keywords: local features modelling, face recognition system, Gaussian mixture models, Feret

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process.

Keywords: Quran, speaker recognition, reciter recognition, Gaussian Mixture Model

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Authors: Fawaz S. Al-Anzi, Dia AbuZeina

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Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit.

Keywords: speech recognition, acoustic features, mel frequency, cepstral coefficients

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Authors: Sameh Abaza, Mohamed Ibrahim, Tarek Mahmoud

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Due to the development in the digital image processing, its wide use in many applications such as medical, security, and others, the need for more accurate techniques that are reliable, fast and robust is vehemently demanded. In the field of security, in particular, speed is of the essence. In this paper, a pattern recognition technique that is based on the use of Hidden Markov Model (HMM), K-means and the Sobel operator method is developed. The proposed technique is proved to be fast with respect to some other techniques that are investigated for comparison. Moreover, it shows its capability of recognizing the normal face (center part) as well as face boundary.

Keywords: HMM, K-Means, Sobel, accuracy, face recognition

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Authors: Vishwa Joshi

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The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

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Authors: Annamarija Paula

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Modern psychology is divided into three distinct domains: cognition, affect, and conation. Historically, psychology devalued the importance of studying the effect in order to explain human behavior as it supposedly lacked both rational thought and a scientific foundation. As a result, affect remained the least studied domain for years to come. However, the last 30 years have marked a significant change in perspective, claiming that not only is affect highly adaptive, but it also plays a crucial role in cognitive processes. Affective states have a crucial impact on human behavior, which led to fundamental advances in the study of affective states on perception and judgment. Positive affect and negative affect are distinct entities and have different effects on social information processing. In addition, emotions of the same valence are manifested in distinct and unique physiological reactions indicating that not all forms of positive or negative affect are the same or serve the same purpose. The effect plays a vital role in perception and judgments, which impacts the validity and reliability of memory retrieval. The research paper analyzes key findings from the past three decades of observational and empirical research on affective states and cognition. The paper also addresses the limitations connected to the findings and proposes suggestions for possible future research.

Keywords: memory, affect, perception, judgement, mood congruency effect

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Authors: Vaishali P. Joshi, Pallav Joshi

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The objective of this study is to examine the differences fashion innovators and non-fashion innovators in their fashion consumption behavior in terms of their pre-purchase behavior, purchase behavior and post purchase behavior. The questionnaire was distributed to a female college student for data collection for achieving the objective of the first part of the study. Question-related to fashion innovativeness and fashion consumption behavior was asked. The sample was comprised of 81 college females ages 18 through 30 who were attending Business Management degree. A series of attitude questions was used to categorize respondents on the Innovativeness Scale. 32 respondents with a score of 21 and above were designated as Fashion innovators and the remainder (49) as Non-fashion innovators. Findings showed that there exist significant differences between innovators and non-innovators in their fashion consumption behavior. Data was analyzed through frequency distribution table. Many differences were found in the behavior of innovators and non-innovators in terms of their pre-purchase, actual purchase, and post-purchase behavior.

Keywords: fashion, innovativeness, consumption behavior, purchase

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Authors: Sean W. T. Bayly, Daniel Glover, Don Horrell, Simon Horrocks, Barnes Callum, Stuart Gibson, Mac Misuira

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We assess suitability of recent, ∼7B parameter, instruction-tuned Language Models for Generative Named Entity Recognition (GNER). Alongside Retrieval Augmented Generation (RAG), and supported by task-specific prompting, our proposed Multi-Level Information Retrieval method achieves notable improvements over finetuned entity-level and sentence-level methods. We conclude that language models directed toward this task are highly capable when distinguishing between positive classes (precision). However, smaller models seem to struggle to find all entities (recall). Poorly defined classes such as ”Miscellaneous” exhibit substantial declines in performance, likely due to the ambiguity it introduces to the prompt. This is partially resolved through a self-verification method using engineered prompts containing knowledge of the stricter class definitions, particularly in areas where their boundaries are in danger of overlapping, such as the conflation between the location ”Britain” and the nationality ”British”. Finally, we explore correlations between model performance on the GNER task with performance on relevant academic benchmarks.

Keywords: generative named entity recognition, information retrieval, lightweight artificial intelligence, prompt engineering, personal information identification, retrieval augmented generation, self verification

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Authors: Bamidele Samson Alobalorun, Ifedotun Roseline Idowu

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Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model.

Keywords: Daugman rubber sheet, feature extraction, Hamming distance, iris recognition system, 2D Gabor wavelet transform

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Authors: Alessandra Turini Bolsoni-Silva, Sonia Regina Loureiro

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It is important to evaluate such variables simultaneously and differentiating types of behavior problems: internalizing, externalizing and with comorbidity of internalizing and externalizing. The objective was to compare, correlate and predict teacher educational practices (educational social skills and negative practices) and children's behaviors (social skills and behavior problems) of children with internalizing, externalizing and combined internalizing and externalizing problems, controlling variables of child (gender and education). A total of 262 children were eligible to compose the participants, considering preschool age from 3 to 5 years old (n = 109) and school age from 6 to 11 (n = 153) years old, and their teachers who were distributed, in designs case-control, non-clinical, with internalizing, externalizing problems and internalizing and externalizing comorbidity, using the Teacher's Report Form (TRF) as a criterion. The instruments were applied with the teachers, after consent from the parents/guardians: a) Teacher’s Report Form (TRF); b) Educational Social Skills Interview Guide for Teachers (RE-HSE-Pr); (c) Socially Skilled Response Questionnaire – Teachers (QRSH-Pr). The data were treated by univariate and multivariate analyses, proceeding with comparisons, correlations and predictions regarding the outcomes of children with and without behavioral problems, considering the types of problems. As main results stand out: (a) group comparison studies: in the Inter group there is emphasis on behavior problems in affection interactions, which does not happen in the other groups; as for positive practices, they discriminate against groups with externalizing and combined problems and not in internalizing ones, positive educational practices – hse are more frequent in the G-Exter and G-Inter+Exter groups; negative practices differed only in the G-Exter and G-Inter+Exter groups; b) correlation studies: it can be seen that the Inter+Exter group presents a greater number of correlations in the relationship between behavioral problems/complaints and negative practices and between children's social skills and positive practices/contexts; c) prediction studies: children's social skills predict internalizing, externalizing and combined problems; it is also verified that the negative practices are in the multivariate model for the externalizing and combined ones. This investigation collaborates in the identification of risk and protective factors for specific problems, helping in interventions for different problems.

Keywords: development, educational practices, social skills, behavior problems, teacher

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Authors: Wale Agbaje

Abstract:

The aim of this research study is to assess the impact of financial statement fraud on profitability of some selected Nigerian manufacturing firms covering (2002-2016). The specific objectives focused on to ascertain the effect of incorrect asset valuation on return on assets (ROA) and to ascertain the relationship between improper expense recognition and return on assets (ROA). To achieve these objectives, descriptive research design was used for the study while secondary data were collected from the financial reports of the selected firms and website of security and exchange commission. The analysis of covariance (ANCOVA) was used and STATA II econometric method was used in the analysis of the data. Altman model and operating expenses ratio was adopted in the analysis of the financial reports to create a dummy variable for the selected firms from 2002-2016 and validation of the parameters were ascertained using various statistical techniques such as t-test, co-efficient of determination (R2), F-statistics and Wald chi-square. Two hypotheses were formulated and tested using the t-statistics at 5% level of significance. The findings of the analysis revealed that there is a significant relationship between financial statement fraud and profitability in Nigerian manufacturing industry. It was revealed that incorrect assets valuation has a significant positive relationship and so also is the improper expense recognition on return on assets (ROA) which serves as a proxy for profitability. The implication of this is that distortion of asset valuation and expense recognition leads to decreasing profit in the long run in the manufacturing industry. The study therefore recommended that pragmatic policy options need to be taken in the manufacturing industry to effectively manage incorrect asset valuation and improper expense recognition in order to enhance manufacturing industry performance in the country and also stemming of financial statement fraud should be adequately inculcated into the internal control system of manufacturing firms for the effective running of the manufacturing industry in Nigeria.

Keywords: Althman's Model, improper expense recognition, incorrect asset valuation, return on assets

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Authors: Maria Pavlova

Abstract:

In our days, it is possible to put the camera on different vehicles like quadcopter, train, airplane and etc. The camera also can be the input sensor in many different systems. That means the object recognition like non separate part of monitoring control can be key part of the most intelligent systems. The aim of this paper is to focus of the object recognition process during vehicles movement. During the vehicle’s movement the camera takes pictures from the environment without storage in Data Base. In case the camera detects a special object (for example human or animal), the system saves the picture and sends it to the work station in real time. This functionality will be very useful in emergency or security situations where is necessary to find a specific object. In another application, the camera can be mounted on crossroad where do not have many people and if one or more persons come on the road, the traffic lights became the green and they can cross the road. In this papers is presented the system has solved the aforementioned problems. It is presented architecture of the object recognition system includes the camera, Raspberry platform, GPS system, neural network, software and Data Base. The camera in the system takes the pictures. The object recognition is done in real time using the OpenCV library and Raspberry microcontroller. An additional feature of this library is the ability to display the GPS coordinates of the captured objects position. The results from this processes will be sent to remote station. So, in this case, we can know the location of the specific object. By neural network, we can learn the module to solve the problems using incoming data and to be part in bigger intelligent system. The present paper focuses on the design and integration of the image recognition like a part of smart systems.

Keywords: camera, object recognition, OpenCV, Raspberry

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Authors: Shih-Yu Lo

Abstract:

Stereotypes exist in almost every society, affecting how people interact with each other. However, to our knowledge, the influence of stereotypes was rarely explored in the context of basic perceptual processes. This study aims to explore how the gender stereotype affects object recognition. Participants were presented with a series of scene pictures, followed by a target display with a man or a woman, holding a weapon or a non-weapon object. The task was to identify whether the object in the target display was a weapon or not. Although the gender of the object holder could not predict whether he or she held a weapon, and was irrelevant to the task goal, the participant nevertheless tended to identify the object as a weapon when the object holder was a man than a woman. The analysis based on the signal detection theory showed that the stereotype effect on object recognition mainly resulted from the participant’s bias to make a 'weapon' response when a man was in the scene instead of a woman in the scene. In addition, there was a trend that the participant’s sensitivity to differentiate a weapon from a non-threating object was higher when a woman was in the scene than a man was in the scene. The results of this study suggest that the irrelevant social cues implied in the visual scene can be very powerful that they can modulate the basic object recognition process.

Keywords: gender stereotype, object recognition, signal detection theory, weapon

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Authors: Ilenia Lanni, Claudia Del Gatto, Allegra Indraccolo, Riccardo Brunetti

Abstract:

Empathy represents a multifaceted construct encompassing affective and cognitive components. Among these, empathic accuracy—defined as the ability to accurately infer another person’s emotions or mental state—plays a pivotal role in fostering empathetic understanding. Emotional contagion, the automatic process through which individuals mimic and synchronize facial expressions, vocalizations, and postures, is considered a foundational mechanism for empathy. This embodied simulation enables shared emotional experiences and facilitates the recognition of others’ emotional states, forming the basis of empathic accuracy. Facial mimicry, an integral part of emotional contagion, creates a physical and emotional resonance with others, underscoring its potential role in enhancing empathic understanding. Building on these findings, the present study explores how manipulating emotional contagion through the enfacement illusion impacts empathic accuracy, particularly in the recognition of complex emotional expressions. The enfacement illusion was implemented as a visuo-tactile multisensory manipulation, during which participants experienced synchronous and spatially congruent tactile stimulation on their own face while observing the same stimulation being applied to another person’s face. This manipulation enhances facial mimicry, which is hypothesized to play a key role in improving empathic accuracy. Following the enfacement illusion, participants completed a modified version of the Diagnostic Analysis of Nonverbal Accuracy–Form 2 (DANVA2-AF). The task included 48 images of adult faces expressing happiness, sadness, or morphed emotions blending neutral with happiness or sadness to increase recognition difficulty. These images featured both familiar and unfamiliar faces, with familiar faces belonging to the actors involved in the prior visuo-tactile stimulation. Participants were required to identify the target’s emotional state as either "happy" or "sad," with response accuracy and reaction times recorded. Results from this study indicate that emotional contagion, as manipulated through the enfacement illusion, significantly enhances empathic accuracy, particularly for the recognition of happiness. Participants demonstrated greater accuracy and faster response times in identifying happiness when viewing familiar faces compared to unfamiliar ones. These findings suggest that the enfacement illusion strengthens emotional resonance and facilitates the processing of positive emotions, which are inherently more likely to be shared and mimicked. Conversely, for the recognition of sadness, an opposite but non-significant trend was observed. Specifically, participants were slightly faster at recognizing sadness in unfamiliar faces compared to familiar ones. This pattern suggests potential differences in how positive and negative emotions are processed within the context of facial mimicry and emotional contagion, warranting further investigation. These results provide insights into the role of facial mimicry in emotional contagion and its selective impact on empathic accuracy. This study highlights how the enfacement illusion can precisely modulate the recognition of specific emotions, offering a deeper understanding of the mechanisms underlying empathy.

Keywords: empathy, emotional contagion, enfacement illusion, emotion recognition

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