Processing Domain Research Articles

Saturating the Maximum Success Probability Bound for Noiseless Linear Amplification Using Linear Optics

A noiseless linear amplifier (NLA) performs the highest-quality amplification allowable under the rules of quantum physics. Unfortunately, these same rules conspire against us via the no-cloning theorem, which constrains NLA operations to the domain of probabilistic processes. Nevertheless, they are useful for a wide variety of quantum protocols, with numerous proposals assuming access to an optimal NLA device that performs with the maximum possible success probability. Here we propose the first linear-optics NLA protocol that asymptotically achieves this success probability bound by modifying the Knill-Laflamme-Milburn near-deterministic teleporter into an amplifier. Published by the American Physical Society 2024

Application of an ensemble CatBoost model over complex dataset for vehicle classification.

The classification of vehicles presents notable challenges within the domain of image processing. Traditional models suffer from inefficiency, prolonged training times for datasets, intricate feature extraction, and variable assignment complexities for classification. Conventional methods applied to categorize vehicles from extensive datasets often lead to errors, misclassifications, and unproductive outcomes. Consequently, leveraging machine learning techniques emerges as a promising solution to tackle these challenges. This study adopts a machine learning approach to alleviate image misclassifications and manage large quantities of vehicle images effectively. Specifically, a contrast enhancement technique is employed in the pre-processing stage to highlight pixel values in vehicle images. In the feature segmentation stage, Mask-R-CNN is utilized to categorize pixels into predefined classes. VGG16 is then employed to extract features from vehicle images, while an autoencoder aids in selecting features by learning non-linear input features and compressing representation features. Finally, the CatBoost (CB) algorithm is implemented for vehicle classification (VC) in diverse critical environments, such as inclement weather, twilight, and instances of vehicle blockage. Extensive experiments are conducted using different large-scale datasets with various machine learning platforms. The findings indicate that CB (presumably a specific method or algorithm) attains the highest level of performance on the large-scale dataset named UFPR-ALPR, with an accuracy rate of 98.89%.

Exploring motivations for algorithm mention in the domain of natural language processing: A deep learning approach

With the formation of the fourth paradigm of scientific research, algorithms have become increasingly important in scientific research. In academic papers, algorithms may be mentioned by scholars with various motivations, using, comparing, or improving algorithms to solve complex research tasks. Identifying these motivations can help scholars discover the relationships between algorithms and further assess their roles and values. Therefore, taking the field of natural language processing (NLP) as an example, this article proposes a complete method to conduct the identification, distribution, and evolution of motivations for mentioning algorithms at the sentence level. Specifically, using manual annotation and machine learning methods, we identify algorithm entities and sentences in the full text of papers, classify motivations for mentioning algorithms by pre-training models and data augmentation techniques, and finally analyze the distribution and evolution of motivations. The results show that the deep learning models trained with the augmented data outperform the traditional machine learning models in the classification task. In academic papers, more than half of the sentences show the direct use of algorithms, while the lowest percentage of motivations are improving algorithms, and the diversity of motivations has been increasing with time. For specific algorithms, grammatical algorithms are mentioned more by the motivation of “description,” while more motivations of “use” are found in the machine learning algorithms category. As time passed, the “use” motivations gradually replaced the “description” motivations for different algorithms, and the number of motivation types decreased significantly. Our research explores the identification, distribution, and evolution of authors’ motivations for mentioning algorithm entities, which could provide a basis for future algorithm relationship identification and influence evaluation using motivations.

RELATIONSHIP BETWEEN SELF-RATED MUSICAL COMPETENCE AND AUDITORY PROCESSING IN INSTRUMENTALISTS

IntroductionAlthough the benefits of musical training have been shown to affect a number of auditory processes, the question of how self-rated musical competence correlates with auditory perception remains largely unexplored. The present study aimed to investigate how self-perceived musical competence correlates with musicians’ ratings of their own abilities in speech perception, spatial hearing, and <i></i>sound qualities.Material and methodsThe <i>Edinburgh Lifetime Musical Experience Questionnaire</i> (ELMEQ) was administered on 57 instrumentalists aged 19 to 53 years (mean = 25.1 ± 4.9 years, 34 males and 23 females). All had normal hearing and had undergone formal training on musical instruments for 1–2 years. All of them also regularly practised music for at least 1–2 h/week. Based on their ELMEQ scores, they were divided into two groups: low (ELMEQ score ≤ 7; <i>n</i> = 12) and high (ELMEQ score ≥ 11; <i>n</i> = 16; self-perceived musical competence. Participants were sent a modified questionnaire from the <i>Speech, Spatial and Qualities of Hearing Scale</i> (SSQ) using Google forms, and the data statistically analysed.ResultsA Mann–Whitney <i>U</i>-test showed that participants with high self-rated musical competence had significantly higher ratings on the spatial hearing and sound qualities sub-domains of SSQ compared to their counterparts who had lower ELMEQ scores. However, both groups scored about the same on the speech perception domain of SSQ. A Spearman test revealed a moderate to strong positive correlation between self-rated musical competence and SSQ ratings in each domain.ConclusionsThe findings show that instrumentalists with high self-rated musical competence exhibited high proficiency in the spatial hearing and sound quality domains of auditory processing.

Strategic interventions for bridging the design-reality gap in property conferment rights: a case study of India’s ambitious initiative

PurposeThis research aims to assess how well the e-governance initiative aligns with its design expectations and on-ground realities for property management to redevelop Delhi, the mega-city of India. Additionally, the study proposes strategic interventions to address any gaps identified, aiming to improve the project’s effectiveness and success.Design/methodology/approachThe research employs a singular exploratory case study methodology to scrutinise the e-governance initiative undertaken by the Government of India to confer property rights. Specifically, the study utilises a qualitative research approach known as design-reality gap (DRG) analysis. The study draws from primary and secondary sources using a mixed-methods data collection strategy. Primary data are gathered through a purposeful and snowball sampling method involving direct engagement with respondents, whilst secondary data are sourced from the project portal.FindingsThe investigation reveals a substantial disparity between the planning and execution phases of e-governance projects. This incongruity predominantly manifests in the domains of process, staffing and skills, as well as objectives and values. The study further finds that strategic collaboration amongst stakeholders and the sustainability of decisions are the most critical factors in ensuring the success of e-governance initiatives and bridging the DRG of e-governance projects.Research limitations/implicationsThis research highlights the complex challenges faced by e-governance projects in technical, human and organisational aspects. The successful implementation and sustainability of these projects require effective strategies to overcome challenges, which management should proactively identify and act on. To improve services, beneficiaries should be encouraged to provide feedback and suggestions, as they play a crucial role in service enhancement. A dynamic feedback loop would be established by creating a two-way interaction between beneficiaries and service providers, leading to iterative service improvement. It is important to note that the study’s findings are more illustrative than conclusive due to the moderate sample size, reflecting its limitations.Originality/valueThe research contributes to the scholarly discourse on e-governance and policy implementation by comprehensively examining the alignment between conceptual design and real-world execution. It also introduces a normalised scale for the DRG framework, mapping its dimensions to deduce specific outcomes. This innovative approach enhances the research’s originality and value, offering insights applicable in both academic and practical domains.

Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases

In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.

Validity and Reliability of the Residency and Immersion Program (PRIme) Assessment Instrument in Enhancing the Quality of New Principals

The Residency and Immersion Program (PRIme) is an induction for new school principals serving their first year. PRIme was implemented in 2014, and it requires a comprehensive evaluation for program improvement. Hence, this study aims to assess the validity and reliability of the PRIme assessment instrument. The assessment data of content validity by professional experts and face validity by new principals were analyzed using CVR, CVI, and FVI methods. Meanwhile, pilot study data was obtained from 53 respondents and further analyzed using the Rasch measurement model. The questionnaire includes 102 items for assessing the domains of input (courses, coaching, and mentoring), process (type of leadership practiced), and product (leadership and organizational management) in the implementation of PRIme. The Rasch analysis results showed that the items in the questionnaire have good content validity, construct validity, and reliability. Therefore, the Rasch analysis has proven that this questionnaire can be used as an instrument to assess PRIme in an effort to enhance the quality of new principals in Malaysia.

Audit Sistem Informasi Pelayanan Bpjs Kesehatan Menggunakan Framework Cobit 5.0

BPJS Health is a legal entity formed to administer the health insurance program with the aim of protecting the entire community with affordable premiums and providing wider service coverage for the entire community. BPJS health services focus on first level health services (FKTP/first level health facilities). Due to the sensitive nature of health information stored and managed by BPJS Health, it is important to ensure that the information systems used are safe from cyber attacks and data leaks. The stages of a BPJS Health service audit are determining audit objectives, identifying services using the framework, capability level analysis, testing controls and evidence at the capability level, verifying results, and compiling audit results reports and recommendations. Audits carried out for services that focus on Process Domains DSS01 and DSS02. The measurement result is 2.5 because the value rounding index means the results are at level 3 (established process), that is, currently the processes in each process domain have been identified and standardized well so that the library service system is stable for implementation, while the expected level is at level 4 so there is a gap of 1.5. To be able to overcome the existing gap, a recommendation was made, namely to maximize the alignment of Company Operational Standards (SOP) with applicable policies.

A Comparative Analysis of ChatGPT-4, ChatGPT-3.5, and Bard (Gemini Pro) in Sarcasm Detection

Understanding nuanced human communication like sarcasm is a significant challenge in the rapidly evolving domains of artificial intelligence (AI) and natural language processing (NLP). This study aims to comparatively analyze the sarcasm detection capabilities of three advanced AI models: ChatGPT-4, ChatGPT-3.5, and Bard (Gemini Pro). Utilizing the Sarcasm Corpus V2, focused on general sarcasm, the research involved testing 100 sentences (50 sarcastic and 50 non-sarcastic) with each model to assess their detection accuracy. The results indicated distinct performance variations among the models. ChatGPT-4 and Bard showed a relatively balanced ability in identifying both sarcastic and non-sarcastic sentences, whereas ChatGPT-3.5 exhibited a stronger accuracy in detecting non-sarcastic sentences but struggled with sarcastic ones. Statistical analysis confirmed that the differences in performance were significant (p < 0.05). These findings have critical implications for the development and application of AI in fields requiring nuanced language understanding, such as social media analysis, customer service, and sentiment analysis. The study highlights the varying strengths and weaknesses of current AI models in processing complex linguistic constructs like sarcasm and underscores the need for continued advancements in this area. Conclusively, this research provides valuable insights into the current state of sarcasm detection in AI, contributing to the broader understanding of AI's language processing capabilities. It also opens avenues for future research, particularly in enhancing AI algorithms for improved sarcasm detection across diverse contexts and languages

A Study of Application of Digital Image Processing in Medical Field and Medical Image Segmentation by Edge Detection

There has been a lot of recent development in the domain of image processing and associated imaging techniques. Medical imaging is about the practice to capture images of different fragments of the body for diagnostic or investigative purposes. The number of imaging procedures performed each week is in the millions. This study presents the promising image processing methods currently in use in the medical field. The rapid expansion of medical imaging can be attributed to advancements in image processing methods, including image recognition, enhancement, and analysis. Utilised in the diagnosis and treatment of patients, image processing techniques have proven to be of immense benefit to surgeons and physicians. The explosion of clinical medical devices is largely attributable to the blend of hardware and image processing techniques, both of which have contributed significantly to medical progress. In recent years, Image segmentation via edge detection plays a vital role by extracting important features of the images like corners, lines and curves to recognized object boundaries which in turns helps the medical expertise to detect diseases in medical analysis and patient care.

MILD combustion stabilization issues through the analysis of hysteresis behaviors: The case of new energy carriers

MILD combustion processes are renewed to reveal a strong resilience to extinction phenomena and/or instabilities, whereas the oxidation process is stabilized trough ignition phenomena. Under MILD conditions, igni-diffusive and/or perfectly mixed kernels, forming during the mixing process between hot products and fresh reactants, are so much diluted and pre-heated to escape classical feed-back flammable flames stabilization mechanisms, while ignition and extinction events merge in a unique condition through “anhysteretic” behaviors. So far, considering methane as reference fuel, it has been largely demonstrated the mentioned “anhysteretic” condition is very conservative and defines a sub-domain of MILD combustion processes, following Cavaliere and de Joannon's definition. Furthermore, the coincidence of ignition and extinction phenomena can occur also preserving hysteresis phenomena. In turns, this condition strongly enlarges the stabilization domain of MILD combustion processes, starting from the upper branch of the hysteresis behaviors to the real extinction, with characteristic unstable loci to consider as further/last opportunity to promote stable operative conditions through the formation of local thermo-kinetic conditions in the combustion chamber during hot products/fresh reactants mixing process (injection configuration/burner design), or by forced ignition events. The hysteresis behaviors of renewable/alternative fuels, relevant within the decarbonization policies of several energy sectors, are thoroughly discussed under MILD conditions through numerical studies in model reactors in order to shed light on common and/or different features, and outline practical rules towards the definition of stable MILD combustion domains. Results show that, as MILD combustion is a chemical kinetics-driven processes, stability issues have to be discussed in relation to fuel nature, albeit with common behavior can be derived. The coincidence between extinction/ignition phenomena is reached for extremely diluted conditions, already ascribable to MILD combustion conditions, thus defining a small sub-domain of the process. This condition can be reached through “hysteretic” or “anhysteretic” behaviors.

Enhancing Automatic Vehicle Number Plate Recognition Systems: A Multidimensional Approach to Improve Efficiency and Applicability

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

Spectrum-Dependent Image Convolutional Processing via a Two-Dimensional Polarization-Sensitive Photodetector.

Currently, the improvement in the processing capacity of traditional processors considerably lags behind the demands of real-time image processing caused by the advancement of photodetectors and the widespread deployment of high-definition image sensors. Therefore, achieving real-time image processing at the sensor level has become a prominent research domain in the field of photodetector technology. This goal underscores the need for photodetectors with enhanced multifunctional integration capabilities than can perform real-time computations using optical or electrical signals. In this study, we employ an innovative p-type semiconductor GaTe0.5Se0.5 to construct a polarization-sensitive wide-spectral photodetector. Leveraging the wide-spectral photoresponse, we realize three-band imaging within a wavelength range of 390-810 nm. Furthermore, real-time image convolutional processing is enabled by configuring appropriate convolution kernels based on the polarization-sensitive photocurrents. The innovative design of the polarization-sensitive wide-spectral GaTe0.5Se0.5-based photodetector represents a notable contribution to the domain of real-time image perception and processing.

Greening in the spotlight: How public inquisitiveness shapes European SMEs' actions in response to climate concerns

AbstractWe examine greening activities among European small and medium‐sized enterprises (SMEs) in the product and process domains, and argue that greater public climate concern in an SME's home country environment primarily associates with greening in the inherently more visible product domain. Moreover, we introduce the concept of public inquisitiveness and propose that greater inquisitiveness prompts SMEs to also pay attention to less visible process greening activities as a response to public climate pressures. We test our ideas using multilevel regression models on a large representative sample of SMEs from 18 European Union (EU) countries. The study's main ideas are supported by the findings, which point to possible trade‐offs between product and process greening among resource‐constrained SMEs, and suggest the general public's inquisitiveness indeed plays a key role in preventing under engagement in less outwardly visible greening strategies. We discuss our study's implications for discourse on how and under which conditions normative institutional forces shape firm‐level sustainable behavior, as well as for SMEs' pro‐environmental stakeholders.

Enhanced Linear and Vision Transformer-Based Architectures for Time Series Forecasting

Time series forecasting has been a challenging area in the field of Artificial Intelligence. Various approaches such as linear neural networks, recurrent linear neural networks, Convolutional Neural Networks, and recently transformers have been attempted for the time series forecasting domain. Although transformer-based architectures have been outstanding in the Natural Language Processing domain, especially in autoregressive language modeling, the initial attempts to use transformers in the time series arena have met mixed success. A recent important work indicating simple linear networks outperform transformer-based designs. We investigate this paradox in detail comparing the linear neural network- and transformer-based designs, providing insights into why a certain approach may be better for a particular type of problem. We also improve upon the recently proposed simple linear neural network-based architecture by using dual pipelines with batch normalization and reversible instance normalization. Our enhanced architecture outperforms all existing architectures for time series forecasting on a majority of the popular benchmarks.

Multi-objective optimization for vector quantization via genetic algorithm

The issue of image compression continues to be a subject of ongoing research within the domain of image processing, particularly in the context of medical applications. The quality of the decompressed image can vary depending on the accuracy of the compression technique, resulting in either fine or distorted details. Therefore, the diagnostic procedure performed by medical professionals is contingent upon the precision of the compression and decompression process. In addition, the compression of medical images serves to decrease the amount of storage required, thereby enabling faster transmission over computer networks through the reduction of their bit size. This paper proposes a hybrid mix of the discrete wavelet transform (DWT) technique and vector quantization (VQ) to improve the compression technique of medical images. The aim of the proposed compression technique is to preserve diagnostic image information while achieving a high compression ratio. First, noise in medical images is caused by salt pepper noise. At the same time, the edges of the images are maintained in sharpness and context. Then, a lossless compression method is applied to the wavelet coefficients of the subband with the lowest frequency, while the thresholding method was used to efficiently construct coefficients for high-frequency sub-bands. This process will produce a traditional VQ, which is estimated via the Genetic Algorithm (GA) with fuzzy clustering. While Arithmetic encoded theory was being utilized to quantize coefficients, the proposed compression technique was evaluated by dividing the image into two levels and three levels of sub-bands, respectively, in two different scenarios. Comparing the decompressed image to the filtered image by means of different evaluation metrics, the proposed method can enhance compression performance and strike a balance between compression ratio and image visual quality.

Trie and LOUDS hybrid model for efficient e-commerce processing in cloud environment

Set superset query is widely used in e-commerce processing and many other domains, particularly in cloud computing environments. Indexing is an efficient way to model e-commerce data. Many existing indexes, however, primarily focus on enhancing either query performance or space efficiency, often neglecting the need to strike a balance between these two factors. We have observed that upper nodes closer to the root of a tree are frequently accessed, while lower nodes near the leaves tend to entail expensive storage costs. To address this issue, we introduce TLI model, a trie and level-ordered unary degree sequence (LOUDS) hybrid model. The upper part of TLI is a trie, which is optimized for superior query performance. The lower part of TLI uses the LOUDS structure. TLI strikes a good balance between query performance and space utilization. To seamlessly integrate these two parts, we have developed efficient connecting strategies. Our simulation results on localhost demonstrate that TLI outperforms its competitors in terms of both space and time efficiency. Remarkably, it enhances query performance by up to 1.89 times, with a modest 6.72% increase in space overhead compared to LOUDS-based alternatives.

Development and validation of career decision making proficiency scale

The purpose of the present research was to develop and examine the psychometric properties of the Career Decision Making Proficiency Scale (CDMPS). The new tool amalgamates various conceptual domains of career exploration process and validates its usage for students and young adults (N=565). The researcher used a survey research design that involved the theoretical evaluation by experts, mandating two subscale divisions for the original 40 items. A deductive approach was considered for the scale development. The content validity assessment by means of CVR- Lawshe’s Test, Fleiss’ Kappa statistics, and the CVI-proportion agreement method retained 34 items. R Studio software was used for dimension reduction techniques and inter-factor bi-variate correlations. The results of the item analysis confirmed 30 items clustered into five dormant constructs. The reliability coefficients of the two sub-scales were found to be high (α= 0.80 and 0.87). Furthermore, the exploratory factor analysis explained 52.45 percent and 50.99 percent of total variance, confirming the factorial internal structural validity of the two sub-scales, respectively. The scale can be administered by career and school counsellors to guide students with their career decisions, to assess career decision difficulties faced by weak students and potential dropouts or repeaters, and to assess students’ confidence, motivation, and abilities in handling problematic situations at the time of deciding a career path.

Railway accident causation prediction with improved transformer model based on lexical information and contextual relationships

The railway system is a prime example of a safety-critical system. Predicting the causes of railway accidents holds immense significance in enhancing railway transportation safety. Previous approaches to railway causation analysis have encountered huge challenges regarding data processing and analytical capabilities. To address this concern, this paper proposes an innovative deep model framework based on the Transformer architecture that utilizes historical data on railway equipment accidents to predict the causes behind such incidents. Firstly, this paper proposes the utilization of Convolutional Block Attention in the domain of text processing, serving as a lexical encoder to augment word semantics acquisition in accident texts. Subsequently, in order to address the deficiency of traditional Transformers that lack positional representation information, we propose incorporating a BiGRU (Bidirectional Gated Recurrent Unit) as a contextual positional information encoder to capture contextual positional information in railway accident data effectively. Finally, considering that accident data reports are discrete tabular data, this study suggests employing cue word techniques for preprocessing accident data to alleviate the model's learning burden. We applied the proposed model to the FRA (Federal Railroad Administration) dataset. The results demonstrate that our model surpasses the current state-of-the-art language models, exhibiting superior performance compared to the optimal model with a notable improvement of 3.56%, 0.42%, and 0.76% in Precision, Recall, and F1-score, respectively. Furthermore, our model accurately predicts accident categories prone to misjudgment even when trained on limited data, outperforming existing language models. The study findings will contribute to the prevention and management of railway accidents.

Cooperative Learning on Sports Confidence among Students of Baseball Elective

Cooperative learning emphasizes collaboration, shared problem-solving, and communal knowledge, positing that learning in groups enhances understanding and practical application. This study aimed to explore the correlation between cooperative learning in a baseball elective and the sports confidence of 208 surveyed students. The findings indicated strong agreements in domains of Positive Interdependence, Interpersonal Skills, and Reflective Group Processing, with slightly lower agreement in Individual Accountability. Holistic Development emerged as the highest indicator of sports confidence, while Performance Assurance was lower, yet all were within agreeable ranges. The study recommends the "Team Synergy: Enhancing Sports Confidence through Cooperative Learning" program, aiming to blend cooperative learning strategies into sports training, thereby bolstering sports confidence and fostering a holistic development of student-athletes.