EXPERT SYSTEM FOR DIAGNOSING BEHAVIORAL DISORDERS USING THE DEMPSTER-SHAFER THEORY ALGORITHM

Abstract: Behavioral disorders, especially in children, can have a significant impact on their development in various domains, such as social, emotional, and academic. The diagnostic process for this disorder can be complicated due to overlapping symptoms and the possibility of complex causal factors. Early identification and appropriate treatment of behavioral disorders is essential to prevent more serious impacts on an individual's psychological well-being. Lack of knowledge and difficulty in accessing psychiatrists to find out about behavioral disorders in children are factors that result in this problem requiring a solution. The aim of this research is to create an expert system that utilizes the Dempster-Shafer Theory algorithm to detect behavioral disorders, thus simplifying the diagnosis process and ensuring accurate results. The Dempster-Shafer theory, as an inference engine, can overcome uncertainty by combining several sources of evidence or data that may overlap or be incomplete, resulting in a stronger conclusion. The main feature of this expert system is its ability to carry out diagnoses based on symptoms and display diagnosis results, disease descriptions, and treatment options. Test accuracy produces a value of 90%, which shows that the Dempster-Shafer Theory approach can diagnose behavioral disorders effectively.


STUDENTS' WRITING SKILL THROUGH DIARY WRITING TECHNIQUE AT SMA NEGERI 5 KOTA TERNATE

Abstract: The study that aims to determine does the use of diary writing technique can significantly improve students’ writing skills at SMA Negeri 5 Kota Ternate. This study used a quantitative method with a pre-experimental design. The researcher involved 20 students as the sample selected by using the convenient sampling technique. The research instrument was in the form of diary writing of students' personal experiences. The finding shows that diary writing technique at class X (IPA) can significantly improve. It provided the data of the pretest is 60.35 and post-test is 82, 95. It is evident that the result of post-test is higher than pretest. The analysis that the statistical value (4.35) is higher than the t-critical value (1.729) at the significant level of (0.05). This alternative hypothesis (Ha) stating that, diary writing technique improve students’ writing skill at SMA Negeri 5 Kota Ternate is accepted and hypothesis null (Ho) stating that diary writing technique does not improve student writing skill at first year student of SMA Negeri 5 Kota Ternate is rejected. This suggests that the use of this technique is effective to improve student writing skill at SMA Negeri 5 Kota Ternate


ENGLISH TEACHER’S STRATEGIES IN TEACHING STUDENTS WITH HEARING IMPAIRMENT AT SMP LUAR BIASA YPAC

Abstract: This research aimed to identify English teachers’ strategies in teaching students with hearing impairment and the challenges which the teacher faced in classroom practices. The research method used is qualitative with a case study approach, The subject of this research was an English teacher at SMPLB YPAC. The data were collected by using observation checklist and interview. The result of research showed that there are two strategies used by teacher at SMPLB YPAC grade 8th, namely the manual method which has two basic components, namely sign language and finger spelling and the oral method, namely lip reading or speech reading. Implementing the strategy, there are challenges faced by teachers. The challenges are that first, students sometimes have difficulty understanding words and teachers sometimes need to repeat words or write them down. Second, the limited vocabulary of students and how to overcome them with the teacher needing to explain the definition and provide examples with the media. Third, the teacher did not push students to do exactly what the teacher says. Last, the need for repetition in delivering material with a long duration compared to regular students and the ability of the teacher to remain patient with students is very necessary in answering the last challenge.


Impact of Political Marginalization and Historical Conflicts on the Sustainability of SMEs in Anambra State, Nigeria

Abstract: The untamed rise in Nigeria’s insecurity is of great concern to residents and observers. Aside from heightened unemployment, Nigeria is a troubled nation in need of international interventions to prevent the growth and spread of terror to various parts of the world. This study assessed the impact of perceived political marginalization and historical conflict on the sustainability of SMEs in Anambra State, Nigeria. To achieve the study intent, the design followed a qualitative pattern. Participants were in two groups: owners of SMEs on one side and Herders and farmers on the other. An in-depth interview was paramount to obtaining needed information. Thematic and Content analysis were adopted. Findings revealed that the willingness to ignore governmental directives increases as perceived marginalization increases. Also, a negative relationship between herders-farmers conflict and the business climate in Anambra state was discovered. The governor of Anambra state needs to consciously set up or empower if any, an internal mechanism under the Ministry of Agriculture to devise workable plans and strategies on how to resolve the existing conflict in order to boost investors’ confidence in the state.


Utilization of Augmented Reality Technology in Independent Speech Therapy Applications

Abstract: One of the uses of information technology is augmented reality technology in the health sector. Augmented reality is used in the development of applications that are used for speech therapy for children with autism or children with speech delays. The method used for the development of speech therapy applications is the extreme programming method. This method can adapt to the development of an application in a short time and quite a lot of changes. The stages in the extreme programming method include identifying system requirements, planning activities during system/application development, system development process, iteration for system improvement until the final iteration, and no more user feedback, system/application production, and system maintenance with data backup and system recovery. After testing the system, it was concluded that three iterations occurred during the development of the speech therapy application. The last test showed that the user accepted the speech therapy application with a percentage of 77,14%. The output of this research is an augmented reality-based speech therapy application that is useful for independent speech therapy for children with speech delays.


The Effectiveness of Emotion Validation Pop-Up Books on the Emotional Development of Preschool Children as a Control for Children’s Mental Health Emergencies

Abstract: One of the psychological problems experienced by children is emotional mental problems which can result in unhealthy emotional and mental disorders. The incidence of this disorder is around 3-10%, in the United States it is around 3-7% while in Germany, Canada and New Zealand it is around 5-10%. In Indonesia, there are still no definite figures regarding the incidence, even though this disorder occurs quite often. The Emotion Validation Pop-Up Book Research contributes to Preschool Children increasing their understanding of felt emotions through an attractive display. This research uses a quasi-experimental design. The number of samples used in the treatment group and control group was 30 students at Kindergarten N Pembina Kab. Kendal uses a simple random sampling technique. Statistical analysis uses independent T test and paired T test. The results showed that the research showed an independent T test (0.000<α) and paired T test (0.000<α). This study concluded that there were differences in the emotional development of preschool children between the control and treatment groups and there were differences before and after in the treatment group in the use of the Preschool Children’s Emotion Validation Pop-Up Book.


A Statistical Analysis of Vietnamese High School English Test Score Data

Abstract: This statistical study, perhaps the first of its kind in Vietnamese academia, has tried to understand the patterns in high school students’ pre-college national exam test scores which hold tremendous significance in students’ college or university admissions. English belongs to one of the three mandatory subjects (the other two being Mathematics and Literature) where students must be tested as part of their overall evaluation. However, since English is a foreign language for the learners, many high school students, especially those from rural areas, struggle to attain an acceptable level of proficiency. Meanwhile, a good proportion of students, particularly those in urban areas and with their own substantial financial resources, do attend extra tutorial classes to gain an edge in English proficiency. In this study we have analyzed the provincial mean and median test scores over a period of three years (2019, 2020 and 2021). It has been noted that a province’s aggregate performance depends heavily on - (i) the province’s per capita gross domestic product (PCGDP), which measures a province's economic prosperity; and (ii) the average number of students per high school (ANSHS) which indicates the overall competitive environment the students encounter which helps them sharpen their language proficiency.


Syllabus Analysis of English for Hotel Receptionist Program with SKKNI in Kartu Prakerja by Mobile English

Abstract: Hotel receptionists who work as frontline in the hospitality industry are supposed to be able to perform their duties no matter where the guests come from. In handling foreigners, English can be used as a medium to communicate and serve guests. To achieve a certain level of communication in English; the employee could take training programs and courses to upskill their language level in ESP. This study intends to analyze a syllabus of the ESP. By conducting this study, the researcher tries to find out the competencies being taught in the program and the suitability of the SKKNI as the standard reference. The method used in this study was qualitative research with a content analysis approach. The subject of this study was a syllabus developed by Mobile English as a learning institution that partners with the Kartu Prakerja Program. The instrument used to examine the study was a form of table. The study's results examined only five competencies taught in the program out of 14 competencies that should be acquired based on the SKKNI. The finding implies that future improvements in the syllabus and the program are needed to help the student be competent in their role as hotel receptionists


Uji Validasi Rancangan Aplikasi Kelengkapan Resume Medis di Klinik Almeta Medika

Abstract: The quality of medical records becomes more valuable if the information written is complete, especially on the medical resume. Complete medical resume writing at Almeta Medika Clinic Banjarmasin has not been carried out, this causes patient information to be incomplete. So a complete medical resume application was designed. This research aims to validate the complete medical resume application at the Almeta Medika Clinic, Banjarmasin. This research method is descriptive qualitative, collecting data using questionnaires and checklists. The subjects in this research were medical records officers, Almeta Medika Banjarmasin clinic managers, IT expert respondents and materials experts. The results of this research were that by testing it was found that the design of the ERD, DFD and application for complete medical resumes at the Almeta Medika Banjarmasin Clinic contained a login, dashboard, 4 components of quantitative analysis and the results of quantitative analysis reports/summaries. ERD and DFD were created using Microsoft Visio and the medical resume application was created using Microsoft Access after identifying user needs and identifying data requirements. The complete medical resume application at Almeta Medika Clinic Banjarmasin has been designed according to the clinic's needs. Validation with medical records officers, Almeta Medika Banjarmasin clinic manager, IT expert respondents and material experts found the appropriate category. Validation testing of the medical resume completeness application at Almeta Medika Clinic Banjarmasin is designed according to the clinic's needs and is feasible to implement.


Survival Analysis of Battalion-Level Commanders(leaders) Using Big Data as Results of Brigade-Level KCTC Training - Focused on Infantry Battalion Defensive Operations -

Abstract: In this study, we conducted a survival analysis on battalion-level commanders(leaders), focusing on infantry battalion defensive operations using the big data of brigade-level KCTC(Korea Combat Training Center) training results.Unlike previous studies, we utilized the brigade-level KCTC training results data for the first time to conduct a survival analysis, and the research subjects were battalion-level commanders(leaders), which can affect the battle. At this time, the battle results were defined, and through cluster analysis, infantry battalions were divided into excellent, average, and insufficient units, and the difference in the survival rate of the commanders was analyzed through the Kaplan-Meier survival analysis. This provided an opportunity to objectively compare the differences between excellent and insufficient units. Subsequently, factors affecting the survival of commanders were derived using the Cox proportional hazard model, and it was possible to confirm the influencing factors from various angles by also using the survival tree model. Significance and limitations confirmed in the research process were presented as policy suggestions and future research directions.


Federated Learning for Healthcare: A Comprehensive Review

Abstract: Recent advancements in deep learning for healthcare and computer-aided laboratory services have sparked a renewed interest in making medical data more accessible. Elevating the quality of healthcare services and delivering improved patient care necessitates a knowledge base rooted in data-driven insights. Deep learning models have proven to excel in this regard, as they are specifically designed to embrace a data-driven approach. These models thrive on exposure to larger datasets, which enables them to continuously improve their performance. However, as healthcare organizations strive to aggregate clinical records onto central servers to construct robust deep learning models, concerns surrounding privacy, data ownership, and legal restrictions have emerged. Safeguarding sensitive medical data while harnessing collective knowledge from multiple healthcare centers is a challenging balancing act. One promising approach to address these concerns is the use of privacy-preserving techniques that allow for the utilization of data from multiple centers without compromising security. Federated learning (FL) is a technique that has emerged to enable the deployment of large machine learning models trained across multiple data centers without the necessity of sharing sensitive information. In this article, we present the most recent findings derived from a systematic literature review focusing on the application of federated learning in healthcare settings. This review offers insights into the current state of research and practical implementations of FL within the healthcare domain. By leveraging federated learning, healthcare institutions can harness the collective power of their data while upholding privacy and data security standards, ultimately leading to more effective and data-driven healthcare solutions.


Extended Reality Technologies in Physical Fitness for Health Promotion

Abstract: This chapter presents a bibliometric analysis of extended reality technologies (i.e., augmented, virtual, and mixed reality) concerning physical fitness by analyzing 251 published documents from Scopus-indexed sources. Results from the analysis indicated that medicine and computer science are the prominent subject areas, and the International Journal of Environmental Research and Public Health and Lecture Notes in Computer Science are the leading publishers in this domain. The analysis further found variability in terms of authorship patterns and international collaboration in 58 documents. In addition to this, the analysis found that there has been a steady increase in publications in this domain, with a substantial surge from 2019 onwards. The United States and China are the leading contributors, with several collaborative networks among countries, particularly among high-income, western nations. Furthermore, the keyword co-occurrences indicated that there is a focus on VR, gamification, and health issues related to older adults.


Special Aspects in Implementing the Project-Based Learning in Russian Universities

Abstract: Abstract Modern Russia is challenged with serious needs for highly qualified engineering personnel as a necessary resource of its economic development. The growing education quality and increasing competitiveness of Russian higher educational establishments are, inter alia, achieved in Russia's innovation universities due to their integration into the global educational environment and to the use of actual teaching methods. Inviting internationally recognized experts into Russian universities allows them to apply the world's best practices to teaching their students and training their faculty members. In recent times, the project-based teaching method is increasingly used at Kazan National Research Technological University (KNRTU), Russia, as a method that enhances the studying motivation of its students. Implementation of the method into the teaching/learning processes in the University is largely determined by inviting a professor from the USA, who has been successfully implementing this method globally for very different audiences for many years. A special feature of his work at KNRTU is the use of the project-based method for differing academic purposes in different groups of trainees, such as bachelor-level students, master-level students, postgraduates, and teaching personnel. The project-based method means that students must attack a problem and propose some ways of solving it. This approach requires from each participant to work proactively, everyone makes his or her own contribution to and is responsible for the outcomes. As compared to conventional methods, this method ensures the high level of all participants' involvement into educational process. In the course of working on the project, students and trainees were proposed to fulfill independently or in teams some tasks within a strictly limited time span, which were aimed at describing and analyzing of a problem-based situation, assessing the state/level/depth of the problem, estimating the evidence and indicators of the problem status, reviewing the problem status according to actual data, and identifying the challenges and approaches to solving the problem. As a result of the activities completed, nine projects have been implemented in the groups of bachelor-level and master-level students on the topics interesting to both the University and the Republic in general. The bachelor-level students chose the following topics for their projects: Career Guidance for Students; Teaching Children to Love Nature and Animals; Communication without Borders; Living with Everyone; and Student Slang Dictionary. Master-level students developed the following projects: Water-Jet Cutting Fire-Control Unit; Naushochketka (A Safety Hat Based on a Protective Cap); Website Adapted for Foreign Students www.cityguest.help; Waste Compactor; and A Heated Scraping Brush for Snow/Ice Car Clearing. Upon completing the lessons with the invited professor, a poll was taken among students that showed that 80 % of the respondents had liked to work on their project. 74 % pointed to their keen interest to working inn a team. 78 % of students are sure that they will further use the knowledge they have gained when working on their project. Everyone pointed to the usefulness of lessons given by a native English speaker. Certain problems occurred in mastering the problem-based method by faculty members, since they had preferred a systematic real-life communication with a native English speaker to the hard work on projects. The professor also used the project-based teaching method for one-time "exchange" lessons with students studying law in English. Questioning these students demonstrated the usefulness of this method even when applied in such a manner. Using project-based teaching/learning implements the principles of interactivity, performance, independence, and students' involvement in the educational process, contributes to training the key competences of future professionals, and essentially activates the students' mental activities regardless of the levels of their preceding preparation or motivation.


Extraction of information and facts from data mining of random sequences for undergraduate research

Abstract: Abstract A general method to extract information and facts from data mining of random sequences in biology and astronomy has been developed. The random sequence analysis has been implemented in several NSF-REU projects using NIH and NASA databases. Examples of RNA sequence with reference to Shannon-entropy based bioinformatics and SDO magnetic topology analysis with reference to solar physics are presented. The contrast to Brookhaven Synchrotron and high energy physics data analysis is also discussed. The feedback of administrating REU projects to our physics teaching for engineering students was found to be valuable and the examples of magnetic reconnection and geomagnetic induced current are presented. Therefore community college REU project provides connectedness awareness in the linking of previous published reports, critical thinking in result interpretation, and career development when going onto a senior college REU program, the top three benefits of college education, according to a 2016 July Money Magazine "Value of College" survey.


Sparse Observation Assimilated Digital Image Correlation for High Fidelity Deformation Field Reconstruction

Abstract: Digital image correlation (DIC) is an image-based deformation measurement method, which has been widely used in multiple cutting-edge fields, benefitted from its noncontact, low cost and high robustness. However, achieving higher accuracy and overcoming the tradeoff between resolution and measurement uncertainty have been recognized as bottleneck problems for DIC. This paper proposes a new method by assimilating sparse observations into DIC, constituting “Assimilated DIC”, to reconstruct deformation fields with high fidelity. In the proposed method, the subpixel registration process in the traditional DIC procedure is viewed as a dynamic model, which constitutes the basis for assimilation. Meanwhile, the sparse observation provides highly accurate guidance for correcting the dynamic model, thus facilitating the update of model data towards the ground-truth. In addition to effectively improving the measurement accuracy without lowering the computational efficiency, Assimilated DIC can obtain a higher resolution without compromising the accuracy performance. And the boundary regions of multi-connected structures, which are generally subject to large errors by traditional methods, can be precisely reconstructed by Assimilated DIC. In this work, the proposed method with two types of observations on strain and displacement has been experimentally validated under various deformation cases, including a rigid-body translation, a heterogeneous deformation, and an extension of a holed plate. These excellent properties of Assimilated DIC have been confirmed in comparison with those of a traditional DIC method.


AYOLOv5: Improved YOLOv5 based on attention mechanism for blood cell detection

Abstract: A crucial component of biological study and clinical trials is the microscopic examination of cells and tissues. Although tremendous progress has been made in computer-assisted microscopy cell detection techniques, especially recently based on deep learning, it still needs to be determined to accurately identify cell targets in the presence of dense and complex cell distribution. In this research, an improved YOLOv5 (AYOLOv5) based on the attention mechanism is suggested to address the issue of the low recognition rate of cell detection caused by this circumstance. Based on YOLOv5, the attention mechanism is introduced to improve the convolutional neural network's features in areas of the picture with a high density of features. The convolutional block attention module (CBAM) and the transformer encoder block are used in this study to develop the attention mechanism. The YOLOv5 integrated convolutional block attention module increases the weight of cell-dense regions in blood cell pictures and aids the network's ability to resist information other than cells. Additionally, the transformer block is introduced to YOLOv5′s processing of upper and lower feature data to improve the network's capacity to gather details about various cell properties, enabling AYOLOv5 to recognize and distinguish blood cells in cell-dense areas. The experiment was done on the dataset BCCD, and the mAP results for cell detection reached 93.3%, better than previously discovered. Also, the validation set's average recognition accuracy increased from 89% to 98%. The experimental results demonstrated that the suggested AYOLOv5 could extract the cells' feature information more effectively, considerably improving the cell pictures and recognition performance.


Beyond the <i>AJR</i>: Applying Screening Algorithms (Artificially) Intelligently

Abstract: Commentary on Landy R, Wang VL, Baldwin DR, et al. Recalibration of a deep learning model for low-dose computed tomographic images to inform lung cancer screening intervals. JAMA Netw Open 2023; 6:e233273; https://doi.org/10.1001/jamanetworkopen.2023.3273. Abstract available at pubmed.ncbi.nlm.nih.gov/36929398/


Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) With Diverse Inter-Correlations and Its Application to Medical Image Classification

Abstract: In real applications, one object (e.g., image) can be described by multiple instances (e.g., image patches) and simultaneously associated with multiple labels. Such applications can be formulated as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-instance multi-label learning (MIML) problems and have been extensively studied currently. Existing MIML methods are useful in many applications but most of which suffer from relatively low accuracy and training efficiency due to: i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the inter-label correlations are neglected; ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the inter-instance correlations cannot be learned directly with other types of correlations due to the missing instance labels; iii) diverse inter-correlations (e.g., inter-label correlations, inter-instance correlations) can only be learned in multiple stages. To resolve these issues, a new single-stage framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">broad multi-instance multi-label learning (BMIML) is proposed. In BMIML, there are three innovative modules: i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">auto-weighted label enhancement learning (AWLEL) based on broad learning system (BLS) is designed, which simultaneously captures the inter-label correlations while traditional BLS cannot; ii) A specific MIML neural network, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scalable multi-instance probabilistic regression (SMIPR) is constructed to effectively estimate the inter-instance correlations, which can provide additional probabilistic information for learning; iii) Finally, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interactive decision optimization (IDO) is designed to combine and optimize the results from AWLEL and SMIPR and form a single-stage framework. Consequently, BMIML can achieve simultaneous learning of diverse inter-correlations between whole images, instances, and labels in single stage for higher classification accuracy and much faster training time. Experiments show that BMIML is highly competitive to (or even better than) existing methods in accuracy and much faster.


Unknown Threats Detection Methods of Smart Contracts

Abstract: With the explosive growth of blockchain platforms and applications, security threats of blockchain also occur frequently. As a decentralized application deployed on the blockchain, smart contracts help the blockchain realize safe and efficient information storage, asset management, and value transfer. Therefore, smart contracts play a vital role in the security of the blockchain. In recent years, security threats against smart contracts have increased, not only causing huge economic losses but also impacting the credit system of the blockchain. Therefore, many researchers have carried out corresponding research on the security threats of smart contracts. Common threat detection methods include formal verification, symbolic execution, fuzzing, etc. Most of these methods are only for known threats, while there is not much work on detecting unknown threats. In order to better deal with unknown threats, we present a review of the typical smart contract security events in recent years, analyze the security threats from contract coding, Ethereum virtual machine, and blockchain characteristics. Further, we compare and summarize the latest unknown threat detection methods. Then, to address the problem that very few unknown threat samples are available, a detection method based on a few-shot learning is proposed.


Semantic Correlation Attention-Based Multiorder Multiscale Feature Fusion Network for Human Motion Prediction

Abstract: Human motion prediction is to predict future human states based on the observed human states. However, current research ignores the semantic correlations between body parts (joints and bones) in the observed human states and motion time; thus, the prediction accuracy is limited. To address this issue, we propose a novel semantic correlation attention-based multiorder multiscale feature fusion network (SCAFF), which includes an encoder and a decoder. In the encoder, a multiorder difference calculation module (MODC) is designed to calculate the multiorder difference information of joint and bone attributes in the observed human states. Then, multiple semantic correlation attention-based graph calculation operators (SCA-GCOs) are stacked to extract the multiscale features of the multiorder difference information. Each SCA-GCO captures joint and bone dependencies of the multiorder difference information, refines them with a semantic correlation attention module (SCAM), and captures temporal dynamics of the refined joint and bone dependencies as the output features. Note that SCAM learns a semantic attention mask describing the semantic correlations between body parts and motion time for feature refinement. Afterward, multiple multiorder feature fusion modules (MOFFs) and multiscale feature fusion modules (MSFFs) are designed to fuse the multiscale features of the multiorder difference information extracted by multiple SCA-GCOs, thus obtaining the motion features of the observed human states. Based on the obtained motion features, the decoder recurrently recruits a composite gated recurrent module (CGRM) and multilayer perceptrons (MLPs) to predict future human states. As far as we know, this is the first attempt to consider the semantic correlations between body parts and motion time in human motion prediction. The results on public datasets demonstrate that SCAFF outperforms existing models.


Parameterized Convex Universal Approximators for Decision-Making Problems

Abstract: Parameterized max-affine (PMA) and parameterized log-sum-exp (PLSE) networks are proposed for general decision-making problems.The proposed approximators generalize existing convex approximators, namely, max-affine (MA) and log-sum-exp (LSE) networks, by considering function arguments of condition and decision variables and replacing the network parameters of MA and LSE networks with continuous functions with respect to the condition variable.The universal approximation theorem of PMA and PLSE is proven, which implies that PMA and PLSE are shape-preserving universal approximators for parameterized convex continuous functions.Practical guidelines for incorporating deep neural networks within PMA and PLSE networks are provided.A numerical simulation is performed to demonstrate the performance of the proposed approximators.The simulation results support that PLSE outperforms other existing approximators in terms of minimizer and optimal value errors with scalable and efficient computation for high-dimensional cases.


The <i>Transform-and-Perform</i> Framework: Explainable Deep Learning Beyond Classification

Abstract: In recent years, visual analytics (VA) has shown promise in alleviating the challenges of interpreting black-box deep learning (DL) models. While the focus of VA for explainable DL has been mainly on classification problems, DL is gaining popularity in high-dimensional-to-high-dimensional (H-H) problems such as image-to-image translation. In contrast to classification, H-H problems have no explicit instance groups or classes to study. Each output is continuous, high-dimensional, and changes in an unknown non-linear manner with changes in the input. These unknown relations between the input, model and output necessitate the user to analyze them in conjunction, leveraging symmetries between them. Since classification tasks do not exhibit some of these challenges, most existing VA systems and frameworks allow limited control of the components required to analyze models beyond classification. Hence, we identify the need for and present a unified conceptual framework, the Transform-and-Perform framework (T&P), to facilitate the design of VA systems for DL model analysis focusing on H-H problems. T&P provides a checklist to structure and identify workflows and analysis strategies to design new VA systems, and understand existing ones to uncover potential gaps for improvements. The goal is to aid the creation of effective VA systems that support the structuring of model understanding and identifying actionable insights for model improvements. We highlight the growing need for new frameworks like T&P with a real-world image-to-image translation application. We illustrate how T&P effectively supports the understanding and identification of potential gaps in existing VA systems.


CGOF++: Controllable 3D Face Synthesis With Conditional Generative Occupancy Fields

Abstract: Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e.g., controlling the shapes, expressions, textures, and poses of the generated face images. However, previous methods focus on controllable 2D image generative models, which are prone to producing inconsistent face images under large expression and pose changes. In this paper, we propose a new NeRF-based conditional 3D face synthesis framework, which enables 3D controllability over the generated face images by imposing explicit 3D conditions from 3D face priors. At its core is a conditional Generative Occupancy Field (cGOF++) that effectively enforces the shape of the generated face to conform to a given 3D Morphable Model (3DMM) mesh, built on top of EG3D [1], a recent tri-plane-based generative model. To achieve accurate control over fine-grained 3D face shapes of the synthesized images, we additionally incorporate a 3D landmark loss as well as a volume warping loss into our synthesis framework. Experiments validate the effectiveness of the proposed method, which is able to generate high-fidelity face images and shows more precise 3D controllability than state-of-the-art 2D-based controllable face synthesis methods.


Secure Task Distribution With Verifiable Re-Encryption in Mobile-Crowdsensing-Assisted Emergency IoT System

Abstract: Extreme events (such as earthquakes, hurricanes, etc.) pose a dual challenge to the reliability and serviceability of IoT systems. With regard to this challenge, by publishing some tasks and then encouraging the public to assist in real-time data collection through their mobile terminals (namely, the mobile crowdsourcing-assisted IoT systems), is expected to play an important role in secondary disaster prevention and personnel rescue in extreme events. However, it has weaknesses in terms of security, flexibility, and efficiency. As an elegant solution, identity-based broadcast proxy re-encryption (PR-IBBE) enables flexible access authorization sharing and efficient broadcast distribution of encrypted tasks via the cloud. However, their security relies on fully-trusted or semi-trusted cloud assumptions, which are hard to be implemented in real-world scenarios. And the cloud is more vulnerable in an emergency event since there is a lack of effective management. Motivated by that, we propose the verifiable PR-IBBE (VPR-IBBE) scheme, which realizes a cross-domain identity-based broadcast task file secure authorization access, and empowers the verifiability and reputability of re-encrypted ciphertext under the untrusted cloud setting. This mechanism ensures that the relevance between the re-encrypted ciphertext and the original ciphertext can be publically verified, so the cloud can defend itself if there is a malicious accusation of forging the re-encrypted ciphertext. Through rigorous formal security proofs, we demonstrate that VPR-IBBE attains the indistinguishability of ciphertext against selective identity chosen ciphertext attack (IND-sID-CPA), and is also resistant to the collusion attack between the untrusted cloud and the cooperative performer. Theoretical comparison and experimental results demonstrate the practicability of our VPR-IBBE scheme, as well as the superiority over representative related works.


Parallel Self-Attention and Spatial-Attention Fusion for Human Pose Estimation and Running Movement Recognition

Abstract: Human pose estimation (HPE) is a fundamental yet promising visual recognition problem. Existing popular methods (e.g., Hourglass and its variants) either attempt to directly add local features element-wisely, or (e.g., vision transformers) try to learn the global relationships among different human parts. However, it remains an open problem to effectively integrate the local-global representations for accurate HPE. In this work, we design four feature fusion strategies on the hierarchical ResNet structure, including direct channel concatenation, element-wise addition and two parallel structures. Both two parallel structures adopt the naive self-attention encoder to model global dependencies. The difference between them is that one adopts the original ResNet BottleNeck while the other employs a spatial-attention module (named SSF) to learn the local patterns. Experiments on COCO Keypoint 2017 show that our SSF for HPE (named SSPose) achieves the best average precision with acceptable computational cost among the compared state-of-the-art methods. In addition, we build a lightweight running dataset to verify the effectiveness of SSPose. Based solely on the keypoints estimated by our SSPose, we propose a regression model to identify valid running movements without training any other classifiers. Our source codes and running dataset are publicly available.


Artificial Intelligence and Pathomics

Abstract: Artificial intelligence (AI) has the potential to transform pathologic diagnosis and cancer patient management as a predictive and prognostic biomarker. AI-based systems can be used to examine digitally scanned histopathology slides and differentiate benign from malignant cells and low from high grade. Deep learning models can analyze patient data from individual or multimodal combinations and identify patterns to be used to predict the response to different therapeutic options, the risk of recurrence or progression, and the prognosis of the newly diagnosed patient. AI-based models will improve treatment planning for patients with prostate cancer and improve the efficiency and cost-effectiveness of the pathology laboratory.


Federated Learning and NFT-Based Privacy-Preserving Medical-Data-Sharing Scheme for Intelligent Diagnosis in Smart Healthcare

Abstract: Historical medical data of the patients have an important place in the healthcare industry for providing the best care to patients through intelligent health diagnosis and prediction of diseases. The existing intelligent health diagnosis systems collect data from medical institutions or laboratories and then use machine learning algorithms to predict diseases. But, in most cases, the medical institutions have incomplete medical data of the patients since a patient may consult different specialists (from various hospitals) during the treatment process. To overcome this problem, we build a smart and secure federated learning framework for intelligent health diagnosis with a blockchain-based incentive mechanism and NFT(Non-Fungible Tokens)-based marketplace. We make use of NFTs to develop clear demarkations on the ownership and accessibility of the data of patients. We create an NFT marketplace that manages access to the historical medical data of patients. A comprehensive incentive mechanism based on several factors, including the quality and relevance of the data, the frequency, and regularity of data uploading, etc., is incorporated to encourage and penalize the patients based on their contributions to the global model. We used the Polyak-averaging technique for aggregating local models to form a global model. The extensive analysis shows that the proposed model achieves comparable performance with the centralized machine learning models while affording better security and access to better data. The results also show the efficacy of the proposed blockchain-based incentive mechanism.


Fedcs: Efficient communication scheduling in decentralized federated learning

Abstract: Decentralized federated learning is a training approach that prioritizes user data privacy protection, while also offering improved scalability and robustness. However, as the number of edge devices participating in training increases, a significant communication overhead arises among devices located in different geographical locations. Therefore, designing a well-thought-out gradient synchronization strategy is crucial for minimizing the overall communication overhead of training. To tackle this issue, this article introduces a 2D-Ring network structure based parameter synchronization strategy and a 2D-attention-based device placement algorithm, aiming to minimize communication overhead. The parameter synchronization strategy devises a two-layer circular communication architecture for the devices involved in training, thereby reducing the overall frequency of parameter synchronization in decentralized federated learning. By taking into account the total communication overhead and the device placement strategy, an optimization problem is formulated. Specifically, a 2D-attention neural network is constructed to optimize the device placement solution based on 2D-Ring network structure, leading to reduced communication overhead. Moreover, an evaluation model is designed to assess the communication overhead in a complex decentralized system during federated training. This enables precise determination of the total communication overhead throughout the training process, providing valuable insights for devising the device placement strategy. Extensive simulations confirm that the proposed approach achieves a substantial reductions of 55% and 64% in the total communication overhead for decentralized federated learning training with 50 and 100 devices, respectively.


Towards Scalable Multi-View Clustering via Joint Learning of Many Bipartite Graphs

Abstract: This paper focuses on two limitations to previous multi-view clustering approaches. First, they frequently suffer from quadratic or cubic computational complexity, which restricts their feasibility for large-scale datasets. Second, they often rely on a single graph on each view, yet lack the ability to jointly explore many versatile graph structures for enhanced multi-view information exploration. In light of this, this paper presents a new Scalable Multi-view Clustering via Many Bipartite graphs (SMCMB) approach, which is capable of jointly learning and fusing many bipartite graphs from multiple views while maintaining high efficiency for very large-scale datasets. Different from the one-anchor-set-per-view paradigm, we first produce multiple diversified anchor sets on each view and thus obtain many anchor sets on multiple views, based on which the anchor-based subspace representation learning is enforced and many bipartite graphs are simultaneously learned. Then these bipartite graphs are efficiently partitioned to produce the base clusterings, which are further re-formulated into a unified bipartite graph for the final clustering. Note that SMCMB has almost linear time and space complexity. Extensive experiments on twenty general-scale and large-scale multi-view datasets confirm its superiority in scalability and robustness over the state-of-the-art.


Combining variational mode decomposition with regularisation techniques to denoise MRI data

Abstract: In this paper, we propose a novel method for removing noise from MRI data by exploiting regularisation techniques combined with variational mode decomposition. Variational mode decomposition is a new decomposition technique for sparse decomposition of a 1D or 2D signal into a set of modes. In turn, regularisation is a method that can translate the ill-posed problem (e.g., image denoising) into a well-posed problem. The proposed method aims to remove the noise from the image in two steps. In the first step, the MR imaging data are decomposed by the 2D variational mode decomposition algorithm. In the second step, for effective suppression of Rician noise from MRI data, we used the fused lasso signal approximator with all modes acquired from the medical scan. The performance of the proposed approach was compared with state-of-the-art reference methods based on different metrics, that is, the peak signal-to-noise ratio, the structural similarity index metrics, the high-frequency error norm, the quality index based on local variance, and the sharpness index. The experiments were performed on the basis of both simulated and real images. The presented results prove the high denoising performance of the proposed algorithm; particularly under heavy noise conditions.


CT-Net: Asymmetric compound branch Transformer for medical image segmentation

Abstract: The Transformer architecture has been widely applied in the field of image segmentation due to its powerful ability to capture long-range dependencies. However, its ability to capture local features is relatively weak and it requires a large amount of data for training. Medical image segmentation tasks, on the other hand, demand high requirements for local features and are often applied to small datasets. Therefore, existing Transformer networks show a significant decrease in performance when applied directly to this task. To address these issues, we have designed a new medical image segmentation architecture called CT-Net. It effectively extracts local and global representations using an asymmetric asynchronous branch parallel structure, while reducing unnecessary computational costs. In addition, we propose a high-density information fusion strategy that efficiently fuses the features of two branches using a fusion module of only 0.05M. This strategy ensures high portability and provides conditions for directly applying transfer learning to solve dataset dependency issues. Finally, we have designed a parameter-adjustable multi-perceptive loss function for this architecture to optimize the training process from both pixel-level and global perspectives. We have tested this network on 5 different tasks with 9 datasets, and compared to SwinUNet, CT-Net improves the IoU by 7.3% and 1.8% on Glas and MoNuSeg datasets respectively. Moreover, compared to SwinUNet, the average DSC on the Synapse dataset is improved by 3.5%.


An Algorithm for Finding Optimal k-Core in Attribute Networks

Abstract: As a structural indicator of dense subgraphs, k-core has been widely used in community search due to its concise and efficient calculation. Many community search algorithms have been expanded on the basis of k-core. However, relevant algorithms often set k values based on empirical analysis of datasets or require users to input manually. Once users are not familiar with the graph network structure, they may miss the optimal solution due to an improper k setting. Especially in attribute social networks, characterizing communities with only k-cores may lead to a lack of semantic interpretability of communities. Consequently, this article proposes a method for identifying the optimal k-core with the greatest attribute score in the attribute social network as the target community. The difficulty of the problem is that the query needs to integrate both structural and textual indicators of the community while fully considering the diversity of attribute scoring functions. To effectively reduce computational costs, we incorporate the topological characteristics of the k-core and the attribute characteristics of entities to construct a hierarchical forest. It is worth noting that we name tree nodes in a way similar to pre-order traversal and can maintain the order of all tree nodes during the forest creation process. In such an attribute forest, it is possible to quickly locate the initial solution containing all query vertices and reuse intermediate results during the process of expanding queries. We conducted effectiveness and performance experiments on multiple real datasets. As the results show, attribute scoring functions are not monotonic, and the algorithm proposed in this paper can avoid scores falling into local optima. With the help of the attribute k-core forest, the actual query time of the Advanced algorithm has improved by two orders of magnitude compared to the BaseLine algorithm. In addition, the average F1 score of our target community has increased by 2.04 times and 26.57% compared to ACQ and SFEG, respectively.


Application of Metaverse as an Immersive Teaching Tool in Engineering Technology Classrooms

Abstract: Abstract Application of Metaverse as an Immersive Teaching Tool in Engineering Technology Classroom The last two years have presented an ever challenging classroom environment for both students and educators, especially when it comes to topics in engineering based on hands-on or case based learning requiring highly interactive class sessions. Online education platforms like Blackboard and Canvas have provided some level of classroom stability by providing a centralized repository for class content, delivery and administration, however whether this translates into a more engaging experience is relatively unknown, especially as classes have moved to a combination of online, hybrid, synchronous and asynchronous models. The Department of Engineering Technology and in particular the "Technology Innovations" class will leverage the metaverse environment in virtual reality (VR) and other highly immersive tools with students. Through the class we want to define a framework for education curriculum design and content delivery in a virtual and highly interactive environment. The development of the metaverse curriculum framework will aim to establish values and principles outlining the parameters for classroom methodology focused on leveraging both current and near future tools to deliver classroom education. Students will have the ability to take the course online through Blackboard and also have the ability to take the course in the metaverse using VR. The course content for both online and metaverse will be the same however the environment and the content delivery methods will be different to test out the level of engagement and knowledge transfer experienced by the student. It is expected, at the end of the course, students will have a valuable skill set in their ability to both learn, present and interact in the metaverse. Finally, as educators we will have an initial evaluation of classroom educational tools for the metaverse.


commonmeta-py now supports metadata lists

Abstract: This week the commonmeta-py Python library adds an important new feature: metadata lists. With this feature commonmeta-py no longer only operates on metadata for a single scholarly work (e.g. a journal article, book, dataset, software, or blog post), but can handle lists of scholarly works.


Impact of Blockchain Technology on Supply Chain Management Efficiency and Transparency in Pakistan

Abstract:
 Abstract
 Purpose: The aim of the study was to influence the impact of blockchain technology on supply chain management efficiency and transparency
 Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
 Findings: A study on the impact of blockchain technology on supply chain management in Pakistan revealed significant improvements in efficiency and transparency. Through blockchain integration, supply chain processes experienced reduced delays and increased automation, leading to streamlined operations. Transparency was notably enhanced, with real-time tracking of goods and secure data sharing among stakeholders, mitigating risks of fraud and error. These findings suggest that adopting blockchain technology holds promise for enhancing supply chain management in Pakistan by fostering greater efficiency and transparency.
 Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, information processing theory & resource dependency theory may be used to anchor future studies on the impact of block chain technology on supply chain management efficiency and transparency. Encourage supply chain stakeholders to adopt blockchain solutions for enhanced transparency, traceability, and efficiency. Advocate for regulatory frameworks that promote the adoption of blockchain technology in supply chains while addressing concerns related to data privacy, interoperability, and standardization.


A Technical Review of the State-of-the-Art Methods in Aspect-Based Sentiment Analysis

Abstract: With the advent and rapid advancement of text mining technology, a computer-based approach used to capture sentiment standpoints from data in textual form is increasingly becoming a promising field. Detailed information about sentiment can be provided using aspect-based sentiment analysis, which can be used in better decision-making. This study aims to study, observe, and classify previous methods used in aspect-based sentiment analysis. A systematic review is adopted as the method used to collect and review papers to achieve this research's aim. Papers focused on sentiment analysis, aspect extraction, and aspect aggregation from different academic databases such as Scopus, ScienceDirect, IEEE Explore, and Web of Science were gathered based on the inclusion and exclusion criteria of the study. The gathered papers were further reviewed to answer the stated research questions. The findings from the research show the most used methods for aspect extraction, sentiment analysis, and aspect aggregation in aspect-based sentiment analysis. This research offers a robust synthesis of evidence to guide further academic exploration in sentiment analysis.


Tin (Sn) Geochemical Mapping Based on a Fixed-Value Method: A Case Illustration in Gejiu Area, Southwest China

Abstract: Geochemical maps play an important role in mineral resource exploration. There are three traditional methods for creating geochemical maps: the cumulative frequency method, the logarithmic interval method, and the Avg±k*Std (where Avg and Std are the abbreviations of average and standard deviation, and k is a multiple of Std) method. However, with the increasing scope of the study area and cumulative data, the limitations of traditional methods, which depend on the amount of data, are exposed. A fixed-value method for Sn geological mapping is proposed to overcome the limitations of traditional methods. In the fixed-value method, Sn concentrations are divided into 19 levels on 18 fixed values ranging from 1 μg/g (corresponding to the detection limit) to 1000 μg/g (corresponding to the cut-off grade of Sn in hard rocks). The 19 levels are mapped in six color tones. The first to fifth levels are the lowest background areas in blue tones, which correspond to Sn concentrations ranging from the minimum to 3.4 μg/g. The sixth to ninth levels are high background areas in yellow tones corresponding to concentrations less than 10 μg/g, the 10th to 12th are low anomaly areas in pink tones less than 28 μg/g, the 13th to 15th are high anomaly areas in red tones less than 200 μg/g (corresponding to the placer cut-off grade), the 16th to 18th in gray tones less than 1000 μg/g, and the 19th level is in black corresponding to Sn ores with Sn concentration not less than 1000 μg/g. The fixed-value method along with three traditional methods was used to contour the Sn geochemical maps in the Gejiu area in Southwest China. The illustration results of the presented fixed-value method and three traditional methods for geochemical mapping of Sn are all feasible for Sn deposit exploration in the Gejiu area, Southwest China. Compared to traditional methods, the presented fixed-value method overcomes the flaws of traditional methods and is also more meaningful in geochemistry.


STR: Secure Computation on Additive Shares Using the Share-Transform-Reveal Strategy

Abstract: The rapid development of cloud computing has probably benefited each of us. However, the privacy risks brought by untrustworthy cloud servers arise the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks while ensuring the security of private data. The tasks to be outsourced are countless; however, the computations involved are similar. In this paper, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) in arbitrary $n\geq 2$ servers. All protocols only require constant rounds of interactions and achieve the low computation complexity. Moreover, the proposed $n$-party protocols ensure the security of private data even though $n-1$ servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols.


Adaptive Collaboration With Training Plan Considering Role Correlation

Abstract: Based on role-based collaboration (RBC), group role assignment (GRA) optimizes a team’s overall performance by assigning the most appropriate individual agents from the team’s viewpoint based on agents’ role-playing abilities. As an extension of GRA, GRA with a training plan (GRATP) deals with the impact of training on team management. Considering the correlation between roles, the training of one agent on one role also affects the performance of the agent in other roles. Moreover, in the adaptive collaboration (AC) problem, the training time also affects significantly the agent’s ability, as an agent’s ability changes over time. However, the existing GRATP models fail to consider these factors in the collaboration process. Therefore, we aim to address the role-correlation-based adaptive GRATP (RCA-GRATP) in this article. This article contributes two aspects to the literature on AC. 1) RCA-GRATP problem is abstracted based on RBC and GRA. To the best of the authors’ knowledge, this is the first article that explicitly considers role correlation in the RBC problems. 2) A comprehensive formalization of RCA-GRATP and two solving algorithms for diverse situations are proposed to solve the formalized problems. Experiments are carried out to verify the effectiveness of the proposed algorithms in diverse scenarios.


Implementation of Pseudo-Random Number Generator Using LFSR

Abstract: Abstract Linear Feedback Shift Registers (LFSR) play a vital role to provide cybersecurity for the data communication system. LFSR is a shift register where the input is linear of its previous states. The most important application of LFSR is the device's ability to generate a pseudo-random sequence of values that are used in encryption and decryption to secure personal data. Moreover, LFSR is used in Cyclic Redundancy Check Calculations (CRC) which can help to detect errors and corruption in data communications as LFSR feedback values can be modified. The main objective of the research is to generate the pseudo-random sequence of values in 3-bit, 4-bit, and 8-bit using the multiple registers with XOR as different taps selection. In the research, we used D(Data) flip flops and XOR alternative taps to identify the difference in the values for different arrangements in circuit diagrams using the software Quartus. After the simulation, we test the circuit on the Field Programmable Gate Array (FPGA) board to check the generated binary random number displaying the LED "on" or in "off" state. We would deeply work on CRC and use different approaches such as Verilog or VHDL code.


Virtual Engineering Summer Camp in the age of COVID-19 Pandemic

Abstract: Abstract COVID-19 pandemic greatly affected the learning experience of students. Prior to the COVID-19 pandemic, many engineering programs worldwide organized engineering summer camps for K-12 students in the summer of every year. These camps benefit K-12 students by providing a good insight into the introduction to engineering and also inducing intellectual curiosity in them through hands-on projects. Most of these sessions usually took place in a face to face setting versus the virtual mode of instruction. In summer 2020, the majority of the engineering summer camps were canceled due to the COVID-19 pandemic. The College of Engineering at the University of Texas Permian Basin organized the virtual engineering summer camp after shipping the required project kits to the students, thereby providing step-by-step instruction of performing the project virtually while also proving hands-on experience for better learning. To our knowledge, this mode of virtual summer camp that provided a hands-on experience to K-12 students was uniquely implemented in the nation by UTPB College of Engineering. This paper provides the ideas implemented to overcome several challenges, the techniques that rightly helped in conducting the virtual summer camp with hands-on experience, and methods to assess student learning.


Design and Development of Augmented Reality Engineering Expeditions - Innovations in Online Engineering Education

Abstract: Abstract This paper presents a framework for the design of augmented reality expeditions in engineering education settings to motivate student learning of engineering fundamentals and careers. The impact of social distancing has been significant for students of all ages and there is a great need for additional and creative online learning resources. The engineering field has an opportunity to not only motivate and support student learning, but to do so by featuring the very innovative nature of engineering as a tool to develop those experiences. A case study regarding the development of NASA-themed aeronautics virtual tours and virtual field trips highlights the capabilities of various software technologies and offers design considerations. The three developed virtual expeditions serve as an opportunity to engage and educate high school and university students. A research plan to collect student feedback on these experiences is also presented.


Outreach and Recruitment with 3D Printing and CAD

Abstract: Abstract Society has been changing faster every day to adapt to changes linked to economic, technological, environmental, and social shifts and uncertainties. In times of uncertainties, everything, including education, is affected negatively. To protect and support the education pipeline, educators, departments, and colleges should invest in recruitment and outreach activities more than ever before. For this purpose, at __________, various off-campus, on-campus, and on-line day-long programs, summer camps, certification programs, and other activities for both K-12 students and school teachers are designed and delivered around the concepts of "3D printing and Computer-Aided Design (CAD)". In this study, the activities will be presented in detail. Students' and teachers' attitudes towards the concepts and activities will be discussed. Lastly, the challenges encountered will be shared with suggested solutions.


Automating Project Team Formation with Heterogeneous Project Preferences and Skill Mix Constraints

Abstract: Abstract It is well established that design team composition is a significant driver of both team member engagement and design project success. Off-the-shelf solutions are available for automating the process of forming design teams with balanced skill mix and diversity profiles. These systems have been successfully applied both at our institution and others to improve student engagement and team performance in design projects with homogenous project opportunities and skill mix requirements (i.e. when forming multiple teams of similar composition to address a common design challenge). However, it has proven challenging to apply available off-the-shelf solutions to form design teams in our Capstone Design program due to heterogeneity in both project opportunities (each project team addresses a unique design challenge) and required skill mixes (which vary by project opportunity). In this paper, we discuss the development and application of a heuristic optimization algorithm that extends the capabilities of existing off-the-shelf project team formation systems, which effectively balance team sizes and diversity profiles within teams, to incorporate students' individual project preferences and project-specific skill mix constraints. Comparing results of applying this algorithm for Capstone Design project team formation with results of applying off-the-shelf solutions for team formation in other design courses with homogenous project opportunities and skill mix requirements, we find that our algorithm satisfies project-specific skill mix constraints and effectively matches students to preferred project opportunities (consistently matching more than 5/6 of students on one of their Top 3 project choices) while offering comparable performance to off-the-shelf project team formation systems in terms of balancing team sizes and diversity profiles within teams. Consistent with findings in prior research, our data indicate that balancing the sizes and diversity of project teams favorably impacts team performance (measured by scores on course assignments) and student engagement (measured by peer evaluations). We are seeking additional insights into these phenomena by incorporating new descriptive variables such as students' project preferences in analyses of Capstone Design project team performance.


Benefits of the virtual platform for K-12 STEM Outreach

Abstract: Abstract The number of students enrolling and graduating with STEM degrees in the Unites States must increase exponentially in order to meet the predicted job shortages in STEM. Perkins Peer Advisement is a grant funded program at College X committed to increasing enrollment and retention of female and nontraditional students in engineering technology programs. Program activities include professional development, mentoring, faculty development, and community outreach. Community outreach is a critical component of the program as it benefits the undergraduate students hosting the events as well as the K-12 students participating in them. Studies indicate that greater exposure to STEM at early levels contributes to narrowing the STEM gap. The COVID 19 pandemic has forced all aspects of society to transition to a virtual platform. The pandemic has forced children to stay home and has limited their physical ability to participate in field trips and lab experiments; however, today's technology allows for students to expand the opportunities accessible to them through a virtual world. This paper will focus on the transition of the program to a virtual platform and the successful expansion of the community outreach component as a result of this transition. The program offers monthly family STEM workshops at the local elementary school. In February, the program will host its annual Girl Day event in celebration of National Engineers Week. The 2020-2021 academic year was the first time these outreach events were held virtually. Although the transition presented a challenge at first, in the end there are many benefits to the virtual platform. This paper will provide a summary of the STEM Outreach activities and provide conference participants with a tool kit that can be applied at their home institutions and local communities.


Incorporating Information Literacy in MET Design Project: Year 2 Implementation

Abstract: Abstract This paper describes the authors' two-year effort to introduce and apply a project-based learning (PBL) pedagogy to a fluid mechanics course in our mechanical engineering technology program (MET). The authors continue efforts begun last year; a close collaboration between the engineering and education faculty and the engineering librarian with the goal of increasing the students' information literacy and developing their MET disciplinary competency. Students were required to work in teams to design a HVAC project using information literacy skills to identify relevant engineering codes and standards, such as the ASHRAE standards and codes. The project goals align with the new ABET criterion 3 (student outcomes 1-5) and criterion 5 (curriculum, content C, D and E). Benefits, limitations, and future directions are also discussed.


Power detection over number fields

Abstract: We describe an algorithm that, given an integral ideal <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="German upper A"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="fraktur">A</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">\mathfrak {A}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> in a number field <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper K"> <mml:semantics> <mml:mi>K</mml:mi> <mml:annotation encoding="application/x-tex">K</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, finds the largest integer <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="k greater-than-or-slanted-equals 1"> <mml:semantics> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>⩾<!-- ⩾ --></mml:mo> <mml:mn>1</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">k\geqslant 1</mml:annotation> </mml:semantics> </mml:math> </inline-formula> such that <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="German upper A"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="fraktur">A</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">\mathfrak {A}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> is a <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="k"> <mml:semantics> <mml:mi>k</mml:mi> <mml:annotation encoding="application/x-tex">k</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-th power, and at the same time computes the ideal <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="German upper B"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="fraktur">B</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">\mathfrak {B}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> such that <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="German upper A equals German upper B Superscript k"> <mml:semantics> <mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="fraktur">A</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="fraktur">B</mml:mi> </mml:mrow> <mml:mi>k</mml:mi> </mml:msup> </mml:mrow> <mml:annotation encoding="application/x-tex">\mathfrak {A} = \mathfrak {B}^k</mml:annotation> </mml:semantics> </mml:math> </inline-formula>. This algorithm does not require the complete factorization of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="German upper A"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="fraktur">A</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">\mathfrak {A}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> into a product of prime ideals; given the maximal order <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="double-struck upper Z Subscript upper K"> <mml:semantics> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="double-struck">Z</mml:mi> </mml:mrow> <mml:mi>K</mml:mi> </mml:msub> <mml:annotation encoding="application/x-tex">\mathbb {Z}_K</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, we prove that it has polynomial time complexity. We apply this algorithm to the reduction of elements of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper K Superscript asterisk"> <mml:semantics> <mml:msup> <mml:mi>K</mml:mi> <mml:mo>∗<!-- ∗ --></mml:mo> </mml:msup> <mml:annotation encoding="application/x-tex">K^*</mml:annotation> </mml:semantics> </mml:math> </inline-formula> modulo <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="k"> <mml:semantics> <mml:mi>k</mml:mi> <mml:annotation encoding="application/x-tex">k</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-th powers.


M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion

Abstract: In federated learning (FL), the communication constraint between the remote clients and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be compressed so as to minimize the loss in accuracy resulting from the communication constraint. This paper proposes “M- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">magnitude weighted L2 distortion + 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">degrees of freedom ” (M22) algorithm, a rate-distortion inspired approach to gradient compression for federated training of deep neural networks (DNNs). In particular, we propose a family of distortion measures between the original gradient and the reconstruction we referred to as “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M -magnitude weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L 2 ” distortion, and we assume that gradient updates follow an i.i.d. distribution – generalized normal or Weibull, which have two degrees of freedom. In both the distortion measure and the gradient distribution, there is one free parameter for each that can be fitted as a function of the iteration number. Given a choice of gradient distribution and distortion measure, we design the quantizer to minimize the expected distortion in gradient reconstruction. To measure the gradient compression performance under a communication constraint, we define the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">per-bit accuracy as the optimal improvement in accuracy that one bit of communication brings to the centralized model over the training period. Using this performance measure, we systematically benchmark the choice of gradient distribution and distortion measure. We provide substantial insights on the role of these choices and argue that significant performance improvements can be attained using such a rate-distortion inspired compressor.


Human–Object Interaction detection via Global Context and Pairwise-level Fusion Features Integration

Abstract: Recent two-stage detector-based methods show superiority in Human-Object Interaction (HOI) detection along with the successful application of transformer. However, these methods are limited to extracting the global contextual features through instance-level attention without considering the perspective of human-object interaction pairs, and the fusion enhancement of interaction pair features lacks further exploration. The human-object interaction pairs guiding global context extraction relative to instance guiding global context extraction more fully utilize the semantics between human-object pairs, which helps HOI recognition. To this end, we propose a two-stage Global Context and Pairwise-level Fusion Features Integration Network (GFIN) for HOI detection. Specifically, the first stage employs an object detector for instance feature extraction. The second stage aims to capture the semantic-rich visual information through the proposed three modules, Global Contextual Feature Extraction Encoder (GCE), Pairwise Interaction Query Decoder (PID), and Human-Object Pairwise-level Attention Fusion Module (HOF). The GCE module intends to extract the global context memory by the proposed crossover-residual mechanism and then integrate it with the local instance memory from the DETR object detector. HOF utilizes the proposed pairwise-level attention mechanism to fuse and enhance the first stage’s multi-layer feature. PID outputs multi-label interaction recognition results with the input of the query sequence from HOF and the memory from GCE. Finally, comprehensive experiments conducted on HICO-DET and V-COCO datasets demonstrate that the proposed GFIN significantly outperforms the state-of-the-art methods.


Real-time mask-wearing detection in video streams using deep convolutional neural networks for face recognition

Abstract: This research aims to develop a real-time mask-wearing detection system using deep convolutional neural networks (CNNs). This is crucial in the coronavirus disease 2019 (COVID-19) pandemic to alert individuals who are not wearing masks early on, thereby reducing the spread of the virus. Since COVID-19 primarily spreads through respiratory droplets and mask-wearing is recommended, our proposed study utilizes computer vision techniques, specifically image processing, to detect masked and unmasked faces. We employ a customized CNN architecture consisting of five convolutional layers, followed by max-pooling layers and fully connected (FC) layers. The final output layer utilizes softmax activation for classification. The model is updated with optimized layer configurations and parameter values. We are developing an application that uses a digital camera as an input device. The application utilizes a dataset comprising 11,792 image samples, which are used for training and testing purposes with the 80:20 ratio. Real-time testing is conducted using human subjects captured by the camera. The experimental results demonstrate that the CNN method achieves a classification accuracy of 99% on the training data and 98.83% during real-time video testing. These findings suggest that the real-time mask detection system using CNN performs effectively.


Unavoidable social contagion of false memory from robots to humans.

Abstract: Many of us interact with voice- or text-based conversational agents daily, but these conversational agents may unintentionally retrieve misinformation from human knowledge databases, confabulate responses on their own, or purposefully spread disinformation for political purposes. Does such misinformation or disinformation become part of our memory to further misguide our decisions? If so, can we prevent humans from suffering such social contagion of false memory? Using a social contagion of memory paradigm, here, we precisely controlled a social robot as an example of these emerging conversational agents. In a series of two experiments (ΣN = 120), the social robot occasionally misinformed participants prior to a recognition memory task. We found that the robot was as powerful as humans at influencing others. Despite the supplied misinformation being emotion- and value-neutral and hence not intrinsically contagious and memorable, 77% of the socially misinformed words became the participants' false memory. To mitigate such social contagion of false memory, the robot also forewarned the participants about its reservation toward the misinformation. However, one-time forewarnings failed to reduce false memory contagion. Even relatively frequent, item-specific forewarnings could not prevent warned items from becoming false memory, although such forewarnings helped increase the participants' overall cautiousness. Therefore, we recommend designing conversational agents to, at best, avoid providing uncertain information or, at least, provide frequent forewarnings about potentially false information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


ACNPU: A 4.75TOPS/W 1080P@30FPS Super Resolution Accelerator With Decoupled Asymmetric Convolution

Abstract: Deep learning-driven superresolution (SR) outperforms traditional techniques but also faces the challenge of high complexity and memory bandwidth. This challenge leads many accelerators to opt for simpler and shallow models like FSRCNN, compromising performance for real-time needs, especially for resource-limited edge devices. This paper proposes an energy-efficient SR accelerator, ACNPU, to tackle this challenge. The ACNPU enhances image quality by 0.34dB with a 27-layer model, but needs 36% less complexity than FSRCNN, while maintaining a similar model size, with the decoupled asymmetric convolution and split-bypass structure. The hardware-friendly 17K-parameter model enables holistic model fusion instead of localized layer fusion to remove external DRAM access of intermediate feature maps. The on-chip memory bandwidth is further reduced with the input stationary flow and parallel-layer execution to reduce power consumption. Hardware is regular and easy to control to support different layers by processing elements (PEs) clusters with reconfigurable input and uniform data flow. The implementation in the 40 nm CMOS process consumes 2333 K gate counts and 198Â KB SRAMs. The ACNPU achieves 31.7 FPS and 124.4 FPS for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 2$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> scales Full-HD generation, respectively, which attains 4.75 TOPS/W energy efficiency.


Mixed-Frequency Signal Processing Implemented by a Multi-Terminal Electrolyte-Gated Transistor-Based Physical Reservoir

Abstract: Constructing highly compact and low hardware consuming multi-timescale physical reservoir is one of challenges in the hardware implementation of reservoir computing (RC) systems. However, state-of-the-art memristor-based reservoir layer based on ionic diffusion mechanisms suffers from relatively narrow temporal dynamic modulation window, limiting the multi-timescale feature extraction performance. In this letter, a proton-driven multi-terminal electrolyte-gated transistor (MTEGT) is proposed to obtain abundant reservoir states by ion-electron coupling through the multi-terminal modulation. The relaxation time of MTEGT ranges nearly two orders of magnitude (0.03 s to 1.0 s) within a 2.0 V bias voltage variation window (−1.2 V to 0.8 V). The MTEGT-based reservoir successfully modulates the amplitude of input signals at different frequencies. Moreover, a multiple superimposed oscillator task (MSO5 task) with five different frequencies is demonstrated with an ultralow normalized mean square error (NMSE) of 0.0009. This paves the way for the development of multi-timescale ion-controlled physical reservoirs with low hardware consumption and integration complexities.


High-Speed Nonlinear Circuit Macromodeling Using Hybrid-Module Clockwork Recurrent Neural Network

Abstract: In the computer-aided design (CAD) area, the recurrent neural network (RNN) has shown notable functionality in generating fast and high-performance models rather than the models in simulation tools. Predicting time sequences is a pervasive and challenging problem that may require identifying the dependencies between sequences that RNN is capable of performing. Despite all its features, conventional RNN still faces challenges such as limited accuracy and a large number of parameters. Therefore, we propose new macromodeling methods for nonlinear circuits called the Clockwork-RNN (CWRNN) and its hybrid version which is a more powerful but simpler implementation of a conventional RNN architecture with relatively little model complexity. In addition, CWRNN inherently models complex dependencies without the need for a large number of parameters. As a result, the computational cost is less than conventional RNN. Moreover, understanding and implementing the CWRNN is relatively simple and provides great flexibility in architectural configuration by introducing modules with several clock rates of exponents of 2. In addition to the above new modeling technique, we proposed the Hybrid-Module CWRNN as another new modeling method that utilizes modules of various exponents of different numbers resulting in further accuracy improvement of the CWRNN. Furthermore, the models obtained from the proposed techniques required much smaller simulation times compared to the current models used in simulation tools. Three nonlinear high-frequency examples have been utilized to verify the benefits of the proposed modeling methods.


Distributed Semi-Supervised Learning With Consensus Consistency on Edge Devices

Abstract: Distributed learning has been increasingly studied in edge computing, enabling edge devices to learn a model collaboratively without exchanging their private data. However, existing approaches assume the private data owned by edge devices are all labeled while the reality is that massive private data are unlabeled and remain to be utilized, which leads to suboptimal performance. To overcome this limitation, we study a new practical problem, Distributed Semi-Supervised Learning (DSSL), to learn models collaboratively with mixed private labeled and unlabeled data on each device. We also propose a novel method <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DistMatch that exploits private unlabeled data by self-training on each device with the help of models from neighboring devices. DistMatch generates pseudo-labels for unlabeled data by properly averaging the predictions of these received models. Furthermore, to avoid self-training with wrong pseudo-labels, DistMatch proposes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consensus consistency loss to filter pseudo-labels with high consensus and force the output of the trained model to be consistent with these pseudo-labels. Extensive evaluation results via our self-developed testbed indicate the proposed method outperforms all baselines on commonly used image classification benchmark datasets.


An improvement for CAST-128 encryption based on magic square and matrix inversion

Abstract: This paper presents two image encryption methods which aim to improve the CAST 128-bit algorithm by increasing the security level of encrypted images. The first improvement uses a magic square of order three, while the second improvement uses a 2×2 matrix over GF(P). Both modifications are used in each round of the CAST algorithm, in place of a standard algorithm which uses XOR to increase the correlation between the plaintext and ciphertext. Simulations are carried out in order to evaluate the image encryption system with regard to complexity, time consumption, histogram, information entropy, differential attacks, noise evaluation, adjacent pixels’ correlation index, National Institute of Standards and Technology (NIST) analyses, mean absolute error, and average difference. The experimental results demonstrate that the encryption and decryption time when using the proposed CAST 128-bit algorithm with magic square is less than the time required for the CAST 128-bit algorithm with the matrix. Conversely, the proposed CAST with the matrix is higher than the CAST with the magic square. Both theoretical analysis and experimental results confirm that the two proposed enhancements to CAST perform effectively with sufficient security levels.


Improving sentiment analysis using text network features within different machine learning algorithms

Abstract: Sentiment analysis poses a significant challenge due to the inherent subjectivity of natural language and the prevalence of unstandardized dialects in social networks. Regrettably, existing literature lacks a dedicated focus on network representation learning for sentiment classification. This paper addresses this gap by investigating ten machine learning algorithms, including support vector machine (SVM), random forest (RF), logistic regression (LR), and Naive Bayes (NB). Our approach integrates text network analysis and sentiment analysis to propose a comprehensive solution. We begin by applying text preprocessing techniques and converting a text corpus into a text network using word co-occurrence. Subsequently, we employ network analysis techniques to extract features based on network topology and node attributes. These network-derived features serve as inputs for sentiment prediction on Yelp reviews. Through the incorporation of diverse text network features and various machine learning algorithms, we achieve significant enhancements in sentiment classification performance. Our evaluation demonstrates an improved area under curve (AUC) of 83% on the Yelp reviews corpus, underscoring the efficacy of integrating network features to enhance sentiment classifiers. This research underscores the critical role of network representation and its potential impact on sentiment analysis, highlighting the prospect of harnessing network features for sentiment classification tasks.


A deep learning-based intelligent decision-making model for tumor and cancer cell identification

Abstract: In the current era, the prevalence of common ailments is leading to an increasing number of fatalities. Various infections, viruses, and other pathogens can cause these illnesses. Some illnesses can give rise to tumors that seriously threaten human health. Distinct forms of tumors exist, including benign, premalignant, and malignant, with cancer being present only in malignant forms. Deep learning (DL) algorithms have emerged as one of the most promising methods for detecting cancers within the human body. However, existing models face criticism for their limitations, such as lack of support for large datasets, and reliance on a limited number of attributes from input images. To address these limitations and enable efficient cancer detection throughout the human body, an intelligent decision-making approach model (IDMA) is proposed. The IDMA is combined with the pre-trained VGG19 for improved training. The IDMA analyses convolutional neural network (CNN) layer images for signs of malignancy and rules out false positives. Various performance indicators, like sensitivity, precision, recall, and F1-score, are used to assess the system's performance. The suggested system has been evaluated and proven to outperform similar current systems, achieving an impressive 98.67% accuracy in detecting cancer cells.


Evaluation of domain sulfur industry for DIA translator using bilingual evaluation understudy method

Abstract: Evaluation is important part of our system development cycle; it also contributes to improving new machine translation (MT) technology optimum via comparing them with the traditional systems available to determine the weaknesses and the effectiveness to be improved in the proposed MT system. This work aiming to make a study that evaluate the performance and effectivness of the domain sulfur industry (DSI) for English-Arabic DIA translator quality. The recent study has conducted evaluating by making a comparison between this programme with the prominent Google translator through applying a rendering of 1,200 English sentences in bilingual evaluation understudy (BLUE) method. The obtain results show that the efficiency of Google translator is about 30.325%, while DIA translator efficiency in domain sulfur industry is about 73.325% and it’s more effective and give a better translation accuracy. The BLUE method efficiency is about (90.478%) compared with the human expert evaluator.


S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking With Adaptive Spatio-Temporal Appearance Representations

Abstract: Following the tracking-by-attention paradigm, this paper introduces an object-centric, transformer-based framework for tracking in 3D. Traditional model-based tracking approaches incorporate the geometric effect of object- and ego motion between frames with a geometric motion model. Inspired by this, we propose STAR-TRACK which uses a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">latent motion model (LMM) to additionally adjust object queries to account for changes in viewing direction and lighting conditions directly in the latent space, while still modeling the geometric motion explicitly. Combined with a novel learnable track embedding that aids in modeling the existence probability of tracks, this results in a generic tracking framework that can be integrated with any query-based detector. Extensive experiments on the nuScenes benchmark demonstrate the benefits of our approach, showing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">state-of-the-art (SOTA) performance for DETR3D-based trackers while drastically reducing the number of identity switches of tracks at the same time.


Hierarchical Task Model Predictive Control for Sequential Mobile Manipulation Tasks

Abstract: Mobile manipulators are envisioned to serve more complex roles in people's everyday lives. With recent breakthroughs in large language models, task planners have become better at translating human verbal instructions into a sequence of tasks. However, there is still a need for a decision-making algorithm that can seamlessly interface with the high-level task planner to carry out the sequence of tasks efficiently. In this work, building on the idea of nonlinear lexicographic optimization, we propose a novel Hierarchical-Task Model Predictive Control framework that is able to complete sequential tasks with improved performance and reactivity by effectively leveraging the robot's redundancy. Compared to the state-of-the-art task-prioritized inverse kinematic control method, our approach has improved hierarchical trajectory tracking performance by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {42\%}$</tex-math></inline-formula> on average when facing task changes, robot singularity and reference variations. Compared to a typical single-task architecture, our proposed hierarchical task control architecture enables the robot to traverse a shorter path in task space and achieves an execution time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {2.3}$</tex-math></inline-formula> times faster when executing a sequence of delivery tasks. We demonstrated the results with real-world experiments on a 9 degrees of freedom mobile manipulator.


Teachers’ digital literacy overview in secondary school

Abstract: This study explored the teachers’ digital literacy in secondary school and deciphered the most suggested way of enhancing secondary teachers’ digital literacy, overview levels, and the trends factors in the last five years of research. The methodologies in this study employed the traditional systematic literature review (SLR), which compiled some previous studies to be examined. The initial studies were only selected from an article published in a reputable journal indexed by Scopus. Of the 118 articles reviewed, 46 were selected to be included in the analysis. Significant findings summarize the fact that most of the last five-year research about teachers in secondary schools was impacted by the rapid development of technology and the demand for 21st-century skills. To summarize, as it is at the intermediate level, most secondary school teachers are digitally literate. However, future research may evaluate digital literacy training programs for teachers to assess what improvements should be made to delivery as well as existing frameworks used to test teachers’ digital literacy.