Potential cognitive risks of generative transformer-based AI chatbots on higher order executive functions.

Abstract: Chat generative retrained transformer (ChatGPT) represents a groundbreaking advancement in Artificial Intelligence (AI-chatbot) technology, utilizing transformer algorithms to enhance natural language processing and facilitating their use for addressing specific tasks. These AI chatbots can respond to questions by generating verbal instructions similar to those a person would provide during the problem-solving process.ChatGPT has become the fastest growing software in terms of user adoption in history, leading to an anticipated widespread use of this technology in the general population. Current literature is predominantly focused on the functional aspects of these technologies, but the field has not yet explored hypotheses on how these AI chatbots could impact the evolutionary aspects of human cognitive development. Thesis: The "neuronal recycling hypothesis" posits that the brain undergoes structural transformation by incorporating new cultural tools into "neural niches," consequently altering individual cognition. In the case of technological tools, it has been established that they reduce the cognitive demand needed to solve tasks through a process called "cognitive offloading." In this theoretical article, three hypotheses were proposed via forward inference about how algorithms such as ChatGPT and similar models may influence the cognitive processes and structures of upcoming generations.By forecasting the neurocognitive effects of these technologies, educational and political communities can anticipate future scenarios and formulate strategic plans to either mitigate or enhance the cognitive influence that these factors may have on the general population. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


Molecular Epidemiology of Plasmodium falciparum Infections Using PCR-based Assays in Jos, Nigeria-cross-sectional Study

Abstract:
Background: Malaria remains a significant health threat globally, with Plasmodium falciparum being the predominant and lethal parasite in Africa. Nigeria is still faced with ongoing cases of asymptomatic malaria, hindering effective control measures.
 Aim: The aim was to generate epidemiological data that will provide good background and guide strategies for driving malaria control efforts, research, and resource allocation in the region.
 Place and Duration of Study: This study was conducted in Jos, Plateau State, Nigeria, where the samples were originally collected within about 16 months between October 2019 and January 2021.
 Methodology: A cross-sectional molecular epidemiological study was conducted using 136 microscopically screened 2 plus (++) and above positive malarial whole blood samples obtained in EDTA bottles from two hospitals in Jos, Plateau State, Nigeria. The DNA extraction was performed according to the manufacturer's instructions using Zymo Research extraction kits. Plasmodium genus and Plasmodium falciparum were detected in the samples using the PCR method and gel electrophoresis.
 Results: In the results, using PCR techniques, 47.8% (65/136) of the total malaria-positive samples collected were confirmed for the presence of the Plasmodium genus. Out of these 65 positive samples, 63 were found to be Plasmodium falciparum.
 Conclusion: This study demonstrates that Plasmodium falciparum remains the predominant malaria species in Jos, Plateau State, comprising approximately 96.9% (63/65) of the malarial cases. This indicates that only about 3% of malaria cases affecting the residents of Jos, Plateau State might be caused by the other four species of malaria parasites (Plasmodium vivax, Plasmodium malariae, Plasmodium ovale, and Plasmodium knowlesi).


Application solutions of highway freight information systems based on quantum communication

Abstract: Abstract To improve the security of data transmission in the highway freight information system, this study is an application plan for the highway freight information system based on quantum communication. This solution is based on quantum communication technology to encrypt and transmit key sensitive data [1] ; it realizes unified management of quantum keys through the quantum key cloud terminal and provides key services for the highway freight information system; it realizes access to the system through the quantum key cloud service platform. The secure use of mobile terminal quantum keys improves the overall security of the road freight information system. This scheme uses the quantum encryption key generated only once, effectively protecting the entire system's security. The quantum key management server and quantum key cloud platform defined in this plan manage terminals and quantum keys respectively, and jointly produce and distribute quantum keys with the help of other hardware facilities and software to provide secure transmission of key information.


CROWD MANAGEMENT DURING MASS EVENTS

Abstract: Mass events organized in Poland in recent years, including sports, entertainment, as well ascultural and social (religious) gatherings, have been consistently gaining popularity, as reflectedin the large number of participants. This is undoubtedly due to the attractiveness of the events onone hand and the positive assessment of the safety level during these events on the other. Yet itis crucial to recognize that beyond their positive aspects, mass events frequently present specificchallenges and threats. Unpredictable and irrational behaviour among participants can result froma variety of factors, posing risks to their health and lives as well as that of bystanders and property.Effective managing of these situations is a genuine challenge for governmental authorities,services and responsible entities. This challenge stems from comprehensive preparatory andorganizational measures designed to prevent crisis situations and, if they occur, promptly regaincontrol. This study aims to present a model of organizational-procedural solutions and explorethe application of modern information technologies to enhance participant safety during massevents. The research methodology involved a comprehensive analysis of scientific, industry, andlegal materials. Additionally, valuable insights were drawn from the author’s first-hand experiencegained through direct observations while fulfilling official duties in ensuring the safety of massevents during real incidents in Poland.


Data-based approach for time-correlated closures of turbulence models

Abstract: Developed turbulent motion of fluid still lacks an analytical description despite more than a century of active research. Nowadays, phenomenological ideas are widely used in practical applications, such as small-scale closures for numerical simulations of turbulent flows. In the present paper, we use a shell model of turbulence to construct a closure intended to have a solid theoretical background and to capture intrinsic probabilistic features of turbulence. Shell models of turbulence are dynamical deterministic systems used to model energy cascade and other key aspects of the Navier-Stokes such as intermittency. We rescale the variables of the Sabra model in a way which leads to hidden symmetries and universal distributions. We then use such fine distributions to write closures, i.e., missing expressions for some of the Sabra variables. Our closures rely on approximating probability density functions using a Gaussian mixture model, which makes them probabilistic by nature and allows us to write time-correlated closures. We also provide a framework where other machine learning tools can be employed with reduced black-box aspects.


Security issues of the gold industry chain based on smart blockchain in the context of the Internet of Things

Abstract: Abstract The purpose is to solve the safety production and management problems of the gold Industrial Chain and give early warning of the safety situation of the gold Industrial Chain. According to the theory of industrial chain security governance and the basic situation of the gold Industrial Chain, this work establishes a gold Industrial Chain model based on smart blockchain and system dynamics (SD), and discusses the application of the gold Industrial Chain in the Internet of Things (IoT) environment. The overall goal of the application of IoT technology to the safety management of intelligent gold mines is to take the gold mine production demand as the driving force. The digitalization of production, electromechanical, safety, dispatching, and other information realizes intelligent digital perception, assists decision-making, guides the safety management of gold mining operations, continuously improves the operation efficiency of the gold mining industry, and drives the development of the industry. Finally, it takes the resource reserve of China’s gold industry from 2011 to 2021 as the research data introduces the weighting method to assess the security situation of China’s gold Industrial Chain from 2011 to 2021. The safety performance of China's environmental industry chain is evaluated through the detailed introduction of the basic information of the gold Industrial Chain. The result shows that the security situation of China’s gold Industrial Chain from 2011 to 2021 shows an overall growth trend, 88.42% higher than in 2014. The security situation of China’s gold Industrial Chain from 2011 to 2021 positively impacted the opening of the domestic gold market and entering the international gold market, improving the security level of China’s gold Industrial Chain. In this work, a gold Industrial Chain security model based on smart blockchain and SD is established to solve the safety problem of the gold Industrial Chain, which can improve the safety level of the gold industry and promote its sustainable development.


Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in onco-hematology

Abstract: Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genomically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In onco-hematology, Hierarchical Dirichlet Mixture Models (HDMM) have become one of the preferred method to cluster the genomics data, that include the presence or absence of gene mutations and cytogenetics anomalies, into components. This work unfolds the standard workflow used in onco-hematology to improve patient stratification and proposes alternative approaches to characterize the components and to assign patient to them, as they are crucial tasks usually supported by a priori clinical knowledge. We propose (a) to compute the parameters of the multinomial components of the HDMM or (b) to estimate the parameters of the HDMM components as if they were Multivariate Fisher’s Non-Central Hypergeometric (MFNCH) distributions. Then, our approach to perform patients assignments to the HDMM components is designed to essentially determine for each patient its most likely component. We show on simulated data that the patients assignment using the MFNCH-based approach can be superior, if not comparable, to using the multinomial-based approach. Lastly, we illustrate on real Acute Myeloid Leukemia data how the utilization of MFNCH-based approach emerges as a good trade-off between the rigorous multinomial-based characterization of the HDMM components and the common refinement of them based on a priori clinical knowledge.


Untouchable them: the effect of chatbot gender on angry customers

Abstract: Purpose This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger. Design/methodology/approach This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study. Findings This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method. Originality/value The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.


IHIBE: A Hierarchical and Delegated Access Control Mechanism for IoT Environments

Abstract: Ensuring authorized access control in the IoT is vital for privacy and safety protection. Our study presents the novel IHIBE framework, which combines IOTA (a distributed ledger technology) with hierarchical identity-based encryption (HIBE), thereby enhancing both IoT security and scalability. This approach secures access tokens and policies while reducing the computational demand on data owners. Our empirical findings reveal a significant performance gap, with access rights delegation on the Raspberry Pi 4 exceeding those on AWS by over 250%. Moreover, our analysis uncovers optimal identity policy depths: up to 640 identities on AWS and 640 on the Raspberry Pi 4 for systems with higher tolerable delays, and 320 identities on AWS versus 160 on the Raspberry Pi 4 for systems with lower tolerable delays. The system shows practical viability, exhibiting insignificant operational time differences compared to Zhang et al.’s schemes, particularly in access rights verification processes, with a minimal difference of 33.35%. Our extensive security assessment, encompassing scenarios like encrypted token theft and compromise of authority, affirms the efficacy of our challenge-response and last-word challenge (LWC) mechanisms. This study underscores the importance of platform choice in IoT system architectures and provides insights for deploying efficient, secure, and scalable IoT environments.


The effect of metaverse on L2 vocabulary learning, retention, student engagement, presence, and community feeling

Abstract: Abstract This study investigates the multifaceted impacts of Metaverse Based Language Teaching on high school L2 vocabulary learning and retention, engagement, community feeling, and diverse dimensions of presence, namely social, cognitive, teaching, and overall presence. Adopting a quasi-experimental design, the study provides an in-depth controlled trial. The participants in this study consist of 86 (43 male, 43 female) high school English language learners in Türkiye, who are assigned to two groups. The control group uses traditional instructional technologies while the experimental group engages with metaverse technology. The lessons are based on social constructivist theory for both groups. Pre- and post-tests are employed to quantitatively assess vocabulary learning and retention and reveal substantial improvements in both areas. A series of carefully selected psychometric scales capture core aspects of engagement, community feeling, and presence. The findings reveal increased levels of engagement and a sense of presence, and connectedness within the language learning community, which confirms the efficacy of the MBLT. However, the improvement in social presence does not reach statistical significance. Cumulatively, the findings of this research transcend mere quantifiable improvements in vocabulary learning and retention. They reveal a holistic transformation of the language learning experience by emphasizing the significance of social interactions and digital embodiment in high school second language education through metaverse. The research highlights the metaverse’s potential in shaping the future of language education, reimagining traditional paradigms, and proposing a new era of immersive, interactive, and transformative pedagogy.


Content Based Recommendation System on Netflix Data

Abstract: After pandemic, OTT platforms are the most common platform to provide entertainment to users. Among all platforms, Netflix has become most the popular one. Data visualization of Netflix data can provide valuable insights and benefits in many ways like understanding viewer preferences, content optimization, personalized recommendation, quality and content performance evaluation, fraud detection to name a few. This research provides exploratory data visualization and provide a content based recommendation system on Netflix data as in real world applications, company uses these recommendation system algorithms to determine which system are better to improve users’ engagement of the platform.


Vigilance in infectious disease emergencies: Expanding the concept

Abstract: In their 2010 book, Lorna Weir and Eric Mykhalovskiy conceptualised the role of vigilance in unknown and emerging infectious disease threats. Theirs is a macro-level account which draws on empirical data to describe vigilance as a set of technical and political arrangements that govern collection, analysis, interpretation and communication of data as it pertains to unknown threats. In this paper we expand their work to detail a conceptual analysis of the role of vigilance at the micro-level during periods of high infectious disease threat. Our data are daily press conferences and associated non-discursive tools in New South Wales (NSW), Australia during times of heightened COVID-19 risk. This paper is a conceptual analysis that draws on theories of vigilance and related concepts to show how a key aspect of vigilance is making previously unseen threats visible or present. Communications formulated and encouraged three types of vigilance as a set of governing relations: institutional or authority-based; individual outward-facing; and individual inward-facing. We also describe the relationship between vigilance and related concepts that are used in response to anticipated public threats. Authority based vigilance involved contact tracing and policing of movement and behaviours. In individual outward facing vigilance people were asked to be alert to, analyse, and react to risk in their immediate environment. Inward facing vigilance required people to gather and react to information about their own behaviours and within their own bodies. There was a relationship between different types of vigilance; as risk increased and authority-based vigilance was less successful in containing the spread of infection, individual vigilance had a stronger role to play. This extension of vigilance at the micro-level sees some of the same unintended consequences as Weir and Mykhalovskiy describe at the global level, particularly in how burdens are inequitably distributed and experienced.


Immersive Realities: A Comprehensive Guide from Virtual Reality to Metaverse

Abstract: This comprehensive literature review explores the diverse landscape of immersive realities, offering a thorough examination of key technologies from Virtual Reality (VR) to the emerging and innovative concept of the Metaverse. The article concentrates the nuances of Extended Reality (XR), Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), providing an in-depth understanding of their individual functionalities, applications, and evolving interplay. The paper also mentions about wearable technologies which play key role in the realization of these immersive realities. The article begins by explaining the fundamental principles of each immersive reality, tracing their technological evolution and highlighting distinctive features that define their user experiences. It then navigates through the complex intersections and synergies between these immersive technologies, shedding light on how they collectively contribute to shaping the future of digital interaction. The paper unfolds how the journey of immersive realities lead to the Metaverse which is relatively a novel paradigm that transcends individual immersive realities, creating a unified, interconnected digital space. The exploration of the Metaverse encompasses its conceptual foundations, potential societal impacts, and the fusion of various immersive technologies within this evolving ecosystem. With an inclusive approach, this paper caters to both novices and seasoned professionals in the field, offering insights into the practical applications associated with different immersive realities. By fostering a comprehensive understanding of these technologies, the paper aims to empower readers to navigate and contribute to the dynamic landscape of immersive realities.


Workshop Strategi Revolusioner Pembelajaran Modern Melalui Integrasi Teknologi Cerdas dan Platform Media Sosial bagi Pendidikan Generasi Digital

Abstract: Education in the current digital era demands the use of smart technology and social media platforms in the learning process to suit the character of the digital generation. However, the competence of lecturers in Indonesia in this regard is still limited. Therefore, the workshop "Revolutionary Strategies for Modern Learning Through the Integration of Smart Technology and Social Media Platforms for Digital Generation Education" is important to implement. This workshop aims to provide understanding and skills to 187 lecturers in implementing technology and social media for learning innovation for digital generation students. Workshop material includes smart technology trends, use of social media platforms, digital evaluation and assessment strategies, as well as discussion of challenges and opportunities for integrating technology and social media in digital era learning. Participants' enthusiasm was quite high and the evaluation results showed that 91% were interested in implementing modern learning strategies after the workshop.


Synergizing cybersecurity in healthcare with novel bioprocessing for sustainable energy-centric water remediation

Abstract: Abstract The introduction of several novel chemicals, materials, and processes with varied levels of complexity in recent decades has been a result of the incredibly rapid technological advancement that has characterised those decades. This in turn has led to an increase in the number of pollutants released into the environment, necessitating their effective removal. The results of environmental monitoring reveal that several pollutants are exceeding the limit in ground water, which raises questions about the efficacy of the wastewater treatment methods now in use. This research proposes a novel method for sustainable smart grid (SG)-based energy analysis in water remediation and network cybersecurity analysis for healthcare application. Here the water-caused damages have been analysed based on a healthcare application using SG energy analysis and the network cyber security analysis is carried out using the federated blockchain model (SGEA_FB). Experimental analysis is carried out in terms of network integrity, throughput, scalability, and training accuracy. According to an analysis of the exergy destruction rates in every method component, power generation subsystem has the greatest exergy destruction rate at 15.4 MW. A training accuracy of 96%, throughput of 86%, network integrity of 76%, and scalability of 73% were all achieved by the suggested method.


The influence of leadership style, business ethics, and environmental performance toward corporate social performance and its impact on corporate financial performance

Abstract: The study presents research clarifying the influence of leadership style, business ethics, and environmental performance on corporate social performance and its implications for corporate financial performance. A survey was done in manufacturing companies of Indonesia’s State Own Enterprise (SOE) and affiliates. Corporate performance is not solely directed towards shareholders or exclusively defined by financial measures; rather, it encompasses a focus on the impact of the organization on the society in which it operates. The style of leadership plays a crucial role in influencing the strategic management process, enabling the organization to implement successful strategies to realize its vision. Business ethics provide advantages to companies by fostering a favorable influence on their competitive edge in the corporate landscape. Environmental concerns are gaining growing significance among various corporate stakeholders, encompassing consumers, shareholders, prospective investors, creditors, regulatory bodies, employees, and the wider public. The finding of the study was that a leadership style had a positive influence on corporate social performance but a negative influence on corporate financial performance. Business ethics had a negative influence on financial performance but a positive influence on corporate social performance and environmental performance. Environmental performance had a negative influence on corporate social performance but positive influence on financial performance. Corporate social performance has a positive influence on corporate financial performance.


Automation of autonomous mobile robot docking based on the counter growth rapidly exploring random tree method

Abstract: Objectives. The article substantiates the relevance of automatic docking of autonomous mobile robots. Specific examples show that the implementation of the automatic docking functions of autonomous robots reveals the potential for creating multi-agent systems with a transformable structure. The aim of the work is to develop means for automatic docking of autonomous mobile robots in complex scenarios and an uncertain environment. Methods. The proposed approach to automating autonomous mobile robot docking is reduced to a modification of the counter-growth rapidly-exploring random tree (RRT) method. It is based on the parallel execution of a decentralized route planning algorithm with mutual coordination of distributed computing processes. The effectiveness of the complex of algorithmic and software tools developed was evaluated using computer and natural simulation methods. The final series of full-scale experiments was carried out on the example of JetBot AI kit Nvidia platforms for automatic docking of autonomous robots. This was performed using the means and methods of intelligent control, visual navigation, technical vision and wireless network communication. Results. The study analyzed the features of automatic docking as one of the tasks of group control of autonomous robots. This is part of multi-agent systems, capable of reconfiguring structures for purposeful changes to the existing set of functional properties and application possibilities. The study also proposes a decentralized modification of the counter-growth RRT method. This allows the movements of autonomous mobile robots in the course of their mutual approach and subsequent docking to be planned. A set of software-algorithmic tools was developed to automate the docking of autonomous robots. A series of model and full-scale experiments were carried out to confirm the effectiveness of the approach developed herein. Conclusions. The modification presented herein of the counter-growth RRT method, traditionally used for planning the movements of manipulators and mobile platforms, is complementary to the tasks it resolves. This enables the docking of autonomous robots to be automated. The results obtained open up the potential for universal schedulers with extended functionality for autonomous robot control systems to be designed.


Teachers’ Perspectives on the Development of 21st Century Skills in Early Childhood Development

Abstract: The study focusses on teachers` perspectives on the development of 21st century skills in Early Childhood Development (ECD). The study was motivated by mixed reactions by various teachers on the competency-based curriculum. A qualitative approach was adopted in order to get in- depth information from the teachers in their natural setting. A case study design was adopted drawing on five schools which were purposively sampled to obtain rich data. Semi- structured interviews were used to generate data. The study comprised ten Early Childhood Development teachers and five school heads who were purposively sampled. Data were analysed using thematic content analysis. The findings reveal that teachers have a good understanding of the 21st century skills. A few teachers, however, conflated the 21st century skills with learning areas. Findings further reveal challenges such as misconceptions on play pedagogies and lack of resources. The study recommends, strategies such as engaging in income generating ECD projects will capacitate schools to buy teaching and learning materials for teachers necessary in developing the 21st century skills. Another recommendation is the implementation of staff development for teachers and administrators on ECD curriculum.


What lies on top lies also beneath? Connecting crop surface modelling to buried archaeology mapping.

Abstract: A recommendation of: Sosic Klindzic Rajna; Vuković Miroslav; Kalafatić Hrvoje; Šiljeg Bartul Digital surface models of crops used in archaeological feature detection – a case study of Late Neolithic site Tomašanci-Dubrava in Eastern Croatia https://doi.org/10.5281/zenodo.7970703


Integration of Augmented Reality Into Glioma Resection Surgery: A Case Report

Abstract: Augmented reality (AR) is an exciting technology that has garnered considerable attention in the field of neurosurgery. Despite this, clinical use of this technology is still in its infancy. An area of great potential for this technology is the ability to display 3D anatomy overlaid with the patient to assist with presurgical and intraoperative decision-making. A 39-year-old woman presented with headaches and was experiencing what was described as a whooshing sound. MRI revealed the presence of a large left frontal mass involving the genu of the corpus callosum, with heterogeneous enhancement and central hemorrhagic necrosis, confirmed to be a glioma. She underwent a craniotomy with intraoperative MRI for resection. An augmented reality system was used to superimpose 3D holographic anatomy onto the patient's head for surgical planning. This report highlights a new AR technology and its immediate application to cranial neurosurgery. It is critical to document new uses of this technology as the field continues to integrate AR as well as other next-generation technologies into practice.


Deep learning forecasting of large induced earthquakes via precursory signals

Abstract: Abstract Precursory phenomena to earthquakes have always attracted researchers’ attention. Among the most investigated precursors, foreshocks play a key role. However, their prompt identification with respect to background seismicity still remains an issue. The task is worsened when dealing with low-magnitude earthquakes. Despite that, seismology and, in particular real-time seismology, can nowadays benefit from the use of Artificial Intelligence (AI) to face the challenge of effective precursory signals discrimination. Here, we propose a deep learning method named PreD-Net (precursor detection network) to address precursory signal identification of induced earthquakes. PreD-Net has been trained on data related to three different induced seismicity areas, namely The Geysers, located in California, USA, Cooper Basin, Australia, Hengill in Iceland. The network shows a suitable model generalization, providing considerable results on samples that were not used during the network training phase of all the sites. Tests on related samples of induced large events, with the addition of data collected from the Basel catalogue, Switzerland, assess the possibility of building a real-time warning strategy to be used to avoid adverse consequences during field operations.


Using design research and human-centered design to address growing pains in a busy, urban emergency department: a faculty, clinician, and student collaboration between nursing, design, and medicine

Abstract: Abstract In 2021, a large urban university-based hospital transitioned to a new two-floor emergency department. Despite the new environment, there were usability and workflow challenges with the space. The authors of this paper created a multidisciplinary, human-centered design collaborative of clinicians, university faculty, and students in an effort to increase emergency department efficiency. After thorough design-research and clinician-focused collaboration, the authors and design team identified the need to improve medical supply retrieval time, which directly impacts patient care and clinician satisfaction. The primary interventions consisted of a redesign that is as follows: (a) created standardized icons related to organ system, (b) increased visibility of supply labels, and (c) reorganized supplies based on usage data. Although a successful project, it was not without several barriers discussed in this article, including design researcher and clinician-level setting and engagement, academic/institutional policies, and conflicting schedules. In addition, the lessons learned from implementing human-centered design concepts into clinical workflow sets forth future research opportunities and inspiration for other institutions to collaborate.


High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit

Abstract: Abstract Reinforcement learning (RL) stands as one of the three fundamental paradigms within machine learning and has made a substantial leap to build general-purpose learning systems. However, using traditional electrical computers to simulate agent-environment interactions in RL models consumes tremendous computing resources, posing a significant challenge to the efficiency of RL. Here, we propose a universal framework that utilizes a photonic integrated circuit (PIC) to simulate the interactions in RL for improving the algorithm efficiency. High parallelism and precision on-chip optical interaction calculations are implemented with the assistance of link calibration in the hybrid architecture PIC. By introducing similarity information into the reward function of the RL model, PIC-RL successfully accomplishes perovskite materials synthesis task within a 3472-dimensional state space, resulting in a notable 56% improvement in efficiency. Our results validate the effectiveness of simulating RL algorithm interactions on the PIC platform, highlighting its potential to boost computing power in large-scale and sophisticated RL tasks.


Determining Intrusion Attacks Against Online Applications Using Cloud-Based Data Security

Abstract: Cloud technology makes it possible for users to access information from anywhere, all the time, on any device, and that is the major cause of the many different types of assaults. In principle, multiple dangers, including data leakage, information leakage, and unauthorized information accessibility, are active in cloud environment layering. Modern technological advancements are made accessible on a daily basis through cloud technology. In the cloud, access control and encryption solutions are more complicated. Because of this greater level, security flaws in online applications and systems are more likely to occur. Somewhere at the ends of the end nodes, a malignant insider can carry out protection assaults. Nevertheless, problems with user privacy and data protection on cloud-based social networking sites continue to exist. Such problems are not known to users. On that social networking site, they post a variety of images, videos, and private information that endures even after eradication. However, some of the data that has been made public was intended to be kept private; as a result, online social information has significantly increased the risk of personally identifiable information leaking. The context of cloud technology depends on the customer capabilities such as quick storing and retrieving offered through cloud computing environments. Dependable cloud providers use a number of methodologies to deliver various digital services, creating a variety of security risks. In this paper, the study of determining intrusive cyber-attacks over the online applications using the cloud data security. Restricting access to shared resources is essential to prevent hackers from stealing vulnerabilities in cloud computing to get unauthorised access to a user's activities as well as information. Gaining access to customer information and obstructing the use of cloud computing are the primary objectives of intrusions on cloud services.


Divergent evolutionary rates of primate brain regions as revealed by genomics and transcriptomics

Abstract: Although the primate brain contains numerous functionally distinct structures that have experienced diverse genetic changes during the course of evolution and development, these changes remain to be explored in detail. Here we utilize two classic metrics from evolutionary biology, the evolutionary rate index (ERI) and the transcriptome age index (TAI), to investigate the evolutionary alterations that have occurred in each area and developmental stage of the primate brain. We observed a higher evolutionary rate for those genes expressed in the non-cortical areas during primate evolution, particularly in human, with the highest rate of evolution being exhibited at brain developmental stages between late infancy and early childhood. Further, the transcriptome age of the non-cortical areas was lower than that of the cerebral cortex, with the youngest age apparent at brain developmental stages between late infancy and early childhood. Our exploration of the evolutionary patterns manifest in each brain area and developmental stage provides important reference points for further research into primate brain evolution.


Recovery of polarization entanglement in partially coherent photonic qubits

Abstract: Partially coherent photonic qubits, owing to their robustness in propagation through random media compared to fully coherent qubits, find applications in free-space communication, quantum imaging, and quantum sensing. However, the reduction of spatial coherence degrades entanglement in qubits, adversely affecting entanglement-based applications. We report the recovery of entanglement in the partially coherent photonic qubits generated using a spontaneous parametric downconversion process despite retaining their multimode nature. This study utilizes an electron multiplying charge-coupled device (EMCCD) to perform coincidence measurements, eliminating the need for raster scanning of single-pixel detectors, which simplifies optical alignment, enhances precision, and reduces time consumption. We demonstrate that the size of apertures used to select biphotons substantially impacts the visibility and S-parameter of polarization-entangled partially coherent qubits. The entanglement is recovered with partial spatial coherence properties by choosing small sizes of the apertures in the captured image plane. This study could help in the advancement of free-space quantum communication, quantum imaging, and quantum metrology.


PMONN: an optical neural network for photonic integrated circuits based on micro-resonator

Abstract: We propose an improved optical neural network (ONN) circuit architecture based on conventional micro-resonator ONNs, called the Phase-based Micro-resonator Optical Neural Network (PMONN). PMONN's core architecture features a Convolutions and Batch Normalization (CB) unit, comprising a phase-based (PB) convolutional layer, a Depth-Point-Wise (DPW) convolutional layer, and a reconstructed Batch Normalization (RBN) layer. The PB convolution kernel uses modulable phase shifts of Add-drop MRRs as learnable parameters and their optical transfer function as convolution weights. The DPW convolution kernel amplifies PB convolution weights by learning the amplification factors. To address the internal covariate shift during training, the RBN layer normalizes DPW outputs by reconstructing the BN layer of the electronic neural network, which is then merged with the DPW layer in the test stage. We employ the tunable DAs in the architecture to implement the merged layer. PMONN achieves 99.15% and 91.83% accuracy on MNIST and Fashion-MNIST datasets, respectively. This work presents a method for implementing an optical neural network on the improved architecture based on MRRs and increases the flexibility and reusability of the architecture. PMONN has potential applications as the backbone for future optical object detection neural networks.


Resource Usage and Optimization Opportunities in Workflows of GitHub Actions

Abstract: Continuous integration and continuous delivery (CI/CD) has become a prevalent practice in software development. GitHub Actions is emerging as a popular platform for implementing CI/CD pipelines, called workflows, especially because the platform offers 2,000 minutes of computation for free to public repositories each month. To understand what these resources are used for and whether CI/CD could be more efficient, this paper presents the first comprehensive empirical study of resource usage and optimization opportunities of GitHub Action workflows. Our findings show that CI/CD imposes significant costs, e.g., $504 per year for an average paid-tier repository. The majority of the used resources is consumed by testing and building (91.2%), which is triggered by pull requests (50.7%), pushes (30.9%), and regularly scheduled workflows (15.5%). While existing optimizations, such as caching (adopted by 32.9% of paid-tier repositories), demonstrate a positive impact, they overall remain underutilized. This result underscores the need for enhanced documentation and tools to guide developers toward more resource-efficient workflows. Moreover, we show that relatively simple changes in the platform, such as deactivating scheduled workflows when repositories are inactive, could result in reductions of execution time between 1.1% and 31.6% over the impacted workflows. Overall, we envision our findings to help improve the resource efficiency of CI/CD pipelines.


Revealing Hidden Threats: An Empirical Study of Library Misuse in Smart Contracts

Abstract: Smart contracts are Turing-complete programs that execute on the blockchain. Developers can implement complex contracts, such as auctions and lending, on Ethereum using the Solidity programming language. As an object-oriented language, Solidity provides libraries within its syntax to facilitate code reusability and reduce development complexity. Library misuse refers to the incorrect writing or usage of libraries, resulting in unexpected results, such as introducing vulnerabilities during library development or incorporating an unsafe library during contract development. Library misuse could lead to contract defects that cause financial losses. Currently, there is a lack of research on library misuse. To fill this gap, we collected more than 500 audit reports from the official websites of five audit companies and 223,336 real-world smart contracts from Etherscan to measure library popularity and library misuse. Then, we defined eight general patterns for library misuse; three of them occurring during library development and five during library utilization, which covers the entire library lifecycle. To validate the practicality of these patterns, we manually analyzed 1,018 real-world smart contracts and publicized our dataset. We identified 905 misuse cases across 456 contracts, indicating that library misuse is a widespread issue. Three patterns of misuse are found in more than 50 contracts, primarily due to developers lacking security awareness or underestimating negative impacts. Additionally, our research revealed that vulnerable libraries on Ethereum continue to be employed even after they have been deprecated or patched. Our findings can assist contract developers in preventing library misuse and ensuring the safe use of libraries.


Domain Knowledge Matters: Improving Prompts with Fix Templates for Repairing Python Type Errors

Abstract: As a dynamic programming language, Python has become increasingly popular in recent years. Although the dynamic type system of Python facilitates the developers in writing Python programs, it also brings type errors at run-time which are prevalent yet not easy to fix. There exist rule-based approaches for automatically repairing Python type errors. The approaches can generate accurate patches for the type errors covered by manually defined templates, but they require domain experts to design patch synthesis rules and suffer from low template coverage of real-world type errors. Learning-based approaches alleviate the manual efforts in designing patch synthesis rules and have become prevalent due to the recent advances in deep learning. Among the learning-based approaches, the prompt-based approach which leverages the knowledge base of code pre-trained models via pre-defined prompts, obtains state-of-the-art performance in general program repair tasks. However, such prompts are manually defined and do not involve any specific clues for repairing Python type errors, resulting in limited effectiveness. How to automatically improve prompts with the domain knowledge for type error repair is challenging yet under-explored.


Application of a novel metaheuristic algorithm inspired by stadium spectators in global optimization problems

Abstract: Abstract This paper presents a novel metaheuristic algorithm inspired by the actions of stadium spectators affecting behavior of players during a match which will be called stadium spectators optimizer (SSO) algorithm. The mathematical model of the SSO algorithm is presented and the performance and efficiency of the presented method is tested on some of the well-known mathematical test functions and also CEC-BC-2017 functions. The SSO algorithm is a parameter-free optimization method since it doesn't require any additional parameter setup at any point throughout the optimization process. It seems urgently necessary to design a novel metaheuristic algorithm that is parameter-free and capable of solving any optimization problem without taking into account extra parameters, as the majority of metaheuristic algorithms rely on the configuration of extra parameters to solve different problems efficiently. A positive point for the SSO algorithm can be seen in the results of the suggested technique, which indicate a partial improvement in performance. The results are compared with those of golf optimization algorithm (GOA), Tiki taka optimization algorithm (TTA), Harris Hawks optimization algorithm (HHO), the arithmetic optimization algorithm (AOA), CMA-ES and EBOwithCMAR algorithms. The statistical tests are carried out for the obtained results and the tests reveal the capability of the presented method in solving different optimization problems with different dimensions. SSO algorithm performs comparably and robustly with the state-of-the-art optimization techniques in 14 of the mathematical test functions. For CEC-BC-2017 functions with ten dimensions, EBOwithCMAR performs better than the proposed method. However, for most functions of CEC-BC-2017 with ten dimensions, the SSO algorithm ranks second after EBOwithCMAR, which is an advantage of the SSO since the proposed method performs better than the well-known CMA-ES optimization algorithm. The overall performance of the SSO algorithm in CEC-BC-2017 functions with 10 dimensions was acceptable, in dimension of 30, 50 and 100, the performance of the proposed method in some functions decreased.


Current Trends and Beyond Conventional Approaches: Advancements in Breast Cancer Surgery through Three-Dimensional Imaging, Virtual Reality, Augmented Reality, and the Emerging Metaverse

Abstract: Breast cancer stands as the most prevalent cancer globally, necessitating comprehensive care. A multidisciplinary approach proves crucial for precise diagnosis and treatment, ultimately leading to effective disease management. While surgical interventions continue to evolve and remain integral for curative treatment, imaging assumes a fundamental role in breast cancer detection. Advanced imaging techniques not only facilitate improved diagnosis but also contribute significantly to the overall enhancement of breast cancer management. This review article aims to provide an overview of innovative technologies such as virtual reality, augmented reality, and three-dimensional imaging, utilized in the medical field to elevate the diagnosis and treatment of breast cancer. Additionally, the article delves into an emerging technology known as the metaverse, still under development. Through the analysis of impactful research and comparison of their findings, this study offers valuable insights into the advantages of each innovative technique. The goal is to provide physicians, surgeons, and radiologists with information on how to enhance breast cancer management.


Challenges and Opportunities for Virtual Reality in Higher Education

Abstract: Virtual reality (VR) has made a successful transition from the entertainment domain to the skills training domain.As expected, there is now considerable research being conducted on utilizing the affordances of VR in higher education.A properly designed VR-based learning environment can engage students not only cognitively but also affectively and behaviorally.This paper provides details of the process of developing VR lessons for introductory level undergraduate courses in STEM disciplines.The lessons were prepared using a web-based VR development environment.Some challenges in developing these lessons were identified, and are shared.The impact of the VR lessons on students' perceptions and attitudes was analyzed using self-reported data that were gathered through surveys.The results of the data analysis are included.


Promoting collaborative learning in architectural engineering design through multi-user augmented reality

Abstract: As a highly interdisciplinary industry, architecture, engineering, and construction (AEC) require effective and seamless collaboration to deliver projects timely and with high quality.To better prepare students with the demanding cooperative skill, collaborative learning has always been an integral part of the education of undergraduate students.Augmented reality (AR) technology has matured rapidly in recent years and has been widely used in various fields such as medical, manufacturing, entertainment, and education.The seamless combination of the real world and virtual environment is one of the most important characteristics of AR technology [1].In this study, we utilized this characteristic to develop a multi-user design application in Microsoft HoloLens.The AR application enables students to participate in the collaborative architectural design by manipulating architectural objects in virtual environment as well as directly communicating with teammates in physical environment.The objective of the research is to enhance collaborative learning in architectural engineering design through multi-user AR.In order to test the feasibility of integrating the AR application into the traditional collaborative design process, we deployed an AR module based on the AR application into two architectural design studio courses including 19 participants as a preliminary case study.This paper reports the effectiveness of the AR module on students' active participation and engagement in collaboration, skills and knowledge gains, and the comfort level of collaborative design in AR.In addition, students' communication behaviors during collaborative design have been assessed by analyzing recorded videos of users' view in AR.


Lessons learned: faculty watch parties are a powerful approach to foster diversity and inclusivity discussions

Abstract: Her research focuses on human-centered AI, developing systems that understand human behavior.She has worked on projects in human activity recognition to recognize activities of daily living (ADLs), intelligent tutoring systems to teach perspective sketching, and cognition-aware computing to measure people's mental workload on spatial visualization tasks.


Work In Progress: A Metro Map-Based Curriculum Visualization for Examining Interrelated Curricula

Abstract: Prior research has indicated that academic curriculum can be difficult for students to understand: especially for new students or students considering multiple degree programs.Based on the existing curriculum visualization literature, we hypothesize that the visualization of multiple curricula in one diagram might aid students in understanding curricular requirements and analyzing the different course paths they could take to complete a desired degree program.This work in progress describes the design and implementation of a visualization of the degree programs within an engineering college at a large University, where the interrelated web of courses and majors is displayed in the form of a metro map.In this metro map, the course requirements of individual majors are represented by metro 'lines', with each represented by a 'station' on each line.If multiple majors require the same course to complete the degree, then the 'line' representing each major will connect to the 'station' for that course.Through this visualization, we aim to create a tool that students could use to better understand the path of their degree program, and the ways in which their program requirements interact with those of related programs.We describe our methods for collecting course data as well as the design and implementation of the visualization.We close with a proposed set of evaluations for determining the effectiveness of this tool, and describe the limitations involved in creating similar visualizations for additional universities.Figure 1: A section of the metro map representation of 15 engineering degree programs.Each chain of colored lines represents a degree, while each circular 'station' represents a single required course.


Digital transformation in engineering education: a gap between teaching and management

Abstract: Techonology and System profesional, with extensive


Results of 2021 Energy Education Stakeholder Survey

Abstract: This paper presents the results of a recent energy education stakeholder survey that was conducted to examine energy industry trends and educational programming needs for the next decade.The survey was sent to over 700 faculty ranging from middle school to university level along with 150 industry representatives.The survey generated a response rate just over 10% for both the educator and industry groups.The top three technologies identified as areas for growth in the next decade were solar photovoltaics, energy storage, and electric vehicles.The biggest obstacle faced by faculty were a lack of tools, materials, and supplies necessary to provide hands-on learning with energy technology.Educators expressed a strong preference for face-to-face instruction that included access to tools and equipment, and also indicated a need for electronic open educational resources that were compliant with website accessibility requirements.The results of the survey are presented along with analysis, conclusions, and recommendations for engineering educational programs that address energy technology.


Abstract: Abstract A Holistic Implementation of Data Science in Clean Energy Engineering Education Advances in technology and economic expansion require increased use of energy. At the same time, technology has significantly improved energy efficiency and allows exploitation of a variety of diverse and plentiful sources of clean energy. This has led to a clean energy revolution, as stated by the Department of Energy [1]. We are witnessing expansion of economic activities driven by advances in clean energy technologies, which is in turn leading to new technological breakthroughs [2]. It is predicted that by 2040, artificial intelligence (AI) applications, in combination with other technologies, will become prevalent in almost every aspect of life, including but not limited to energy, healthcare, and transportation. Empowered by simultaneous increases in high-quality data, computing resources and communication infrastructure, AI will challenge societies to gain most of the benefits and at the same time reduce negative social effects [3]. This paper describes the development of a program that provides students with knowledge and skills in various aspects of energy, economics, and data science, calling attention to the areas in the program impacted by the newly developed courses, and expands on several activities designed to engage students through active learning and research experiences.


Using GitHub as a Teaching Tool for Programming Courses

Abstract: GitHub has become the most popular code management platform in the software development industry.It allows developers to manage their software development projects and collaborate with each other.Recently, educators also started using GitHub as a teaching tool for programming courses by hosting code samples and managing student assignments.In this study, we examine how GitHub is being used in academia, and we discuss the motivations and the benefits of using this platform.We also present authors' experience of using GitHub in programming courses of a software engineering program.We discuss the benefits and challenges of using GitHub and GitHub classroom in the classroom.


Memory-based Distribution Shift Detection for Learning Enabled Cyber-Physical Systems with Statistical Guarantees

Abstract: Incorporating learning based components in the current state-of-the-art cyber physical systems (CPS) has been a challenge due to the brittleness of the underlying deep neural networks. On the bright side, if executed correctly with safety guarantees, this has the ability to revolutionize domains like autonomous systems, medicine and other safety critical domains. This is because, it would allow system designers to use high dimensional outputs from sensors like camera and LiDAR. The trepidation in deploying systems with vision and LiDAR components comes from incidents of catastrophic failures in the real world. Recent reports of self-driving cars running into difficult to handle scenarios is ingrained in the software components which handle such sensor inputs. The ability to handle such high dimensional signals is due to the explosion of algorithms which use deep neural networks. Sadly, the reason behind the safety issues is also due to deep neural networks themselves. The pitfalls occur due to possible over-fitting, and lack of awareness about the blind spots induced by the training distribution. Ideally, system designers would wish to cover as many scenarios during training as possible. But, achieving a meaningful coverage is impossible. This naturally leads to the following question: Is it feasible to flag out-of-distribution (OOD) samples without causing too many false alarms? Such an OOD detector should be executable in a fashion that is computationally efficient. This is because OOD detectors often are executed as frequently as the sensors are sampled. Our aim in this paper is to build an effective anomaly detector. To this end, we propose the idea of a memory bank to cache data samples which are representative enough to cover most of the in-distribution data. The similarity with respect to such samples can be a measure of familiarity of the test input. This is made possible by an appropriate choice of distance function tailored to the type of sensor we are interested in. Additionally, we adapt conformal anomaly detection framework to capture the distribution shifts with a guarantee of false alarm rate. We report the performance of our technique on two challenging scenarios: a self-driving car setting implemented inside the simulator CARLA with image inputs and autonomous racing car navigation setting with LiDAR inputs. From the experiments, it is clear that a deviation from in-distribution setting can potentially lead to unsafe behavior. Although it should be noted that not all OOD inputs lead to precarious situations in practice, but staying in-distribution is akin to staying within a safety bubble and predictable behavior. An added benefit of our memory based approach is that the OOD detector produces interpretable feedback for a human designer. This is of utmost importance since it recommends a potential fix for the situation as well. In other competing approaches such a feedback is difficult to obtain due to reliance on techniques which use variational autoencoders.


Supplementary Figure S1 from Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures

Abstract: Supplementary Figure S1 shows that the best-fit piecewise linear model was found when the turning point was selected at the age of 46; the AIC was 2.00-8.81 units smaller than the AICs of the models when the turning point was selected at other ages. It also shows that the best-fit piecewise linear model was found when the turning point was selected at the age of 65; the AIC was 1.55-5.58 units smaller than the AICs of the models when the turning point was selected at other ages.


Supplementary Methods S1 from Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures

Abstract: Supplementary Methods S1 describes the five models that were used for fitting the age-related trend of total variance and familial correlations in Cirrus


Data from Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures

Abstract:
AbstractBackground:
Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus.
Methods:
We measured Cirrus for 3,195 breast cancer–free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants.
Results:
There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%).
Conclusion:
Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins.
Impact:
Early-life factors, shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognized as a critical window of vulnerability to breast carcinogens.


“Influencer marketing is not a way around the law”: Regulatory Compliance and Law Enforcement in the Canadian Social Media Influencer Field

Abstract:
In 2019, Competition Bureau Canada (“the Bureau”) sent letters to approximately 100 brands and marketing agencies that engage in influencer marketing—advising them to ensure that their marketing practices are in compliance with the law. Against this background, this research uses semi-structured interviews to examine the regulatory compliance efforts of 21 influencer intermediaries who liaise between brands and social media influencers. The research assesses these intermediaries’ disclosure practices together with their thoughts on the Bureau’s targeted outreach and law enforcement in the field. Additionally, the research explores how the intermediaries adjust their regulatory compliance efforts to technological innovation on social media platforms.

This research contributes four main findings. First, intermediaries consign regulatory expectations for disclosure to a secondary level of importance when working to meet the needs of brands and influencers. Second, there is limited intermediary knowledge of the Bureau’s legal standing and activities in the Canadian influencer field. Third, there are various algorithmic (e.g., algorithmic deprioritization) and non-algorithmic (e.g., lengthy disclosure processes) challenges to maintaining high standards of compliance in evolving digital environments. Finally, intermediaries have access to forms of social, cultural, and technical power that can be maximized to influence high standards of compliance. This research contributes to a growing body of scholarship focused on the perspectives of professionals who manage influencer marketing collaborations.


Predicting Student Retention via Expectancy Value Theory Using Data Gathered before the Semester Begins

Abstract: Prior to joining UofL, he worked as a Lecturer in his home country (Brazil) for three years, teaching and mentoring low-income, first-generation STEM students from rural communities.Additionally, Dr. Dourado worked as an instructor at the University of Central Florida for two years, primarily serving Hispanic first-generation


Board 166: Experiences from ImageSTEAM Workshop for the Middle School (Work In Progress)

Abstract: The rise of computing and artificial intelligence (AI) will transform our society and it is clear that students will be forced to engage with AI in their careers.Currently, the United States lacks adequate infrastructure or capacity to support the teaching of AI in the K-12 curriculum.To address these challenges, we introduce the use of visual media as a key bridge technology to engage students in grades 6-8 with AI topics, through a recent NSF funded ITEST program, labeled ImageSTEAM.Specifically, we focus on the idea of a computational camera, which rethinks the sensing interface between the physical world and intelligent machines and enables students to ponder how sensors and perception fundamentally will augment science and technology in the future.Two workshops have been conducted, the first in summer 2021, and the second in summer 2022.The first workshop was delivered virtually due to the persistent COVID-19 environment at the time.The second workshop had one week conducted in-person and the second week was conducted virtually.Teachers and students participated in the workshops and their experiences will be shared and discussed at the conference.In addition, teacher use of the skills and knowledge learned from the workshops will be shared.


Optimization of Multi-target Continuous Dynamic Trajectory for Unmanned Aerial Vehicles

Abstract: In the problem of trajectory optimization for unmanned aerial vehicles (UAVs) passing through multiple target points, continuous velocity and acceleration in the process of optimization are essential for maintaining a feasible trajectory. This paper proposes a multi-segment holistic Bezier shaping method (MHBSM) for dynamic trajectory optimization. The MHBSM not only achieves holistic optimization of UAVs passing through a multi-target trajectory but also ensures high-order continuity at the junctions of trajectory segments. Unlike methods that generate non-dynamic trajectories by connecting arcs and straight lines (such as Dubins curves) without considering speed changes, MHBSM optimizes the trajectory while taking into account the dynamics of UAVs. The method optimizes the differential flat output space of a fixed-wing UAV dynamic model and transforms the dynamic optimization problem into a three-dimensional position optimization problem. The problem is then further transformed into an optimization of control points using the Bezier curve. The resulting trajectory satisfies the dynamic constraints. In comparison to the Gaussian pseudospectral method (GPM), MHBSM achieves similar optimization results with only a 2.58% difference, while requiring approximately 1.36% of the computational time on average. The MHBSM demonstrates excellent convergence performance while satisfying the constraints of continuous acceleration and obstacle avoidance.


Protecting quantum correlations of negative quantum states using weak measurement under non-Markovian noise

Abstract: Abstract The weak measurement (WM) and quantum measurement reversal (QMR) are crucial in protecting the collapse of quantum states. The idea of WM and QMR has recently been used to protect and enhance quantum correlations and universal quantum teleportation (UQT) protocols. Here, we study the quantum correlations, maximal fidelity, and fidelity deviation of the two-qubit negative quantum states developed using discrete Wigner functions with(without) WM and QMR. To take into account the effect of a noisy environment, we evolve the states via non-Markovian amplitude damping and random telegraph noise quantum channels. To benchmark the performance of negative quantum states, we calculate their success probability. We compare our results with the two-qubit maximally entangled Bell state. Interestingly, we observe that some negative quantum states perform better with WM and QMR than the Bell state for different cases under evolution via noisy quantum channels.


News Recommender System Considering Temporal Dynamics and Accuracy-Diversity Tradeoff

Abstract:
News recommender systems aim to personalize users experience for online news readers and help them discover relevant and interesting news items from a broad and diverse search space. However, recommending news is a challenging task. There are hundreds of news articles published every day, many of which quickly become obsolete and irrelevant to the readers. Readers’ preferences (interests) also exhibit dynamic behavior and the relevance of readers’ preferences strongly depend on the context. Some of the readers’ preferences are long-term, reflecting the personality or behavior, whereas others are short-term, showing their current interests. External events, such as breaking news and trends, also influence readers’ interests. Although the high recommendation accuracy is appreciated, focusing on it too much sacrifices diversity and limits readers’ options.

The main contribution of this research is to design a recommender system to tackle the specific challenges of news recommendations. To address these challenges, we propose a recommendation strategy that seamlessly integrates readers’ long-term and short-term preferences when recommending news items. In addition to high accuracy, we focus on promoting reasonable diversity in news recommendations. To achieve a balanced objective (high accuracy and reasonable diversity), we formulate a combined optimization strategy that includes both of these aspects in the recommendation process.

More specifically, to achieve high accuracy, we propose novel latent factor models such as matrix factorization and a generalized linear model. In addition, we propose a regularized latent factor model to achieve reasonable diversity while maintaining high accuracy. Then, for the same purpose, we propose a deep learning-based framework composed of network components of news modelling and reader modelling. We use different regularization terms in the latent factor model to achieve accuracy-diversity balance, and the neural attention mechanism in deep neural networks for the same purpose.

We also introduce a new evaluation metric to measure the tradeoff performance with respect to accuracy and diversity. Experiments on real-world data have demonstrated the effectiveness of our proposed approach on quality factors such as accuracy, diversity as well as this tradeoff metric. Our model has achieved an improvement on this new tradeoff metric by 10-30% when compared to traditional and state-of-the-art recommendation algorithms.


Entanglement-based quantum digital signatures over deployed campus network

Abstract: The quantum digital signature protocol offers a replacement for most aspects of public-key digital signatures ubiquitous in today's digital world. A major advantage of a quantum-digital-signatures protocol is that it can have information-theoretic security, whereas public-key cryptography cannot. Here we demonstrate and characterize hardware to implement entanglement-based quantum digital signatures over our campus network. Over 25 hours, we collect measurements on our campus network, where we measure sufficiently low quantum bit error rates (<5% in most cases) which in principle enable quantum digital signatures at over 50 km as shown through rigorous simulation accompanied by a noise model developed specifically for our implementation. These results show quantum digital signatures can be successfully employed over deployed fiber. Moreover, our reported method provides great flexibility in the number of users, but with reduced entanglement rate per user. Finally, while the current implementation of our entanglement-based approach has a low signature rate, feasible upgrades would significantly increase the signature rate.


Managing and Billing Software for Hypermarket

Abstract: It previews the whole system design while in the free demo session and clear your queries by experiencing Optech’s digital dashboard. In the navigation system, we optimize the UI to be straightforward and comprehend for all sets of people so the employees grasp this entire operation. The Optech software has been developed with VB.net and MY SQL Server. The system is efficient in generating reports, which will help in the maintenance of the hypermarket easily. This Optech software can store a lot of records in the database. It’s a user-friendly software, which can be used for service, sales and report. It has an unlimited storage and application can be used in both online and offline. Reports for billing can be generated by data wise or pricewise.


Emerging Innovations and Global Challenges on Curriculum Design: Case Study of Teaching Security in Embedded System Design

Abstract: Abstract Emerging innovations and global challenges are not only overwhelming human health and the environment but also complicating the training of the next generations of the workforce that will shape the future of the global economy. In particular, a topic of immediate concern is the security of embedded systems. Embedded systems are ubiquitous and are found in medical devices, nuclear plants, and chemical processing systems to name a few applications. The ever-increasing complexity of modern embedded systems has raised the potential for security breaches that may compromise the integrity and functions of these products. Although embedded systems design has been a core curriculum in most electrical and computer engineering programs, incorporating security-training skills at the undergraduate level remains challenging. Despite the enormity of security challenges, they are often ignored during the initial design and development period of an embedded system, thus leaving many devices vulnerable. We postulate that this design shortcoming is perhaps due, in part, to the teaching inadequacy of embedded security in early engineering education. In this paper we propose a framework to design an undergraduate curriculum that will incorporate security concepts in embedded systems courses. The curriculum will create new learning materials and teaching strategies that will focus on security concepts in early phases of embedded systems design. Because it is almost impossible to add security to an existing embedded system, the significance of this paper is to enforce life-learning awareness of security concerns in embedded systems design in early training of undergraduate students. The undergraduate curriculum will integrate security concerns, challenges, and concepts in all aspects of embedded systems design. We propose new learning materials and teaching strategies that will focus on securing the hardware and software programming of the central processing unit and its peripherals at the low-level layer allowing the upper-level layer to implement security standards with a greater efficiency. We will also provide advanced concepts that mitigate security breaches either by low-level encryption solutions or dead-end program destruction. The long-term goal of this paper is to promote security awareness in embedded systems at the undergraduate level.


Abstract: Abstract Cybersecurity Issues in Crowdsourcing Engineering Initiatives As crowdsourced engineering-related projects become more wide-spread and global, we must pay attention to cybersecurity issues that can emerge. Sharing data is fundamental to crowdsourcing, and that requires attention to protecting privacy. This issue is exacerbated if participants use common technologies, e.g., cell phones, that are used for other purposes and may be subject to cybersecurity vulnerabilities. Cybersecurity issues can arise during recruitment, by which social media is a prevalent tool, to projects in that bad actors can solicit input into malicious and false projects. Furthermore, the results of crowdsourced engineering projects may provide foundations for policy and legislation; thus, integrity must be maintained and bad actors prevented from manipulating data and/or results. Governance of crowdsourced projects must include attention to cybersecurity issues, regardless of their scope and scale. The exploitation of cybersecurity vulnerabilities are often result from ethical lapses. Just as engineers respect engineering professional ethics, so must citizens who participate in gathering data. In this paper, we will share a taxonomy that identifies how professional engineering ethics are represented and brought to life in 21st century crowdsourced engineering projects, which include commercial initiatives from Anheuser-Busch, Nokia, and FedEx as well as research initiatives like the Digital Humanitarian Network, the Humanitarian OpenStreetMap Team, and KeralaRescue.


Improving the Learning Experience of Neurodiverse Students in a Fluid Mechanics Course During the COVID-19 Pandemic

Abstract: Abstract The goals of this study were (a) to assess whether the adoption of a comprehensive suite of inclusive academic interventions within a large engineering course (Fluid Mechanics) would increase the engagement and learning outcomes of neurodivergent students enrolled in the course and (b) to observe student perceptions of program activities with the end goal of determining whether or not students perceived the program interventions to be helpful. The course interventions were conceived and implemented within the broader context of a research project that aims to transform the academic practices within an engineering department in the Northeast United States to embrace the unique strengths of neurodiverse students and improve the educational experience for all students. Over the course of the semester, three surveys were handed out to students on a voluntary basis. Of a class of 52, 50, 48, and 39 participant responses were received for the three surveys, respectively. Each survey included several multiple-choice (or T/F) and open-ended questions, including aspects related to the instructor effectiveness, the course activities and assessments, and the student experience. Students were also asked whether they identify as neurodivergent during the first survey. After the course was completed, the analysis of survey responses was conducted using a qualitative research protocol. First, mapping of survey questions with the goals of the study was done, then student responses were organized in a matrix and underwent various rounds of coding. The process of coding is an iterative process that results in a refinement of label codes which can result in the identification of emerging themes. Some of the emerging themes that were identified by the students as barriers were the difficulty of exams, advanced mathematics content, and course mode of delivery that was hybrid due to the pandemic. Themes identified as supports were alternate exam modalities, course content and delivery, lecture videos, TA/recitation videos, lab videos, and live-streamed lectures. It was found that on several occasions that students who identify as neurodivergent reported supports and challenges differently than the remaining students who responded to the survey. At the beginning of the semester, only 29% of neurodiverse student responses expressed a positive outlook on how effective the learning experience would be in this course. Interestingly, as the course progressed, 71% of neurodiverse student responses indicated that the course was providing an effective learning experience for them.


Sentiment Analysis Through the Application of Machine Learning Algorithms

Abstract: Sentiment analysis, also called opinion mining. It is a natural language processing (NLP) task that involves fixing the sentiment tone expressed in a piece of text including a review, tweet, post, comment, or news article, using machine learning algorithms. There are various social networking sites where people express their views, opinions, and emotions freely. All such posts are recorded and analyzed to determine emotions of the people. Present study recovers posts and finds emotions and polarity of posts. To determine emotions and polarity of posts, various techniques are used.


Comparison and optimization of different routing methods for meshed QKD networks using trusted nodes

Abstract: Quantum key distribution (QKD) appears as a promising technique for encrypted communication, preserving security even in the presence of a future powerful quantum computer. At the same time, communication infrastructure becomes increasingly complex, and the exponentially increasing traffic volume makes the application of QKD a challenging task. In addition, current hardware for QKD modules is still subject to research to gain maturity, to guarantee secret key rates, and to mitigate key rate fluctuations and instabilities. Our study analyzes and optimizes five routing algorithms to efficiently use the secret keys as a resource in meshed national-wide networks. By fulfilling distinct security and performance levels, the algorithms are compared with respect to key performance indicators and optimized for blocking probabilities, load balancing, or the management traffic amount. Depending on the application, our study allows operators to choose a well-suited algorithm and gives performance estimates, including an assessment of the performance gap to globally optimized algorithms.


Hubble Meets Webb: Image-to-Image Translation in Astronomy

Abstract: This work explores the generation of James Webb Space Telescope (JWSP) imagery via image-to-image translation from the available Hubble Space Telescope (HST) data. Comparative analysis encompasses the Pix2Pix, CycleGAN, TURBO, and DDPM-based Palette methodologies, assessing the criticality of image registration in astronomy. While the focus of this study is not on the scientific evaluation of model fairness, we note that the techniques employed may bear some limitations and the translated images could include elements that are not present in actual astronomical phenomena. To mitigate this, uncertainty estimation is integrated into our methodology, enhancing the translation's integrity and assisting astronomers in distinguishing between reliable predictions and those of questionable certainty. The evaluation was performed using metrics including MSE, SSIM, PSNR, LPIPS, and FID. The paper introduces a novel approach to quantifying uncertainty within image translation, leveraging the stochastic nature of DDPMs. This innovation not only bolsters our confidence in the translated images but also provides a valuable tool for future astronomical experiment planning. By offering predictive insights when JWST data are unavailable, our approach allows for informed preparatory strategies for making observations with the upcoming JWST, potentially optimizing its precious observational resources. To the best of our knowledge, this work is the first attempt to apply image-to-image translation for astronomical sensor-to-sensor translation.


Blind watermarking algorithm with 2D-SCCM chaotic system encryption based on SURF and RHFMs

Abstract: Abstract In this paper, a digital watermarking algorithm was proposed based on local feature regions and radial harmonic Fourier moments. It used a quantized modulation algorithm to embed the watermark information into the frequency domain information of the image. Compared with traditional watermark directedly embedded into the pixel values, the algorithm enhanced the invisibility of the watermark. Many existing digital watermarking algorithms neglected the security of the embedding process, because of more attention on the effect of post-embedding and extraction. To overcome this problem, we proposed a two-dimensional hyperchaotic system named 2D-SCCM chaotic system, which used classical sine mapping and cubic mapping for coupling. Before embedding, the correlation among the pixels of the watermarked image was disrupted, resulting in a full process security enhancement. Combining the clustering algorithm with feature point extraction, the obtained local feature region was more stable and suitable for watermark embedding. Results showed that the improved watermark embedding algorithm was more effective for resisting geometric attacks such as cropping attacks. Moreover, the PSNR values of the final watermark-embedded images were all greater than 50 dB, indicating a good balance between imperceptibility and robustness.&#xD;


The Bounded Gaussian Mechanism for Differential Privacy

Abstract: The Gaussian mechanism is one differential privacy mechanism commonly used to protect numerical data. However, it may be ill-suited to some applications because it has unbounded support and thus can produce invalid numerical answers to queries, such as negative ages or human heights in the tens of meters. One can project such private values onto valid ranges of data, though such projections lead to the accumulation of private query responses at the boundaries of such ranges, thereby harming accuracy. Motivated by the need for both privacy and accuracy over bounded domains, we present a bounded Gaussian mechanism for differential privacy, which has support only on a given region. We present both univariate and multivariate versions of this mechanism and illustrate a significant reduction in variance relative to comparable existing work.


Quantum differential privacy under noise channels

Abstract: Abstract Quantum differential privacy is a way to protect privacy in quantum computing. It perturbs the final state of quantum computing by a specific quantum noise channel and makes the output useless to the attackers, so as to achieve privacy protection. In this paper, we study the quantum differential privacy under three channels: bit flip channel, phase flip channel and depolarizing channel. We calculate the privacy parameters for these three channels based on the proportional distance. We show the privacy protection capability by calculating the decay rates of the trace distances of two arbitrary quantum states before and after passing through the channels. Under the same privacy budget, we compare the fidelities, the trace distances between the real outputs and the outputs after entering the channels, and analyze the privacy protection performance of different quantum channels in detail.


Programming Computational Electromagnetic Applications Assisted by Large Language Models [Em Programmer’s Notebook]

Abstract: This article discusses the possibilities and limitations of using large language models (LLMs) in software development with applications in computational electromagnetics (EM). Three tasks are discussed: code translation, code generation, and code description. The tests showed that LLMs are generally very useful. Even when errors occurred, they could provide useful hints to find a solution.


Nichilismo storiografico e nuovi media nella RPC Ricerca accademica e controllo politico

Abstract: This paper provides an overview of the phenomenon of ‘online historical nihilism’ ( wangluo lishi xuwuzhuyi 网络历史虚无主义) in the People’s Republic of China. This research maps the political and academic discussion on the new form of nihilism through the analysis of Chinese official documents and scholarly debates. The first part of the article highlights the political and academic relevance of historical nihilism in China, emphasizing its relationship with new media. The second part focuses on the Chinese academic discussion of online historical nihilism. The final section examines the norms and ad hoc initiatives promoted by state-run online portals and private online platforms aimed at limiting forms of online historical nihilism.


Correction: Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

Abstract: [This corrects the article DOI: 10.1371/journal.pone.0296107.].


Low-Cost Training of Image-to-Image Diffusion Models with Incremental Learning and Task/Domain Adaptation

Abstract: Diffusion models specialized in image-to-image translation tasks, like inpainting and colorization, have outperformed the state of the art, yet their computational requirements are exceptionally demanding. This study analyzes different strategies to train image-to-image diffusion models in a low-resource setting. The studied strategies include incremental learning and task/domain transfer learning. First, a base model for human face inpainting is trained from scratch with an incremental learning strategy. The resulting model achieves an FID score almost equivalent to that of its batch learning equivalent while significantly reducing the training time. Second, the base model is fine-tuned to perform a different task, image colorization, and, in a different domain, landscape images. The resulting colorization models showcase exceptional performances with a minimal number of training epochs. We examine the impact of different configurations and provide insights into the ability of image-to-image diffusion models for transfer learning across tasks and domains.


ContextualHomeLLM: A Context-Aware Large Language Model for Smart Homes

Abstract: ContextualHomeLLM is a state-of-the-art Large Language Model (LLM) specifically designed to enhance user interactions and smart home management. Leveraging the power of Transformer architecture, this model is trained to understand, interpret, and respond to user queries, commands, and contextual cues within the smart home ecosystem. By integrating advanced contextual understanding, personalized recommendations, and real-time control, ContextualHomeLLM offers an innovative approach to seamless smart home control and assistance.


Sentiment Analysis of Product Reviews Based on Bi-LSTM and Max Pooling

Abstract: Product reviews are discussions about the value of the product, for which customers will have different emotional expressions. It is necessary to analyze whether customers have negative, positive, or other emotions about the value of the product, to facilitate manufacturers and sales manufacturers to upgrade and improve the product and analyze customers’ demand for the product. A text sentiment analysis model is constructed through sentiment dictionary and machine learning, a benchmark model is set up, and a method combining Bi-LSTM (Bi-directional Long Short-Term Memory), and Max pooling is proposed to construct a deep learning model of text sentiment analysis. Through experiments, the accuracy of the model in the sentiment classification of product reviews reaches 0.9332, which is higher than the result of SVM (Support Vector Machine) (0.9214) in machine learning and the benchmark model (Gradient Boosting) (0.8972).


EnigmaScan: Streamlining Matrix Puzzle Solving through Image Recognition

Abstract: In an era increasingly shaped by technology, the enduring allure of word puzzles, such as word searches, crosswords, and Sudoku, remains steadfast. Yet, manually tackling these puzzles can prove to be a time-intensive and frequently exasperating endeavor. To meet this challenge, we suggest employing image recognition-based algorithms designed for puzzle-solving, specifically targeting Sudoku, word searches, and crossword puzzles. EnigmaScan is publicly accessible at https://github.com/theNewtonCode/EnigmaScan, allowing users to promptly solve their puzzles in real-time by uploading snapshots of unsolved Sudoku, word searches, or crosswords. This ground-breaking tool is designed to streamline and hasten the puzzle-solving process for users of all proficiency levels, thereby promoting a more enjoyable and accessible interaction with these timeless pastimes. The proposed algorithms exhibit remarkable efficiency, solving these puzzles within seconds and achieving an accuracy of 100%.


Research on Chinese Spam SMS Classification Based on ECA-TextCNN Model

Abstract: In response to the issues of traditional Chinese spam SMS classification models, which fail to comprehensively extract text features and obtain representative text features, we propose a text classification model based on the ECA-TextCNN model, combining an efficient channel attention mechanism and TextCNN classification model. Firstly, the model utilizes a skip-gram pre-training model for text representation. Secondly, it employs convolutional kernels of different sizes for feature extraction. Furthermore, ECA-Net is used to assign corresponding weights to each channel feature. Finally, the feature vectors are inputted into the softmax layer to obtain the classification results. The experiments are conducted on the publicly available Message80W Chinese SMS dataset. The results show that compared to the baseline model, the text classification model based on ECA-TextCNN exhibits varying degrees of improvement in classification accuracy, precision, recall, and F1 value.


Positive Solution of Moving Platform Position Based on Bat-Iterative Algorithm

Abstract: To address the forward position problem of a 6-DOF moving platform, a Bat-iterative method leveraging the Bat algorithm was proposed as a solution. By combining the Bat algorithm with the traditional Newton-Raphson iterative algorithm, the convergence speed and accuracy of the algorithm can be improved while the convergence and global search ability are guaranteed. The concrete implementation process of the algorithm is given, and the corresponding positive solution problem of 6-DOF parallel mechanism is solved by using the algorithm, and the corresponding operation example is given. At the same time, the inverse solution method is used to obtain the data set for error analysis and comparison. In summary, the comparison results demonstrate that the Bat-iterative algorithm offers notable benefits such as fast computation speed and high precision. As a result, it proves to be well-suited for practical engineering applications, meeting the necessary requirements effectively.


Predictive Modelling for Wordle Game: Analysing Word Attributes and Future Trends

Abstract: Wordle, an engaging online crossword puzzle, challenges players to decipher a hidden five-letter word. In this manuscript, our primary focus lies in predicting the distribution of reported results in the Wordle game. To accomplish this, we devised and compared multiple predictive models, seeking the most accurate and stable solution. The foundation of our research revolves around training 31 distinct word attributes through five diverse multi-output regression data processing models. Then we choose a model whose average error of the test set error is very small, indicating that the prediction accuracy of our model is better, and also more stable than other models. Finally, to illustrate the practical application of our model, we provide a concrete example of predicting the associated percentages of (1, 2, 3, 4, 5, 6, X) for the word “EERIE” in future game instances.


Citation Analysis of Post Graduate Theses Submitted to Department of Renewable Energy Sources in College of Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, Maharashtra

Abstract: Citation analysis helps to understand the different ways that users look for material. Citation analysis can be used to calculate the obsolescence rate. Finding the rate of obsolescence of publications in the field of renewable energy sources is the aim of this study. Citation analysis is being done on M.Tech. (Agril.Engg.) PG theses that were submitted to the College of Agricultural Engineering and Technology's department of Renewable Energy Sources. The study uses theses that were submitted between 2017 and 2021. There are 14 theses in total, with 707 citations. Analysis is also conducted to determine the Authorship pattern and Types of Information Sources that the researchers mentioned in their theses.


Unary Quantum Finite State Automata with Control Language

Abstract: We study quantum finite automata with control language (QFCs), a theoretical model for finite memory hybrid systems coupling a classical computational framework with a quantum component. We constructively show how to simulate measure-once, measure-many, reversible, and Latvian QFAs by QFCs, emphasizing the size cost of such simulations. Next, we prove the decidability of testing the periodicity of the stochastic events induced by a given QFC. Thanks to our QFA simulations, we can extend such a decidability result to measure-once, measure-many, reversible, and Latvian QFAs as well. Finally, we focus on comparing the size efficiency of quantum and classical finite state automata on unary regular language recognition. We show that unary regular languages can be recognized by isolated cut point QFCs for which the size is generally quadratically smaller than the size of equivalent DFAs.