Issue Of Recognition Research Articles

The Fall of the First Republic of Armenia in the Context of Relations with the Entente Countries, USA and Russia

The article discusses the issues of the Western orientation of the government of the First Republic with its Entente allies, and significant failures in foreign policy choices. It is emphasised that the leadership of the Republic of Armenia in 1918‒1920. placed excessively unjustified hopes on the Entente countries in resolving issues of recognition of independence and sovereignty and disputed territories. Reliance on the Entente countries during the First Republic (May 1918 ‒ December 1920) did not guarantee the continued existence and security of the Armenian republic. It is noted that the Treaty of Sèvres, signed by the Entente powers (Great Britain, France) and the United States on August 10, 1920, was deceptive and was not recognised by either the Sultan’s government or the Kemalists. In this regard, the article states that, however, not a single European state has expressed a desire to help resolve territorial issues through diplomatic pressure on Turkey or the use of military force. It is also shown that as a result, the RA government ignored the Armenian-Russian agreement signed on August 10 and tried to resolve issues independently, which at the end of 1920 led to complete defeat in the Armenian-Turkish war and capitulation. It is demonstrated that Soviet Russia really assessed the current situation for intervention with the goal of rapid Sovietisation of Armenia, creating an Armenian buffer, instead of the First Republic, creating the Soviet Republic. It is concluded that the orientation of the government and major failures in army construction turned out to be the dominant factors and reasons for the decline of the First Republic of Armenia at the end of 1920. The ineffectiveness of the militarydiplomatic support of the Entente states and the underestimation of the Russian factor are revealed. The RA’s orientation toward its European allies and the United States turned out to be disastrous, since it belatedly became obvious that the Armenian leadership had made a mistake in its calculations and turned out to be short-sighted, since it did not even try to act faster in order to win real and necessary military-political support from Russia. As a result, we can come to the conclusion that after a steep fall at the end of 1920, the First Republic of Armenia, on the basis of the Armenian-Russian agreement signed on December 2, acquired a new historical perspective for further existence in the form of a Soviet socialist republic.

Ghosts of solid air: contested heritage and augmented reality in public space

ABSTRACT This article critically reflects on the design and development of a new Augmented Reality (AR) experience addressing issues of contested heritage in the historic built environment. The experience – Ghosts of Solid Air – is a 45-minute interactive theatrical narrative for mobile phones that tells a critical story about the legacies of colonialism and histories of protest and disobedience that have shaped contemporary Britain. Audiences follow the story from Trafalgar Square to Parliament Square in central London, encountering varied contested monuments and activist figures from the past along the route. This article describes the main elements of the experience before tracing the evolution of the project and its relationship to shifting debates on contested heritage in the UK since 2020. We conclude with reflections on the challenges and opportunities of AR when it comes to opening up new modes of heritage engagement, paying particular attention to questions of justice and participation that transcend issues of representation, recognition, and reinterpretation.

Advancements in Arabic Sign Language Recognition: A Method based on Deep Learning to Improve Communication Access

This research anticipates the issue of real-time sign language recognition as a means of facilitating the daily communication of the hearing-impaired persons. Improvement in the rate of real time sign language recognition will help in boosting interaction in the society and access to services for the hearing-impaired persons. This paper outlines a step-by-step guide on fine tuning an object detection model for the AASL using the SSD architecture. The AASL dataset which includes total of 7,857 samples of more than 200 patients are first preprocessed where the images are scaled, normalized, and augmented. In SSD model, the base of the network is the VGG16 network and few extra layers for feature extraction and for auxiliary and prediction of objects are added. Particularly in object detection, Intersection over Union (IoU), and mean Average Precision (mAP) are used in addition to the confusion matrix metrics comprising accuracy, precision, recall, and F1-score. The proposed model provides good recognition accuracy of 98 percent. This feature checks its capability of real-time identification of sign languages and shows 25% efficiency. The efficiency of the proposed method is explained when comparing the results of the modified VGG16-based SSD with other methodologies. As for the improvement of communication for the hearing-impaired individuals, the present work demonstrates that the deep learning methods can be significantly effective; however, suggesting that more research efforts should be directed to real-time solutions and that the datasets should be expanded.

Analysis of Revenue Recognition Issues in the Online Game Industry under the New Standards

With the continuous development of the Internet and the implementation of the new revenue standards officially announced by the Ministry of Finance in 2017, the recognition of revenue has a significant impact on the online game industry, which is related to the healthy development of the entire online game industry. Accounting treatment is also based on the recognition of revenue. This paper uses the investigation method and literature research method to study the online game industry revenue recognition under the new revenue criteria, which mainly includes three aspects: revenue recognition time point, transaction complexity, precipitation income, and puts forward suggestions, aiming to provide reference for the future online game industry revenue recognition related issues.

Overview of Semantic Segmentation Algorithms Based on Threshold Method and Deep Learning

With the development of hardware, the technology of artificial intelligence is more and more developed, and computer vision is once again a craze. Semantic segmentation is an important issue in computer recognition. Semantic segmentation makes the computer recognize the objects in a given image and classify and present them, which can save huge human time and cost, and the mature algorithms are more accurate than manual. The paper will focus on four sections: Firstly, different semantic segmentation algorithms were introduced. Then, the performance, advantages, and disadvantages of representative algorithms in each method were compared and analyzed, and the applications of semantic segmentation were listed. Finally, the optimization of semantic segmentation algorithms was introduced. The aim is to clarify the idea of the algorithms, the design intention, and the correlation between them, to understand the limitations of different algorithms, to enable the researcher to choose the most appropriate method, and to gain further new inspiration.

Degrees of Freedom: Rethinking Higher Education Credentials in South Africa

The shifting landscape of higher education and the labour market has necessitated re-evaluating conventional degree programmes, leading to an exploration of macro-credentialing as a link between university offerings and industry requirements. This paper examines the traditional dominance of the degree and the concurrent rise of strategic partnerships between higher education institutions and businesses, with the goal of developing credentials more closely aligned with the dynamic needs of the modern workforce. Traditionally, university degrees have been viewed as the bedrock of educational achievement and a prerequisite for professional success. However, rapid technological advancements and changing economic demands have exposed limitations in the degree model, particularly its often inflexible curriculum and lengthy completion time. Consequently, there is growing interest, especially within Private higher education, in macro-credentialing, which encompasses an array of non-degree awards that can be directly linked to specific skill sets and employment opportunities. Through qualitative analysis of partnership models across diverse industries and countries, this study identifies core elements contributing to the successful integration of macro-credentials into educational and professional spheres. These elements include co-creating curricula (between industry and the university), shared governance structures between educational and corporate entities, and robust assessment methodologies that validate learner competencies consistent with industry standards. Additionally, the study explores potential challenges and resistance within academia and industry, including credential recognition issues, balancing theoretical knowledge versus practical skills, and economic impacts on traditional degree pathways. It proposes strategic approaches to mitigate these challenges, emphasising the importance of transparency, stakeholder engagement, and continuous evaluation. The incorporation of macro-credentialing via university (both public and private), TVET college and Community College - Business partnerships possesses considerable potential to tackle future student discipline trajectories, the dire unemployment of South African youth and the growing needs of upskilling via life-long learning. It demands continuous innovation in educational models to guarantee they promote individual career achievements and societal progress in an increasingly intricate global economy. This paradigm transition from the conventional degree to a more adaptable, competency-based credentialing structure signifies a crucial development in matching education and industry.

“Idol talks!” AI-driven image to text to speech: illustrated by an application to images of deities

This work aims to provide an innovative solution to enhance the accessibility of images by an innovative image to text to speech system. It is applied to Hindu and Christian divine images. The method is applicable, among others, to enhance cultural understanding of these images by the visually impaired. The proposed system utilizes advanced object detection techniques like YOLO V5 and caption generation techniques like ensemble models. The system accurately identifies significant objects in images of Deities. These objects are then translated into descriptive and culturally relevant text through a Google text-to-speech synthesis module. The incorporation of text generation techniques from images introduces a new perspective to the proposed work. The aim is to provide a more comprehensive understanding of the visual content and allow visually impaired individuals to connect with the spiritual elements of deities through the immersive experience of auditory perception through a multimodal approach to make them feel inclusive in the community. This work is also applicable to preserve Cultural Heritage, Tourism and integrating with Virtual Reality (VR) and Augmented Reality (AR). Images of the artistic cultural legacy are hardly available in annotated databases, particularly those featuring idols. So we gathered, transcribed, and created a new database of Religious Idols in order to satisfy this requirement. In this paper, we experimented how to handle an issue of religious idol recognition using deep neural networks. In order to achieve this outcome, the network is first pre-trained on various deep learning models, and the best one which outperforms others is chosen. The proposed model achieves an accuracy of 96.75% for idol detection, and an approximate 97.06% accuracy for text generation according to the BLEU score.

Research on music genre recognition method based on deep learning

In this paper, we explore music genre recognition using deep learning methods, examining the application of feature extraction, model construction, and performance evaluation for different music genres. During the data preparation and preprocessing stages, data augmentation and normalization techniques were employed to enhance the model’s generalization capabilities. By constructing multilayer convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we achieved automatic recognition of music genres. In the experimental results analysis, we compared the accuracy and training time of different models, validating the effectiveness of deep learning in the field of music genre recognition. The limitations of deep learning methods and future research directions are also discussed, providing a reference for further studies in music information processing. This study delves into the issue of music genre recognition and proposes a deep learning-based approach. This method leverages neural networks to extract features and learn from audio data, enabling accurate classification of different music genres. Extensive experiments have demonstrated that our method achieves highly satisfactory results in music genre recognition tasks. Furthermore, we optimized the deep learning models, improving their generalization capabilities and accuracy. Our research offers the music industry an efficient and accurate method for music genre recognition, providing new perspectives and technical support for research and applications in the music field.

Claiming rock art, claiming indigeneity: Spaces and scales of recognition in Khoe and San identity claims

Based on research conducted in South Africa, this work revolves around the observation that certain rock art sites (Wildebeest Kuil, Northern Cape; Game Pass Shelter, KwaZulu-Natal) are utilized within identity and political frameworks—and a question: what role do rock art sites play in the construction of Khoe and San indigeneity? Contemporary indigeneity is closely linked to issues of recognition, which are themselves related to the restitution of rights, objects, and knowledge. Acknowledging the central role of objects in identity claims, this article proposes to analyze the place of these two rock art sites in the political construction of Khoe and San indigeneity. Based on a corpus of interviews, observations, legal texts, and press articles, certain uses of rock art sites will be presented as strategic means within a broader context of claims.

An IoT solution that detects human physical activities to support health assessment based on sensors and supervised learning algorithms

At the moment, human physical activities can be detected by activity recognition systems to help with health status evaluation. One of the key issues in human activity recognition (HAR) systems is cost, speed, and accuracy. With the advancement of mobile devices and increasingly sophisticated microprocessors, along with the integration of various sensors including accelerometers, human activity recognition has become easier and faster. However, the accuracy of activity classification based on accelerometer data depends on many different factors, which can lead to potential deviations in results. Through various experiments, we have found that selecting optimal features is crucial for the performance of classification. In this paper, we propose a simple feature set that yields very promising results with an accuracy of up to 99%.

Developing an Entrepreneurship Training Framework for Graphic Design Students at the Tshwane University of Technology, South Africa

Entrepreneurship education has become increasingly important worldwide, including for graduates in South Africa. Like their counterparts in other emerging economies, South African graduates face challenges in starting their businesses. This study focused on Graphic Design graduates at the Tshwane University of Technology (TUT), who encountered difficulties in establishing their design businesses. The research aimed to develop an entrepreneurship education framework for the Integrated Communication Design (ICD) programme at Tshwane University of Technology. A qualitative method was used along with an interpretivism research philosophy to examine small and micrographic design businesses in Tshwane’s central business district. In this article, twelve participants were chosen through purposive sampling. Four main themes were revealed in this article namely, core entrepreneurial skills, curriculum challenges in practical education, challenges faced by graphic design graduates, and strategies for improving business performance. These findings informed the proposed framework for entrepreneurial education for Tshwane University of Technology. The study identified ten key challenges for graphic design graduates, such as a lack of contracts, mentorship, and financial support, difficulties in hiring skilled staff, limited business knowledge, reliance on government contracts, excessive subcontracting, brand recognition issues, cash flow problems, and corruption. The study concluded by emphasising the urgent need for a shift in graphic design education, particularly in South Africa. It is recommended that graphic design educational institutions and the government join hands in finding ways to sponsor these entrepreneurs financially and non-financially to make their transition into the corporate world as smooth as possible. Keywords: Curriculum, entrepreneurship education, graphic design, higher education, Integrated Communication Design (ICD), small business

Few-Shot Image Classification of Crop Diseases Based on Vision-Language Models.

Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision-Language Models (VLMs), which enhance the multimodal synergy for better crop disease classification than the traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to solely using the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing the prompt quality. Secondly, we integrate cross-attention and SE (Squeeze-and-Excitation) Attention into the training-free mode VLCD(Vision-Language model for Crop Disease classification) and the training-required mode VLCD-T (VLCD-Training), respectively, for prompt text processing, enhancing the classifier weights by emphasizing the key text features. The experimental outcomes conclusively prove our method's heightened classification effectiveness in few-shot crop disease scenarios, tackling the data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure.

Development and Initial Testing of an Artificial Intelligence-Based Virtual Reality Companion for People Living with Dementia in Long-Term Care.

Background/Objectives: Feelings of loneliness are common in people living with dementia (PLWD) in long-term care (LTC). The goals of this study were to describe the development of a novel virtual companion for PLWD living in LTC and assess its feasibility and acceptability. Methods: The computer-generated virtual companion, presented using a head-mounted virtual reality display, was developed in two stages. In Stage 1, the virtual companion asked questions designed to encourage conversation and reminiscence. In Stage 2, more powerful artificial intelligence tools allowed the virtual companion to engage users in nuanced discussions on any topic. PLWD in LTC tested the application at each stage to assess feasibility and acceptability. Results: Ten PLWD living in LTC participated in Stage 1 (4 men and 6 women; average 82 years old) and Stage 2 (2 men and 8 women; average 87 years old). Session lengths ranged from 0:00 to 5:30 min in Stage 1 and 0:00 to 53:50 min in Stage 2. Speech recognition issues and a limited repertoire of questions limited acceptance in Stage 1. Enhanced conversational ability in Stage 2 led to intimate and meaningful conversations with many participants. Many users found the head-mounted display heavy. There were no complaints of simulator sickness. The virtual companion was best suited to PLWD who could engage in reciprocal conversation. After Stage 2, response latency was identified as an opportunity for improvement in future versions. Conclusions: Virtual reality and artificial intelligence can be used to create a virtual companion that is acceptable and enjoyable to some PLWD living in LTC. Ongoing innovations in hardware and software will allow future iterations to provide more natural conversational interaction and an enhanced social experience.

Exploration of the Links Between Psychosocial Well-being and Face Recognition Skills in a French-Speaking Sample.

Face recognition abilities vary tremendously in the general population. People at the lower end of the spectrum, those with developmental prosopagnosia, report stress, anxiety or social interaction issues due to their poor face recognition abilities. It is thus important to develop adequate diagnostic tools convenient to use for clinicians and to examine relationships between face recognition skills and negative affects. In the present study, we provide a validated French translation of the 20-item prosopagnosia index (PI20), a self-report measure used to detect people with developmental facial identity recognition deficits (Shah et al., 2015; Tsantani et al., 2021). We also examined links between face recognition skills measured with the PI20 and a standard face recognition test (Cambridge face memory test-CFMT; Duchaine & Nakayama, 2006) and measures of social anxiety (social interaction anxiety scale, social phobia scale) and negative affects (state trait anxiety scale, Beck depression inventory). We did not find any significant correlation between the CFMT and measures of psychosocial well-being and only found a weak positive association between the PI20 and social interaction anxiety. Although this association is weak and warrants further research, raising awareness about developmental face recognition issues may help improve the well-being of people with facial identity recognition deficits and provide new investigation or intervention avenues for clinicians who treat patients with social interaction anxiety.

Improved semantic-guided network for skeleton-based action recognition

A fundamental issue in skeleton-based action recognition is the extraction of useful features from skeleton joints. Unfortunately, the current state-of-the-art models for this task have a tendency to be overly complex and parameterized, which results in low model training and inference time efficiency for large-scale datasets. In this work, we develop a simple but yet an efficient baseline for skeleton-based Human Action Recognition (HAR). The architecture is based on adaptive GCNs (Graph Convolutional Networks) to capture the complex interconnections within skeletal structures automatically without the need of a predefined topology. The GCNs are followed and empowered with an attention mechanism to learn more informative representations. This paper reports interesting accuracy on a large-scale dataset NTU-RGB+D 60, 89.7% and 95.0% on respectively Cross-Subject, and Cross-View benchmarks. On NTU-RGB+D 120, 84.6% and 85.8% over Cross-Subject and Cross-Setup settings, respectively. This work provides an improvement of the existing model SGN (Semantic-Guided Neural Networks) when extracting more discriminant spatial and temporal features.

Subjective signs of criminal offense provided by art. 374 of the Criminal Code of Ukraine

It is noted that in order to establish the content of the right to defense, it is necessary to examine the subjective signs of the criminal offense provided for in Art. 374 of the Criminal Code of Ukraine. It is emphasized that the subject of violation of the right to defense is special and clearly defined in Art. 374 of the Criminal Code of Ukraine - investigator, investigator, prosecutor, judge. The signs of a violation of the right to defense by a special subject have been determined. The prerequisites for the legality of a person occupying the position of inquirer, investigator, prosecutor, judge, as well as the peculiarities of the legal personality of a special subject of a criminal offense provided for in Art. 374 of the Criminal Code of Ukraine. The qualified composition of this criminal offense was analyzed. It was determined that the purpose and motive of the violation of the right to defense are not important for the qualification of a criminal offense under Art. 374 of the Criminal Code of Ukraine. It is noted that the absence of signs of a special subject in a person excludes the possibility of bringing him to criminal responsibility as the perpetrator of a crime that can be committed by a special subject, it is expedient to agree with those scientists who maintain the position that the issue of recognition of a person who is a general subject of a crime, a co-perpetrator of a crime with a special subject should be decided individually, based on specific circumstances, taking into account the specifics of the objective features (objective side) of the crime. The subject of violation of the right to protection is special, the characteristics of which are defined in Part 2 of Art. 18 of the Criminal Code of Ukraine, according to which the special subject of a criminal offense is a convicted natural person who committed a criminal offense at the age from which criminal responsibility can arise, the subject of which can only be a certain person. Taking into account the above, it can be stated that the inquirer, investigator, prosecutor, judge as special subjects are not only characterized by the features of a general subject, such as age and sanity, but also belong to persons with special legal personality - the ability of a person in to acquire a certain legal status and act as a bearer of the subjective rights and legal duties of an inquirer, investigator, prosecutor, judge.

A Study of Difficulties Faced by Nam Can Tho University Non-English-majored Students in the Listening Skill of the Level-3-5 VSTEP

This study investigates the difficulties faced by non-English-majored students at Nam Can Tho University in mastering listening skills for the Level-3-5 VSTEP test. A total of 100 students participated in the study, which utilized a 52-item questionnaire to identify factors influencing listening comprehension. The study categorized these factors into six clusters: background knowledge, phonological awareness, word recognition, the speaker, the listener, and the physical setting.The results revealed that background knowledge significantly impacts listening comprehension, with students often struggling with unfamiliar topics and cultural references. Phonological awareness was another key factor, as students had difficulty recognizing spoken words, particularly when they were reduced or spoken quickly. Word recognition issues were highlighted, with students finding it challenging to identify words in complex sentences and at high speeds. The characteristics of the speaker, including speech rate and accent, also affected comprehension, along with the absence of visual cues.Listener-related factors such as anxiety and difficulties with incomplete information were significant barriers. Additionally, physical setting issues, including background noise and poor audio quality, were found to impact listening comprehension adversely.The study concludes that addressing these factors through targeted instructional strategies and improving the listening environment could enhance students’ listening skills. Future research should explore additional factors and strategies to support non-English-majored students in overcoming listening challenges.

Introduction: the turn to racism and anti-racism in Latin America

ABSTRACT We are currently witnessing a turn to racism and anti-racism in Latin America. The recognition of racism is not new, but the attention and challenge to racism, in such an orchestrated way, is. What are the signals of such turn to racism and anti-racism? What are the overall lessons for Latin America and from Latin America to global antiracist efforts? This introduction looks at the arguments of the articles in this special issue to highlight how issues of racial visibility, naming racism, racial data, legal rights and recognition, entrepreneurship, mestizo identity, the possibilities of alliances, racially-aware struggles against class (and gendered) oppression, are key to understanding this turn. While we do not claim that these articles cover the full extent of this turn in Latin America, we suggest that analysing how this turn appears in Latin American opens useful ways of thinking about anti-racism more widely.

Developing and valuation for Techniques Fingerprint Recognition System

الأنماط المميزة التي يلاحظها على أطراف الأصابع الإنسان هي نتيجة لمزيج من حواف وعمق الموجودة على سطح الجلد. تحقق هذه الأنماط تطورها الكامل خلال فترة الحمل وتبقى مستقرة طوال عمر الفرد. تشير بصمات الأصابع إلى الانطباعات التي تشكلها الأنماط الفريدة الموجودة على أطراف الأصابع. يمكن للكدمات الناتجة عن الإصابات العرضية، مثل الجروح والحروق، أن تؤثر بشكل مؤقت على رؤية بصمات الأصابع. ومع ذلك، بعد شفاء الآفة بالكامل، سيتم استعادة الأنماط الأصلية. يعد التعرف على بصمات الأصابع مجالًا ديناميكيًا للغاية ويتم بحثه على نطاق واسع في مجال القياسات الحيوية. تشير المقالة إلى الشكل المختصر للتقنية الحسابية المستخدمة للتأكد من التوافق بين بصمتين بشريتين متميزتين. في هذه المشكلة الصعبة المتعلقة بالتعرف على الأنماط، من الضروري تقليل معدلين للخطأ: معدل القبول الزائف (FAR) ومعدل الرفض الزائف (FRR). معدلات الخطأ هذه تتنافس مع بعضها البعض. لقد سهّل تطوير القدرات الحاسوبية إنشاء أنظمة التحقق من بصمات الأصابع (AFIS)، مما أدى إلى زيادة كبيرة في النشاط البحثي، خاصتا في العشرين عامًا الماضية. تهدف هذه المقالة إلى تقديم مراجعة شاملة للمشكلة المحيطة بالتعرف على بصمات الأصابع، بما في ذلك تحليل التصميم الأساسي وصعوبات التنفيذ، إلى جانب تقييم التطورات المستقبلية المحتملة.

Design of an Intelligent Radio Frequency Identification (RFID) Based Cashless Vending Machine for Sales of Drinks

This study examines the evolution of vending machines, emphasizing their integration with RFID technology for cashless transactions. Vending machines have transformed human-machine interaction, offering convenient access to products and services. Transitioning from coin-operated to RFID-enabled systems has revolutionized the industry, enhancing security, reducing costs, and improving user experience. Through exploration of technical specifications and design objectives, the research highlights RFID vending machines' potential to reshape consumer behavior and optimize operations. Traditional cash-based vending machines face challenges such as limited storage, recognition issues, and security concerns. To address these, the paper proposes an intelligent RFID-based cashless vending machine for drink sales. The system incorporates RFID technology for payment, allowing users to swipe cards and select drinks without cash involvement. Prototype development involved software design, utilizing C-language for multiproduct vending. Comparatively, the RFID-based system outperforms cash-based counterparts in efficiency, security, and sales tracking, presenting a superior solution for drink sales.