Search results for: human action classification

Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: K. W. A. M. Kokila, Matsui Kenichi

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Sri Lanka’s disaster management policy aims to protect lives and developments in disaster affected areas by effectively using resources for disaster risk reduction, emergency management, and community awareness. However, funding for these action programs has posed a serious challenge to the country’s economy. This paper examines the extent to which private-public partnerships (PPPs) can facilitate and expedite disaster management works. In particular, it discusses the results of the questionnaire survey among policymakers, government administrators, NGOs, and private businesses. This questionnaire was conducted in 2017. All respondents were selected based on their experience in PPP projects in the past. The survey focused on clarifying the effectiveness of past PPP projects as well as their efficiency and transparency. The respondents also provided their own opinions and suggestions to improve the future PPP projects in Sri Lanka. The questionnaire was distributed to fifteen persons. The results show that almost all respondents think that PPP projects are beneficial and important for future disaster risk management in Sri Lanka. The respondents, however, showed some reservation about effectiveness and transparency of the PPP process. This paper also discusses the results on the respondents’ perceptions about their capacity regarding human resources and management. This paper, overall, sheds light on technological, financial and human resource management practices in developed countries as well as policy and legislation provisions regarding PPP projects.

Keywords: disaster management, policy, private public partnership, projects

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Authors: Shazmeen Maroof

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The study is motivated by a desire to investigate whether there is substance to claims that, relative to traditional donors, China disregards human rights considerations when allocating overseas aid. While the stated policy of the U.S. is that consideration of potential aid recipients’ respect for human rights is mandatory, some quantitative studies have cast doubt on whether this is reflected in actual allocations. There is a lack of academic literature that formally assesses the extent to which the two countries' aid allocations differ; which is essential to test whether the criticisms of China's aid policy in comparison to that of the U.S. are justified. Using data on two standard human rights measures, 'Political Terror Scale' and 'Civil Liberties', the study analyse the two donors’ aid allocations among 125 countries over the period 2000 to 2014. The bivariate analysis demonstrated that a significant share of China’s aid flow to countries with poor human rights record. At the same time, the U.S. seems little different in providing aid to these countries. The empirical results obtained from the Fractional Logit model also provided some support to the general pessimism regarding China’s provision of aid to countries with poor human rights record, yet challenge the optimists expecting better targeted aid from the U.S. These findings are consistent with the split between humanitarian and non-humanitarian aid and in the sample of countries whose human rights record is below some threshold level.

Keywords: China's aid policy, foreign aid allocation, human rights, United States Foreign Assistance Act

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Authors: Elizabeth Carthy

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There is growing recognition that sexual exploitation and abuse (SEA) perpetrated by humanitarian workers is widespread, most recently affirmed by allegations of high-ranking Oxfam officials paying women for sex in post-earthquake Haiti. SEA covers a range of gendered abuses, including rape, sexual assault, and ‘transactional’ or ‘survival’ sex. Holding individuals legally accountable for such behaviors is difficult in all contexts even more so in fragile and conflict-affected settings. Transactional sex, for the purposes of this paper, refers to situations where humanitarian workers exchange aid or assistance for sexual services. This paper explores existing organizational accountability measures relating to transactional sex engaged in by international humanitarian workers through a descriptive and interpretive case study approach-examining the situation in Haiti. It comparatively analyses steps the United Nations has taken to combat this problem. Then it examines the possibility of domestic legal accountability for such conduct in Haiti. Finally, the paper argues that international human rights law can fill in potential gaps in domestic legal frameworks to ensure states hold humanitarian workers and potentially organizations accountable for engaging in and/or perpetuating this gendered abuse of power.

Keywords: gender-based violence, humanitarian action, international human rights law, sexual exploitation

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Authors: Zainab Olabisi Tairu

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The human species is facing some of the most challenging issues encountered as civilization and development occurs. The most salient factors threatening all species globally are habitats loss and degradation, overexploitation, competition with unwanted invasive species, pollution, global climate and various individual lifestyles of indigenous species. In order to avoid or minimize the effect of our actions on the environment and to balance employee work life with their private life, Green Human Resource is important and must be practiced in every organization including Higher Learning Institutions. This study addressed Green HRM from an institutional perspective, University systems are involved in numerous and complex social, educational and extra-curricular activities. The University community must be challenged to rethink and re-construct their environmental policies and practices in order to contribute to sustainable development. Many institutions only look at sustainability from the technology improvement aspect and waste management. People are the principal actors for sustainability development at the institutional level. The aim of the study is to explore the concept of Green Human Resource Management at a case site. Divine Word University (DWU) an Institution of Higher Education that embraced the ‘Printing & Paper use Policy’, also commonly referred to as the ‘paperless policy’, the use of solar as an alternative source of energy, water conservation and improvement in internet technology (IT) with the aim of becoming a green institution in effort to help save the environment. This study used Participatory Action Research as the Overarching methodological framework and Egg of sustainability and Wellbeing as the theoretical perspective in analyzing the data, engaging Case study strategy and a mixed method design at DWU. Focus group interview were conducted with three departments at the University, semi-structure interviews with the senior managers, survey questionnaire administered to students and staff with a sample size of 176 participants, in addition, policy documents were also exploited as extra source of data. Waste management including e-waste appeared to be one of the main concerns at DWU. A vast majority of DWU staff and students expressed the need for their institution to do more on sustainability education. The findings revealed that members of the community are not fully integrated like the Egg of sustainability and wellbeing in order to achieve sustainable development goal. The concept of Green Human Resource Management in Universities lies with the idea that Universities must bear profound responsibilities to manage its stakeholders in an environmental friendly way. Human resource management can help local institutions to recognize the need for changes of lifestyle, production, consumption as well as the end product in order to combat or at least reduce human Induced which produce or aggravate it.

Keywords: sustainability, environmental management, higher education institutions, green human resource management

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Authors: Ceren Kaya, Okan Erkaymaz, Orhan Ayar, Mahmut Özer

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Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%).

Keywords: diabetic retinopathy, discrete wavelet transform, multi-layer perceptron, radial basis function, video-oculography (VOG)

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Yoshimoto Kurihara, Tad Gonsalves

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Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

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Authors: Mazlum Çelik, Burcu Gürkan Ercan, Ahmet Ayaz, Hilal Yakut İpekoğlu, Furkan Baltacı, Mustafa Kurtoğlu, Bilge Kalkavan, Sinem Küçükyılmaz, Hikmet Çağrı Yardımcı, Şeyma Sevgican, Cemile Gökçe Elkovan, Bilal Çayır, Mehmet Emin Düzcan

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The earthquake research literature generally examines emotional, cognitive, and behavioral responses after an earthquake. Studies concerning the behavioral responses to earthquakes reveal that after the earthquake, people either flee in a panic or do not act according to the stereotype that they act irrationally and anti-socially and sometimes give rational and adaptive reactions. However, the rareness of research dealing with human behavior experiencing the earthquake moment makes it necessary to pay particular attention to these behavior patterns. In this direction, this study aims to examine human behavior indoors in case of rising earthquake intensity. In Turkey, located on geography in the earthquake zone, devastating earthquakes took place, such as in "Istanbul" with a magnitude of 7.4 in 1999 and in "Elazığ" with a magnitude of 6.8 in 2020. Occurred recently, the "Kahramanmaraş" earthquake affected 11 provinces, with a magnitude of 7.7 and 7.6 in 2023. In addition, there is expected to be a devastating earthquake in Istanbul, experts warn. For this reason, it is essential to understand human behavior for disaster risk. Management and pre-disaster preparedness to be effective and efficient and to take realistic measures to protect human life. Mazlum Çelik, Burcu Gürkan Ercan, Ahmet Ayaz, Hilal Yakut İpekoğlu, Furkan Baltacı, Mustafa Kurtoğlu, Bilge Kalkavan, Sinem Küçükyılmaz, Hikmet Çağrı Yardımcı, Şeyma Sevgican, Cemile Gökçe Elkovan, Bilal Çayır, Mehmet Emin Düzcan. In this study, which is currently part of a project supported by The Scientific and Technological Council of Turkey (TUBITAK), the indoor recordings during the earthquakes in Elazig on January 24, 2020, and in İzmir on October 30, 2020, are examined, and the people's behavior during the earthquake is analyzed. In this direction, video recordings taken from the YouTube archives of İzmir and Elazığ Disaster and Emergency Management Presidency (AFAD) Directorates and metropolitan municipalities are examined. The researchers have created an observation form in line with the information in the relevant literature to classify people's behavior during an earthquake. It is intended to determine the behavioral patterns by classifying according to the form and video analysis of the people heading toward the door, remaining stable, taking protective measures, turning to people, and engaging in "other" behaviors outside of these behaviors during the earthquake. A total of 60 video analyzes are carried out from Elazığ and İzmir. The descriptive statistic has been used with the SPSS 23.0 package program in the data analysis. It is found that in the event of an increase in the severity of the earthquake, unlike Elazığ, in İzmir, protective action is preferred to the act of remaining stable. In addition, it is observed that with the increase in the earthquake's intensity, women attempt to take more protective action while men head toward the door. In contrast, a rise is observed in the behavior of young people heading toward the door and taking protective actions, while there is a decrease in their behavior directing to people. These findings, unlike the literature, reveal that human behavior during earthquakes cannot be reduced to a single behavior pattern, such as drop-cover-hold-on. The results show that it is necessary to understand the behaviors of individuals during the earthquake and to develop practical policy proposals for combating earthquakes by considering sociocultural, geographical, and demographic variables.

Keywords: descriptive analysis, earthquake, human behaviour, disaster policy.

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Authors: Prajwal Raj Gyawali, Aastha Dahal

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The tension between human rights and loans provided by international development banks with hidden conditions in the pretext of development is a complex issue with significant implications for the rights of citizens in borrowing countries. It is important for all parties involved, including international banks, borrowing countries, and affected communities, to consider and respect human rights in the negotiation and implementation of development projects. Yet, it is rare for human rights actors or communities to have a seat at the negotiation table when loans are finalized. In our research, we conducted negotiation simulations in law schools to examine how international loan negotiations would play out if human rights actors and communities had seats at the table. We ran the negotiation simulations in Bangladesh, Nepal and India. We found that the presence of community groups and human rights actors makes a difference in loan outcomes. While the international development loan was accepted as opposed to rejected by negotiators in three countries, the cultural values of the respective countries played a significant part in terms of the final agreement. We present the findings and their implications for the design of human rights courses in law schools as well as larger policy implications for expanding the participation of actors in international development loan negotiations.

Keywords: law, development, debt, human rights

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Authors: Mohsen Qasemi

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In the international economic order multilateral trading system which established by General Agreement on Tariffs and Trade 1947 (GATT) was dominant until about two decades ago. Regional Trade Agreements (RTAs) have changed this order and become an important phenomenon. One of the main objectives of the World Trade Organization (WTO) as a central institution of multilateral trading system is raising standards of living. There are many scholars who suggest that WTO should take steps to protect human rights in its activities. Although it has always been opposing views who declare that since WTO has no explicit rule for human rights, it has no human rights related obligations. At the time that the WTO was established, member states began to join RTAs and since then, the escalating growth of these agreements and their effects on multilateral trading system has been controversial. There are some aspects of RTAs that have received too little attention from scholars. It is important to take a different view and evaluate the RTAs based on non-commercial aspects. The present paper seeks to answer this question: which system could be more useful in protecting human rights, RTAs or WTO?

Keywords: WTO, RTAs, human rights, multilateral trading system, non discrimination

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Qing Wu, Fei-Yan Li, Heng-Chang Zhang

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Multitask learning support vector machines (SVMs) have recently attracted increasing research attention. Given several related tasks, the single-task learning methods trains each task separately and ignore the inner cross-relationship among tasks. However, multitask learning can capture the correlation information among tasks and achieve better performance by training all tasks simultaneously. In addition, the asymmetric squared loss function can better improve the generalization ability of the models on the most asymmetric distributed data. In this paper, we first make two assumptions on the relatedness among tasks and propose two multitask learning proximal support vector machine algorithms, named MTL-a-PSVM and EMTL-a-PSVM, respectively. MTL-a-PSVM seeks a trade-off between the maximum expectile distance for each task model and the closeness of each task model to the general model. As an extension of the MTL-a-PSVM, EMTL-a-PSVM can select appropriate kernel functions for shared information and private information. Besides, two corresponding special cases named MTL-PSVM and EMTLPSVM are proposed by analyzing the asymmetric squared loss function, which can be easily implemented by solving linear systems. Experimental analysis of three classification datasets demonstrates the effectiveness and superiority of our proposed multitask learning algorithms.

Keywords: multitask learning, asymmetric squared loss, EMTL-a-PSVM, classification

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Authors: Tomas O. Ortega, Cecilia T. Reyes, Cecile O. Legaspi, Cylde G. Abayon, Anna Mae C. Relingo, Mary Eden M. Teruel

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A study was conducted to evaluate the performance in the delivery of environmental management programs of the local government of Malay, Aklan, Philippines. The samples were determined by adopting the Multi-Stage Random Probability Sampling technique. The 150 respondents were drawn from barangays with larger shares of the population based on the Philippine Statistical Authority’s Data on Census Population and Housing for the year 2015. The qualified sample respondents were selected using the Kish Grid. Female respondents were targeted for even numbered questionnaires while male respondents were targeted for odd numbers. The four major core concepts namely awareness, availment, satisfaction and need for action were used in measuring the rating of the respondents and presented in frequency and percentage distributions. The reasons for their response were likewise gathered. The study inferred that a large portion of the respondents was profoundly aware of the environmental management programs implemented by their local government unit especially the solid waste management and the clean-up programs/projects. Programs to control air pollution and waste water management obtained the least awareness ratings from the respondents. A high percentage of respondents had availed of environmental management programs, particularly solid waste management. Overall, majority of the respondents were satisfied with the environmental management programs rendered by the local government unit and therefore needs less action. It is recommended that the local government unit must strengthen air pollution control program. Appropriate action must be taken to support the people’s interest in this program most particularly to the individuals who burn their garbage. Seminars and training-workshops about appropriate waste disposal will most likely help settle this issue.

Keywords: availment, awareness, environmental management, need for action, satisfaction

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Authors: Valeriy M. Kondratev, Ekaterina A. Vyacheslavova, Talgat Shugabaev, Alexander S. Gudovskikh, Alexey D. Bolshakov

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Modern sensorics imposes strict requirements on the biosensors characteristics, especially technological feasibility, and selectivity. There is a growing interest in the analysis of human health biological markers, which indirectly testifying the pathological processes in the body. Such markers are acids and alkalis produced by the human, in particular - ammonia and hydrochloric acid, which are found in human sweat, blood, and urine, as well as in gastric juice. Biosensors based on modern nanomaterials, especially low dimensional, can be used for this markers detection. Most classical adsorption sensors based on metal and silicon oxides are considered non-selective, because they identically change their electrical resistance (or impedance) under the action of adsorption of different target analytes. This work demonstrates a feasible frequency-resistive method of electrical impedance spectroscopy data analysis. The approach allows to obtain of selectivity in adsorption sensors of a resistive type. The method potential is demonstrated with analyzis of impedance spectra of silicon nanowires in the presence of NH3 and HCl vapors with concentrations of about 125 mmol/L (2 ppm) and water vapor. We demonstrate the possibility of unambiguous distinction of the sensory signal from NH3 and HCl adsorption. Moreover, the method is found applicable for analysis of the composition of ammonia and hydrochloric acid vapors mixture without water cross-sensitivity. Presented silicon sensor can be used to find diseases of the gastrointestinal tract by the qualitative and quantitative detection of ammonia and hydrochloric acid content in biological samples. The method of data analysis can be directly translated to other nanomaterials to analyze their applicability in the field of biosensory.

Keywords: electrical impedance spectroscopy, spectroscopy data analysis, selective adsorption sensor, nanotechnology

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Risikat Oladoyin S. Dauda, Onyebuchi Iwegbu

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This study investigates the response of human development outcomes to selected macroeconomic indicators in Nigeria. Human development outcomes is measured by human development index while the selected macroeconomic variables are inflation rate, real interest rate, government capital expenditure, real exchange rate, current account balance, and savings. Structural Vector Autoregression (SVAR) technique is employed in examining the response of human development index to the macroeconomic shocks. The result from the forecast error variance decomposition and Impulse-Response analysis reveals that fiscal policy (government capital expenditure) shock is the greatest determinant of human development outcomes. This result reiterates the role which the government plays in improving the welfare of the citizenry. The fiscal policy tool is pivotal in human development which comes in the form of investment in education, health, housing, and infrastructure. Further conclusion drawn from this study is that human development outcome positively and significantly responds to shocks from real interest rate, a monetary policy transmission variable and is felt greatly in the short run period. The policy implication of this study is that if capital budget implementation falls below expectations, human development will be engendered. Hence, efforts should be made to ensure that full implementation and appraisal of government capital expenditure is taken sacrosanct as any shock from such plan, engenders human development outcome.

Keywords: human development outcome, macroeconomic outcomes, structural vector autoregression, SVAR

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Authors: Subhadeep Mandal, Sujoy Bhattacharya, Pabitra Mitra, Diya Guha Roy, Seema Bhattacharya

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E-commerce firms allow customers to evaluate and review the things they buy as a positive or bad experience. The e-commerce transaction processes are made up of a variety of diverse organizations and activities that operate independently but are connected together to complete the transaction (from placing an order to the goods reaching the client). After a negative shopping experience, clients frequently disregard the critical assessment of these businesses and submit their feedback on an all-over basis, which benefits certain enterprises but is tedious for others. In this article, we solely dealt with negative reviews and attempted to distinguish between negative reviews where the e-commerce firm is explicitly blamed by customers for a bad purchasing experience and other negative reviews.

Keywords: e-commerce, online shopping, customer reviews, customer behaviour, text analytics, n-grams classification

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Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, P. L. Goh, Grace H. B. Foo, M. L. Leong

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This paper presents the evaluation of various soil testing methods such as the four-probe soil electrical resistivity method and cone penetration test (CPT) that can complement a newly developed novel rapid soil classification scheme using computer vision, to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from the local construction industry are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labor-intensive. Thus, a rapid classification method is needed at the SGs. Four-probe soil electrical resistivity and CPT were evaluated for their feasibility as suitable additions to the computer vision system to further develop this innovative non-destructive and instantaneous classification method. The computer vision technique comprises soil image acquisition using an industrial-grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the following three items were targeted to be added onto the computer vision scheme: the apparent electrical resistivity of soil (ρ) measured using a set of four probes arranged in Wenner’s array, the soil strength measured using a modified mini cone penetrometer, and w measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay,” and a mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay” and are feasible as complementing methods to the computer vision system.

Keywords: computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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Authors: Jamshid Qodirov, Sirojiddin Komolov, Ravilov Mirahmad, Olimjon Mirzayev

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Nowadays, natural language processing tools play a crucial role in our daily lives, including various techniques with text processing. There are very advanced models in modern languages, such as English, Russian etc. But, in some languages, such as Uzbek, the NLP models have been developed recently. Thus, there are only a few NLP models in Uzbek language. Moreover, there is no such work that could show which Uzbek NLP model behaves in different situations and when to use them. This work tries to close this gap and compares the Uzbek NLP models existing as of the time this article was written. The authors try to compare the NLP models in two different scenarios: sentiment analysis and sentence similarity, which are the implementations of the two most common problems in the industry: classification and similarity. Another outcome from this work is two datasets for classification and sentence similarity in Uzbek language that we generated ourselves and can be useful in both industry and academia as well.

Keywords: NLP, benchmak, bert, vectorization

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Authors: Adegboyega Adeolapo Ola, Gerelene Jagganath

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One of the greatest challenges of human right in the world, especially African states is the use of child soldiers in armed conflict, constituting a major source of destruction of lives and properties. Mostly, they are in developing countries with the situation in Sub-Saharan Africa, the abduction and employment of children as soldiers is a form of exploitative labour that is tantamount to slavery. Since the end of cold war, Child soldier has increased in Africa countries like Angola, Liberia, Sierra Leone and Uganda. This study examines the main cause of the recruitment and use of child soldiers and its challenges to human right. It further assesses the role of international regional bodies and various governments in curbing child soldiers with a view to proffer suggestions on how to address some of the resultant threat of human right. The study posits that the control of small arms and light weapons is essential in curtailing the spread of child soldier and abuse of human right. This hopefully should result in the sustainability of human/child right in African continent. It is a recommendation of this study that, in order to sustain human right in the region, all Africa leaders, government and regional bodies; such as African Union, Economic Community of West African States, South African Development Community among others, should cooperate and work together to address the issue of illicit small arms, which could eventually lead to child soldier.

Keywords: arms control, child soldier, human right, small arms

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Authors: Sikhumbuzo Zondi, Serges A. Kamga

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Human security and human rights are mutually reinforcing concepts given that human security addresses questions related to human conditions such as the safety of individuals and the protection of individual rights and civil liberties. It does this by suggesting that the proper referent for security should be the individual and not the nation-state, due to the individual’s vulnerability to threats such as malnutrition and poverty, conflicts, exploitation and marginalization, despotism and climate change. Due to the primacy of the individual, human security comfortably expand to the notion of social justice, given that for far too-long, many individuals around the world have been denied of their basic human rights through racial discrimination, unfair labour and segregation policies and as a result encountered widespread social, environmental and economic injustices which are evident in the current structural division of the world between the developed north and the underdeveloped or developing south. In light of this view, ensuring freedom from want and freedom from fear, for all individuals is arguably the sound route to addressing and remedying the global ills of our time and a way to promoting human rights for all. The promotion of human security provides an important part of human/societal progress because inclusive security facilitates development and human rights protection, while insecurity reduces people’s growth and investment prospects and prolongs historical injustices. Therefore, this paper seeks to show that human security and human rights complements one another and that this correlation provides the necessary mechanisms for addressing and remedying the historical injustices that still affect most of the world’s population. It will look at linkages between human security and the individual right to equality and freedom from discrimination, right to life, liberty, and personal security; development; own property; adequate living standard; education; desirable work and to join trade unions; participate in government and in free elections; social security and equality before the law. The paper considers these human rights and liberties as vital for securing the core values of human life while at the same addressing socio-economic injustices that still persist in the contemporary world. The paper will be a desktop study using qualitative research methods on two case studies in Africa namely Cameroun and South Africa.

Keywords: justice, human security, human rights, injustices

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Authors: Faris Tarlochan, Siva Mahesh Tangutooru

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Lateral Geniculate Nucleus (LGN) is the relay center in the visual pathway as it receives most of the input information from retinal ganglion cells (RGC) and sends to visual cortex. Low threshold calcium currents (IT) at the membrane are the unique indicator to characterize this firing functionality of the LGN neurons gained by the RGC input. According to the LGN functional requirements such as functional mapping of RGC to LGN, the morphologies of the LGN neurons were developed. During the neurological disorders like glaucoma, the mapping between RGC and LGN is disconnected and hence stimulating LGN electrically using deep brain electrodes can restore the functionalities of LGN. A computational model was developed for simulating the LGN neurons with three predominant morphologies, each representing different functional mapping of RGC to LGN. The firings of action potentials at LGN neuron due to IT were characterized by varying the stimulation parameters, morphological parameters and orientation. A wide range of stimulation parameters (stimulus amplitude, duration and frequency) represents the various strengths of the electrical stimulation with different morphological parameters (soma size, dendrites size and structure). The orientation (0-1800) of LGN neuron with respect to the stimulating electrode represents the angle at which the extracellular deep brain stimulation towards LGN neuron is performed. A reduced dendrite structure was used in the model using Bush–Sejnowski algorithm to decrease the computational time while conserving its input resistance and total surface area. The major finding is that an input potential of 0.4 V is required to produce the action potential in the LGN neuron which is placed at 100 µm distance from the electrode. From this study, it can be concluded that the neuroprostheses under design would need to consider the capability of inducing at least 0.4V to produce action potentials in LGN.

Keywords: Lateral Geniculate Nucleus, visual cortex, finite element, glaucoma, neuroprostheses

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Authors: Andy H. Clark

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This research looks into the relationship between language comprehension and production with a specific focus on the role of salience in shaping these processes. Salience, our most immediate perception of what is most probable out of all possible situations and outcomes strongly affects our perception and action in language production and comprehension. This study investigates the impact of geographic and emotional attachments to the target language on the differences in the learners’ comprehension and production abilities. Using quantitative research methods (Qualtrics, SPSS), this study examines preferential choices of two groups of Japanese English language learners: those residing in the United States and those in Japan. By comparing and contrasting these two groups, we hope to gain a better understanding of how salience of linguistics cues influences language processing.

Keywords: intercultural pragmatics, salience, production, comprehension, pragmatics, action, perception, cognition

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Rita Magdalena, R. D. Atmaja, Sofia Saidah, Ocky Tiaramukti

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Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time.

Keywords: carcinoma, colorectal cancer, k-nearest neighbor, lymphoma, principle component analysis

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Authors: Dyana Zainudin, Atta Ur-Rahman, Thaier Hamed

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This paper explains about human nature concept as to understand the significance of information security in employees’ mentality including leaders in an organisation. By studying on a theory concept of the latest Von Solms fourth waves, information security governance basically refers to the concept of a set of methods, techniques and tools that responsible for protecting resources of a computer system to ensure service availability, confidentiality and integrity of information. However, today’s information security dilemma relates to the acceptance of employees mentality. The major causes are a lack of communication and commitment. These types of management in an organisation are labelled as immoral/amoral management which effects on information security compliance. A recovery action is taken based on ‘learn a lesson from incident events’ rather than prevention. Therefore, the paper critically analysed the Von Solms fourth waves’ theory with current human events and its correlation by studying secondary data and also from qualitative analysis among employees in public sectors. ‘Three-dimensions to failure’ of information security dilemma are explained as deny, don’t know and don’t care. These three-dimensions are the most common vulnerable behaviour owned by employees. Therefore, by avoiding the three-dimensions to failure may improve the vulnerable behaviour of employees which is often related to immoral/amoral management.

Keywords: information security management system, information security behaviour, information security governance, information security culture

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Authors: Ploychompoo Kittikunchotiwut

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This study examines the relationships between human resource practices, knowledge management capacity, and innovation performance. The data were collected by using a questionnaire from 241 firms in the hotels in Thailand. The hypothesized relationships among variables are examined by using ordinary least square (OLS) regression analysis. The findings show that human resource practices have a positive effect on knowledge management capacity. Besides, knowledge management capacity was found to positively affect innovation performance. Finally, the limitations of the study and directions for future research are discussed.

Keywords: human resource practices, knowledge management capacity, innovation performance

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Authors: Natthawut Arin

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In forensic contexts, competency to stand trial assessments are the most common referrals. The defendants may attempt to endorse psychopathology symptoms and feign incompetent. Coaching, which can be teaching them test-taking strategies to avoid detection of psychopathological symptoms feigning. Recently, the Symptom Validity Testings (SVTs) were created to detect feigning. Moreover, the works of the literature showed that the effects of coaching on SVTs may be more robust to the effects of coaching. Thai Symptom Validity Test (SVT-Th) was designed as SVTs which demonstrated adequate psychometric properties and ability to classify between feigners and honest responders. Thus, the current study to examine the utility as the robustness of SVT-Th in the detection of feigned psychopathology. Participants consisted of 120 were recruited from undergraduate courses in psychology, randomly assigned to one of three groups. The SVT-Th was administered to those three scenario-experimental groups: (a) Uncoached group were asked to respond honestly (n=40), (b) Symptom-coached without warning group were asked to feign psychiatric symptoms to gain incompetency to stand trial (n=40), while (c) Test-coached with warning group were asked to feign psychiatric symptoms to avoid test detection but being incompetency to stand trial (n=40). Group differences were analyzed using one-way ANOVAs. The result revealed an uncoached group (M = 4.23, SD.= 5.20) had significantly lower SVT-Th mean scores than those both coached groups (M =185.00, SD.= 72.88 and M = 132.10, SD.= 54.06, respectively). Classification rates were calculated to determine the classification accuracy. Result indicated that SVT-Th had overall classification accuracy rates of 96.67% with acceptable of 95% sensitivity and 100% specificity rates. Overall, the results of the present study indicate that the SVT-Th yielded high adequate indices of accuracy and these findings suggest that the SVT-Th is robustness against coaching.

Keywords: incompetency to stand trial, coaching, robustness, classification accuracy

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Authors: Amin Osama Amin Mohamed Elbaramawy

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In the last period of time, about ten years ago, wars caused violations of human rights in many places, and despite international condemnations, they did not stop, and the truth is that international law was unable to stop them. The global wars and conflicts that the world has been witnessing for more than ten years have caused the displacement of millions of people in all parts of the earth, causing a violation of the human rights of those people. Despite international condemnations of these conflicts, these conflicts have not stopped and have not been resolved until now. Therefore, I call for international law and international courts to be more effective and not just in words, taking into account the speed in this due to the increase in those wars and conflicts every day and new violations every day.

Keywords: war, freedom, human rights, international law

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