Search results for: electronic intelligence

Authors: Z. M. T. Lim, W. Q. Yow

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Diversity in individual problem-solving approaches, culture and nationality have been shown to have positive effects on collaborative creative processes in organizational and scholastic settings. For example, diverse graduate and organizational teams consisting of members with both structured and unstructured problem-solving styles were found to have more creative ideas on a collaborative idea generation task than teams that comprised solely of members with either structured or unstructured problem-solving styles. However, being different may not always provide benefits to the collaborative creative process. In particular, speaking different languages may hinder mutual engagement through impaired communication and thus collaboration. Instead, sharing similar languages may have facilitative effects on mutual engagement in collaborative tasks. However, no studies have explored the relations between language diversity and adults’ collaborative creative problem solving. Sixty-four Singaporean English-speaking bilingual undergraduates were paired up into similar or dissimilar language pairs based on the second language they spoke (e.g., for similar language pairs, both participants spoke English-Mandarin; for dissimilar language pairs, one participant spoke English-Mandarin and the other spoke English-Korean). Each participant completed the Ravens Progressive Matrices Task individually. Next, they worked in pairs to complete a collaborative divergent thinking task where they used mind-mapping techniques to brainstorm ideas on a given problem together (e.g., how to keep insects out of the house). Lastly, the pairs worked on a collaborative insight problem-solving task (Triangle of Coins puzzle) where they needed to flip a triangle of ten coins around by moving only three coins. Pairs who had prior knowledge of the Triangle of Coins puzzle were asked to complete an equivalent Matchstick task instead, where they needed to make seven squares by moving only two matchsticks based on a given array of matchsticks. Results showed that, after controlling for intelligence, similar language pairs completed the collaborative insight problem-solving task faster than dissimilar language pairs. Intelligence also moderated these relations. Among adults of lower intelligence, similar language pairs solved the insight problem-solving task faster than dissimilar language pairs. These differences in speed were not found in adults with higher intelligence. No differences were found in the number of ideas generated in the collaborative divergent thinking task between similar language and dissimilar language pairs. In conclusion, sharing similar languages seem to enrich collaborative creative processes. These effects were especially pertinent to pairs with lower intelligence. This provides guidelines for the formation of groups based on shared languages in collaborative creative processes. However, the positive effects of shared languages appear to be limited to the insight problem-solving task and not the divergent thinking task. This could be due to the facilitative effects of other factors of diversity as found in previous literature. Background diversity, for example, may have a larger facilitative effect on the divergent thinking task as compared to the insight problem-solving task due to the varied experiences individuals bring to the task. In conclusion, this study contributes to the understanding of the effects of language diversity in collaborative creative processes and challenges the general positive effects that diversity has on these processes.

Keywords: bilingualism, diversity, creativity, collaboration

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Authors: B. Adcock, H. van Rensburg

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Education is ever-changing to keep up with innovative technological development and the rapid acceleration of globalisation. This chapter introduces the historical development and transformation of teaching in distance education from correspondence to on-line learning in Queensland universities. It furthermore investigates changes to the delivery models of distance education that have impacted on teaching at tertiary level in Queensland, and reflects on the social changes that have taken place during the past 100 years. This includes an analysis of the following five different periods in time: Foundation period (1911-1919) including World War I; 1920-1939 including the Great Depression; 1940-1970s, including World War II and the post war reconstruction; and the current technological era (1980s to present). In Queensland, the concept of distance education was begun by the University of Queensland (UQ) in 1911, when it began offering extension courses. The introduction of modern technology, in the form of electronic delivery, dramatically changed tertiary distance education due to political initiatives. The inclusion of electronic delivery in education signifies change at many levels, including policy, pedagogy, curriculum and governance. Changes in delivery not only affect the way study materials are delivered, but also the way courses are be taught and adjustments made by academics to their teaching methods.

Keywords: distance education, innovative technological development, on line education, tertiary education

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Authors: Rumana Ali

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The presence of nonlinear and power electronic switching devices produce distorted output and harmonics into the system. This paper presents a technique to analyze harmonics using digital series oscilloscope (DSO). In power distribution system further measurements are done by DSO, and the waveforms are analyzed using FFT program. The results of this proposed work are helpful for the investigator to install an appropriate compensating device to mitigate the harmonics, in turn, improve the power quality. This case study is carried out at AIT Chikmagalur. It is done as a starting step towards the improvement of energy efficiency at AIT Chikmagalur, and with an overall aim of reducing the electricity bill with a complete energy audit of the institution. Strategies were put forth to reach the above objective: The following strategies were proposed to be implemented to analyze the power quality in EEE department of the institution. Strategy 1: The power factor has to be measured using the energy meter. Power factor improvement may reduce the voltage drop in lines. This brings the voltages at the socket in the labs closer to the nominal voltage of 230V, and thus power quality improves. Strategy 2: The harmonics at the power inlet has to be measured by means of a DSO. The DSO waveform is analyzed using FFT to know the percentage harmonic up to the 13th harmonics of 50Hz. Reduction in the harmonics in the inlet of the EEE department may reduce line losses and therefore reduces energy bill to the institution.

Keywords: harmonic analysis, energy bill, power quality, electronic switching devices

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Authors: Ioan Corneliu Salisteanu, Valentin Dogaru Ulieru, Mihaita Nicolae Ardeleanu, Alin Pohoata, Bogdan Salisteanu, Stefan Broscareanu

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Creating a personalized statistic for an individual within the population using IT systems, based on the searches and intercepted spheres of interest they manifest, is just one 'atom' of the artificial intelligence analysis network. However, having the ability to generate statistics based on individual data intercepted from large demographic areas leads to reasoning like that issued by a human mind with global strategic ambitions. The DiaMOTO device is a technical sensory system that allows the interception of car events caused by a driver, positioning them in time and space. The device's connection to the vehicle allows the creation of a source of data whose analysis can create psychological, behavioural profiles of the drivers involved. The SiaMOTO system collects data from many vehicles equipped with DiaMOTO, driven by many different drivers with a unique fingerprint in their approach to driving. In this paper, we aimed to explain the software infrastructure of the SiaMOTO system, a system designed to monitor and improve driver driving behaviour, as well as the criteria and algorithms underlying the intelligent analysis process.

Keywords: artificial intelligence, data processing, driver behaviour, driver monitoring, SiaMOTO

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Authors: Benlakhdar Mohamed Larbi, Yagoub Asma

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Purpose: This study aims at highlighting the significance and importance of utilizing marketing information system (MKIS) on decision-making, by clarifying the need for quick and efficient decision-making due to time saving and preventing of duplication of work. Design, methodology, approach: The study shows the roles of each part of MKIS for developing marketing strategy, which present a real challenge to individuals and institutions in an era characterized by uncertainty and clarifying the importance of each part separately, depending on decision type and the nature of the situation. The empirical research method was evaluated by specialized experts, conducted by means of questionnaires. Correlation analysis was employed to test the validity of the procedure. Results: The empirical study findings confirmed positive relationships between the level of utilizing and adopting ‘decision support system and marketing intelligence’ and the success of an organizational decision-making, and provide the organization with a competitive advantage as it allows the organization to solve problems. Originality/value: The study offer better understanding of performance- increasing market share as an organizational decision making based on marketing information system.

Keywords: database, marketing research, marketing intelligence, decision support system, decision-making

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Authors: Hsin Lee, Hsuan Lee

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Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

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Authors: Włodzimierz Lewandowski, Renata Świsłocka, Aleksandra Golonko, Grzegorz Świderski, Monika Kalinowska

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Caffeic acid (3,4-dihydroxycinnamic) is distributed in a free form or as ester conjugates in many fruits, vegetables and seasonings including plants used for medical purpose. Caffeic acid is present in propolis – a substance with exceptional healing properties used in natural medicine since ancient times. The antioxidant, antibacterial, antiinflammatory and anticarcinogenic properties of caffeic acid are widely described in the literature. The biological activity of chemical compounds can be modified by the synthesis of their derivatives or metal complexes. The structure of the compounds determines their biological properties. This work is a continuation of the broader topic concerning the investigation of the correlation between the electronic charge distribution and biological (anticancer and antioxidant) activity of the chosen phenolic acids and their metal complexes. In the framework of this study the synthesis of new metal complexes of sodium, magnesium, aluminium, iron (III) ruthenium (III) and osmium (III) with caffeic acid was performed. The spectroscopic properties of these compounds were studied by means of FT-IR, FT-Raman, UV-Vis, ¹H and ¹³C NMR. The quantum-chemical calculations (at B3LYP/LAN L2DZ level) of caffeic acid and selected complexes were done. Moreover the antioxidant properties of synthesized complexes were studied in relation to selected stable radicals (method of reduction of DPPH and method of reduction of ABTS). On the basis of the differences in the number, intensity and locations of the bands from the IR, Raman, UV/Vis and NMR spectra of caffeic acid and its metal complexes the effect of metal cations on the electronic system of ligand was discussed. The geometry, theoretical spectra and electronic charge distribution were calculated by the use of Gaussian 09 programme. The geometric aromaticity indices (Aj – normalized function of the variance in bond lengths; BAC - bond alternation coefficient; HOMA – harmonic oscillator model of aromaticity and I₆ – Bird’s index) were calculated and the changes in the aromaticity of caffeic acid and its complexes was discussed. This work was financially supported by National Science Centre, Poland, under the research project number 2014/13/B/NZ7/02-352.

Keywords: antioxidant properties, caffeic acid, metal complexes, spectroscopic methods

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Authors: Soraya Abtouche, Zeyneb Ghoualem, Syrine Daoudi, Lina Ouldmohamed, Xavier Assfeld

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This work reports density functional theory calculations of the optimized geometries, molecular reactivity, energy gap,and thermodynamic properties of the free base (H2P) and their Zn (II) metallated (ZnP), bearing one, two, or three carboxylic acid groups using the hybrid functional B3LYP, Cam-B3lYP, wb97xd with 6-31G(d,p) basis sets. When donating groups are attached to the molecular dye, the bond lengths are slightly decreased, which is important for the easy transfer of an electron from donating to the accepting group. For all dyes, the highest occupied molecular orbital/lowest occupied molecular orbital analysis results in positive outcomes upon electron injection to the semiconductor and subsequent dye regeneration by the electrolyte. The ionization potential increases with increasing conjugation; therefore, the compound dye attached to one carboxylic acid group has the highest ionization potential. The results show higher efficiencies of those sensitized with ZnP. These results have been explained, taking into account the electronic character of the metal ion, which acts as a mediator in the injection step, and, on the other hand, considering the number of anchoring groups to which it binds to the surface of TiO2.

Keywords: DSSC, porphyrin, TD-DFT, electronic properties, donor-acceptor groups

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Authors: David S. Byrne

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The purpose of this paper is to understand how phone record analysis can enable identification of subjects in communication with a target of a terrorist plot. This study also sought to understand the advantages of the implementation of simulations to develop the skills of future intelligence analysts to enhance national security. Through the examination of phone reports which in essence consist of the call traffic of incoming and outgoing numbers (and not by listening to calls or reading the content of text messages), patterns can be uncovered that point toward members of a criminal group and activities planned. Through temporal and frequency analysis, conclusions were drawn to offer insights into the identity of participants and the potential scheme being undertaken. The challenge lies in the accurate identification of the users of the phones in contact with the target. Often investigators rely on proprietary databases and open sources to accomplish this task, however it is difficult to ascertain the accuracy of the information found. Thus, this paper poses two research questions: how effective are freely available web sources of information at determining the actual identification of callers? Secondly, does the identity of the callers enable an understanding of the lifestyle and habits of the target? The methodology for this research consisted of the analysis of the call detail records of the author’s personal phone activity spanning the period of a year combined with a hypothetical theory that the owner of said phone was a leader of terrorist cell. The goal was to reveal the identity of his accomplices and understand how his personal attributes can further paint a picture of the target’s intentions. The results of the study were interesting, nearly 80% of the calls were identified with over a 75% accuracy rating via datamining of open sources. The suspected terrorist’s inner circle was recognized including relatives and potential collaborators as well as financial institutions [money laundering], restaurants [meetings], a sporting goods store [purchase of supplies], and airline and hotels [travel itinerary]. The outcome of this research showed the benefits of cellphone analysis without more intrusive and time-consuming methodologies though it may be instrumental for potential surveillance, interviews, and developing probable cause for wiretaps. Furthermore, this research highlights the importance of building upon the skills of future intelligence analysts through phone record analysis via simulations; that hands-on learning in this case study emphasizes the development of the competencies necessary to improve investigations overall.

Keywords: hands-on learning, intelligence analysis, intelligence education, phone record analysis, simulations

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Authors: Noor Azwa Azreen Binti Abd. Aziz, Muhamad Zaim Bin Mohd Rozi

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Moving towards the 4th Industrial Revolution, artificial intelligence (AI) is now more popular than ever. This subject is gaining significance every day and is continually expanding, often merging with other fields. Instead of merely being passive observers, there are benefits to understanding modern technology by delving into its inner workings. However, in a world teeming with digital information, the impact of AI on the spread of disinformation has garnered significant attention. The dissemination of inaccurate or misleading information is referred to as misinformation, posing a serious threat to democratic society, public debate, and individual decision-making. This article delves deep into the connection between AI and the dissemination of false information, exploring its potential, risks, and ethical issues as AI technology advances. The rise of AI has ushered in a new era in the dissemination of misinformation as AI-driven technologies are increasingly responsible for curating, recommending, and amplifying information on online platforms. While AI holds the potential to enhance the detection and mitigation of misinformation through natural language processing and machine learning, it also raises concerns about the amplification and propagation of false information. AI-powered deepfake technology, for instance, can generate hyper-realistic videos and audio recordings, making it increasingly challenging to discern fact from fiction.

Keywords: artificial intelligence, digital information, disinformation, ethical issues, misinformation

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Authors: Diogo Ferreira Lima Filho, José Roberto Amazonas

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Nanotechnology opens the door to new paradigms that introduces a variety of novel tools enabling a plethora of potential applications in the biomedical, industrial, environmental, and military fields. This work proposes an integrated node model by applying the same concepts of TCNet to networks of nanodevices where the nodes are cooperatively interconnected with a low-complexity Mealy Machine (MM) topology integrating in the same electronic system the modules necessary for independent operation in wireless sensor networks (WSNs), consisting of Rectennas (RF to DC power converters), Code Generators based on Finite State Machine (FSM) & Trellis Decoder and On-chip Transmit/Receive with autonomy in terms of energy sources applying the Energy Harvesting technique. This approach considers the use of a Graphene Composite Substrate (GCS) for the integrated electronic circuits meeting the following characteristics: mechanical flexibility, miniaturization, and optical transparency, besides being ecological. In addition, graphene consists of a layer of carbon atoms with the configuration of a honeycomb crystal lattice, which has attracted the attention of the scientific community due to its unique Electrical Characteristics.

Keywords: composite substrate, energy harvesting, finite state machine, graphene, nanotechnology, rectennas, wireless sensor networks

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Authors: Dinesh Kumar Maurya, Sapan Mohan Saini

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A theoretical formalism to calculate the structural, electronic and optical properties of orthorhombic crystals from first principle calculations is described. This is applied first time to new YNi4Si-type DyNi4Si compound. Calculations are performed using full-potential augmented plane wave (FPLAPW) method in the framework of density functional theory (DFT). The Coulomb corrected local-spin density approximation (LSDA+U) in the self-interaction correction (SIC) has been used for exchange-correlation potential. Our optimized results of lattice parameters show good agreement to the previously reported experimental study. Analysis of the calculated band structure of DyNi4Si compound demonstrates their metallic character. We found Ni-3d states mainly contribute to density of states from -5.0 eV to the Fermi level while the Dy-f states peak stands tall in comparison to the small contributions made by the Ni-d and R-d states above Fermi level, which is consistent with experiment, in DNi4Si compound. Our calculated optical conductivity compares well with the experimental data and the results are analyzed in the light of band-to-band transitions. We also report the frequency-dependent refractive index n(ω) and the extinction coefficient k(ω) of the compound.

Keywords: band structure, density of states, optical properties, LSDA+U approximation, YNi4Si- type DyNi4Si compound

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Authors: L. K. Davis

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The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.

Keywords: 14-3-3 docking genes, synthetic protein design, time-based DNA codes, writing DNA code from scratch

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Authors: Steffon Campbell

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This research explores the potential of using artificial intelligence (AI) autoethnographies to study, document, explore, and understand aspects of Caribbean culture. As a digital research methodology, AI autoethnography merges computer science and technology with ethnography, providing a fresh approach to collecting and analyzing data to generate novel insights. This research investigates how AI autoethnography can best be applied to understanding the various complexities and nuances of Caribbean culture, as well as examining how technology can be a valuable tool for enriching study of the region. By applying AI autoethnography to Caribbean studies, the research aims to produce new and innovative ways of discovering, understanding, and appreciating the Caribbean. The study found that AI autoethnographies can offer a valuable method for exploring Caribbean culture. Specifically, AI autoethnographies can facilitate experiences of self-reflection, facilitate reconciliation with the past, and provide a platform to explore and understand the cultural, social, political, and economic concerns of Caribbean people. Findings also reveal that these autoethnographies can create a space for people to reimagine and reframe the conversation around Caribbean culture by enabling them to actively participate in the process of knowledge creation. The study also finds that AI autoethnography offers the potential for cross-cultural dialogue, allowing participants to connect with one another over cultural considerations and engage in meaningful discourse.

Keywords: artificial intelligence, autoethnography, caribbean, culture

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Authors: Jeffrey Chang, Barun Dasgupta

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E-business technologies, whereby business transactions are conducted remotely using the Internet, present unique opportunities and challenges for business. E-business technologies are applicable to a wide range of organizations and small and medium-sized enterprises (SMEs) are no exception. There is an established body of literature about e-business, looking at definitions, concepts, benefits and challenges. In general, however, the research focus has been on larger organizations, not SMEs. In an attempt to redress the balance of research, this paper looks at e-business technologies specifically from a small business perspective. It seeks to identify the possible barriers that SMEs might face when considering adoption of the e-business concept and practice as part of their business process change initiatives and implementation. To facilitate analysis of these barriers a conceptual framework has been developed which outlines the key conceptual and practical challenges of e-business implementation in SMEs. This is developed following a literature survey comprised of three categories: characteristics of SMEs, issues of IS/IT use in SMEs and general e-business adoption and implementation issues. The framework is then empirically assessed against 7 SMEs who have yet to implement e-business or whose e-business efforts have been unsatisfactory. Conclusions from the case studies can be used to verify the framework, and set parameters for further larger scale empirical investigation.

Keywords: business process change, disruptive technologies, electronic business (e-Business), electronic commerce (e-Commerce), ICT adoption, small and medium sized enterprises (SMEs)

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Authors: Stephan Boehm, Julia Engel, Judith Eisser

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During the last decade communication in the Internet transformed from a broadcast to a conversational model by supporting more interactive features, enabling user generated content and introducing social media networks. Another important trend with a significant impact on electronic commerce is a massive usage shift from desktop to mobile devices. However, a presentation of product- or service-related information accumulated on websites, micro pages or portals often remains the pivot and focal point of a customer journey. A more recent change of user behavior –especially in younger user groups and in Asia– is going along with the increasing adoption of messaging applications supporting almost real-time but asynchronous communication on mobile devices. Mobile apps of this type cannot only provide an alternative for traditional one-to-one communication on mobile devices like voice calls or short messaging service. Moreover, they can be used in mobile commerce as a new marketing and sales channel, e.g., for product promotions and direct marketing activities. This requires a new way of customer interaction compared to traditional mobile commerce activities and functionalities provided based on mobile web-sites. One option better aligned to the customer interaction in mes-saging apps are so-called chatbots. Chatbots are conversational programs or dialog systems simulating a text or voice based human interaction. They can be introduced in mobile messaging and social media apps by using rule- or artificial intelligence-based imple-mentations. In this context, a comparative analysis is conducted to examine the impact of using traditional websites or chatbots for promoting a product in an impulse purchase situation. The aim of this study is to measure the impact on the customers’ user experi-ence and emotions. The study is based on a random sample of about 60 smartphone users in the group of 20 to 30-year-olds. Participants are randomly assigned into two groups and participate in a traditional website or innovative chatbot based mobile com-merce scenario. The chatbot-based scenario is implemented by using a Wizard-of-Oz experimental approach for reasons of sim-plicity and to allow for more flexibility when simulating simple rule-based and more advanced artificial intelligence-based chatbot setups. A specific set of metrics is defined to measure and com-pare the user experience in both scenarios. It can be assumed, that users get more emotionally involved when interacting with a system simulating human communication behavior instead of browsing a mobile commerce website. For this reason, innovative face-tracking and analysis technology is used to derive feedback on the emotional status of the study participants while interacting with the website or the chatbot. This study is a work in progress. The results will provide first insights on the effects of chatbot usage on user experiences and emotions in mobile commerce environments. Based on the study findings basic requirements for a user-centered design and implementation of chatbot solutions for mobile com-merce can be derived. Moreover, first indications on situations where chatbots might be favorable in comparison to the usage of traditional website based mobile commerce can be identified.

Keywords: chatbots, emotions, mobile commerce, user experience, Wizard-of-Oz prototyping

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Authors: B. Pavan Kumar Reddy, P. Michael Preetam Raj, Souri Banerjee, Souvik Kundu

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In this work, the fabrication and characterization of copper-doped zinc oxide (Cu:ZnO) based memristor devices with aluminum (Al) and indium tin oxide (ITO) metal electrodes are reported. The thin films of Cu:ZnO was synthesized using low-cost and low-temperature chemical process. The Cu:ZnO was then deposited onto ITO bottom electrodes using spin-coater technique, whereas the top electrode Al was deposited utilizing physical vapor evaporation technique. Ellipsometer was employed in order to measure the Cu:ZnO thickness and it was found to be 50 nm. Several surface and materials characterization techniques were used to study the thin-film properties of Cu:ZnO. To ascertain the efficacy of Cu:ZnO for memristor applications, electrical characterizations such as current-voltage (I-V), data retention and endurance were obtained, all being the critical parameters for next-generation memory. The I-V characteristic exhibits switching behavior with asymmetrical hysteresis loops. This work imputes the resistance switching to the positional drift of oxygen vacancies associated with respect to the Al/Cu:ZnO junction. Further, a non-linear curve fitting regression techniques were utilized to determine the equivalent circuit for the fabricated Cu:ZnO memristors. Efforts were also devoted in order to establish its potentiality for different electronic applications.

Keywords: copper doped, metal-oxides, oxygen vacancies, resistive switching

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Authors: Rohan M. Dalal, Elena Pirondini, Shanu Somvanshi

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Introduction: The current state of reproductive health outcomes in lower-income countries is poor, with inadequate knowledge and culture among adolescent boys. Moreover, boys have traditionally not been a priority target. To explore the opportunity to educate adolescent boys in the developing world regarding accurate reproductive health information, the purpose of this study is to investigate how adolescent boys in the developing world engage and use technology, utilizing cell phones. This electronic survey and video interview study were conducted to determine the feasibility of a mobile phone platform for an educational video game specifically designed for boys that will improve health knowledge, influence behavior, and change health outcomes, namely teen pregnancies. Methods: With the assistance of Plan India, a subsidiary of Plan International, informed consent was obtained from parents of adolescent males who participated in an electronic survey and video interviews via Microsoft Teams. An electronic survey was created with 27 questions, including topics of mobile phone usage, gaming preferences, and sexual and reproductive health, with a sample size of 181 adolescents, ages 11-25, near New Delhi, India. The interview questions were written to explore more in-depth topics after the completion of the electronic survey. Eight boys, aged 15, were interviewed for 40 minutes about gaming and usage of mobile phones as well as sexual and reproductive health. Data/Results. 154 boys and 27 girls completed the survey. They rated their English fluency as relatively high. 97% of boys (149/154) had access to mobile phones. The majority of phones were smartphones (97%, 143/148). 48% (71/149) of boys borrowed cell phones. The most popular phone platform was Samsung (22%, 33/148). 36% (54/148) of adolescent males looked at their phones 1-10 times per day for 1-2 hours. 55% (81/149) of the boys had parental restrictions. 51% (76/148) had 32 GB of storage on their phone. 78% (117/150) of the boys had wifi access. 80% (120/150) of respondents reported ease in downloading apps. 97% (145/150) of male adolescents had social media, including WhatsApp, Facebook, and YouTube. 58% (87/150) played video games. Favorite video games included Free Fire, PubG, and other shooting games. In the video interviews, the boys revealed what made games fun and engaging, including customized avatars, progression to higher levels, realistic interactive platforms, shooting/guns, the ability to perform multiple actions, and a variety of worlds/settings/adventures. Ideas to improve engagement in sexual and reproductive health classes included open discussions in the community, enhanced access to information, and posting on social media. Conclusion: This study involving an electronic survey and video interviews provides an initial foray into understanding mobile phone usage among adolescent males and understanding sexual and reproductive health education in New Delhi, India. The data gathered from this study support using mobile phone platforms, and this will be used to create a serious video game to educate adolescent males about sexual and reproductive health in an attempt to lower the rate of unwanted pregnancies in the world.

Keywords: adolescent males, India, mobile phone, sexual and reproductive health

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Authors: Said Abdullah Al Saifi

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The goal of this research is to examine the impact of trust, motivation, and national culture on knowledge sharing within the context of electronic mail. This study is quantitative and survey based. In order to conduct the research, 200 students from a leading university in New Zealand were chosen randomly to participate in a questionnaire survey. Motivation and trust were found to be significantly and positively related to knowledge sharing. The research findings illustrated that face saving, face gaining, and individualism positively moderates the relationship between motivation and knowledge sharing. In addition, collectivism culture negatively moderates the relationship between motivation and knowledge sharing. Moreover, the research findings reveal that face saving, individualism, and collectivism culture positively moderate the relationship between trust and knowledge sharing. In addition, face gaining culture negatively moderates the relationship between trust and knowledge sharing. This study sets out several implications for researchers and practitioners. The study produces an integrative model that shows how attributes of national culture impact knowledge sharing through the use of emails. A better understanding of the relationship between knowledge sharing and trust, motivation, and national culture differences will increase individuals’ ability to make wise choices when sharing knowledge with those from different cultures.

Keywords: knowledge sharing, motivation, national culture, trust

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Authors: Emine Akar

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The majority of privacy-related regulation has traditionally focused on concepts that are perceived to be well-understood or easily describable, such as certain categories of data and personal information or images. In the past century, such regulation appeared reasonably suitable for its purposes. However, technologies such as AI, combined with ever-increasing capabilities to collect, process, and store “big data”, not only require calibration of these traditional understandings but may require re-thinking of entire categories of privacy law. In the presentation, it will be explained, against the background of various emerging technologies under the umbrella term “emotional artificial intelligence”, why modern privacy law will need to embrace human emotions as potentially private subject matter. This argument can be made on a jurisprudential level, given that human emotions can plausibly be accommodated within the various concepts that are traditionally regarded as the underlying foundation of privacy protection, such as, for example, dignity, autonomy, and liberal values. However, the practical reasons for regarding human emotions as potentially private subject matter are perhaps more important (and very likely more convincing from the perspective of regulators). In that respect, it should be regarded as alarming that, according to most projections, the usefulness of emotional data to governments and, particularly, private companies will not only lead to radically increased processing and analysing of such data but, concerningly, to an exponential growth in the collection of such data. In light of this, it is also necessity to discuss options for how regulators could address this emerging threat.

Keywords: AI, privacy law, data protection, big data

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Authors: A. Mohamed Mydeen, Pallapa Venkataram

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The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.

Keywords: knowledge representation, pervasive computing, agent technology, ECA rules

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Authors: Jiwuer Jilili, Iogann Tolbatov, Mousumi U. Kahaly

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Metal to insulator transition and interfacial magnetism of the LaNiO₃ based superlattice are main interest due to thickness dependent electronic response and tunable magnetic behavior. We investigate the structural, electronic, and magnetic properties of recently experimentally synthesized (LaNiO₃)n/(CaMnO₃)m superlattices with varying LaNiO₃ thickness using density functional theory. The effect of the on-site Coulomb interaction is discussed. In switching from zero to finite U value for Ni atoms, LaNiO₃ shows transitions from half-metallic to metallic character, while spinning ordering changes from paramagnetic to ferromagnetic (FM). For CaMnO₃, U < 3 eV on Mn atoms results in G-type anti-FM spin ordering whereas increasing U value yields FM ordering. In superlattices, metal to insulator transition was achieved with a reduction of LaNiO₃ thickness. The system with one layer of LaNiO₃ yields insulating character. Increasing LaNiO₃ to two layers and above results in the onset of the metallic character with a major contribution from Ni and Mn 3d eg states. Our results for interfacial ferromagnetism, induced Ni magnetic moments and novel antiferromagnetically coupled Ni atoms are consistent with the recent experimental findings. The possible origin of the emergent magnetism is proposed in terms of the exchange interaction and Anderson localization.

Keywords: density functional theory, interfacial magnetism, metal-insulator transition, Ni magnetism.

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Authors: Aicha Bouhlala, Sabah Chettibi

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A systematic investigation on structural, electronic, and magnetic properties of Ce₀.₇₅A₀.₂₅O₂ (A = W, Mo) is performed using first-principles calculations within the framework Full-Potential Linear Augmented Plane Wave (FP-LAPW) method based on the Density Functional Theory (DFT). The exchange-correlation potential has been treated using the generalized gradient approximation (WC-GGA) developed by Wu-Cohen. The host compound CeO2 was doped with transition metal atoms W and Mo in the doping concentration of 25% to replace the Ce atom. In structural properties, the equilibrium lattice constant is observed for the W-doped CeO₂ compound which exists within the value of 5.314 A° and the value of 5.317 A° for Mo-doped CeO2. The present results show that Ce₀.₇₅A₀.₂₅O₂ (A=W, Mo) systems exhibit semiconducting behavior in both spin channels. Although undoped CeO₂ is a non-magnetic semiconductor. The band structure of these doped compounds was plotted and they exhibit direct band gap at the Fermi level (EF) in the majority and minority spin channels. In the magnetic properties, the doped atoms W and Mo play a vital role in increasing the magnetic moments of the supercell and the values of the total magnetic moment are found to be 1.998 μB for Ce₀.₇₅W₀.₂₅O₂ and to be 2.002 μB for Ce₀.₇₅Mo₀.₂₅O₂ compounds. Calculated results indicate that the magneto-electronic properties of the Ce₁₋ₓAₓO₂(A= W, Mo) oxides supply a new way to the experimentalist for the potential applications in spintronics devices.

Keywords: FP-LAPW, DFT, CeO₂, properties

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

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2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: artificial intelligence, machine learning, deep learning, convolutional neural networks

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Authors: Mariia Khramova, Sergio Martinez, Duc Nguyen

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Modern electronic devices, particularly smartphones, are characterised by extremely high environmental footprint and short product lifecycle. Every year manufacturers release new models with even more superior performance, which pushes the customers towards new purchases. As a result, millions of devices are being accumulated in the urban mine. To tackle these challenges the concept of circular economy has been introduced to promote repair, reuse and recycle of electronics. In this case, electronic devices, that previously ended up in landfills or households, are getting the second life, therefore, reducing the demand for new raw materials. Smartphone reuse is gradually gaining wider adoption partly due to the price increase of flagship models, consequently, boosting circular economy implementation. However, along with reuse of communication device, circular economy approach needs to ensure the data of the previous user have not been 'reused' together with a device. This is especially important since modern smartphones are comparable with computers in terms of performance and amount of data stored. These data vary from pictures, videos, call logs to social security numbers, passport and credit card details, from personal information to corporate confidential data. To assess how well the data privacy requirements are followed on smartphones second-hand market, a sample of 100 Android smartphones has been purchased from IT Asset Disposition (ITAD) facilities responsible for data erasure and resell. Although devices should not have stored any user data by the time they leave ITAD, it has been possible to retrieve the data from 19% of the sample. Applied techniques varied from manual device inspection to sophisticated equipment and tools. These findings indicate significant barrier in implementation of circular economy and a limitation of smartphone reuse. Therefore, in order to motivate the users to donate or sell their old devices and make electronic use more sustainable, data privacy on second-hand smartphone market should be significantly improved. Presented research has been carried out in the framework of sustainablySMART project, which is part of Horizon 2020 EU Framework Programme for Research and Innovation.

Keywords: android, circular economy, data privacy, second-hand phones

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Authors: Umamaheswari Shanmugam, Silvia Ronchi

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that can use the large amount and variety of data generated during healthcare services every day; one of the significant advantages of these new technologies is the ability to get experience and knowledge from real-world use and to improve their performance continuously. Healthcare systems and institutions can significantly benefit because the use of advanced technologies improves the efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and protect patients' safety. The evolution and the continuous improvement of software used in healthcare must consider the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device's approval. Still, they are necessary to ensure performance, quality, and safety. At the same time, they can be a business opportunity if the manufacturer can define the appropriate regulatory strategy in advance. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems

Procedia PDF Downloads 88

Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

Procedia PDF Downloads 110

Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

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The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter

Procedia PDF Downloads 145

Authors: Soheila Sadeghi

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— The growing integration of Artificial Intelligence (AI) into Human Resources (HR) processes has transformed the way organizations manage recruitment, performance evaluation, and employee engagement. While AI offers numerous advantages—such as improved efficiency, reduced bias, and hyper-personalization—it raises significant concerns about employee well-being, job security, fairness, and transparency. The study examines how AI shapes employee perceptions, job satisfaction, mental health, and retention. Key findings reveal that: (a) while AI can enhance efficiency and reduce bias, it also raises concerns about job security, fairness, and privacy; (b) transparency in AI systems emerges as a critical factor in fostering trust and positive employee attitudes; and (c) AI systems can both support and undermine employee well-being, depending on how they are implemented and perceived. The research introduces an AI-employee well-being Interaction Framework, illustrating how AI influences employee perceptions, behaviors, and outcomes. Organizational strategies, such as (a) clear communication, (b) upskilling programs, and (c) employee involvement in AI implementation, are identified as crucial for mitigating negative impacts and enhancing positive outcomes. The study concludes that the successful integration of AI in HR requires a balanced approach that (a) prioritizes employee well-being, (b) facilitates human-AI collaboration, and (c) ensures ethical and transparent AI practices alongside technological advancement.

Keywords: artificial intelligence, human resources, employee well-being, job satisfaction, organizational support, transparency in AI

Procedia PDF Downloads 29