Search results for: legal recognition

Authors: Mingsiang Chen

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The increasing transactions among countries worldwide have brought about a trend of comparative law research in the legal community. An important branch of legal research, i.e., constitutional law, is no exception to the trend. The comparative study of constitutional law takes various forms, and one of these is to study the use of foreign law by constitutional courts. There are, in essence, three sources of foreign law usually used by constitutional courts: foreign constitutions, decisions by foreign constitutional courts, and legal theories developed by foreign scholars. There are two types of using foreign law by constitutional courts: citing any of the forenamed sources for reference purpose, ruling based on the contents or logic of any of the forenamed sources. This paper examines all the decisions handed down by the Constitutional Court of Taiwan from 1990 to 2017. Its purpose is to seek out the occasions, the extent, the significance, and the approach of such usage.

Keywords: comparative constitutional law, constitutional court, judicial review, Taiwan judiciary

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

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Authors: Meruyert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova, Madina Ermaganbetova

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This research paper explores the process of creating a proctoring system by evaluating the implementation of practical face recognition algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As an outcome, a proctoring system will be created, enabling the conduction of tests and ensuring academic integrity checks within the system. Due to the correct operation of the system, test works are carried out. The result of the creation of the proctoring system will be the basis for the automation of the informational, educational portal developed by machine learning.

Keywords: artificial intelligence, education portal, face recognition, machine learning, proctoring

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Authors: Assel Jaxylykova, Alexnder Pak

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This study utilized a Self-Organizing Map (SOM) for unsupervised learning on the CONLL-2003 dataset for Named Entity Recognition (NER). The process involved encoding words into 300-dimensional vectors using FastText. These vectors were input into a SOM grid, where training adjusted node weights to minimize distances. The SOM provided a topological representation for identifying and clustering named entities, demonstrating its efficacy without labeled examples. Results showed an F1-measure of 0.86, highlighting SOM's viability. Although some methods achieve higher F1 measures, SOM eliminates the need for labeled data, offering a scalable and efficient alternative. The SOM's ability to uncover hidden patterns provides insights that could enhance existing supervised methods. Further investigation into potential limitations and optimization strategies is suggested to maximize benefits.

Keywords: named entity recognition, natural language processing, self-organizing map, CONLL-2003, semantics

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Authors: Felix O. Omosele

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Several studies have established that electronic voting machines are prone to multi-faceted challenges. One of which is their capacity to lose votes after the ballots might have been cast. Therefore, the international consensus appears to favour the use of electronic voting machines that are accompanied with verifiable audit paper audit trail (VVPAT). At present, there is no known study that has evaluated the impacts (or otherwise) of this verification and auditing on the authentication, admissibility and evidential weight of electronically-obtained electoral data. This legal inquiry is important as elections are sometimes won or lost in courts and on the basis of such data. This gap will be filled by the present research work. Using the United States of America as a case study, this paper employed a qualitative legal analysis of several of its appellate courts’ judicial opinions. This analysis equally unearths the necessary statutory rules and regulations that are important to the research problem. The objective of the research is to highlight the roles played by VVPAT on electoral evidence- as seen from the eyes of the court. The preliminary outcome of this qualitative analysis shows that the admissibility and weight attached to ‘Computer Evidence from e-voting machines (CEEM)’ are often treated with general standards applied to other computer-stored evidence. These standards sometimes fail to embrace the peculiar challenges faced by CEEM, particularly with respect to their tabulation and transmission. This paper, therefore, argues that CEEM should be accorded unique consideration by courts. It proposes the development of a legal standard which recognises verification and auditing as ‘weight enhancers’ for electronically-obtained electoral data.

Keywords: admissibility of computer evidence, electronic voting, qualitative legal analysis, voting machines in the USA

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Authors: Ali Cheraghian, Farshid Hajati, Soheila Gheisari, Yongsheng Gao

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In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.

Keywords: Gabor filter, discrete cosine transform, 2.5d face recognition, pose

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Authors: Anandkumar Rshindhe

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Patents are considered as good monopoly in India. It is a mechanism by which the inventor is encouraged to do invention and also to make available to the society at large with a new useful technology. Patent system does not provide any protection to the invention itself but to the claims (rights) which the patentee has identified in relation to his invention. Thus the patentee is granted monopoly to the extent of his recognition of his own rights in the form of utilities and all other utilities of invention are for the public. Thus we find both benefit to the inventor and the public at large that is the ultimate consumer. But developing any such technology is not free of cost. Inventors do a lot of investment in the coming out with a new technologies. One such example if of Pharmaceutical industries. These pharmaceutical Industries do lot of research and invest lot of money, time and labour in coming out with these invention. Once invention is done or process identified, in order to protect it, inventors approach Patent system to protect their rights in the form of claim over invention. The patent system takes its own time in giving recognition to the invention as patent. Even after the grant of patent the pharmaceutical companies need to comply with many other legal formalities to launch it as a drug (medicine) in market. Thus major portion in patent term is unproductive to patentee and whatever limited period the patentee gets would be not sufficient to recover the cost involved in invention and as a result price of patented product is raised very much, just to recover the cost of invent. This is ultimately a burden on consumer who is paying more only because the legislature has failed to provide for the delay and loss caused to patentee. This problem can be effectively remedied if Patent Term extension is done. Due to patent term extension, the inventor gets some more time in recovering the cost of invention. Thus the end product is much more cheaper compared to non patent term extension.The basic question here arises is that when the patent period granted to a patentee is only 20 years and out of which a major portion is spent in complying with necessary legal formalities before making the medicine available in market, does the company with the limited period of monopoly recover its investment made for doing research. Further the Indian patent Act has certain provisions making it mandatory on the part of patentee to make its patented invention at reasonable affordable price in India. In the light of above questions whether extending the term of patent would be a proper solution and a necessary requirement to protect the interest of patentee as well as the ultimate consumer. The basic objective of this paper would be to check the implications of Extending the Patent term on Indian Consumers. Whether it provides the benefits to the patentee, consumer or a hardship to the Generic industry and consumer.

Keywords: patent term extention, consumer interest, generic drug industry, pharmaceutical industries

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Authors: Marco Khalaf Ayad Milhaail

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In thinking about the role of legal rules and their impact on social ethics and social structures, scholars have explored many issues related to gender, power, and ideology. First, it provides a framework for defining feminist legal studies through an overview of the field's evolution in terms of equality, rights, and justice. Secondly, it encourages those interested in equality, rights, and justice regarding women's issues to participate in international comparative law research. Third, we must emphasize that those seeking solutions to disability and discrimination must be aware of the need to confront the so-called undermining of culture. Therefore, an effective way for women to solve this problem is to rely heavily on international law, which establishes basic legal principles such as gender equality, rights, and justice and can help create a domestic environment. Woman has gained many advantages by adopting the law of Divorce in the Islamic Sharea. Any Egyptian woman can get divorce by letting her rightful rights and wealth to her husband in return for her freedom.

Keywords: stability, harsh environments, techniques, thermal, properties, materials, applications, brittleness, fragility, disadvantages, bank, branches, profitability, setting prediction, effective target, measurement, evaluation, performance, commercial, business, profitability, sustainability, financial, system, banks

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Authors: Nidal F. Shilbayeh, Remah W. Al-Khatib, Sameer A. Nooh

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Arabic offline handwriting recognition systems are considered as one of the most challenging topics. Arabic Handwritten Numeral Strings are used to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. Segmentation of connected numerals is the main bottleneck in the handwritten numeral recognition system.  This is in turn can increase the speed and efficiency of the recognition system. In this paper, we proposed algorithms for automatic segmentation and feature extraction of Arabic handwritten numeral strings based on Watershed approach. The algorithms have been designed and implemented to achieve the main goal of segmenting and extracting the string of numeral digits written by hand especially in a courtesy amount of bank checks. The segmentation algorithm partitions the string into multiple regions that can be associated with the properties of one or more criteria. The numeral extraction algorithm extracts the numeral string digits into separated individual digit. Both algorithms for segmentation and feature extraction have been tested successfully and efficiently for all types of numerals.

Keywords: handwritten numerals, segmentation, courtesy amount, feature extraction, numeral recognition

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Authors: Tomyslav Sledevič, Artūras Serackis, Gintautas Tamulevičius, Dalius Navakauskas

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This paper presents a comparative evaluation of features extraction algorithm for a real-time isolated word recognition system based on FPGA. The Mel-frequency cepstral, linear frequency cepstral, linear predictive and their cepstral coefficients were implemented in hardware/software design. The proposed system was investigated in the speaker-dependent mode for 100 different Lithuanian words. The robustness of features extraction algorithms was tested recognizing the speech records at different signals to noise rates. The experiments on clean records show highest accuracy for Mel-frequency cepstral and linear frequency cepstral coefficients. For records with 15 dB signal to noise rate the linear predictive cepstral coefficients give best result. The hard and soft part of the system is clocked on 50 MHz and 100 MHz accordingly. For the classification purpose, the pipelined dynamic time warping core was implemented. The proposed word recognition system satisfies the real-time requirements and is suitable for applications in embedded systems.

Keywords: isolated word recognition, features extraction, MFCC, LFCC, LPCC, LPC, FPGA, DTW

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Authors: Heng Yang, Tao Luo, Yakun Zhang, Kai Wang, Wei Qin, Liang Xie, Ye Yan, Erwei Yin

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Recent years have seen that audio-visual recognition has shown great potential in a strong noise environment. The existing method of audio-visual recognition has explored methods with ResNet and feature fusion. However, on the one hand, ResNet always occupies a large amount of memory resources, restricting the application in engineering. On the other hand, the feature merging also brings some interferences in a high noise environment. In order to solve the problems, we proposed an effective framework with bidirectional distillation. At first, in consideration of the good performance in extracting of features, we chose the light model, Efficientnet as our extractor of spatial features. Secondly, self-distillation was applied to learn more information from raw data. Finally, we proposed a bidirectional distillation in decision-level fusion. In more detail, our experimental results are based on a multi-model dataset from 24 volunteers. Eventually, the lipreading accuracy of our framework was increased by 2.3% compared with existing systems, and our framework made progress in audio-visual fusion in a high noise environment compared with the system of audio recognition without visual.

Keywords: lipreading, audio-visual, Efficientnet, distillation

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Authors: Paulo Fernando Silva Filho, Elcio Hideiti Shiguemori

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The selection of specific landmarks for an Unmanned Aerial Vehicles’ Visual Navigation systems based on Automatic Landmark Recognition has significant influence on the precision of the system’s estimated position. At the same time, manual selection of the landmarks does not guarantee a high recognition rate, which would also result on a poor precision. This work aims to develop an automatic landmark selection that will take the image of the flight area and identify the best landmarks to be recognized by the Visual Navigation Landmark Recognition System. The criterion to select a landmark is based on features detected by ORB or AKAZE and edges information on each possible landmark. Results have shown that disposition of possible landmarks is quite different from the human perception.

Keywords: clustering, edges, feature points, landmark selection, X-means

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Authors: Abdelkader Ababneh

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Heritage management planning is increasingly important throughout the international context, particularly in the developing countries. Jordan has important and unique heritage resources due to its natural topography and climate, but also to its history and old sites. A high number of these archaeological sites are in very good state of preservation. Most natural sites and resources are privately managed while archaeological heritage sites are publicly managed within national legal texts and with some referencing to international legal documents. This study examines the development of cultural heritage management in Jarash, and questions if this heritage has been managed in an appropriate manner. The purpose of this paper is to define and review the stakeholders in charge of the management of the archaeological site of Jarash, the legal texts, laws and documents adopted to apply the site management. Relations and coordination between stakeholders and the challenge of the planning process is also the focus of this paper. A review of pertinent academic, technical studies, reports and projects literature pertaining to the heritage management planning in general and related to the site of Jarash in particular coupled with field study of the site served as the background of the information base for the study. Current context of actors, legislative framework, planning policies and initiatives for the site of Jarash reveal important and continuous challenge for managing the site. Recommendations suggest reviewing and restructuring the entity responsible of the sites management. It is also recommended to review their applied policies and a redevelopment of the legislative frame work.

Keywords: heritage management, stakeholders, legal protection, Jarash

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Authors: Marieta Safta

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The separation and balance of state powers constitute the basis of the rule of law. Observance of this principle requires framing of public authorities within the limits of competence established by the Constitution and the law, as well as loyal cooperation between them. From this perspective, the attribution of the constitutional courts for settling legal conflicts of a constitutional nature is an important tool for correcting the tendencies of violation of these limits, as well as for identifying solutions for situations that do not find an explicit regulation in the constitutional texts. The present study analyzes the jurisprudence of the Constitutional Court of Romania in the field of legal conflicts of a constitutional nature, revealing, together with the presentation of conflict situations, the vulnerabilities of the constitutional reference texts. It is also highlighted the role of the constitutional courts in the evolution of constitutional law institutions, even in terms of defining and redefining the regime of the forms of government. The conclusion of the study, beyond the subject of legal conflicts of a constitutional nature, bears on the necessity, even more so in this matter, of the certainty of jurisdictional interpretation. This certainty cannot be achieved as long as the interpretation is not authoritative; consequently, the assurance of the effectiveness of constitutional justice constitute a key issue of the rule of law.

Keywords: legal conflicts of constitutional nature, the Constitutional Court of Romania, the separation and balance of powers in the state, the effectiveness of constitutional justice

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Authors: Alaaeldin Hamdy Ahmed Mohammed

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Introduction: This study examines sports’ fans and non-interested public perception and recognition of the problems that have negative impacts upon the Egyptian sports, particularly football, through caricatures. Eight caricature paintings were designed to express eight problems affecting the Egyptian sports and its development. These paintings were distributed on two groups of the fans and the non-interested public. Methods: The study was limited to eight caricatures representing the eight issues which are: the impact of stopping the sports activity on athletes, the effect of clubs’ disagreement, fanaticism between the members of the ultras of different clubs, the negative impact of the mingling of politics into sports, the negative role of the clubs affects the professionalism of the promising players, the conflict between the national organization responsible for sports, the breaking in of the fans to the playgrounds, the impact of the lack of planning on the national team. The Results: The results showed that both sports fans and those who are not interested in sports recognized the problems that the caricatures refer to and criticizes exaggeration although the rate was higher for the fans. These caricatures contributed also in their recognition of the danger of the negative impact of these problems on the Egyptian sports, particularly football which is the most common at the Egyptian sports fans. Discussion: This finding echoes the conclusion that caricatures are distinctive in the adults’ facial stimuli that are either systematically exaggerated recognition of them.

Keywords: caricature, fans, football, sports

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Authors: Susanna Menis

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The context of this study revolves around the theme of consent and the construction of unlawfulness in judicial decisions. It aims to explore the formation of societal perceptions of unlawfulness within the context of consensual sexual acts leading to harmful consequences. This study investigates how judges create legal rules that reflect social solidarity and protect against violence. Specifically, the research aims to understand the justification behind criminalising consensual sexual activity when categorised under different offences. The main question addressed in this study will evaluate the way judges create legal rules that they believe reflect social solidarity and protect against violence. The study employs a historical genealogy approach as its methodology. This approach allows for tracing back the original formation of societal perspectives on unlawfulness, thus highlighting the socially constructed nature of the present understanding. The data for this study will be collected through an extensive literature review, examining historical legal cases and documents that shape the understanding of unlawfulness. This will provide a comprehensive view of how social attitudes toward private sexual relations influenced the creation of legal rules. The theoretical importance of this research lies in its contribution to socio-legal scholarship. This study adds to the existing knowledge on the topic by exploring questions of unconscious bias and its origins. The findings shed light on how and why individuals possess unconscious biases, particularly within the judicial system. In conclusion, this study investigates judicial decisions concerning consensual sexual acts and the construction of unlawfulness. By employing a historical genealogy approach, the research sheds light on how judges create legal rules that reflect social solidarity and aim to protect against violence. The theoretical importance of this study lies in its contribution to understanding unconscious bias and its origins within the judicial system. Through data collection and analysis procedures, this study aims to provide valuable insights into the formation of social attitudes towards private sexual relations and its impact on legal rulings.

Keywords: consent, sexual offences, offences against the person, legal genealogy, social construct

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Authors: Daliyah S. Aljutaili, Redna A. Almutlaq, Suha A. Alharbi, Dina M. Ibrahim

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All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.

Keywords: currency recognition, feature detection and description, SIFT algorithm, SURF algorithm, speeded up and robust features

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Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

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The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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Authors: Sandhya Baskaran, Hari Kumar Nagabushanam

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Sarcasm sentiment recognition is an area of natural language processing that is being probed into in the recent times. Even with the advancements in NLP, typical translations of words, sentences in its context fail to provide the exact information on a sentiment or emotion of a user. For example, if something bad happens, the statement ‘That's just what I need, great! Terrific!’ is expressed in a sarcastic tone which could be misread as a positive sign by any text-based analyzer. In this paper, we are presenting a unique real time ‘word with its tone’ spotting technique which would provide the sentiment analysis for a tone or pitch of a voice in combination with the words being expressed. This hybrid approach increases the probability for identification of special sentiment like sarcasm much closer to the real world than by mining text or speech individually. The system uses a tone analyzer such as YIN-FFT which extracts pitch segment-wise that would be used in parallel with a speech recognition system. The clustered data is classified for sentiments and sarcasm score for each of it determined. Our Simulations demonstrates the improvement in f-measure of around 12% compared to existing detection techniques with increased precision and recall.

Keywords: sarcasm recognition, tone-word spotting, natural language processing, pitch analyzer

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Authors: Saurabh Farkya, Govinda Surampudi

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Optical Character Recognition is one of the current major research areas. This paper is focussed on recognition of Devanagari script and its sound generation. This Paper consists of two parts. First, Optical Character Recognition of Devnagari handwritten Script. Second, speech synthesis of the recognized text. This paper shows an implementation of support vector machines for the purpose of Devnagari Script recognition. The Support Vector Machines was trained with Multi Domain features; Transform Domain and Spatial Domain or Structural Domain feature. Transform Domain includes the wavelet feature of the character. Structural Domain consists of Distance Profile feature and Gradient feature. The Segmentation of the text document has been done in 3 levels-Line Segmentation, Word Segmentation, and Character Segmentation. The pre-processing of the characters has been done with the help of various Morphological operations-Otsu's Algorithm, Erosion, Dilation, Filtration and Thinning techniques. The Algorithm was tested on the self-prepared database, a collection of various handwriting. Further, Unicode was used to convert recognized Devnagari text into understandable computer document. The document so obtained is an array of codes which was used to generate digitized text and to synthesize Hindi speech. Phonemes from the self-prepared database were used to generate the speech of the scanned document using concatenation technique.

Keywords: Character Recognition (OCR), Text to Speech (TTS), Support Vector Machines (SVM), Library of Support Vector Machines (LIBSVM)

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Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Dalhat Binta Dan - Ali

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The reality of the new world economic order and the ever increasing importance of biotechnology in the global economy have necessitated the ratification of the Cartagena Protocol on Biosafety and the recent promulgation of Biosafety Act in Nigeria 2015. The legal regimes are anchored on the need to create an enabling environment for the flourishing of bio-trade and also to ensure the safety of the environment and human health. This paper critically examines the legal framework on biosafety by taking a cursory look at its philosophical foundation, key issues and milestones. The paper argues that the extant laws, though a giant leap in the establishment of a legal framework on biosafety, it posits that the legal framework raises debate and controversy on the difficulties of risk assessment on biodiversity and human health, other challenges includes lack of sound institutional capacity and the regimes direction of a hybrid approach between environmental conservation and trade issues. The paper recommend the need for the country to do more in the area of stimulating awareness and establishment of a sound institutional capacity to enable the law ensure adequate level of protection in the field of safe transfer, handling, and use of genetically modified organisms (GMOs) in Nigeria.

Keywords: Cartagena protocol, biosafety, issues, challenges, biotrade, genetically modified organism (GMOs), environment

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Authors: Abdus-Samii Imam Arikewuyo

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The human rights phenomenon increasingly gains universal prominence in the contemporary age. This embraces the clamour for a just treatment of individuals in society. The human rights agitation is a global pursuit which virtually gave birth to many national and international human rights organizations. In particular, Nigeria accedes to a number of human rights covenants. Invariably, there are some provisions which are recognized as inalienable rights of man in his society by which his intrinsic worth and dignity are protected by law. Nonetheless, the constituents of human rights differ in various societies. Conversely, Islam, as a complete code of life, guarantees the rights of a man vis-à-vis the rights of others in his environment regardless of place and time. Human rights pressure in Nigeria in recent times prompted proactive steps to address the issue through various legal instruments. Amazingly, the struggle appears to be a rhetorical noise because the human rights violation subsists. This provokes the present research on a comparative study of the Nigerian legal provisions and the Islamic law perspectives on human rights. It is discovered that the first is simply theoretical, while the other contains both the theoretical framework and the practical measures for its enforcement. The study adopts analytical and descriptive methods. It concludes with the assertion that the Islamic law provisions are all-embracing, universal and more efficacious. Hence, it recommends the adoption of the Islamic law approach to human rights issues.

Keywords: human rights, Nigerian legal provisions, shariah law, comparative study, charter

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Authors: A. Degale Desta, Tamirat Kebamo

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Background: Recent advances in computer vision and pattern recognition have significantly improved activity recognition through video analysis, particularly with the application of Deep Convolutional Neural Networks (CNNs). One-stage detectors now enable efficient video-based recognition by simultaneously predicting object categories and locations. Such advancements are highly relevant in educational settings where CCTV surveillance could automatically monitor academic activities, enhancing security and classroom management. However, current datasets and recognition systems lack the specific focus on campus environments necessary for practical application in these settings.Objective: This study aims to address this gap by developing a dataset and testing an automated activity recognition system specifically tailored for educational campuses. The EthioCAD dataset was created to capture various classroom activities and teacher-student interactions, facilitating reliable recognition of academic activities using deep learning models. Method: EthioCAD, a novel video-based dataset, was created with a design science research approach to encompass teacher-student interactions across three domains and 18 distinct classroom activities. Using the Roboflow AI framework, the data was processed, with 4.224 KB of frames and 33.485 MB of images managed for frame extraction, labeling, and organization. The Ultralytics YOLOv8 model was then implemented within Google Colab to evaluate the dataset’s effectiveness, achieving high mean Average Precision (mAP) scores. Results: The YOLOv8 model demonstrated robust activity recognition within campus-like settings, achieving an mAP50 of 90.2% and an mAP50-95 of 78.6%. These results highlight the potential of EthioCAD, combined with YOLOv8, to provide reliable detection and classification of classroom activities, supporting automated surveillance needs on educational campuses. Discussion: The high performance of YOLOv8 on the EthioCAD dataset suggests that automated activity recognition for surveillance is feasible within educational environments. This system addresses current limitations in campus-specific data and tools, offering a tailored solution for academic monitoring that could enhance the effectiveness of CCTV systems in these settings. Conclusion: The EthioCAD dataset, alongside the YOLOv8 model, provides a promising framework for automated campus activity recognition. This approach lays the groundwork for future advancements in CCTV-based educational surveillance systems, enabling more refined and reliable monitoring of classroom activities.

Keywords: deep CNN, EthioCAD, deep learning, YOLOv8, activity recognition

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Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: Jun Gil Ahn, Jong Kang Park, Jong Tae Kim

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In this paper, we analyze major components of activity recognition (AR) in wearable device with 9-axis sensors and sensor fusion filters. 9-axis sensors commonly include 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. We chose sensor fusion filters as Kalman filter and Direction Cosine Matrix (DCM) filter. We also construct sensor fusion data from each activity sensor data and perform classification by accuracy of AR using Naïve Bayes and SVM. According to the classification results, we observed that the DCM filter and the specific combination of the sensing axes are more effective for AR in wearable devices while classifying walking, running, ascending and descending.

Keywords: accelerometer, activity recognition, directiona cosine matrix filter, gyroscope, Kalman filter, magnetometer

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Authors: Koray U. Erbas

Abstract:

Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Carlos Vera, Tetyana Baydyk, Ernst Kussul, Graciela Velasco, Miguel Aparicio

Abstract:

This article describes the Random Subspace Neural Classifier (RSC) for the recognition of meteors in the night sky. We used images of meteors entering the atmosphere at night between 8:00 p.m.-5: 00 a.m. The objective of this project is to classify meteor and star images (with stars as the image background). The monitoring of the sky and the classification of meteors are made for future applications by scientists. The image database was collected from different websites. We worked with RGB-type images with dimensions of 220x220 pixels stored in the BitMap Protocol (BMP) format. Subsequent window scanning and processing were carried out for each image. The scan window where the characteristics were extracted had the size of 20x20 pixels with a scanning step size of 10 pixels. Brightness, contrast and contour orientation histograms were used as inputs for the RSC. The RSC worked with two classes and classified into: 1) with meteors and 2) without meteors. Different tests were carried out by varying the number of training cycles and the number of images for training and recognition. The percentage error for the neural classifier was calculated. The results show a good RSC classifier response with 89% correct recognition. The results of these experiments are presented and discussed.

Keywords: contour orientation histogram, meteors, night sky, RSC neural classifier, stars

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Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

Abstract:

Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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Authors: Irina A. Martynenko, Viktoriia V. Pikalova

Abstract:

At the moment, there are more than a thousand law schools in Russia. The program of preparation in each of them without exception includes English language course. It is believed that lawyers in Russia are best trained at the MGIMO University, the All-Russian State University of Justice, Kutafin Moscow State Law University, Peoples’ Friendship University of Russia, Lomonosov Moscow State University, St. Petersburg State University, Diplomatic Academy of Russian Foreign Ministry and some others. Currently, the overwhelming majority of universities operate using the two-level system of education: bachelor's plus master's degree. Foreign languages are taught at both levels. The main example of consideration used throughout this paper is Kutafin Moscow State Law University being one of the best law schools in the country. The article examines traditions of teaching legal English in Russia and highlights problem arising in this process. The authors suggest ways of solving them in the scope of modern views and practice of teaching English for specific purposes.

Keywords: Kutafin Moscow State Law University, legal English, Russia, teaching

Procedia PDF Downloads 230