Search results for: human machine interface

Authors: Abdullah Aljumah, Tariq Ahamad

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The simplest possible human-computer interaction is mobile cloud computing as it emerges and makes the use of all modern-day human-oriented technology. The main aim of this idea is the QoS (quality of service) by using user-friendly and reliable software over the global network in order to make it economical by reducing cost, reliable, and increase the main storage. Since we studied and went through almost all the existing related work in this area and we came up with some challenges that will rise or might be rising for some basic areas in mobile cloud computing and mostly stogie and security area. In this research article, we suggest some recommendation for mobile cloud computing and for its security that will help in building more powerful tools to handle all this pressure.

Keywords: Cloud Computing, MCC, SAAS, computer interaction

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Authors: Konstantinos G. Papaioannou, Panagiotis K. Serdaris

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This study, examines the level of implementation of Human Resource Management (HRM) activities in law enforcement agencies in Greece. Recognizing that HRM is crucial for maximizing organizational performance, the study aims to evaluate its application within Greek law enforcement. A quantitative-descriptive survey was conducted, involving 996 executives from Greek Law Enforcement Agencies (477 from the Hellenic Police and 519 from the Hellenic Coast Guard), through random sampling. The survey, revealed significant concerns regarding the minimal implementation of HRM practices, in both agencies. The findings indicate that HRM practices, such as HR planning, recruitment, job position, selection, training and development, personnel management, compensation, labor relations and health and safety, are minimally applied. Neither the Hellenic Police nor the Hellenic Coast Guard appears to follow a comprehensive HRM plan. The study, contributes both theoretically and practically by highlighting the lack of HRM implementation in these agencies. The data suggest that by adopting strategic HRM practices, these organizations can enhance personnel performance and better fulfill their societal roles. Future research should extend to law enforcement agencies in other countries to draw more representative conclusion.

Keywords: coastguard, human resources management, law enforcement agencies, performance management, police

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Authors: Jonathan Turner, Julie Doyle, Laura O’Philbin, Dympna O’Sullivan

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People living with dementia should be at the centre of decision-making regarding goals for daily living. These goals include basic activities (dressing, hygiene, and mobility), advanced activities (finances, transportation, and shopping), and meaningful activities that promote well-being (pastimes and intellectual pursuits). However, there is limited involvement of people living with dementia in the design of technology to support their goals. A project is described that is co-designing intelligent computer-based support for, and with, people affected by dementia and their carers. The technology will support self-management, empower participation in shared decision-making with carers and help people living with dementia remain healthy and independent in their homes for longer. It includes information from the patient’s care plan, which documents medications, contacts, and the patient's wishes on end-of-life care. Importantly for this work, the plan can outline activities that should be maintained or worked towards, such as exercise or social contact. The authors discuss how to integrate care goal information from such a care plan with data collected from passive sensors in the patient’s home in order to deliver individualized planning and interventions for persons with dementia. A number of scientific challenges are addressed: First, to co-design with dementia patients and their carers computerized support for shared decision-making about their care while allowing the patient to share the care plan. Second, to develop a new and open monitoring framework with which to configure sensor technologies to collect data about whether goals and actions specified for a person in their care plan are being achieved. This is developed top-down by associating care quality types and metrics elicited from the co-design activities with types of data that can be collected within the home, from passive and active sensors, and from the patient’s feedback collected through a simple co-designed interface. These activities and data will be mapped to appropriate sensors and technological infrastructure with which to collect the data. Third, the application of machine learning models to analyze data collected via the sensing devices in order to investigate whether and to what extent activities outlined via the care plan are being achieved. The models will capture longitudinal data to track disease progression over time; as the disease progresses and captured data show that activities outlined in the care plan are not being achieved, the care plan may recommend alternative activities. Disease progression may also require care changes, and a data-driven approach can capture changes in a condition more quickly and allow care plans to evolve and be updated.

Keywords: care goals, decision-making, dementia, self-care, sensors

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Authors: Jessie Fazel, Kristen Smith

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Human trafficking is a multi-billion-dollar criminal industry that affects 24.9 million people around the world. There are several different types of trafficking, the most common being sex trafficking, labor trafficking, and domestic servitude. Survival sex is common in the pediatric population, as they engage in sex for food, a place to sleep, or other basic needs. Statistics show that health care workers are at a unique advantage to help identify victims and get them the help they need, as 88% of trafficked victims encounter a health care worker while being trafficked. Unfortunately, victims don’t usually self-identify that they are being trafficked and the situations they face can vary dramatically. It is imperative to remember that traditional red flags are not always present in the pediatric population. Risk factors and red flags with their history and physical exam are one of the best indicators that health care providers need to be vigilant in looking at. There are numerous barriers for disclosure in the healthcare setting. Periods of time before and after disclosure are often emotionally difficult and could be dangerous for the victim. It is extremely important to have a plan in place for intervention if the victim does disclose trafficking. A trauma informed approach to medical and mental health interventions, that focus on safety, are vital in this population. This is happening where you live and you can make a difference in their lives.

Keywords: human trafficking, public health, emergency medicine, sexual health

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Authors: Daewook Kim, Gilwon Yoon

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Intrabody communication (IBC) is a new way of transferring data using human body as a medium. Minute current can travel though human body without any harm. IBC can remove electrical wires for human area network. IBC can be also a secure communication network system unlike wireless networks which can be accessed by anyone with bad intentions. One of the IBC systems is based on frequency shift keying modulation where individual data are transmitted to the external devices for the purpose of secure access such as digital door lock. It was found that the quality of IBC data transmission was heavily dependent on ground configurations of electronic circuits. Reliable IBC transmissions were not possible when both of the transmitter and receiver used batteries as circuit power source. Transmission was reliable when power supplies were used as power source for both transmitting and receiving sites because the common ground was established through the grounds of instruments such as power supply and oscilloscope. This was due to transmission dipole size and the ground effects of floor and AC power line. If one site used battery as power source and the other site used the AC power as circuit power source, transmission was possible.

Keywords: frequency shift keying, ground, intrabody, communication, door lock

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Authors: Rajendra Roul

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The main objective of the project is to bring attention towards the performance of an aerofoil when exposed to the fluid medium inside the wind tunnel. This project aims at involvement of civil as well as mechanical engineering thereby making itself as a multidisciplinary project. The airfoil of desired size is taken into consideration for the project to carry out effectively. An aerofoil is the shape of the wing or blade of propeller, rotor or turbine. Lot of experiment have been carried out through wind-tunnel keeping aerofoil as a reference object to make a future forecast regarding the design of turbine blade, car and aircraft. Lift and drag now become the major identification factor for any design industry which shows that wind tunnel testing along with software analysis (ANSYS) becomes the mandatory task for any researchers to forecast an aerodynamics design. This project is an initiative towards the mitigation of drag, better lift and analysis of wake surface profile by investigating the surface pressure distribution. The readings has been taken on airfoil model in Wind Tunnel Testing Machine (WTTM) at different air velocity 20m/sec, 25m/sec, 30m/sec and different angle of attack 00,50,100,150,200. Air velocity and pressures are measured in several ways in wind tunnel testing machine by use to measuring instruments like Anemometer and Multi tube manometer. Moreover to make the analysis more accurate Ansys fluent contribution become substantial and subsequently the CFD simulation results. Analysis on an Aerofoil have a wide spectrum of application other than aerodynamics including wind loads in the design of buildings and bridges for structural engineers.

Keywords: wind-tunnel, aerofoil, Ansys, multitube manometer

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Authors: Munaf Rashid

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For content-based human action retrieval systems, search accuracy is often inferior because of the following two reasons 1) global information pertaining to videos is totally ignored, only low level motion descriptors are considered as a significant feature to match the similarity between query and database videos, and 2) the semantic gap between the high level user concept and low level visual features. Hence, in this paper, we propose a method that will address these two issues and in doing so, this paper contributes in two ways. Firstly, we introduce a method that uses both global and local information in one framework for an action retrieval task. Secondly, to minimize the semantic gap, a user concept is involved by incorporating features weight updating (FWU) Relevance Feedback (RF) approach. We use statistical characteristics to dynamically update weights of the feature descriptors so that after every RF iteration feature space is modified accordingly. For testing and validation purpose two human action recognition datasets have been utilized, namely Weizmann and UCF. Results show that even with a number of visual challenges the proposed approach performs well.

Keywords: relevance feedback (RF), action retrieval, semantic gap, feature descriptor, codebook

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Authors: Stellina Jolly

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July 18th to August 19th 2013 has gone down in the history of India for undertaking the country’s first environment referendum. The Supreme Court had ruled that the Vedanta Group's bauxite mining project in the Niyamgiri Hills of Orissa will have to get clearance from the gram sabha, which will consider the cultural and religious rights of the tribals and forest dwellers living in Rayagada and Kalahandi districts. In the Niyamgiri hills, people of small tribal hamlets were asked to voice their opinion on bauxite mining in their habitat. The ministry has reiterated its stand that mining cannot be allowed on the Niyamgiri hills because it will affect the rights of the Dongria Kondhs. The tribal person who occupies the Niyamgiri Hills in Eastern India accomplished their first success in 2010 in their struggle to protect and preserve their existence, culture and land against Vedanta a London-based mining giant. In August, 2010 Government of India revoked permission for Vedanta Resources to mine bauxite from hills in Orissa State where the Dongria Kondh live as forest dwellers. This came after various protests and reports including amnesty report wherein it highlighted that an alumina refinery in eastern India operated by a subsidiary of mining company. Vedanta was accused of causing air and water pollution that threatens the health of local people and their access to water. The abuse of human rights by corporate is not a new issue it has occurred in Africa, Asia and other parts of the world. Paper focuses on the instances and extent of human right especially in terms of environment violations by corporations. Further Paper details on corporations and sustainable development. Paper finally comes up with certain recommendation including call for a declaration by United Nations on Corporate environment Human Rights Liability.

Keywords: environment, corporate, human rights, sustainable development

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Authors: Rodrigo Lentini, Beatrice Ionascu, Friederike A. Eyssel, Scandar Copti, Mohamad Eid

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The haptic modality has brought a new dimension to human computer interaction by engaging the human sense of touch. However, designing appropriate haptic stimuli, and in particular tactile stimuli, for various applications is still challenging. To tackle this issue, we present an intuitive system that facilitates the authoring of tactile gestures for various applications. The system transforms a hand gesture into a tactile gesture that can be rendering using a home-made haptic jacket. A case study is presented to demonstrate the ability of the system to develop tactile gestures that are recognizable by human subjects. Four tactile gestures are identified and tested to intensify the following four emotional responses: high valence – high arousal, high valence – low arousal, low valence – high arousal, and low valence – low arousal. A usability study with 20 participants demonstrated high correlation between the selected tactile gestures and the intended emotional reaction. Results from this study can be used in a wide spectrum of applications ranging from gaming to interpersonal communication and multimodal simulations.

Keywords: tactile stimulation, tactile gesture, emotion reactions, arousal, valence

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: Bae Doobyong, Yoo Jaejun, Park Ilgyu, Choi Seowon, Oh Chang Kook

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Variation of slip coefficient in slip-critical connections of curved plates. This paper presents the results of analytical investigations of slip coefficients in slip-critical bolted connections of curved plates. It may depend on the contact stress distribution at interface and the flexibility of spliced plate. Non-linear FEM analyses have been made to simulate the behavior of bolted connections of curved plates with various radiuses of curvature and thicknesses.

Keywords: slip coefficient, curved plates, slip-critical bolted connection, radius of curvature

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Sultanbayeva Gulmira Serikbayevna, Golovchun Aleftina Anatolyevna

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The development of any nation is directly related to the development of human capital in it. A human development is an increase its intellectual potential, its compliance with the requirements of time, present and future society. Demands of globalization cannot limit the processes of national traditions. The education system must be formed on the basis of international practice of cultural development. In Kazakhstan, where modernization changes are rapidly developing, the education system should be formed in two ways: first, on a national basis, and secondly, based on global best practices. There is the need to recognize and promote the importance of education as a value. The world community considers the problem of spiritual values. Along with individual values, spiritual values are also universal values. Formation of values such as the presence in young people a sense of respect for their homeland, social responsibility, respect the culture and traditions of its people is the most important task than the possession of material goods. When forming the intellectual nation, values in the field of education and science become investments for the development of the society, as well as education and science today transformed into the most important capital.

Keywords: human capital, humanitarian technology, intangible assets, intelligent nation, society of knowledge

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Authors: María Elena Menéndez Ibáñez

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After the terrorist attacks of 9/11, new measures were adopted by powerful countries and regions like the United States and the European Union in order to safeguard their security. In 2002, the US created the Department of Homeland Security with one sole objective; to protect American soil and people. The US adopted new policies that made every immigrant a potential terrorist and a threat to their national security. Stronger border control became one of the key elements of the fight against organized crime and terrorism. The main objective of this paper is to compare some of the most important and radical measures adopted by the US, even those that resulted in systematic violations of human rights, with some of the European measures adopted after the 2015 Paris attacks of 2015, such as unlawful detainment of prisoners and other measures against foreigners. Through the Schengen agreement, the European Union has tried to eliminate tariffs and border controls, in order to guarantee successful economic growth. Terrorists have taken advantage of this and have made the region vulnerable to attacks. Authorities need to strengthen their surveillance methods in order to safeguard the region and its stability. Through qualitative methods applied to social sciences, this research will also try to explain why some of the mechanisms proven to be useful in the US would not be so in Europe, especially because they would result in human rights violations. Finally, solutions will be offered that would not put the whole Schengen Agreement at risk. Europe cannot reinstate border control, without making individuals vulnerable to human rights violations.

Keywords: border control, immigration, international cooperation, national security

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Authors: Riaan Kleyn

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Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.

Keywords: computer vision, wine grapes, machine learning, machine harvested grapes

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Authors: María José Benítez Jiménez

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Article 3 of the European Convention for the Protection of Human Rights and Fundamental Freedoms (E.C.H.R.) proclaims that no one may be subjected to torture, punishment or degrading treatment. The legislative correlate in Spain is embodied in Article 15 of the Spanish Constitution, and there must be an overlapping interpretation of both precepts on the ideal plane. While it is true that there are not many cases in which the European Court of Human Rights (E.C.t.H.R. (The Strasbourg Court)) has sanctioned Spain for its failure to investigate complaints of torture, it must be emphasized that the tendency to violate Article 3 of the Convention appears to be on the rise, being necessary to know possible factors that may be affecting it. This paper addresses the analysis of sentences that directly or indirectly reveal the violation of Article 3 of the European Convention. To carry out the analysis, sentences of the Strasbourg Court have been consulted from 2012 to 2016, being able to address any previous sentences to this period if it provided justified information necessary for the study. After the review it becomes clear that there are two key groups of subjects that request a response to the Strasbourg Court on the understanding that they have been tortured or degradingly treated. These are: immigrants and terrorists. Both phenomena, immigration and terrorism, respond to patterns that have mutated in recent years, and it is important for this study to know if national regulations begin to be dysfunctional.

Keywords: E.C.H.R., E.C.t.H.R. sentences, Spanish Constitution, torture

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Authors: Hilal Ahmad Punoo

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Conjugated linoleic acid (CLA) is a mixture of positional and geometric isomers of octadecadienoic acid with two conjugated double bonds. Of more than a dozen isomers of CLA found naturally in dairy and meat products from ruminants, c-9, t-11 and t-10, c-12 are the two isomers with known physiological importance, including anticarcinogenic, antidiabetic, antilipogenic, and antiatherosclerotic effects. Conjugated linoleic acids (CLA) may influence the onset and severity of several chronic diseases, including various cancers, atherosclerosis, obesity, bone density loss, and diabetes. These findings are of special interest to the agriculture community, because dietary sources of CLA are almost exclusively beef and dairy products. Thus, a better understanding of the specific isomers and mechanisms responsible for these positive effects of CLA on human health would be both prudent and economically beneficial. To date, research related to the advantages of CLA consumption on human health has been conducted using experimental laboratory animals and cell culture systems. These data consistently show that relatively low levels of CLA can influence risk of cancer. Further, very recent investigations suggest that the predominate CLA isoform (cis-9, trans-11 CLA or rumenic acid) found in beef and milk fat possesses anticarcinogenic effects but does not alter body composition; the trans-10, cis-12 CLA has been shown to inhibit lipogenesis. Clearly, further work, especially using human subjects, will be required to characterize the potential benefits of CLA consumption on human health. Moreover, we suggest that foods naturally containing high amounts of CLA (e.g., beef and dairy products) be considered as meeting the definition of functional foods.

Keywords: conjugated linoleic acid, potential health benefits, fats, animals, humans

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Authors: Rahul Mohite, Lahcen Ouarbya

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This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

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Authors: Azza Taha Zaki

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Modern drama was characterised by realism and naturalism as dominant literary movements that focused on contemporary people and their issues to reflect the status of modern man and his environment. However, some modern dramatists have often fallen on classical mythology in ancient Greek tragedies to create a sense of the universality of the human experience. The tragic overtones of classical myths have helped modern dramatists in their attempts to create an enduring piece by evoking the majestic grandeur of the ancient myths and the heroic struggle of man against forces he cannot fight. Myths have continued to appeal to modern playwrights not only for the plot and narrative material but also for the vision and insight into the human experience and human condition. This paper intends to study how the reworking of Sophocles’ Antigone by Jean Anouilh in his Antigone, written in 1942 at the height of the Second World War and during the German occupation of his country, France, fits his own purpose and his own time. The paper will also offer an analysis of the vision in both plays to show how Anouilh has used the classical Antigone freely to produce a modern vision of the dilemma of man when faced by personal and national conflicts.

Keywords: Anouilh, Antigone, drama, Greek tragedy, modern, myth, sophocles

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Authors: Long Zhang, Ian Hampson, Loretta O' Donnell

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Practitioners in various industries, especially in the finance industry that conventionally benefit from financial capital and resources, appear to be increasingly aware of the importance of human capital resources (HCR) after the 2008 Global Financial Crisis. Scholars from diverse fields have conducted extensive and fruitful research on HCR within their own disciplines. This review suggests that the mainstream of pure quantitative research alone is insufficient to provide precise or comprehensive understanding of HCR. The complex relationships and interactions in HCR call for more integrative and cross-disciplinary research to more holistically understand complex and intricate HCRs. The complex nature of HCR requires deep qualitative exploration based on in-depth data to capture the everydayness of organizational activities and to register its individuality and variety. Despite previous efforts, a systematic and holistic integration of HCR research among multiple disciplines is lacking. Using a retrospective analysis of articles published in the field of economics, finance and management, including psychology, human resources management (HRM), organizational behaviour (OB), industrial and organizational psychology (I-O psychology), organizational theory, and strategy literatures, this study summaries and compares the major perspectives, theories, and findings on HCR research. A careful examination of the progress of the debates of HCR definitions and measurements in distinct disciplines enables an identification of the limitations and gaps in existing research. It enables an analysis of the interplay of these concepts, as well as that of the related concepts of intellectual capital, social capital, and Chinese guanxi, and how they provide a broader perspective on the HCR-related influences on firms’ competitive advantage. The study also introduces the themes of Environmental, Social and Governance, or ESG based investing, as the burgeoning body of ESG studies illustrates the rising importance of human and non-financial capital in investment process. The ESG literature locates HCR into a broader research context of the value of non-financial capital in explaining firm performance. The study concludes with a discussion of new directions for future research that may help advance our knowledge of HCR.

Keywords: human capital resources, social capital, Chinese guanxi, human resources management

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Authors: Xiaoqing Wang, Junfeng Wang, Xiaolan Zhu

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Android operating system has been recognized by most application developers because of its good open-source and compatibility, which enriches the categories of applications greatly. However, it has become the target of malware attackers due to the lack of strict security supervision mechanisms, which leads to the rapid growth of malware, thus bringing serious safety hazards to users. Therefore, it is critical to detect Android malware effectively. Generally, the permissions declared in the AndroidManifest.xml can reflect the function and behavior of the application to a large extent. Since current Android system has not any restrictions to the number of permissions that an application can request, developers tend to apply more than actually needed permissions in order to ensure the successful running of the application, which results in the abuse of permissions. However, some traditional detection methods only consider the requested permissions and ignore whether it is actually used, which leads to incorrect identification of some malwares. Therefore, a machine learning detection method based on the actually used permissions combination and API calls was put forward in this paper. Meanwhile, several experiments are conducted to evaluate our methodology. The result shows that it can detect unknown malware effectively with higher true positive rate and accuracy while maintaining a low false positive rate. Consequently, the AdaboostM1 (J48) classification algorithm based on information gain feature selection algorithm has the best detection result, which can achieve an accuracy of 99.8%, a true positive rate of 99.6% and a lowest false positive rate of 0.

Keywords: android, API Calls, machine learning, permissions combination

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Authors: Dmitriy Babenko, Sergey Yegorov, Ilya Korshukov, Aidana Sultanbekova, Valentina Barkhanskaya, Tatiana Bashirova, Yerzhan Zhunusov, Yevgeniya Li, Viktoriya Parakhina, Svetlana Kolesnichenko, Yeldar Baiken, Aruzhan Pralieva, Zhibek Zhumadilova, Matthew S. Miller, Gonzalo H. Hortelano, Anar Turmuhambetova, Antonella E. Chesca, Irina Kadyrova

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Introduction: The rapidly evolving COVID-19 pandemic, along with the re-emergence of pathogens causing acute respiratory infections (ARI), has necessitated the development of novel diagnostic tools to differentiate various causes of ARI. MALDI-MS, due to its wide usage and affordability, has been proposed as a potential instrument for diagnosing SARS-CoV-2 versus non-SARS-CoV-2 ARI. The aim of this study was to investigate the potential of MALDI-MS in conjunction with a machine learning model to accurately distinguish between symptomatic infections caused by SARS-CoV-2 and non-SARS-CoV-2 during both the early and later phases of the pandemic. Furthermore, this study aimed to analyze mass spectrometry (MS) data obtained from nasal swabs of healthy individuals. Methods: We gathered mass spectra from 252 samples, comprising 108 SARS-CoV-2-positive samples obtained in 2020 (Covid 2020), 7 SARS-CoV- 2-positive samples obtained in 2023 (Covid 2023), 71 samples from symptomatic individuals without SARS-CoV-2 (Control non-Covid ARVI), and 66 samples from healthy individuals (Control healthy). All the samples were subjected to RT-PCR testing. For data analysis, we employed the caret R package to train and test seven machine-learning algorithms: C5.0, KNN, NB, RF, SVM-L, SVM-R, and XGBoost. We conducted a training process using a five-fold (outer) nested repeated (five times) ten-fold (inner) cross-validation with a randomized stratified splitting approach. Results: In this study, we utilized the Covid 2020 dataset as a case group and the non-Covid ARVI dataset as a control group to train and test various machine learning (ML) models. Among these models, XGBoost and SVM-R demonstrated the highest performance, with accuracy values of 0.97 [0.93, 0.97] and 0.95 [0.95; 0.97], specificity values of 0.86 [0.71; 0.93] and 0.86 [0.79; 0.87], and sensitivity values of 0.984 [0.984; 1.000] and 1.000 [0.968; 1.000], respectively. When examining the Covid 2023 dataset, the Naive Bayes model achieved the highest classification accuracy of 43%, while XGBoost and SVM-R achieved accuracies of 14%. For the healthy control dataset, the accuracy of the models ranged from 0.27 [0.24; 0.32] for k-nearest neighbors to 0.44 [0.41; 0.45] for the Support Vector Machine with a radial basis function kernel. Conclusion: Therefore, ML models trained on MALDI MS of nasopharyngeal swabs obtained from patients with Covid during the initial phase of the pandemic, as well as symptomatic non-Covid individuals, showed excellent classification performance, which aligns with the results of previous studies. However, when applied to swabs from healthy individuals and a limited sample of patients with Covid in the late phase of the pandemic, ML models exhibited lower classification accuracy.

Keywords: SARS-CoV-2, MALDI-TOF MS, ML models, nasopharyngeal swabs, classification

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Authors: Nick Farid, Roghoyeh Salmeh

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The planning and operation of the power grid is becoming much more complex because of the introduction of renewable energy resources, the digitalization of the electricity industry, as well as the coupling of efficiency and greener energy trends. These changes, along with new trends, make interactions between grid users and the other stakeholders more complex. This paper focuses on the main “physical” and “logical” interactions between grid users and the grid stakeholders, both from power system equipment and information management standpoints, and proposes a new interoperability model for Smart Grids.

Keywords: user interface, interoperability layers, grid architecture framework, smart grid

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Onyekachi Ohagwu

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Human trafficking remains a pressing issue globally, with Nigeria serving as a source, transit, and destination country. In response to this crisis, the Edo State Task Force Against Human Trafficking (ETAHT), in collaboration with local partners and international organizations such as the International Organization for Migration, has implemented various initiatives, including technical and vocational education and training (TVET) programmes. This research article examines the effectiveness of the ETAHT TVET programme in providing a second chance for female returnee migrants in Nigeria. Through qualitative analysis, including in-depth interviews and case studies, the study evaluates the impact of the programme on participants' lives, socio-economic reintegration, and empowerment. Findings suggest that the ETAHT TVET programme plays a significant role in empowering female returnees, fostering self-reliance, and reducing the risk of re-trafficking. The article concludes with recommendations for enhancing the programme's effectiveness and scalability.

Keywords: Edo State, human trafficking, TVET programme, female returnee migrants, empowerment, socio-economic reintegration

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Authors: Khan Shahid, Shahid Zakia

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The article demonstrates how Theology and Islamic Philosophy can be illuminated and enhanced through the application of the SOUL framework (Sincere act, Optimization effort, Ultimate goal, Law compliance). The study explores historical development using a phenomenological approach and integrates the SOUL framework to enrich Theology and Islamic Philosophy. The proposed framework highlights the significance of these elements, ultimately leading to a deeper understanding of Theology and Islamic Philosophy.

Keywords: SOUL framework, illuminating human identity, theology, Islamic Philosophy, sincerity act, optimization effort, ultimate goals, law compliance

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

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Authors: Emily Schlebes, Christian Hundhausen, Jens W. Fischer

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The glycosaminoglycan hyaluronan (HA) is a major component of the extracellular matrix, typically produced by fibroblasts of the connective tissue but also by immune cells. Here, we investigated the capacity of human peripheral blood CD8 T cells from healthy donors to produce HA and to express HA receptors as well as HA degrading enzymes. Further, we evaluated the effect of pharmacological HA inhibition on CD8 T cell function. Using immunocytochemistry together with quantitative PCR analysis, we found that HA synthesis is rapidly induced upon antibody-induced T cell receptor (TCR) activation and almost exclusively mediated by HA synthase 3 (HAS3). TCR activation also resulted in the upregulation of HA receptors CD44, hyaluronan-mediated motility receptor (HMMR), and layilin (LAYN), although kinetics and strength of expression varied greatly between subjects. The HA-degrading enzymes HYAL1 and HYAL2 were detected at low levels and induced by cell activation in some individuals. Interestingly, expression of HAS3, HA receptors, and hyaluronidases were modulated by the proinflammatory cytokines IL-6 and IL-1bβ in most subjects. To assess the functional role of HA in CD8 T cells, we performed carboxyfluorescein succinimidyl ester (CFSE) based proliferation assays and cytokine analysis in the presence of the HA inhibitor 4- Methylumbelliferone (4-MU). Despite significant inter-individual variation with regard to the effective dose, 4-MU resulted in the inhibition of CD8 T cell proliferation and reduced release of TNF-α and IFN-γ. Collectively, these data demonstrate that human CD8 T cells respond to TCR stimulation with a synthesis of HA and expression of HA-related genes. They further suggest that HA inhibition may be helpful in interfering with pathogenic T cell activation in human disease.

Keywords: CD8 T cells, extracellular matrix, hyaluronan, hyaluronan synthase 3

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Authors: Luiz Drude de Lacerda, Kevin Luiz Cordeiro Ferrer do Carmo, Victor Lacerda Moura, Rayone Wesley Santos de Oliveira, Moisés Fernandes Bezerra

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Fish is a food source well recognized for its health benefits. However, the consumption of fish, especially carnivorous species, is the main path of human exposure to Hg, a widely distributed pollutant on the planet and that accumulates along food chains. Studies on the impacts on public health by fish intake show existing toxic risks even when at low concentrations. This study quantifies, for the first time, the concentrations of Hg in muscle tissue of the nine most commercialized fish species in the fish market of São Luís (MA) in north Brazil and estimates the consequent human exposure through consumption. Concentrations varied according to trophic level, with the highest found in the larger carnivorous species; the Yellow hake (Cynoscion acoupa) (296.4 ± 241.2 ng/g w.w) and the Atlantic croaker (Micropogonias undulatus) (262.8 ± 89.1 ng/g w.w.), whereas the lowest concentrations were recorded in iliophagous Mullets (Mugil curema) (20.5 ± 9.6 ng/g w.w.). Significant correlations were observed between Hg concentrations and individual length in only two species: the Flaming catfish (Bagre marinus) and the Atlantic bumper (Chloroscombrus crysurus). Given the relatively uniform size of individuals of the other species and/or the small number of samples, this relationship was not found for the other species. The estimated risk coefficients, despite the relatively low concentrations of Hg, suggest that yellow hake and Whitemouth croaker (Micropogonias furnieri), fish most consumed by the local population, present some risk to human health (> 1) HQ and THQ, depending on the frequency of their consumption.

Keywords: contamination, fish, human exposure, risk assessment

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