Search results for: international classification of functioning

Authors: Dingyang Hu, Dan Liu

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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

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Authors: Antonio Marques, Graça Guedes

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The Portuguese footwear industry had in the last five years a remarkable performance in the exportation values, the trade balance and others economic indicators. After a long period of difficulties and with a strong reduction of companies and employees since 1994 until 2009, the Portuguese footwear industry changed the strategy and is now a success case between the international players of footwear. Only the Italian industry sells footwear with a higher value than the Portuguese and the distance between them is decreasing year by year. This paper analyses how the Portuguese footwear companies innovate and make innovation, according the classification proposed by the Oslo Manual. Also analyses the strategy follow in the innovation process, as suggested by Freeman and Soete, and shows the linkage between the type of innovation and the strategy of innovation. The research methodology was qualitative and the strategy for data collection was the case study. The qualitative data will be analyzed with the MAXQDA software. The economic results of the footwear companies studied shows differences between all of them and these differences are related with the innovation strategy adopted. The companies focused in product and marketing innovation, oriented to their target market, have higher ratios “turnover per worker” than the companies focused in process innovation. However, all the footwear companies in this “low-tech” industry create value and contribute to a positive foreign trade of 1.310 million euros in 2013. The growth strategies implemented has the participation of the sectorial organizations in several innovative projects. And it’s obvious that cooperation between all of them is a critical element to the performance achieved by the companies and the innovation observed. Can conclude that the Portuguese footwear sector has in the last years an excellent performance (economic results, exportation values, trade balance, brands and international image) and his performance is strongly related with the strategy in innovation followed, the type of innovation and the networks in the cluster. A simplified model, called “Ace of Diamonds”, is proposed by the authors and explains the way how this performance was reached by the seven companies that participate in the study (two of them are the leaders in the setor), and if this model can be used in others traditional and “low-tech” industries.

Keywords: footwear, innovation, “low-tech” industry, Oslo manual

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Authors: Meenakshi Barsaul Chauhan, Aastha Chauhan, Shilpa Hurmade, Rajeev Sen, Jyotsna Sen, Monika Dalal

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Introduction: Ovarian masses are common forms of neoplasm in women and represent 2/3rd of gynaecological malignancies. A pre-operative suggestion of malignancy can guide the gynecologist to refer women with suspected pelvic mass to a gynecological oncologist for appropriate therapy and optimized treatment, which can improve survival. In the younger age group preoperative differentiation into benign or malignant pathology can decide for conservative or radical surgery. Imaging modalities have a definite role in establishing the diagnosis. By using International Ovarian Tumor Analysis (IOTA) classification with sonography, costly radiological methods like Magnetic Resonance Imaging (MRI) / computed tomography (CT) scan can be reduced, especially in developing countries like India. Thus, this study is being undertaken to evaluate the role of clinical methods and sonography for diagnosis of the nature of the ovarian tumor. Material And Methods: This prospective observational study was conducted on 40 patients presenting with ovarian masses, in the Department of Obstetrics and Gynaecology, at a tertiary care center in northern India. Functional cysts were excluded. Ultrasonography and color Doppler were performed on all the cases.IOTA rules were applied, which take into account locularity, size, presence of solid components, acoustic shadow, dopper flow etc . Magnetic Resonance Imaging (MRI) / computed tomography (CT) scans abdomen and pelvis were done in cases where sonography was inconclusive. In inoperable cases, Fine needle aspiration cytology (FNAC) was done. The histopathology report after surgery and cytology report after FNAC was correlated statistically with the pre-operative diagnosis made clinically and sonographically using IOTA rules. Statistical Analysis: Descriptive measures were analyzed by using mean and standard deviation and the Student t-test was applied and the proportion was analyzed by applying the chi-square test. Inferential measures were analyzed by sensitivity, specificity, negative predictive value, and positive predictive value. Results: Provisional diagnosis of the benign tumor was made in 16(42.5%) and of the malignant tumor was made in 24(57.5%) patients on the basis of clinical findings. With IOTA simple rules on sonography, 15(37.5%) were found to be benign, while 23 (57.5%) were found to be malignant and findings were inconclusive in 2 patients (5%). FNAC/Histopathology reported that benign ovarian tumors were 14 (35%) and 26(65%) were malignant, which was taken as the gold standard. The clinical finding alone was found to have a sensitivity of 66.6% and a specificity of 90.9%. USG alone had a sensitivity of 86% and a specificity of 80%. When clinical findings and IOTA simple rules of sonography were combined (excluding inconclusive masses), the sensitivity and specificity were 83.3% and 92.3%, respectively. While including inconclusive masses, sensitivity came out to be 91.6% and specificity was 89.2. Conclusion: IOTA's simple sonography rules are highly sensitive and specific in the prediction of ovarian malignancy and also easy to use and easily reproducible. Thus, combining clinical examination with USG will help in the better management of patients in terms of time, cost and better prognosis. This will also avoid the need for costlier modalities like CT, and MRI.

Keywords: benign, international ovarian tumor analysis classification, malignant, ovarian tumours, sonography

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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: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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Authors: John Hardy, Paul Lushenko

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he twenty-first century international security order has been fraught with challenges to the credibility and stability of the post-Cold War status quo. Although the American-led international system has rarely been threatened directly by dissatisfied states, an underlying challenge to the international security order has emerged in the form of a slow-burning abnegation of small but significant aspects of the status quo. Meanwhile, those security challenges which have threatened to destabilize order in the international system have not clearly belonged to the traditional notions of diplomacy and armed conflict. Instead, the main antagonists have been both states and non-state actors, the issues have crossed national and international boundaries, and contestation has occurred in a ‘gray zone’ between peace and war. Gray zone conflicts are not easily categorized as military operations, national security policies or political strategies, because they often include elements of diplomacy, military operations, and statecraft in complex combinations. This study applies three approaches to conceptualizing the gray zone in which many contemporary conflicts take place. The first approach frames gray zone conflicts as a form of coercive diplomacy, in which armed force is used to add credibility and commitment to political threats. The second approach frames gray zone conflicts as a form of discrete military operation, in which armed force is used sparingly and is limited to a specific issue. The third approach frames gray zones conflicts as a form of proxy war, in which armed force is used by or through third parties, rather than directly between belligerents. The study finds that each approach to conceptualizing the gray zone accounts for only a narrow range of issues which fall within the gap between traditional notions of peace and war. However, in combination, all three approaches are useful in explicating the gray zone and understanding the character of contemporary security challenges which defy simple categorization. These findings suggest that coercive diplomacy, discrete military operations, and proxy warfare provide three overlapping lenses for conceptualizing the gray zone and for understanding the gray zone conflicts which threaten international security in the early twenty-first century.

Keywords: gray zone, international security, military operations, national security, strategy

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Authors: Mohamed Saad, Huda Ismail

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The purpose of this research is to examine the impact of cryptocurrency classification on money laundering crimes and to analyze how the preferences of criminals differ according to the type of digital currency used. Specifically, we aim to explore the roles of stablecoins, utility coins, and privacy tokens in facilitating or hindering money laundering activities and to identify the key factors that influence the choices of criminals in using these cryptocurrencies. To achieve our research objectives, we used a dataset for the most highly traded cryptocurrencies (32 currencies) that were published on the coin market cap for 2022. In addition to conducting a comprehensive review of the existing literature on cryptocurrency and money laundering, with a focus on stablecoins, utility coins, and privacy tokens, Furthermore, we conducted several Multivariate analyses. Our study reveals that the classification of cryptocurrency plays a significant role in money laundering activities, as criminals tend to prefer certain types of digital currencies over others, depending on their specific needs and goals. Specifically, we found that stablecoins are more commonly used in money laundering due to their relatively stable value and low volatility, which makes them less risky to hold and transfer. Utility coins, on the other hand, are less frequently used in money laundering due to their lack of anonymity and limited liquidity. Finally, privacy tokens, such as Monero and Zcash, are increasingly becoming a preferred choice among criminals due to their high degree of privacy and untraceability. In summary, our study highlights the importance of understanding the nuances of cryptocurrency classification in the context of money laundering and provides insights into the preferences of criminals in using digital currencies for illegal activities. Based on our findings, our recommendation to the policymakers is to address the potential misuse of cryptocurrencies for money laundering. By implementing measures to regulate stable coins, strengthening cross-border cooperation, fostering public-private partnerships, and increasing cooperation, policymakers can help prevent and detect money laundering activities involving digital currencies.

Keywords: crime, cryptocurrency, money laundering, tokens.

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Authors: Thusitha B. Abeysekara, M. A. Nihal Chandrathilake

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The aim of this research is to examine the legal mechanism of resolving maritime disputes by arbitration, and it would be a reference point on the analysis of USA approaches. In doing so, the research aims to analyse the relevant legal principles in the context of current maritime arbitration practices in selected jurisdictions. The research also aims to analyse the advantages and applicability of arbitration in maritime dispute settlements over the litigation and further approaches the role of specialist maritime arbitration institutes in the USA and the position of international merchant organizations in maritime arbitration. Further, research values the legislative aspects of maritime arbitration. The study would evaluate the contemporary issues in maritime arbitration practices in the USA and further analyses the statistical information on maritime arbitration. Finally, the research made remarks to often parallel consequence in USA legal systems in maritime arbitration and despite the fundamental divergences of the applicable principles and practices of maritime arbitration. The research finally suggests the doctrine should reshape with equitable remedies and international maritime arbitration practices with its institutional impact rather than using as statutory rules related maritime arbitration.

Keywords: arbitration, international shipping, maritime dispute, New York convention

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Authors: Moein Izadi, Ali Mohammadzadeh

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Detection of damaged parts of roads after earthquake is essential for coordinating rescuers. In this study, an approach is presented for the semi-automatic detection of damaged roads in a city using pre-event vector maps and both pre- and post-earthquake QuickBird satellite images. Damage is defined in this study as the debris of damaged buildings adjacent to the roads. Some spectral and texture features are considered for SVM classification step to detect damages. Finally, the proposed method is tested on QuickBird pan-sharpened images from the Bam City earthquake and the results show that an overall accuracy of 81% and a kappa coefficient of 0.71 are achieved for the damage detection. The obtained results indicate the efficiency and accuracy of the proposed approach.

Keywords: SVM classifier, disaster management, road damage detection, quickBird images

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Authors: Ella Sèdé Maforikan

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Accurate land cover mapping is essential for effective environmental monitoring and natural resources management. This study focuses on assessing the classification performance of two satellite datasets and evaluating the impact of different input feature combinations on classification accuracy in the Beterou catchment, situated in the northern part of Benin. Landsat-8 and Sentinel-2 images from June 1, 2020, to March 31, 2021, were utilized. Employing the Random Forest (RF) algorithm on Google Earth Engine (GEE), a supervised classification categorized the land into five classes: forest, savannas, cropland, settlement, and water bodies. GEE was chosen due to its high-performance computing capabilities, mitigating computational burdens associated with traditional land cover classification methods. By eliminating the need for individual satellite image downloads and providing access to an extensive archive of remote sensing data, GEE facilitated efficient model training on remote sensing data. The study achieved commendable overall accuracy (OA), ranging from 84% to 85%, even without incorporating spectral indices and terrain metrics into the model. Notably, the inclusion of additional input sources, specifically terrain features like slope and elevation, enhanced classification accuracy. The highest accuracy was achieved with Sentinel-2 (OA = 91%, Kappa = 0.88), slightly surpassing Landsat-8 (OA = 90%, Kappa = 0.87). This underscores the significance of combining diverse input sources for optimal accuracy in land cover mapping. The methodology presented herein not only enables the creation of precise, expeditious land cover maps but also demonstrates the prowess of cloud computing through GEE for large-scale land cover mapping with remarkable accuracy. The study emphasizes the synergy of different input sources to achieve superior accuracy. As a future recommendation, the application of Light Detection and Ranging (LiDAR) technology is proposed to enhance vegetation type differentiation in the Beterou catchment. Additionally, a cross-comparison between Sentinel-2 and Landsat-8 for assessing long-term land cover changes is suggested.

Keywords: land cover mapping, Google Earth Engine, random forest, Beterou catchment

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Authors: Yaojun Wang, Yaoqing Wang

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Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio.

Keywords: case-based reasoning, decision tree, stock selection, machine learning

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: David Nchinda Keming

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The present study is focused on both the national and international collective fight against Boko Haram terrorism on Cameroon and the rule played by the Lake Chad Basin Countries (LCBCs) and the global community to suffocate the sect’s activities in the region. Although countries of the Lake Chad Basin include: Cameroon, Chad, Nigeria and Niger others like Benin also joined the course. The justification for the internationalisation of the fight against Boko Haram could be explained by the ecological and international climatic importance of the Lake Chad and the danger posed by the sect not only to the Lake Chad member countries but to global armed, civil servants and the international political economy. The study, therefore, kick start with Cameroon’s reaction to Boko Haram’s terrorist attacks on its territory. It further expounds on Cameroon’s request on bilateral diplomacy from members of the UN Security Council for an international collective support to staple the winds of the challenging sect. The study relies on the hypothesis that Boko Haram advanced terrorism on Cameroon was more challenging to the domestic military intelligence thus forcing the government to seek for bilateral and multilateral international collective support to secure its territory from the powerful sect. This premise is tested internationally via (multilateral cooperation, bilateral response, regional cooperation) and domestically through (solidarity parade, religious discourse, political manifestations, war efforts, the vigilantes and the way forward). To accomplish our study, we made used of the mixed research methodologies to interpret the primary, secondary and tertiary sources consulted. Our results reveal that the collective response was effectively positive justified by the drastic drop in the sect’s operations in Cameroon and the whole LCBCs. Although the sect was incapacitated, terrorism remains an international malaise and Cameroon hosts a fertile ground for terrorists’ activism. Boko Haram was just weakened and not completely defeated and could reappear someday even under a different appellation. Therefore, to absolutely eradicate terrorism in general and Boko Haram in particular, LCBCs must improve their military intelligence on terrorism and continue to collaborate with advanced experienced countries in fighting terrorism.

Keywords: Boko Haram, terrorism, domestic, international, response

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Authors: Fathi Kallel, Abdulelah Alabd Uljabbar, Abdulrahman Aldukhail, Abdulaziz Alomran

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The brain is an important organ in our body since it is responsible about the majority actions such as vision, memory, etc. However, different diseases such as Alzheimer and tumors could affect the brain and conduct to a partial or full disorder. Regular diagnosis are necessary as a preventive measure and could help doctors to early detect a possible trouble and therefore taking the appropriate treatment, especially in the case of brain tumors. Different imaging modalities are proposed for diagnosis of brain tumor. The powerful and most used modality is the Magnetic Resonance Imaging (MRI). MRI images are analyzed by doctor in order to locate eventual tumor in the brain and describe the appropriate and needed treatment. Diverse image processing methods are also proposed for helping doctors in identifying and analyzing the tumor. In fact, a large Computer Aided Diagnostic (CAD) tools including developed image processing algorithms are proposed and exploited by doctors as a second opinion to analyze and identify the brain tumors. In this paper, we proposed a new advanced CAD for brain tumor identification, classification and feature extraction. Our proposed CAD includes three main parts. Firstly, we load the brain MRI. Secondly, a robust technique for brain tumor extraction is proposed. This technique is based on both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). DWT is characterized by its multiresolution analytic property, that’s why it was applied on MRI images with different decomposition levels for feature extraction. Nevertheless, this technique suffers from a main drawback since it necessitates a huge storage and is computationally expensive. To decrease the dimensions of the feature vector and the computing time, PCA technique is considered. In the last stage, according to different extracted features, the brain tumor is classified into either benign or malignant tumor using Support Vector Machine (SVM) algorithm. A CAD tool for brain tumor detection and classification, including all above-mentioned stages, is designed and developed using MATLAB guide user interface.

Keywords: MRI, brain tumor, CAD, feature extraction, DWT, PCA, classification, SVM

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Authors: Andrea Bellucci, Martina Tofi

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The aim of paper is to analyze business models of bancassurance in Italy for life business. The life insurance business is very developed in the Italian market and banks branches have 80% of the market share. Given its maturity, the life insurance market needs to consolidate its organizational form to allow for the development of non-life business, which nowadays collects few premiums but represents a great opportunity to enlarge the market share of bancassurance using its strength in the distribution channel while the market share of independent agents is decreasing. Starting with the main business model of bancassurance for life business, this paper will analyze the performances of life companies in the Italian market by balance sheet indicators and by main discriminant variables of business models. The study will observe trends from 2013 to 2015 for the Italian market by exploiting a database managed by Associazione Nazionale delle Imprese di Assicurazione (ANIA). The applied approach is based on a bottom-up analysis starting with variables and indicators to define business models’ classification. The statistical classification algorithm proposed by Ward is employed to design business models’ profiles. Results from the analysis will be a representation of the main business models built by their profile related to indicators. In that way, an unsupervised analysis is developed that has the limit of its judgmental dimension based on research opinion, but it is possible to obtain a design of effective business models.

Keywords: bancassurance, business model, non life bancassurance, insurance business value drivers

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Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

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Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

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Authors: Hamid Vahidkia

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Central to the World Court’s mission is the assurance of universal custom “as prove of a common hone acknowledged as law.” Understudies of the Court’s law have long been mindful that the Court has been superior at applying standard law than characterizing it. However until Nicaragua v. Joined together States, small hurt was done. For within the strongly challenged cases earlier to Nicaragua, the Court overseen to inspire commonalities in factious structure that floated its decisions toward the standard standards certain in state hone. The Court’s need of hypothetical unequivocality basically implied that a career opportunity emerged for a few eyewitnesses like me to endeavor to supply the lost hypothesis of custom.

Keywords: law, international law, jurisdication, customary

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Authors: Magdy B. Eteiba, Wael Ismael Wahba, Shimaa Barakat

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This paper presents a fault identification, classification and fault location estimation method based on Discrete Wavelet Transform and Adaptive Network Fuzzy Inference System (ANFIS) for medium voltage cable in the distribution system. Different faults and locations are simulated by ATP/EMTP, and then certain selected features of the wavelet transformed signals are used as an input for a training process on the ANFIS. Then an accurate fault classifier and locator algorithm was designed, trained and tested using current samples only. The results obtained from ANFIS output were compared with the real output. From the results, it was found that the percentage error between ANFIS output and real output is less than three percent. Hence, it can be concluded that the proposed technique is able to offer high accuracy in both of the fault classification and fault location.

Keywords: ANFIS, fault location, underground cable, wavelet transform

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Authors: Valerie Habbel, Magdalena Orth, Johanna Richter, Steffen Schimko

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Cooperation between development actors and the private sector is becoming increasingly important, as it is expected to mobilize additional resources to achieve the Sustainable Development Goals (SDGs), among other things. However, whether the goals of cooperation are achieved has so far only been explored in evaluations and studies of individual projects and instruments. The evaluation synthesis attempts to close this gap by systematically analyzing existing evidence (evaluations and academic studies) from national and international development cooperation on private sector engagement. Overall, the evaluations and studies considered report mainly positive effects on investors and donors, intermediaries, partner countries, and target groups. However, various analyses, including on the quality of the evaluations, point to a positive bias in the results. The evaluation synthesis makes recommendations on the definition of indicators, the measurement and evaluation of impacts and additionality, knowledge management, and the consideration of transaction costs in cooperation with private actors.

Keywords: evaluation synthesis, private sector engagement, international development, sustainable development

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Authors: Hung-Sheng Lin, Cheng-Hsuan Li

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Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE.

Keywords: feature extraction, kernel method, double nearest proportion feature extraction, kernel double nearest feature extraction

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Authors: Marzena Trojanczyk, Mariusz Jaworski, Ewa Dmoch Gajzlerska

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Purpose: The purpose of this paper is to assess the relationship between the level of quality of life and willingness to take treatment in patients with eating disorders as anorexia, bulimia and compulsive bingeing. Material and methods: The subjects consisted of 99 women with eating disorders: anorexia, n = 33; bulimia, n = 35; compulsive overeating, n = 31 and 35 women in the control group. The study used an original questionnaire to assess the overall quality of life, as well as selected areas of the physical, mental, social and spiritual satisfaction. The subjects were also asked about the level of motivation for treatment, and the importance of the disease in the lives of patients. Statistical analyses were performed using the statistical program SPSS 18.0. Results: Women with eating disorders in particular groups did not differ with respect to each other in the aspect of overall quality of life, satisfaction with the development of the spiritual, social functioning and mental health. The severity level of the disease in the lives of patients showed a negative correlation with social functioning in women with anorexia nervosa. In the case of patients with compulsive bingeing a positive relationship between the level of importance of the disease and the satisfaction of spiritual development is reported. Conclusions: Concerning the inferior quality of life, there is no relationship between a willingness to take treatment and the importance of the disease in the lives of patients with anorexia, bulimia and compulsive bingeing.

Keywords: anorexia, bulimia, compulsive overeating, quality of life

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Authors: Aly Elshafei, Daniela Romano

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With the development of autonomous vehicles, a human-machine interaction (HMI) system is needed for a safe transition of control when a takeover request (TOR) is required. An important part of the HMI system is the ability to monitor the level of situational awareness (SA) of any driver in real-time, in different scenarios, and without any pre-calibration. Presenting state-of-the-art machine learning models used to measure SA is the purpose of this systematic review. Investigating the limitations of each type of sensor, the gaps, and the most suited sensor and computational model that can be used in driving applications. To the author’s best knowledge this is the first literature review identifying online and offline classification methods used to measure SA, explaining which measurements are subject or session-specific, and how many classifications can be done with each classification model. This information can be very useful for researchers measuring SA to identify the most suited model to measure SA for different applications.

Keywords: situational awareness, autonomous driving, gaze metrics, EEG, ECG

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Authors: MD. Kamruzzaman

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As a developing country, Bangladesh has seen an increase in total GDP in recent years. But it can be further improved by developing “Health-Care” (HC) services because it has enormous infrastructure problems all over the country. Bangladesh's HC system is now clearly poised to undergo reform at any process level, including prevention, diagnosis, and treatment. Although the Bangladeshi government is trying to develop the HC sector, due to health corruption in this sector, the improvement has not accelerated yet. For this reason, lots of Bangladeshi people are facing acute diseases. Regarding the prevention, diagnosis, and treatment of disease, this research will illustrate the law relating to health and HC to ensure excellent health and well-being. Firstly, this paper investigates health under Bangladeshi law from different perspectives related to the HC system. A massive gap has been investigated in this research after comparing Bangladeshi and international health law (HL). Secondly, a practical scenario is investigated and compared with international HC law. It is evident that the Bangladeshi HC system did not achieve a satisfactory standard level concerning international law. A staggering 70% of Bangladesh's population lives in rural areas, with no restrictions on access to hospitals and clinics. However, it is clear that proper HC infrastructure and some new medical practices are urgently needed to ensure HC quality. Finally, this research provides suggestions for developing a HC system to ensure the health of all Bangladeshi people that needs to be immediately implemented by the Bangladeshi government. This research has practical implications in the HC system for any developing country to maintain their citizen's safety.

Keywords: HC system, law relating, bangladeshi HL, international HL, human HC suggestions

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: Chongya Song, Kang Yen, Alexander Pons, Jin Liu

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In the field of machine learning, the ensemble has been employed as a common methodology to improve the performance upon multiple base classifiers. However, the true predictions are often canceled out by the false ones during consensus due to a phenomenon called “curse of correlation” which is represented as the strong interferences among the predictions produced by the base classifiers. In addition, the existing practices are still not able to effectively mitigate the problem of imbalanced classification. Based on the analysis on our experiment results, we conclude that the two problems are caused by some inherent deficiencies in the approach of consensus. Therefore, we create an enhanced ensemble algorithm which adopts a designed rank-based chain-mode consensus to overcome the two problems. In order to evaluate the proposed ensemble algorithm, we employ a well-known benchmark data set NSL-KDD (the improved version of dataset KDDCup99 produced by University of New Brunswick) to make comparisons between the proposed and 8 common ensemble algorithms. Particularly, each compared ensemble classifier uses the same 22 base classifiers, so that the differences in terms of the improvements toward the accuracy and reliability upon the base classifiers can be truly revealed. As a result, the proposed rank-based chain-mode consensus is proved to be a more effective ensemble solution than the traditional consensus approach, which outperforms the 8 ensemble algorithms by 20% on almost all compared metrices which include accuracy, precision, recall, F1-score and area under receiver operating characteristic curve.

Keywords: consensus, curse of correlation, imbalance classification, rank-based chain-mode ensemble

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Authors: Jasna Hrncic

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Poor family relations predict depression, but also to other mental health issues. Mediating effect of self-efficacy and differentiation of self and moderating effect of decreased accessibility and/or success of other adaptive and defensive mechanisms for overcoming social disadvantages could explain depression as a specific outcome of dysfunctional family relations. The present study analyzes the mediation effect of self-efficacy and differentiation of self from poor family functioning to depressive symptoms and the moderation effect of deprivation of liberty on the listed mediation effect. Deprivation of liberty has, as a general consequence, a decreased accessibility and/or success of many adaptive and defensive mechanisms. It is hypothesized that: 1) self-efficacy and differentiation of self will mediate between family functioning and depressiveness in the total sample, and 2) deprivation of liberty will moderate the stated relations. Cross-sectional study was conducted among 323 male juveniles in Serbia divided in three groups: 98 adolescents deprived of their liberty due to antisocial behavior (incarcerated antisocial group - IAG), 121 adolescents with antisocial behavior in their natural setting (antisocial control group - CAG) and 105 adolescents in general population (general control group - CGG). The CAG was included along with GCG to control the possible influence that comorbidity of antisocial behavior and depressiveness could have on results. Instruments for family relations assessment were: for a whole family of origin the emotional exchange scale and individuation scale from GRADIR by Knezevic, and for a relationship with mother PCS-YSR and CRPBI by barber, and intimacy, rejection, sacrifice, punishment, demands, control and internal control by Opacic and Kos. Differentiation of self (DOS) is measured by emotional self scale (Opacic), self-efficacy (SE) by general incompetence scale by Bezinovic, and depression by BDI (Back), CES-D (Radloff) and D6R (Momirovic). Two-path structural equation modeling based on most commonly reported fit indices, showed that the mediation model had unfavorable fit to our data for total sample [(χ2 (1, N = 324) = 13.73); RMSEA= .20 (90% CI= [.12, .30]); CFI= .98; NFI= .97; AIC=31.73]. Path model provided an adequate fit to the data only for AIG - and not to the data from ACG and GCG. SE and DOS mediated the relationship between PFF and depressiveness. Test of the indirect effects revealed that 23.85% of PFF influences on depressiveness is mediated by these two mediators (the quotient of mediated effect = .24). Test of specific indirect effects showed that SE mediates 22.17%, while DOS mediates 1.67% of PFF influence on depressiveness. Lack of expected mediation effect could be explained by missing other potential mediators (i.e., relationship with that father, social skills, self-esteem) and lower variability of both predictor and criterion variable due to their low levels on the whole sample and on control subsamples. Results suggested that inaccessibility and/or successfulness of other adaptive and defensive mechanisms for overcoming social disadvantages has a strong impact on the mediation effect of self/efficacy and differentiation of self from poor family functioning to depressive symptoms. Further researches could include other potential mediators and a sample of clinically depressed people.

Keywords: antisocial behavior, mediating effect, moderating effect, natural setting, incarceration

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Authors: Luciane Lauffer

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Over the past decade, the expansion of women’s football as a competitive sport has gained more attention from the media and researchers. However, the practice of the sport is not new, and in Asia and Oceania, women’s football has emerged as a common physical activity in many countries since the 1970s. This study recovers the major occurrences that made women’s football possible in an international context, also resulting from the main achievements of the feminist movement in most Westernized countries. Using archival research, the author reviews documents that compose the history of the women’s game, marked by many imposed barriers imposed by social and gender norms. This materials present how women managed their sport in their respective countries and regions, mostly prompted by a spirit of cooperation and partnerships that allowed the staging of major international events. The findings point out that, despite the layers of gendered boundaries that attempted to contain the expansion of the sport, women from Asia and Oceania made the sport flourish and eventually achieving recognition at the international level.

Keywords: women’s football, gender norms, game development, Asia-pacific

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Authors: G. Alex Ambrose, Melissa Paulsen, Abrar Fitwi, Masud Akbari

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In 2016, a U.S. business college was awarded a sub grant to work with FHI360, a nonprofit human development organization, to support a university in Afghanistan funded by the State Department’s U.S. Agency for International Development (USAID). A newly designed Master’s Degree in Finance and Accounting is being implemented to support Afghanistan’s goal of 20% females in higher education and industry by 2020 and to use finance and accounting international standards to attract capital investment for economic development. This paper will present a case study to describe the co-construction of an approach to an International Flipped Faculty Development Model grounded in blended learning theory. Like education in general, faculty development is also evolving from the traditional face to face environment and interactions to the fully online and now to a best of both blends. Flipped faculty development is both a means and a model for careful integration of the strengths of the synchronous and asynchronous dynamics and technologies with the combination of intentional sequencing to pre-online interactions that prepares and enhances the face to face faculty development and mentorship residencies with follow-up post-online support. Initial benefits from this model include giving the Afghan faculty an opportunity to experience and apply modern teaching and learning strategies with technology in their own classroom. Furthermore, beyond the technological and pedagogical affordances, the reciprocal benefits gained from the mentor-mentee, face-to-face relationship will be explored. Evidence to support this model includes: empirical findings from pre- and post-Faculty Mentor/ Mentee survey results, Faculty Mentorship group debriefs, Faculty Mentorship contact logs, and student early/end of semester feedback. In addition to presenting and evaluating this model, practical challenges and recommendations for replicating international flipped faculty development partnerships will be provided.

Keywords: educational development, faculty development, international development, flipped learning

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Authors: Afaf Alharbi, Qianni Zhang

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The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper introduces a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple-instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation of an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: attention multiple instance learning, MIL and transfer learning, histopathological slides, cancer tissue classification

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Authors: Amichai Ben ari, Daniella Margalit

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Medical procedures, such as surgery, may have traumatic significance for some children. This study examines the relationship between maltreatment in children and the development Pediatric Medical Traumatic Stress (PMTS). To this end, differences in the level of distress of children after surgery were examined between two groups: children who were maltreated ("children at risk") and children from the control group ("children who are not at risk"). The study involved 230 parents of children who came to the hospital to undergo surgery. Parents filled out demographic questionnaires to measure socioeconomic variables and psychological questionnaires to measure the distress of the child and parent before surgery. After 6 months from the time of surgery, the parents again filled in the questionnaire measuring the child's distress. The results of the study showed that the level of distress experienced by children at risk after surgery was significantly higher relative to children who are not at risk. It was also found that the level of distress experienced by parents of children at risk in relation to their child’s surgery is significantly higher compared to parents of children who are not at risk. Finally, it was found that the variables: (1) pre-morbid psychological functioning of the child. (2) Parental and family functioning in daily life. (3) Exposure of the child to traumatic events. (4) Support factors for the family. Are variables that predict the development of PMTS in children after surgery, but only for children at risk and not for children who are not at risk. The significance of the findings in relation to the need to identify at-risk populations in the hospitals and the policies derived from them were discussed, and several directions were raised for further research.

Keywords: children at risk, pediatric medical traumatic stress (PMTS), PTSD, medical procedures

Procedia PDF Downloads 111