Social networks as an effective higher education institution promotion tool in the Republic of Armenia

Abstract: Relevance. The research relevance is determined by the modern context, in which Internet marketing is crucial for the promotion of any product, including educational resources. Purpose. The study aims to provide a detailed review and analysis of marketing strategies aimed at using social media, in particular Instagram, to improve the promotion of higher education institutions. Methodology. The following scientific research methods were used: comparative analysis, systematisation, and survey. Results. The study reveals the role and significance of social media in the Armenian educational system and considers the latter as an effective tool for promoting Internet marketing and educational resources. The study highlights social networks as a platform for the use of hidden and viral Internet marketing methods. Higher education institution activity on the Instagram platform, namely the European University of Armenia and the American University of Armenia, has been studied. The study determined that the effective use of social media can have a positive impact on the reputation and attractiveness of higher education institutions. The study has shown that awareness of these aspects of media literacy is an important factor in increasing interaction with the audience and creating a positive image of the university, which is of particular importance in the context of attracting potential students and their parents. Conclusions. The study conducted a survey of applicants to higher education institutions in Yerevan, which confirms that social media is an important tool for promoting universities, facilitating active participation of prospective students, creating an attractive image and opportunities for further development of marketing strategies. The results obtained can be used to develop programmes for the promotion of higher education institutions of the Republic of Armenia on social media, helping to increase their popularity and attract the attention of new students.


Revolutionizing Employee Well-being and Performance: Personalized Emotional Chatbots for Banking Professionals in Tamil Nadu

Abstract: In this article, we delve into personalized emotional chatbots and their potential to enhance the well-being and performance of banking employees in Tamil Nadu. With the banking sector in the region experiencing heightened levels of strain, it is imperative to consider the impact of employee stress and burnout. By harnessing innovative technologies and psychological principles, personalized emotional chatbots offer customized support to mitigate these hurdles. Through carefully examining relevant research and real-life examples, this article illuminates the advantages and possible implementations of these chatbots in the banking industry, ultimately promoting a more positive work culture and increased job satisfaction.


Supporting the Development of Classroom Reading Corner Program at Madrasah Ibtidaiyah Muhammadiyah 01 Pekanbaru Title

Abstract: The habit of reading should be introduced to children at an early age for them to continue reading throughout their lives. A well-planned and facilitated program for habituating readers is therefore necessary. This Community Service activities aim to provide assistance for the development of the Class Reading Corner Program at Madrasah Ibtidaiyah Muhammadiyah 01 Pekanbaru. Following a review of the school's problems regarding the lack of reading corners, a limited number of children's reading books, and the lack of governance of class reading corners, these activities were carried out based on the results of an analysis of these problems. As part of this program, 48 sets of children's picture books were handed over, including mini bookshelves. The program also socialized development of reading corner program and made a simulation of making picture books involving 14 teachers, 1 administrative staff, and 1 headmaster. This program employed a direct observation and questionnaire for the evaluation. The observation showed that three classroom reading corners are present and in use at the school. A satisfaction level of 89.6% was obtained from activity participants after analyzing the questionnaire, an increase of 29.20% over that obtained prior to socialization and simulation. Furthermore, 93.75 % of the participants wanted a reading corner and 100% indicated that there was a need to enhance or improve its quality. The results from these studies suggest that this mentoring program has achieved a positive effect by raising awareness of the importance of increased reading proficiency in schools.


The Design of a C1 Document Data Extraction Application Using a Tesseract-Optical Character Recognition Engine

Abstract: The 2019 election process used the Vote Counting Information System, also known as Sistem Informasi Penghitungan Suara (Situng), to provide transparency in the recapitulation process. The data displayed in Situng is from document C1 for 813,336 voting stations in Indonesia. The data collected from the C1 document is entered and uploaded into Situng by the officers of the Municipal General Election Commission (GEC). Since this process is performed by humans, it is not immune to errors. In the recapitulation process of the 2019 election results, there were 269 data entry errors, and the data entry process also did not run according to the specified target, resulting in delays. Furthermore, there were cases of C1 document modification, raising concerns about the data's authenticity. To avoid human errors and increase data entry speed, automatic data entry is a plausible option. The data entered are text data in image documents with the same template format, so that optical character recognition (OCR) can be used to read the text while improving image quality and alignment, resulting in a more accurate OCR reading area. In this study, we developed a C1 document data extraction application using the waterfall SDLC method, which has undergone a systematic and thorough process. The application was developed using Tesseract optical character recognition. Tesseract is an open-source OCR engine and command-line program that allows for the recognition of text characters within a digital image. The accuracy obtained by using this method is still not optimal as a substitute for Situng's data entry officer. To guarantee the integrity of the C1 document, we use the RSA-2048 digital signature scheme. The use of the Tesseract-OCR Engine for character recognition, combined with digital signature capabilities, provides a comprehensive solution to reduce the human error factor that can lead to miscalculations and inaccurate processes.


Development of an Early Warning System Using Social Media for Flood Disaster

Abstract: This research paper introduces an innovative prototype system that uses IoT technologies to monitor floodwater levels. Integration of an ultrasonic sensor, ESP8266 microcontroller, Arduino IDE, and the ThingSpeak platform aims to establish a robust flood monitoring solution. The paper provides a thorough exploration of the system's background, the problem it addresses, the methodology employed, and the obtained results, along with insights into future research directions. The study meticulously describes the design, implementation and programming code for data collection and transmission within the system. Through extensive field testing and meticulous data analysis, the paper evaluates the precision and effectiveness of the proposed flood monitoring solution. In particular, research underscores the advantages of IoT, emphasizing real-time data collection, logging, and analysis as essential components for efficient flood management. Additionally, the paper elucidates step-by-step instructions for configuring Telegram notifications through the ThingSpeak React app, enhancing the practical applicability of the developed system. The research effectively highlights the potential of IoT in flood monitoring, showcasing its superior accuracy and effectiveness compared to traditional methods. By demonstrating the feasibility and advantages of IoT in the context of flood monitoring, this study contributes valuable information, enriching existing knowledge, and paving the way for future advances in the field. Research encourages the continued exploration of advanced techniques to strengthen flood monitoring and management strategies. Ultimately, this work presents a comprehensive IoT-based prototype for floodwater monitoring, offering valuable information and fostering the promising role of IoT technologies in this critical domain.


A Novel Framework for Information Security During the SDLC Implementation Stage: A Systematic Literature Review

Abstract: This research delves into the critical aspects of information security during the implementation stage of the Software Development Life Cycle (SDLC). By employing a systematic literature review, the study synthesizes findings from various digital repositories, including IEEE Xplore, ACM Digital Library, Scopus, and ScienceDirect, to outline a comprehensive framework addressing the implementation stage's unique security challenges. This research contributes to the field by proposing a novel assurance model for software development vendors that focuses on enhancing information security measures during the implementation stage. The study's findings reveal 12 key steps organizations can adopt to mitigate security risks and enhance information security measures during this critical phase. These steps provide actionable insights and strategies tailored to support security protocols effectively. The article concludes that by incorporating these steps, organizations can significantly improve their security posture, ensuring the integrity and reliability of the software development process, particularly during the implementation stage. This approach not only addresses immediate security concerns but also sets a precedent for future research and practice in secure software development, particularly in the critical implementation stage of the SDLC.


Investigating the Impact of ReLU and Sigmoid Activation Functions on Animal Classification Using CNN Models

Abstract: VGG16 is a convolutional neural network model used for image recognition. It is unique in that it only has 16 weighted layers, rather than relying on a large number of hyperparameters. It is considered as one of the best vision model architectures. This study compares the performance of ReLU (rectified linear unit) and sigmoid activation functions in CNN models for animal classification. To choose which model to use, we tested 2 state-of-the-art CNN architectures: the default VGG16 with the proposed method VGG16. A data set consisting of 2,000 images of five different animals was used. The results show that ReLU achieves higher classification accuracy than sigmoid. The model with ReLU on convolutional and fully connected layers achieved the highest accuracy of 97.56% on the test dataset. However, further experiments and considerations are needed to improve the results. Research aims to find better activation functions and identify factors that influence model performance. The data set consists of animal images collected from Kaggle, including cats, cows, elephants, horses, and sheep. It is divided into training and test sets (ratio 80:20). The CNN model has two convolution layers and two fully connected layers. ReLU and sigmoid activation functions with different learning rates are used. Evaluation metrics include precision, precision, recall, F1 score, and test cost. ReLU outperforms sigmoid in accuracy, precision, recall, and F1 score. However, other factors such as the size, complexity and parameters of the data set must be taken into account. This study emphasizes the importance of choosing the right activation function for better classification accuracy. ReLU is identified as effective in solving the vanish gradient problem. These findings can guide future research to improve CNN models in animal classification.


AUV Surfacing Control With Adversarial Attack Against DLaaS Framework

Abstract: In this study, we consider surfacing control problems for the autonomous underwater vehicle in three-dimensional space under emergencies. Most model-based controllers cannot effectively solve these problems due to the unknown task environment model. Moreover, existing deep reinforcement learning (DRL) methods have some limitations, such as slow convergence or overestimation bias. For these purposes, we propose a model-free DRL algorithm based on the Deep Deterministic Policy Gradient within the paradigm of deep learning as a service (DLaaS). The algorithm combines existing expert episodes as demonstration and transitions from the interaction between the agent and task environment, which potentially reduce risk from adversarial attack. We propose a variable delay learning mechanism to improve the performance of the proposed algorithm. The simulation results show that our method can converge faster and has a more robust performance with adversarial attack than the existing DRL method under the premise of solving the AUV surfacing control tasks for safety.


Public and Private Clouds in Hands-on, Online Learning

Abstract: Adoption of cloud computing is a clear trend in online learning.What types of cloud platforms are available?What are the pros and the cons of different cloud options?We will discuss cloud options for hands-on education, based on our experience with multiple cloud platforms.There are different ways of implementing cloud computing.A public cloud is owned and operated by an external service provider.Computing resources are allocated on a pay-per-usage basis.A private cloud is owned and managed by local institutions.Computing resources are provisioned to internal users on demand.Private clouds are highly customizable and may be adjusted to better meet the needs of local users.However, setting up a private cloud requires expertise and resources.Access to public clouds can be set up quickly but users possess less control.In a diversified online learning environment, multiple cloud platforms may be necessary.


The Design and Implementation of an Intelligent Letter Box

Abstract: A major cost of transportation for the US postal services is collecting mail from blue collection boxes across the country, with their numbers steadily declining over the years, to save on fuel costs.This cost could be significantly curtailed if boxes that contain mail at collection times can be identified.This paper provides a detailed study and implementation of a smart mail detection and alerting system.The system detects if any mail is placed in the blue collection box and sends an alert to the local post office indicating whether the box should or should not be visited.


Research Experience for Secondary School Teachers on Renewable Energy: Design and Implementation of a Small Scale Solar Tracker

Abstract: This paper describes the experiences gained during the first year implementation of a Research Experiences for Teachers (RET) in Renewable Energy (ENERGY) at Georgia Southern University (GSU).RET is a NSF grant program that supports the professional development for secondary school teachers with emphasis on hands-on laboratory research and experiences in STEM fields.The paper also refers to the teachers' experiences gained at the GSU Engineering Research Labs during the summer 2017.Participants in the RET program completed 280 hours (seven weeks) of training and research experience in renewable energy topics.About 50 hours were devoted to lectures and workshops, and the rest, about 230 hours, were research activities, collaborating in research teams with undergraduate and graduate students, and engineering professors.Participants also developed lessons for their courses that were delivered to their own students during the 2017-2018 school year.The lessons were directly related to the experiences that teachers gained in the summer.


A Personalized Computer Based Tutor for Vector Algebra

Abstract: Many of the world's challenges today need to be solved through advances in one or more of the STEM disciplines.This calls for preparing our students to be tomorrow's leading engineers and scientists.It is vital that students in a freshmen class understand how to analyze vectors, and split an arbitrary vector into components along different directions.From a student point of view, concepts in vectors can seem challenging, especially if they do not have strong knowledge background in geometry and trigonometry.We made a computer based personalized tutoring system using state of the art tools provided by Carnegie Mellon University's Cognitive Tutor Authoring Tools (CTAT).In this work, we describe steps that went into building this highly effective tutoring system that challenges the students with a problem, provides immediate feedback, and offers need based hints at different levels, leading the student to solve the problem correctly.


Improved and formal proposal for device-independent quantum private query <sup>*</sup>

Abstract: Abstract In this paper, we present a novel quantum private query (QPQ) scheme that is fully device-independent. As far as we know, this is the first QPQ scheme that uses EPR pairs and offers full device independence. Our approach takes into account the self-testing of shared EPR pairs and the self-testing of projective measurement operators in a mistrustful scenario where neither the client nor the server trusts each other. To achieve full device independence, we also exploit self-testing of a specific class of POVM operators used by the client in our scheme. Additionally, we evaluate the performance of our proposal formally and derive an upper limit of the maximum cheating probabilities (when the protocol does not terminate) for both the dishonest client and the dishonest server.


A Study of Online Assessment Tools to Practice Programming and Their Effect on Students Grades

Abstract: Abstract "Practice makes perfect" is an old phrase that proves truth in many aspects of the life of a computer engineer. Students in programming courses are reminded constantly by their instructors to practice in order to become better developers. Traditionally, book exercises have been used or assigned to students for practicing programming. However unless these exercises are counted for credit, some students will lack the motivation to do them. On the other hand, assigning too many problems for credit can become a time consuming activity for both students and faculty as well as a grading burden for instructors. It is also known that there are a lot of online resources for practicing programming, but students can get overwhelmed with so many tools. In this work in progress paper we present our preliminary results of how using online assessment tools can help student practice and improve their programming skills. The tools used provide immediate feedback and automatic grading. The hypothesis is that these tools help students to practice more and by giving them immediate feedback and quick grading, they get better at programming and consequently get better test scores. Preliminary data collected shows this to be the case. In this paper we present different scenarios how these tools were used and their effect in the final exam results in different semesters.


Introducing Emerging Computer Engineering Research to Community College Students through a Summer Internship Project on Development of a Mobile Gesture Recognition System

Abstract: Abstract Community colleges play an important role in STEM education because they enroll almost half of the nation's undergraduate students. Undergraduate research experience has been proven effective to engage and retain students in the STEM fields. In order to provide community college students with opportunities and experiences in cutting-edge engineering research, a cooperative internship program between a community college and a public comprehensive university has been developed. In summer 2018, the program engaged 20 community college students in six 10-week engineering research projects conducted at the four-year university. This paper mainly describes the computer engineering internship project, in which four sophomore students from the community college developed a mobile gesture recognition system by integrating bio-signal processing, machine learning, real-time system design, and mobile and cloud computing technologies. The project was conducted in a research lab at the four-year university and was supervised by a faculty advisor and a graduate student mentor. The research outcome of the project as well as the results of the pre- and post-program surveys show that the internship program was a great success in allowing the student interns to gain valuable computer engineering research experience and strengthening their confidence and interest in pursuing a STEM profession. The program also helped the students improve their skills in teamwork, time management, scientific writing, and presentation.


A Minimum Discounted Reward Hamilton–Jacobi Formulation for Computing Reachable Sets

Abstract: We propose a novel formulation for approximating reachable sets through a minimum discounted reward optimal control problem. The formulation yields a continuous solution that can be obtained by solving a Hamilton-Jacobi equation. Furthermore, the numerical approximation to this solution is the unique fixed-point to a contraction mapping. This allows for more efficient solution methods that are not applicable under traditional formulations for solving reachable sets. Lastly, this formulation provides a link between reinforcement learning and learning reachable sets for systems with unknown dynamics, allowing algorithms from the former to be applied to the latter. We use two benchmark examples, double integrator, and pursuit-evasion games, to show the correctness of the formulation as well as its strengths in comparison to previous work.


Indistinguishability Obfuscation from Well-Founded Assumptions

Abstract: At least since the initial public proposal of public-key cryptography based on computational hardness conjectures, cryptographers have contemplated the possibility of a "one-way compiler" that translates computer programs into "incomprehensible" but equivalent forms. And yet, the search for such a "one-way compiler" remained elusive for decades. We examine a formalization of this concept with the notion of indistinguishability obfuscation (iO) . Roughly speaking, iO requires that the compiled versions of any two equivalent programs (with the same size and running time) be indistinguishable from any efficient adversary. Finally, we show how to construct iO in such a way that we can prove the security of our iO scheme based on well-studied computational hardness conjectures in cryptography.


Multiview Deep Anomaly Detection: A Systematic Exploration

Abstract: Anomaly detection (AD), which models a given normal class and distinguishes it from the rest of abnormal classes, has been a long-standing topic with ubiquitous applications. As modern scenarios often deal with massive high-dimensional complex data spawned by multiple sources, it is natural to consider AD from the perspective of multiview deep learning. However, it has not been formally discussed by the literature and remains underexplored. Motivated by this blank, this article makes fourfold contributions: First, to the best of our knowledge, this is the first work that formally identifies and formulates the multiview deep AD problem. Second, we take recent advances in relevant areas into account and systematically devise various baseline solutions, which lays the foundation for multiview deep AD research. Third, to remedy the problem that limited benchmark datasets are available for multiview deep AD, we extensively collect the existing public data and process them into more than 30 multiview benchmark datasets via multiple means, so as to provide a better evaluation platform for multiview deep AD. Finally, by comprehensively evaluating the devised solutions on different types of multiview deep AD benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines and hopefully provide other researchers with beneficial guidance and insight into the new multiview deep AD topic.


A Pseudoinversion-Free Method for Weight Updating in Broad Learning System

Abstract: Neural networks have evolved into one of the most critical tools in the field of artificial intelligence. As a kind of shallow feedforward neural network, the broad learning system (BLS) uses a training process based on random and pseudoinverse methods, and it does not need to go through a complete training cycle to obtain new parameters when adding nodes. Instead, it performs rapid update iterations on the basis of existing parameters through a series of dynamic update algorithms, which enables BLS to combine high efficiency and accuracy flexibly. The training strategy of BLS is completely different from the existing mainstream neural network training strategy based on the gradient descent algorithm, and the superiority of the former has been proven in many experiments. This article applies an ingenious method of pseudoinversion to the weight updating process in BLS and employs it as an alternative strategy for the dynamic update algorithms in the original BLS. Theoretical analyses and numerical experiments demonstrate the efficiency and effectiveness of BLS aided with this method. The research presented in this article can be regarded as an extended study of the BLS theory, providing an innovative idea and direction for future research on BLS.


Subtype-Aware Dynamic Unsupervised Domain Adaptation

Abstract: Unsupervised domain adaptation (UDA) has been successfully applied to transfer knowledge from a labeled source domain to target domains without their labels. Recently introduced transferable prototypical networks (TPNs) further address class-wise conditional alignment. In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying fine-grained subtype structure and the cross-domain within-class compactness have not been fully investigated. To counter this, we propose a new approach to adaptively perform a fine-grained subtype-aware alignment to improve the performance in the target domain without the subtype label in both domains. The insight of our approach is that the unlabeled subtypes in a class have the local proximity within a subtype while exhibiting disparate characteristics because of different conditional and label shifts. Specifically, we propose to simultaneously enforce subtype-wise compactness and class-wise separation, by utilizing intermediate pseudo-labels. In addition, we systematically investigate various scenarios with and without prior knowledge of subtype numbers and propose to exploit the underlying subtype structure. Furthermore, a dynamic queue framework is developed to evolve the subtype cluster centroids steadily using an alternative processing scheme. Experimental results, carried out with multiview congenital heart disease data and VisDA and DomainNet, show the effectiveness and validity of our subtype-aware UDA, compared with state-of-the-art UDA methods.


Distributed Process Monitoring for Multiagent Systems Through Cognitive Learning

Abstract: Multi-agent systems are usually large-scaled with a growing degree of intelligence and integration. Direct applications of traditional (centralized) methods will become incompetent for effective process monitoring of multi-agent systems. It necessitates the cognitive learning strategies that determine the effective interactions among subsystems or individuals. Therefore, in order to improve monitoring performance, this paper targets the development of a new distributed process monitoring method that has the cognitive learning ability by embedding an adaptive pickup rule. The proposed cognitive learning-based method can reduce the computation loads in both off-line and online phases because only necessary information exchange (or communication topology) is involved. Furthermore, the threshold used for system monitoring is obtained by developing a fast search algorithm based on statistical learning theory. Case studies on the wastewater treatment system, which can be regarded as a typical multi-agent system, demonstrate the superiority of the proposed distributed process-monitoring method.


Chaotic image encryption algorithm with improved bonobo optimizer and DNA coding for enhanced security

Abstract: Image encryption involves applying cryptographic approaches to convert the content of an image into an illegible or encrypted format, reassuring that illegal users cannot simply interpret or access the actual visual details. Commonly employed models comprise symmetric key algorithms for the encryption of the image data, necessitating a secret key for decryption. This study introduces a new Chaotic Image Encryption Algorithm with an Improved Bonobo Optimizer and DNA Coding (CIEAIBO-DNAC) for enhanced security. The presented CIEAIBO-DNAC technique involves different processes such as initial value generation, substitution, diffusion, and decryption. Primarily, the key is related to the input image pixel values by the MD5 hash function, and the hash value produced by the input image can be utilized as a primary value of the chaotic model to boost key sensitivity. Besides, the CIEAIBO-DNAC technique uses the Improved Bonobo Optimizer (IBO) algorithm for scrambling the pixel position in the block and the scrambling process among the blocks takes place. Moreover, in the diffusion stage, DNA encoding, obfuscation, and decoding process were carried out to attain encrypted images. Extensive experimental evaluations and security analyses are conducted to assess the outcome of the CIEAIBO-DNAC technique. The simulation outcome demonstrates excellent security properties, including resistance against several attacks, ensuring it can be applied to real-time image encryption scenarios.


Wearable and interactive multicolored photochromic fiber display

Abstract: Endowing flexible and adaptable fiber devices with light-emitting capabilities has the potential to revolutionize the current design philosophy of intelligent, wearable interactive devices. However, significant challenges remain in developing fiber devices when it comes to achieving uniform and customizable light effects while utilizing lightweight hardware. Here, we introduce a mass-produced, wearable, and interactive photochromic fiber that provides uniform multicolored light control. We designed independent waveguides inside the fiber to maintain total internal reflection of light as it traverses the fiber. The impact of excessive light leakage on the overall illuminance can be reduced by utilizing the saturable absorption effect of fluorescent materials to ensure light emission uniformity along the transmission direction. In addition, we coupled various fluorescent composite materials inside the fiber to achieve artificially controllable spectral radiation of multiple color systems in a single fiber. We prepared fibers on mass-produced kilometer-long using the thermal drawing method. The fibers can be directly integrated into daily wearable devices or clothing in various patterns and combined with other signal input components to control and display patterns as needed. This work provides a new perspective and inspiration to the existing field of fiber display interaction, paving the way for future human-machine integration.


Kernel Path for Semisupervised Support Vector Machine

Abstract: Semisupervised support vector machine (S3VM) is a powerful semisupervised learning model that can use large amounts of unlabeled data to train high-quality classification models. The choice of kernel parameters in the kernel function determines the mapping between the input space and the feature space and is crucial to the performance of the S3VM. Kernel path algorithms have been widely recognized as one of the most efficient tools to trace the solutions with respect to a kernel parameter. However, existing kernel path algorithms are limited to convex problems, while S3VM is nonconvex problem. To address this challenging problem, in this article, we first propose a kernel path algorithm of S3VM (KPS3VM), which can track the solutions of the nonconvex S3VM with respect to a kernel parameter. Specifically, we estimate the position of the breakpoint by monitoring the change of the sample sets. In addition, we also use an incremental and decremental learning algorithm to deal with the Karush-Khun-Tucker violating samples in the process of tracking the solutions. More importantly, we prove the finite convergence of our KPS3VM algorithm. Experimental results on various benchmark datasets not only validate the effectiveness of our KPS3VM algorithm but also show the advantage of choosing the optimal kernel parameters.


Data-Knowledge-Driven Self-Organizing Fuzzy Neural Network

Abstract: Fuzzy neural networks (FNNs) hold the advantages of knowledge leveraging and adaptive learning, which have been widely used in nonlinear system modeling. However, it is difficult for FNNs to obtain the appropriate structure in the situation of insufficient data, which limits its generalization performance. To solve this problem, a data-knowledge-driven self-organizing FNN (DK-SOFNN) with a structure compensation strategy and a parameter reinforcement mechanism is proposed in this article. First, a structure compensation strategy is proposed to mine structural information from empirical knowledge to learn the structure of DK-SOFNN. Then, a complete model structure can be acquired by sufficient structural information. Second, a parameter reinforcement mechanism is developed to determine the parameter evolution direction of DK-SOFNN that is most suitable for the current model structure. Then, a robust model can be obtained by the interaction between parameters and dynamic structure. Finally, the proposed DK-SOFNN is theoretically analyzed on the fixed structure case and dynamic structure case. Then, the convergence conditions can be obtained to guide practical applications. The merits of DK-SOFNN are demonstrated by some benchmark problems and industrial applications.


When Sparse Neural Network Meets Label Noise Learning: A Multistage Learning Framework

Abstract: Recent methods in network pruning have indicated that a dense neural network involves a sparse subnetwork (called a winning ticket), which can achieve similar test accuracy to its dense counterpart with much fewer network parameters. Generally, these methods search for the winning tickets on well-labeled data. Unfortunately, in many real-world applications, the training data are unavoidably contaminated with noisy labels, thereby leading to performance deterioration of these methods. To address the above-mentioned problem, we propose a novel two-stream sample selection network (TS3-Net), which consists of a sparse subnetwork and a dense subnetwork, to effectively identify the winning ticket with noisy labels. The training of TS3-Net contains an iterative procedure that switches between training both subnetworks and pruning the smallest magnitude weights of the sparse subnetwork. In particular, we develop a multistage learning framework including a warm-up stage, a semisupervised alternate learning stage, and a label refinement stage, to progressively train the two subnetworks. In this way, the classification capability of the sparse subnetwork can be gradually improved at a high sparsity level. Extensive experimental results on both synthetic and real-world noisy datasets (including MNIST, CIFAR-10, CIFAR-100, ANIMAL-10N, Clothing1M, and WebVision) demonstrate that our proposed method achieves state-of-the-art performance with very small memory consumption for label noise learning. Code is available at https://github.com/Runqing-forMost/TS3-Net/tree/master.


TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations

Abstract: Sequential recommendation aims to choose the most suitable items for a user at a specific timestamp given historical behaviors. Existing methods usually model the user behavior sequence based on transition-based methods such as Markov chain. However, these methods also implicitly assume that the users are independent of each other without considering the influence between users. In fact, this influence plays an important role in sequence recommendation since the behavior of a user is easily affected by others. Therefore, it is desirable to aggregate both user behaviors and the influence between users, which are evolved temporally and involved in the heterogeneous graph of users and items. In this article, we incorporate dynamic user-item heterogeneous graphs to propose a novel sequential recommendation framework. As a result, the historical behaviors as well as the influence between users can be taken into consideration. To achieve this, we first formalize sequential recommendation as a problem to estimate conditional probability given temporal dynamic heterogeneous graphs and user behavior sequences. After that, we exploit the conditional random field to aggregate the heterogeneous graphs and user behaviors for probability estimation and employ the pseudo-likelihood approach to derive a tractable objective function. Finally, we provide scalable and flexible implementations of the proposed framework. Experimental results on three real-world datasets not only demonstrate the effectiveness of our proposed method but also provide some insightful discoveries on the sequential recommendation.


FAT: Frequency-Aware Transformation for Bridging Full-Precision and Low-Precision Deep Representations

Abstract: Learning low-bitwidth convolutional neural networks (CNNs) is challenging because performance may drop significantly after quantization. Prior arts often quantize the network weights by carefully tuning hyperparameters such as nonuniform stepsize and layerwise bitwidths, which are complicated since the full- and low-precision representations have large discrepancies. This work presents a novel quantization pipeline, named frequency-aware transformation (FAT), that features important benefits: 1) instead of designing complicated quantizers, FAT learns to transform network weights in the frequency domain to remove redundant information before quantization, making them amenable to training in low bitwidth with simple quantizers; 2) FAT readily embeds CNNs in low bitwidths using standard quantizers without tedious hyperparameter tuning and theoretical analyses show that FAT minimizes the quantization errors in both uniform and nonuniform quantizations; and 3) FAT can be easily plugged into various CNN architectures. Using FAT with a simple uniform/logarithmic quantizer can achieve the state-of-the-art performance in different bitwidths on various model architectures. Consequently, FAT serves to provide a novel frequency-based perspective for model quantization.


Using Decomposed Error for Reproducing Implicit Understanding of Algorithms

Abstract: Abstract Reproducibility is important for having confidence in evolutionary machine learning algorithms. Although the focus of reproducibility is usually to recreate an aggregate prediction error score using fixed random seeds, this is not sufficient. Firstly, multiple runs of an algorithm, without a fixed random seed, should ideally return statistically equivalent results. Secondly, it should be confirmed whether the expected behaviour of an algorithm matches its actual behaviour, in terms of how an algorithm targets a reduction in prediction error. Confirming the behaviour of an algorithm is not possible when using a total error aggregate score. Using an error decomposition framework as a methodology for improving the reproducibility of results in evolutionary computation addresses both of these factors. By estimating decomposed error using multiple runs of an algorithm and multiple training sets, the framework provides a greater degree of certainty about the prediction error. Also, decomposing error into bias, variance due to the algorithm (internal variance), and variance due to the training data (external variance) more fully characterises evolutionary algorithms. This allows the behaviour of an algorithm to be confirmed. Applying the framework to a number of evolutionary algorithms shows that their expected behaviour can be different to their actual behaviour. Identifying a behaviour mismatch is important in terms of understanding how to further refine an algorithm as well as how to effectively apply an algorithm to a problem.


Emerging Scientific Topic Discovery by Analyzing Reliable Patterns of Infrequent Synonymous Biterms

Abstract: Emerging scientific topics are those topics that the number of related articles was small in the past but has grown dramatically in recent years. Automatic discovery of emerging scientific topics has become increasingly necessary because of the exponentially increasing of research papers. Such discovery enables broad applications, such as optimizing resource allocations for promising research areas, predicting future technology trends, finding knowledge gaps and new concepts, and recommending personalized research directions. In this paper, we provide a framework of emerging topic discovery methods using Infrequent Synonymous Biterm (ISB), which automatically extracts the dedicated knowledge from the infrequent patterns of synonymous biterms in a corpus (e.g., paper titles); each term in a synonymous biterm represents a collaborating supertopic, whose collaboration originates an emerging topic. In particular, we propose an Analyzing Reliable Patterns of Infrequent Synonymous Biterms (ARPISB) method, which guarantees the quality of the result emerging topics by adaptively giving larger weights to more reliable ISB. Extensive experiments on five subfields' scholarly papers demonstrate the significant and robust improvement of the accuracy of emerging scientific topic discovery.


Negative Instance Guided Self-Distillation Framework for Whole Slide Image Analysis

Abstract: Histopathology image classification is an important clinical task, and current deep learning-based whole-slide image (WSI) classification methods typically cut WSIs into small patches and cast the problem as multi-instance learning. The mainstream approach is to train a bag-level classifier, but their performance on both slide classification and positive patch localization is limited because the instance-level information is not fully explored. In this article, we propose a negative instance-guided, self-distillation framework to directly train an instance-level classifier end-to-end. Instead of depending only on the self-supervised training of the teacher and the student classifiers in a typical self-distillation framework, we input the true negative instances into the student classifier to guide the classifier to better distinguish positive and negative instances. In addition, we propose a prediction bank to constrain the distribution of pseudo instance labels generated by the teacher classifier to prevent the self-distillation from falling into the degeneration of classifying all instances as negative. We conduct extensive experiments and analysis on three publicly available pathological datasets: CAMELYON16, PANDA, and TCGA, as well as an in-house pathological dataset for cervical cancer lymph node metastasis prediction. The results show that our method outperforms existing methods by a large margin. Code will be publicly available.


A Robust Multilabel Method Integrating Rule-Based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance

Abstract: Model transparency, label correlation learning and robustness to label noise are crucial for multilabel learning. However, few existing methods consider these three characteristics simultaneously. To address this challenge, we propose the robust multilabel Takagi-Sugeno-Kang fuzzy system (R-MLTSK-FS) with three mechanisms. First, we design a soft label learning mechanism to reduce the effect of label noise by explicitly measuring the interactions between labels, which is also the basis of the other two mechanisms. Second, rule-based multi-output TSK FS is used as the base model to efficiently model the inference relationship between features and soft labels in a more transparent way than many existing multilabel models. Third, to further improve the performance of multilabel learning, we build a correlation enhancement learning mechanism based on the soft label space and the fuzzy feature space. Extensive experiments are conducted to demonstrate the superiority of the proposed method.


GeoLinter: A Linting Framework for Choropleth Maps

Abstract: Visualization linting is a proven effective tool in assisting users to follow established visualization guidelines. Despite its success, visualization linting for choropleth maps, one of the most popular visualizations on the internet, has yet to be investigated. In this paper, we present GeoLinter, a linting framework for choropleth maps that assists in creating accurate and robust maps. Based on a set of design guidelines and metrics drawing upon a collection of best practices from the cartographic literature, GeoLinter detects potentially suboptimal design decisions and provides further recommendations on design improvement with explanations at each step of the design process. We perform a validation study to evaluate the proposed framework's functionality with respect to identifying and fixing errors and apply its results to improve the robustness of GeoLinter. Finally, we demonstrate the effectiveness of the GeoLinter - validated through empirical studies - by applying it to a series of case studies using real-world datasets.


A Dual-Attention Learning Network With Word and Sentence Embedding for Medical Visual Question Answering

Abstract: Research in medical visual question answering (MVQA) can contribute to the development of computer-aided diagnosis. MVQA is a task that aims to predict accurate and convincing answers based on given medical images and associated natural language questions. This task requires extracting medical knowledge-rich feature content and making fine-grained understandings of them. Therefore, constructing an effective feature extraction and understanding scheme are keys to modeling. Existing MVQA question extraction schemes mainly focus on word information, ignoring medical information in the text, such as medical concepts and domain-specific terms. Meanwhile, some visual and textual feature understanding schemes cannot effectively capture the correlation between regions and keywords for reasonable visual reasoning. In this study, a dual-attention learning network with word and sentence embedding (DALNet-WSE) is proposed. We design a module, transformer with sentence embedding (TSE), to extract a double embedding representation of questions containing keywords and medical information. A dual-attention learning (DAL) module consisting of self-attention and guided attention is proposed to model intensive intramodal and intermodal interactions. With multiple DAL modules (DALs), learning visual and textual co-attention can increase the granularity of understanding and improve visual reasoning. Experimental results on the ImageCLEF 2019 VQA-MED (VQA-MED 2019) and VQA-RAD datasets demonstrate that our proposed method outperforms previous state-of-the-art methods. According to the ablation studies and Grad-CAM maps, DALNet-WSE can extract rich textual information and has strong visual reasoning ability.


Co-Propagation of QKD & 6 Tb/s (60 × 100G) DWDM Channels With ∼17 dBm Total WDM Power in Single and Multi-Span Configurations

Abstract: We report co-propagation experiments of the quantum channel (at 1310 nm) of a Quantum Key Distribution (QKD) system with Dense Wavelength Division Multiplexing (DWDM) data channels in the 1550 nm range. Two configurations are assessed. The first one is a single span configuration where various lengths of Standard Single Mode Fiber (SSMF) (from 20 to 70 km) are used and the total WDM channels power is varied. The Secure Key Rate (SKR) and the Quantum Bit Error Ratio (QBER) are recorded showing that up to ~17 dBm total power of 30 or 60 channels at 100 Gb/s can coexist with the quantum channel. A metric to evaluate the co-propagation efficiency is also proposed to better evaluate the ability of a QKD system to provide secure keys in a co-propagation regime. The second experiment is a three spans link with a cascade of three QKD systems and two trusted nodes in a 184 km total link length. We report the transmission of a coherent 400 Gb/s Dual Polarization DP-16QAM (Quadrature Amplitude Modulation) channel that transports a QKD secured 100 GbE data stream, with other fifty-four 100 Gb/s WDM channels. Encryption is demonstrated at the same time as co-propagation.


Perceptions of the Agency and Responsibility of the NHS COVID-19 App on Twitter: Critical Discourse Analysis

Abstract: Since September 2020, the National Health Service (NHS) COVID-19 contact-tracing app has been used to mitigate the spread of COVID-19 in the United Kingdom. Since its launch, this app has been a part of the discussion regarding the perceived social agency of decision-making algorithms. On the social media website Twitter, a plethora of views about the app have been found but only analyzed for sentiment and topic trajectories thus far, leaving the perceived social agency of the app underexplored.We aimed to examine the discussion of social agency in social media public discourse regarding algorithm-operated decisions, particularly when the artificial intelligence agency responsible for specific information systems is not openly disclosed in an example such as the COVID-19 contact-tracing app. To do this, we analyzed the presentation of the NHS COVID-19 App on Twitter, focusing on the portrayal of social agency and the impact of its deployment on society. We also aimed to discover what the presentation of social agents communicates about the perceived responsibility of the app.Using corpus linguistics and critical discourse analysis, underpinned by social actor representation, we used the link between grammatical and social agency and analyzed a corpus of 118,316 tweets from September 2020 to July 2021 to see whether the app was portrayed as a social actor.We found that active presentations of the app-seen mainly through personalization and agency metaphor-dominated the discourse. The app was presented as a social actor in 96% of the cases considered and grew in proportion to passive presentations over time. These active presentations showed the app to be a social actor in 5 main ways: informing, instructing, providing permission, disrupting, and functioning. We found a small number of occasions on which the app was presented passively through backgrounding and exclusion.Twitter users presented the NHS COVID-19 App as an active social actor with a clear sense of social agency. The study also revealed that Twitter users perceived the app as responsible for their welfare, particularly when it provided instructions or permission, and this perception remained consistent throughout the discourse, particularly during significant events. Overall, this study contributes to understanding how social agency is discussed in social media discourse related to algorithmic-operated decisions This research offers valuable insights into public perceptions of decision-making digital contact-tracing health care technologies and their perceptions on the web, which, even in a postpandemic world, may shed light on how the public might respond to forthcoming interventions.


Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud

Abstract: An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency, and running time. Therefore, the proposed work motivates to develop a new framework, named as Gaussian adapted Markov model (GAMM)—overhauled fluctuation analysis (OFA), for an efficient big data streaming in the cloud systems. The purpose of this work is to efficiently manage the time-bounded big data streaming applications with reduced error rate. In this study, the gating strategy is also used to extract the set of features for obtaining nonlinear distribution of data and fat convergence solution, used to perform the fluctuation analysis. Moreover, the layered architecture is developed for simplifying the process of resource forecasting in the streaming applications. During experimentation, the results of the proposed stream model GAMM-OFA are validated and compared by using different measures.


10 Gb/s classical secure key distribution based on temporal steganography and private chaotic phase scrambling

Abstract: Secure distribution of high-speed digital encryption/decryption keys over a classical fiber channel is strongly pursued for realizing perfect secrecy communication systems. However, it is still challenging to achieve a secret key rate in the order of tens of gigabits per second to be comparable with the bit rate of commercial fiber-optic systems. In this paper, we propose and experimentally demonstrate a novel solution for high-speed secure key distribution based on temporal steganography and private chaotic phase scrambling in the classical physical layer. The encryption key is temporally concealed into the background noise in the time domain and randomly phase scrambled bit-by-bit by a private chaotic signal, which provides two layers of enhanced security to guarantee the privacy of key distribution while providing a high secret key rate. We experimentally achieved a record classical secret key rate of 10 Gb/s with a bit error rate lower than the hard-decision forward error correction (HD-FEC) over a 40 km standard single mode fiber. The proposed solution holds great promise for achieving high-speed key distribution in the classical fiber channel by combining steganographic transmission and chaotic scrambling.


Grounding spatial relations in text-only language models

Abstract: This paper shows that text-only Language Models (LM) can learn to ground spatial relations like left of or below if they are provided with explicit location information of objects and they are properly trained to leverage those locations. We perform experiments on a verbalized version of the Visual Spatial Reasoning (VSR) dataset, where images are coupled with textual statements which contain real or fake spatial relations between two objects of the image. We verbalize the images using an off-the-shelf object detector, adding location tokens to every object label to represent their bounding boxes in textual form. Given the small size of VSR, we do not observe any improvement when using locations, but pretraining the LM over a synthetic dataset automatically derived by us improves results significantly when using location tokens. We thus show that locations allow LMs to ground spatial relations, with our text-only LMs outperforming Vision-and-Language Models and setting the new state-of-the-art for the VSR dataset. Our analysis show that our text-only LMs can generalize beyond the relations seen in the synthetic dataset to some extent, learning also more useful information than that encoded in the spatial rules we used to create the synthetic dataset itself.


Non-Fluent Synthetic Target-Language Data Improve Neural Machine Translation

Abstract: When the amount of parallel sentences available to train a neural machine translation is scarce, a common practice is to generate new synthetic training samples from them. A number of approaches have been proposed to produce synthetic parallel sentences that are similar to those in the parallel data available. These approaches work under the assumption that non-fluent target-side synthetic training samples can be harmful and may deteriorate translation performance. Even so, in this paper we demonstrate that synthetic training samples with non-fluent target sentences can improve translation performance if they are used in a multilingual machine translation framework as if they were sentences in another language. We conducted experiments on ten low-resource and four high-resource translation tasks and found out that this simple approach consistently improves translation performance as compared to state-of-the-art methods for generating synthetic training samples similar to those found in corpora. Furthermore, this improvement is independent of the size of the original training corpus, the resulting systems are much more robust against domain shift and produce less hallucinations.


Connecting Actors With the Introduction of Mobile Technology in Health Care Practice Placements (4D Project): Protocol for a Mixed Methods Study

Abstract: The learning process in clinical placements for health care students is a multifaceted endeavor that engages numerous actors and stakeholders, including students, clinical tutors, link teachers, and academic assessors. Successfully navigating this complex process requires the implementation of tasks and mentorships that are synchronized with educational and clinical processes, seamlessly embedded within their respective contexts. Given the escalating number of students and the rising demand for health care services from the general population, it becomes imperative to develop additional tools that support the learning process. These tools aim to simplify day-to-day clinical practice, allowing a concentrated focus on value-based activities. This paper introduces a project funded by the European Commission that involves 5 European countries. The project's objective is to comprehensively outline the entire process of development and ultimately implement mobile technology in practice placements. The project tackles the existing gap by constructing tailored mobile apps designed for students, teachers, tutors, and supervisors within each participating organization. This approach leverages practice-based learning, mobile technology, and technology adoption to enhance the overall educational experience.This study aims to introduce mobile technology in clinical practice placements with the goal of facilitating and enhancing practice-based learning. The objective is to improve the overall effectiveness of the process for all stakeholders involved.The "4D in the Digitalization of Learning in Practice Placement" (4D Project) will use a mixed methods research design, encompassing 3 distinct study phases: phase 1 (preliminary research), which incorporates focus groups and a scoping review, to define the problem, identify necessities, and analyze contextual factors; phase 2 (collaborative app development), which involves researchers and prospective users working together to cocreate and co-design tailored apps; and phase 3, which involves feasibility testing of these mobile apps within practice settings.The study's potential impact will primarily focus on improving communication and interaction processes, fostering connections among stakeholders in practice placements, and enhancing the assessment of training needs. The literature review and focus groups will play a crucial role in identifying barriers, facilitators, and factors supporting the integration of mobile technology in clinical education. The cocreation process of mobile learning apps will reveal the core values and needs of various stakeholders, including students, teachers, and health care professionals. This process also involves adapting and using mobile apps to meet the specific requirements of practice placements. A pilot study aimed at validating the app will test and assess mobile technology in practice placements. The study will determine results related to usability and design, learning outcomes, student engagement, communication among stakeholders, user behavior, potential issues, and compliance with regulations.Health care education, encompassing disciplines such as medicine, nursing, midwifery, and others, confronts evolving challenges in clinical training. Essential to addressing these challenges is bridging the gap between health care institutions and academic settings. The introduction of a new digital tool holds promise for empowering health students and mentors in effectively navigating the intricacies of the learning process.DERR1-10.2196/53284.


Augmented Reality Registration System for Visualization of Skull Landmarks

Abstract: Augmented reality (AR) is an emerging technology in neurosurgery with the potential to become a strategic tool in the delivery of care and education for trainees. Advances in technology have demonstrated promising use for improving visualization and spatial awareness of critical neuroanatomic structures. In this report, we employ a novel AR registration system for the visualization and targeting of skull landmarks. A markerless AR system was used to register 3-dimensional reconstructions of suture lines onto the head via a head-mounted display. Participants were required to identify craniometric points with and without AR assistance. Targeting error was measured as the Euclidian distance between the user-defined location and the true craniometric point on the subjects' heads. All participants successfully registered 3-dimensional reconstructions onto the subjects' heads. Targeting accuracy was significantly improved with AR (3.59 ± 1.29 mm). Across all target points, AR increased accuracy by an average of 19.96 ± 3.80 mm. Posttest surveys revealed that participants felt the technology increased their confidence in identifying landmarks (4.6/5) and that the technology will be useful for clinical care (4.2/5). While several areas of improvement and innovation can further enhance the use of AR in neurosurgery, this report demonstrates the feasibility of a markerless headset-based AR system for visualizing craniometric points on the skull. As the technology continues to advance, AR is expected to play an increasingly significant role in neurosurgery, transforming how surgeries are performed and improving patient care.


Inequality-Constrained 3D Morphable Face Model Fitting

Abstract: 3D morphable model (3DMM) fitting on 2D data is traditionally done via unconstrained optimization with regularization terms to ensure that the result is a plausible face shape and is consistent with a set of 2D landmarks. This paper presents inequality-constrained 3DMM fitting as the first alternative to regularization in optimization-based 3DMM fitting. Inequality constraints on the 3DMM's shape coefficients ensure face-like shapes without modifying the objective function for smoothness, thus allowing for more flexibility to capture person-specific shape details. Moreover, inequality constraints on landmarks increase robustness in a way that does not require per-image tuning. We show that the proposed method stands out with its ability to estimate person-specific face shapes by jointly fitting a 3DMM to multiple frames of a person. Further, when used with a robust objective function, namely gradient correlation, the method can work "in-the-wild" even with a 3DMM constructed from controlled data. Lastly, we show how to use the log-barrier method to efficiently implement the method. To our knowledge, we present the first 3DMM fitting framework that requires no learning yet is accurate, robust, and efficient. The absence of learning enables a generic solution that allows flexibility in the input image size, interchangeable morphable models, and incorporation of camera matrix.


BLOCKSCRIPTS -- A BLOCKCHAIN SYSTEM FOR UNIVERSITY TRANSCRIPTS

Abstract: Abstract The concept of blockchains started around 2008 with Bitcoin, which is a cryptocurrency. More recently, the application of Blockchains has been expanded well beyond cryptocurrency. As part of a capstone project for the Master of Science in Computer Science, a Blockchain that holds student transcript information and/or diplomas was created. The information is stored in a digital format that is immutable and can be retrieved easily by any authorized student, faculty, or interested third party. Having immutable records in a digital blockchain format allows students to have an official record that cannot be changed yet be publicly available for authorized viewers, satisfying the need for secure communication channels. This is beneficial for the student, the university, and any prospective employers. Creating and storing records in this way also ensures the digital transcripts and diplomas cannot be altered in any way. Additionally, Blockscripts provides non-repudiation and a more secure platform for storing transcripts due to the nature of decentralized data storage. As such, if one node in the blockchain network goes offline, other nodes in the network will persist and maintain accurate copies of the transcript ledger data. This also negates the risk associated with a data center, especially in regard to disaster recovery (i.e. fires or earthquakes) which could potentially have the capacity to destroy localized data. Another benefit to be realized from storing transcripts and diplomas on a blockchain is the processing time saved. Time is saved for the person requesting the transcript, the registrar's office producing it, and the third party receiving the transcript. In many universities, a transcript request can take up to three weeks to be fulfilled; by using blockchain technology it is likely that requesting parties can receive a requested transcript the same day. A user interface (UI) has been created that allows a student to request his/her transcript to be viewed by a particular entity. Blockscripts also enables agencies or universities to verify a degree or degrees that may be held by a particular student or graduate. The student’s transcripts/original data is owned and initially published to a blockchain by the university they attended. It is made viewable by encrypting with either the student’s public key or the public key of a third party; the student is able to decrypt his/her data with their private key and a third party is able to decrypt the transcript data with their private key. This ensures that the transcript will be publicly available, yet the contents will remain private and available only to authorized parties. If the student wants to send their transcripts to someone else, they include those parties’ public keys in the transcript request. Each transcript request can include a fixed fee for processing; the university administration can take a portion while the validating network node (miner) takes the remainder for providing the service of validation, data storage, and transmission costs.


Instilling Entrepreneurial Mindset by Vertical Integration of Engineering Projects

Abstract: The goal of this research project is to instill an entrepreneurial mindset by vertically integrating a single design project among two consecutive freshmen engineering design classes.The paper describes the context and background of two vertically integrated classes.Example work from students show the interpretation of the entrepreneurial mindset.A survey measures the effectiveness of the entrepreneurial mindset among students that are taking part in the project.Next, there are recommendations based on the information gathered during the implementation, including specific recommendations about the types of projects, constraints and methodologies.Other vertically integrated classes and curricula can use this case-study as a starting point for introducing entrepreneurial mindset.Lastly, there is a current discussion of case studies of vertical integration among student groups in non-consecutive semesters of a program, and student groups from completely different programs.


Hierarchical image-to-image translation with nested distributions modeling

Abstract: Unpaired image-to-image translation among category domains has achieved remarkable success in past decades. Recent studies mainly focus on two challenges. For one thing, such translation is inherently multi-modal (i.e. many-to-many mapping) due to variations of domain-specific information (e.g., the domain of house cat contains multiple sub-modes), which is usually addressed by predefined distribution sampling. For another, most existing multi-modal approaches have limits in handling more than two domains with one model, i.e. they have to independently build two distributions to capture variations for every pair of domains. To address these problems, we propose a Hierarchical Image-to-image Translation (HIT) method which jointly formulates the multi-domain and multi-modal problem in a semantic hierarchy structure by modeling a common and nested distribution space. Specifically, domains have inclusion relationships under a particular hierarchy structure. With the assumption of Gaussian prior for domains, distributions of domains at lower levels capture the local variations of their ancestors at higher levels, leading to the so-called nested distributions. To this end, we propose a nested distribution loss in light of the distribution divergence measurement and information entropy theory to characterize the aforementioned inclusion relations among domain distributions. Experiments on ImageNet, ShapeNet, and CelebA datasets validate the promising results of our HIT against state-of-the-arts, and as additional benefits of nested modeling, one can even control the uncertainty of multi-modal translations at different hierarchy levels.


The Effects of Online Course Design on Student Course Satisfaction

Abstract: Her research interests revolve around the investigations of how learning analytics informs instructional design to foster learning behaviors and strategies associ-


Tablet vs. smartphone use for freehand sketching and spatial visualization in large engineering graphics courses

Abstract: Abstract This abstract is intended as a presentation only (no full paper). Mobile devices are becoming more prevalent and a common part of the education experience. Students can access their devices at any time to perform assignments or review material. Mobile apps can have the added advantage of being able to automatically grade student work and provide instantaneous feedback. However, numerous challenges remain in implementing effective mobile educational apps. One challenge is the small screen size of smartphones. A freehand sketching and spatial visualization mobile app was implemented in two college freshman engineering graphics course to teach students how to sketch orthographic and isometric assignments. The app provides automatic grading and hint feedback to help students when they are stuck. The app was originally developed for iPads, but the wide prevalence of smartphones led to porting the software to iPhone and Android phones. The sketching assignments on a smartphone screen require more frequent zooming and panning, These classes were the first to use the spatial visualization training app on their smartphones to study if the experience was the same as on larger screen sizes using iPad tablets. Students were assigned sketching problems as homework using their personal devices. Students were administered a pre- and post- spatial visualization test (PSVT-R, a reliable, well-validated instrument) to assess learning gains. The trial analysis focused on students who entered the course with limited spatial visualization experience as identified based on a score of ≤70% on the PSVT:R since students entering college with low PSVT:R scores are at higher risk of dropping out of STEM majors. In the first course, almost 60% of the class failed the pre-test, which was a significantly higher percentage than the same trial conducted at another large institution. Among the low-performing students in the first class, 35% of those who used the app showed significant progress, raising their test scores above 70% bringing them out of the at-risk range for dropping out of engineering compared to 71% at the other institution. These mixed results could be attributed to the software not being ready for prime-time during the initial trial. In addition to the PSVT-R instrument, a survey was conducted at both institutions to evaluate student usage and their impressions of the app. Students found the app engaging, easy to use, and something they would do whenever they had “a free moment”. In the first trial, 79% of the students recommended the app to a friend if they are struggling with spatial visualization skills as opposed to 95% in the second course. This presentation will compare the implementation of the mobile spatial visualization sketching app in two large college engineering classroom using smartphones vs. past trials using tablets, highlight the app’s impact in increasing self-efficacy in spatial visualization and sketching, and present a detailed comparison of the results of the pre- and post- test and survey related to smartphone usage.


Iterative Improvement in Flipped Classroom Teaching of Lower Division Engineering Courses

Abstract: Abstract Lower division engineering courses are important yet hard to teach as many students find these highly abstracted material hard to comprehend. Recent studies have suggested that flipped classroom teaching has potential to improve the teaching and learning of lower division engineering courses. While some educators are optimistic about the potential impact that flipped classroom teaching might provide, there are still many challenges to overcome during implementation. One of the major challenges faced by course developers, especially first-time developers, is to manage the heavy workload needed to design an effective practice that is suitable for their unique student body. This paper presents an iterative framework to help ease the adoption and continuous improvement of flipped classroom teaching, so that students' learning experience can be optimized over time. The framework is composed of two phases – the initial design phase and the iterative tuning phase. In the initial design phase, a strategy that allows an instructor to quickly convert the existing course materials used for other lecturing methods into those suitable for flipped classroom teaching is introduced. This process involves designing a course structure according to its objectives, rearranging course content, and patching additional resources. Then, the iterative tuning for improvement will be explained, with a focus on identifying and addressing the most critical areas for improvement. Since the course instructor only focuses on resolving the most critical issues every semester, the workload becomes manageable. The flipped classroom teaching of a lower division mechanical engineering course "ME 2040 – Circuit Analysis for Mechanical Engineers" is used as example to explain details about the utilization of the proposed course development and implementation process. Towards the end, the effectiveness of the development process in improving students' learning outcomes is discussed.


Students are improving their spatial skills in introductory engineering class through projects utilizing 3D printers and laser cutters

Abstract: Abstract A growing body of research indicates that well-developed spatial skills are important to student success in many STEM disciplines, including many engineering majors. This research has further found that spatial skills are malleable and can be improved with targeted training activities. This presentation will share strategies and successes with incorporating spatial visualization skills training in a freshman-level introduction to engineering course. This course is a general introduction that also includes orientation to the engineering profession, academic success strategies, problem solving skills, and introductory math and science applications to engineering. The course prerequisite is college algebra. Much of the spatial skills training content is combined with introductory design and teamwork activities and in total makes up approximately twenty percent of the course. Students engage in two team projects which are supported by a series of spatial skills practice worksheets. The worksheets prompt students to sketch blocks of varying height in both elevation and isometric views and perform mental rotations about one and two axes. The team projects utilizing the OnShape CAD system, a cloud-based parametric solid modeling system that is available at no cost to educators and students. The first project has teams of three students creating a Soma puzzle; a 3×3×3 cube assembled from seven pieces made out of unit cubes that has 240 unique solutions. The class follows a detailed explanation of how to create one of the seven pieces, then each student is required to create and share two of the remaining six pieces. Each student is then required to generate an assembly with two unique solutions. Groups then modify and 3D print their parts. There will be 3D printed models available at the presentation for participants to find their own solutions. The second project has students working first as individual designers and then in pairs to designing a desktop organizer which will be created out of 3mm plywood on the laser cutter. After their initial sketches, individual students create a 3D model with card stock and tape. Students then pair up and negotiate for a final design that emphasizes their own ideas. Together students create a base CAD model, from which, they generate their own versions. Finally, they use the laser cutter and assembly their finished product. There will be models available at the presentation. Students’ pre- and post- test scores on the Purdue Spatial Visualization Test: Rotations (PSVT:R) assessment showed statistically significant improvement with mean scores increasing by 6% (p < 0.0002, N = 76).


WORKSHOP: Spatial Vis™: A sketching app with automatic grading to teach spatial visualization skills to introductory engineering students.

Abstract: Abstract Objectives: Spatial visualization (SV) refers to the ability to manipulate geometric shapes in one’s mind and is important in many STEM fields. Studies have shown that SV skills are learnable, and SV training can increase GPAs and graduate rates in engineering. Additionally, sketching has been shown to be an important part of increasing 3D visualization skills, which is key for creativity and team communication. However, few engineering curricula include SV training, and adding new content to an already full program can be difficult. The Spatial Vis™ App, developed by eGrove Education, was designed with the potential to teach SV training outside of scheduled class time. It allows for freehand sketches of isometric and orthographic assignments to be graded automatically and for hints to be provided when needed. The app has been tested in a number of trials at universities across the country and has been shown to be an engaging and effective tool in improving SV skills. The app is currently available on Android and Apple smartphones and tablets, as well as advanced touchscreen Chromebooks. Features of the app include automatic grading, personalized hints for students when stuck, teacher data for tracking student progress, gradual increase in difficulty of assignments, a gamification start system to encourage persistence, and several test questions the end of each lesson where the Hint and Peek questions have been disabled. Results from trials using the technology in a number of university classroom settings, which have demonstrated the efficacy of the app based on a pre- and post- standardized spatial visualization test, will be discussed. Targeted audience: First year engineering or engineering graphics college or community college instructors; K-12 pre-engineering teachers and administrators. Activities Planned: This workshop will provide an overview of the benefits and best practices of using the Spatial Vis app in your classroom. Participants will be given a license to the app to use on their own smartphone devices to engage in interactive spatial visualization (SV) activities. Learning Outcomes: Instructors will learn 1) spatial visualization skills such as 2D rotations, 3D isometric views, and 2D orthographic projections; 2) how to use the app including the algorithm that automatically grades students’ sketches and provides guidance when requested; and 3) best practices for how to implement the SV training in their curriculum. You won’t want to miss this interactive workshop! Approximate length: 2 hours Required supplies: Android or Apple touchscreen device such as a smartphone or tablet.


Visual Relationship Detection for Workplace Safety Applications

Abstract: Applications of object and visual relationship detection for safety and security applications are in its infancy. State-of-the-art computer vision research is largely focused on improving mean Average Precision and mean Average Recall performance on standard, general datasets such as the Verbs in COmmon objects in COntext and the Visual Genome dataset and rarely mention the potential of such models in safety and security scenarios. We propose to train and develop an object and visual relationship detection neural network to be used as part of the back-end model for a decision support system. We use a naive Bayesian Network to determine scenarios where our proposed object and visual relationship detection network is error-prone. We also release a graphical user interface which demonstrates how our back-end neural network and naive Bayesian Network can be used for hazardous workplace safety and security applications. All models and source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/th-truong/rd-gui</uri> for future research and applications of object and visual relationship detection models in the safety and security sector.


Margin-Aware Adaptive-Weighted-Loss for Deep Learning Based Imbalanced Data Classification

Abstract: In supervised learning algorithms, the class imbalance problem often leads to generate results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal aspect of separating the overlapping classes. This is the reason why most of these methods are prone to overfit on the training data. To this end, we propose a novel loss function, namely Margin-Aware Adaptive-Weighted (MAAW) loss. Here, we first use the large margin softmax to leverage intra-class compactness and inter-class separability. Further to learn an unbiased representation of the classes, we put forward a dynamically weighted loss for imbalanced data classification. This weight dynamically adapts on every minibatch based on the inverse class frequencies. In addition, it takes care of the hard-to-train samples by using the confidence scores to learn discriminative hidden representations of the data. The overall framework is found to be effective when evaluated on two widely used datasets, namely CIFAR-10 and Fashion-MNIST. Additional experiments on HAM10000 and APTOS-2019 BD datasets prove the robustness of our methodology. The source codes of the proposed methodology is provided under the GitHub repository <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/rishavpramanik/MAAW</uri>


Unsupervised Domain Adaptation for Medical Image Segmentation Using Transformer With Meta Attention

Abstract: Image segmentation is essential to medical image analysis as it provides the labeled regions of interest for the subsequent diagnosis and treatment. However, fully-supervised segmentation methods require high-quality annotations produced by experts, which is laborious and expensive. In addition, when performing segmentation on another unlabeled image modality, the segmentation performance will be adversely affected due to the domain shift. Unsupervised domain adaptation (UDA) is an effective way to tackle these problems, but the performance of the existing methods is still desired to improve. Also, despite the effectiveness of recent Transformer-based methods in medical image segmentation, the adaptability of Transformers is rarely investigated. In this paper, we present a novel UDA framework using a Transformer for building a cross-modality segmentation method with the advantages of learning long-range dependencies and transferring attentive information. To fully utilize the attention learned by the Transformer in UDA, we propose Meta Attention (MA) and use it to perform a fully attention-based alignment scheme, which can learn the hierarchical consistencies of attention and transfer more discriminative information between two modalities. We have conducted extensive experiments on cross-modality segmentation using three datasets, including a whole heart segmentation dataset (MMWHS), an abdominal organ segmentation dataset, and a brain tumor segmentation dataset. The promising results show that our method can significantly improve performance compared with the state-of-the-art UDA methods.


Sensor Data Protection Through Integration of Blockchain and Camouflaged Encryption in Cyber-Physical Manufacturing Systems

Abstract: Abstract The advancement of sensing technology enables efficient data collection from manufacturing systems for monitoring and control. Furthermore, with the rapid development of the Internet of Things (IoT) and information technologies, more and more manufacturing systems become cyber-enabled, facilitating real-time data sharing and information exchange, which significantly improves the flexibility and efficiency of manufacturing systems. However, the cyber-enabled environment may pose the collected sensor data with high risks of cyber-physical attacks during the data and information sharing. Specifically, cyber-physical attacks could target the manufacturing process and/or the data transmission process to maliciously tamper the sensor data, resulting in false alarms or failures in anomaly detection in monitoring. In addition, cyber-physical attacks may also enable illegal data access without authorization and cause the leakage of key product/process information. Therefore, it becomes critical to develop an effective approach to protect data from these attacks so that the cyber-physical security of the manufacturing systems can be assured in the cyber-enabled environment. To achieve this goal, this paper proposes an integrative blockchain-enabled data protection method by leveraging camouflaged asymmetry encryption. A real-world case study that protects the cyber-physical security of collected sensor data in additive manufacturing is presented to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time (less than 0.05 ms), and the risk of unauthorized data access is significantly reduced as well.


sEMG-Based Inter-Session Hand Gesture Recognition via Domain Adaptation with Locality Preserving and Maximum Margin

Abstract: Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods.


Deep Kernel Principal Component Analysis for multi-level feature learning

Abstract: Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction. Modern deep learning tools have achieved great empirical success, but a framework for deep principal component analysis is still lacking. Here we develop a deep kernel PCA methodology (DKPCA) to extract multiple levels of the most informative components of the data. Our scheme can effectively identify new hierarchical variables, called deep principal components, capturing the main characteristics of high-dimensional data through a simple and interpretable numerical optimization. We couple the principal components of multiple KPCA levels, theoretically showing that DKPCA creates both forward and backward dependency across levels, which has not been explored in kernel methods and yet is crucial to extract more informative features. Various experimental evaluations on multiple data types show that DKPCA finds more efficient and disentangled representations with higher explained variance in fewer principal components, compared to the shallow KPCA. We demonstrate that our method allows for effective hierarchical data exploration, with the ability to separate the key generative factors of the input data both for large datasets and when few training samples are available. Overall, DKPCA can facilitate the extraction of useful patterns from high-dimensional data by learning more informative features organized in different levels, giving diversified aspects to explore the variation factors in the data, while maintaining a simple mathematical formulation.


Double Transformer Super-Resolution for Breast Cancer ADC Images

Abstract: Diffusion-weighted imaging (DWI) has been extensively explored in guiding the clinic management of patients with breast cancer. However, due to the limited resolution, accurately characterizing tumors using DWI and the corresponding apparent diffusion coefficient (ADC) is still a challenging problem. In this paper, we aim to address the issue of super-resolution (SR) of ADC images and evaluate the clinical utility of SR-ADC images through radiomics analysis. To this end, we propose a novel double transformer-based network (DTformer) to enhance the resolution of ADC images. More specifically, we propose a symmetric U-shaped encoder-decoder network with two different types of transformer blocks, named as UTNet, to extract deep features for super-resolution. The basic backbone of UTNet is composed of a locally-enhanced Swin transformer block (LeSwin-T) and a convolutional transformer block (Conv-T), which are responsible for capturing long-range dependencies and local spatial information, respectively. Additionally, we introduce a residual upsampling network (RUpNet) to expand image resolution by leveraging initial residual information from the original low-resolution (LR) images. Extensive experiments show that DTformer achieves superior SR performance. Moreover, radiomics analysis reveals that improving the resolution of ADC images is beneficial for tumor characteristic prediction, such as histological grade and human epidermal growth factor receptor 2 (HER2) status.


WBC YOLO-ViT: 2 Way - 2 stage white blood cell detection and classification with a combination of YOLOv5 and vision transformer

Abstract: Accurate detection and classification of white blood cells, otherwise known as leukocytes, play a critical role in diagnosing and monitoring various illnesses. However, conventional methods, such as manual classification by trained professionals, must be revised in terms of accuracy, efficiency, and potential bias. Moreover, applying deep learning techniques to detect and classify white blood cells using microscopic images is challenging owing to limited data, resolution noise, irregular shapes, and varying colors from different sources. This study presents a novel approach integrating object detection and classification for numerous type-white blood cell. We designed a 2-way approach to use two types of images: WBC and nucleus. YOLO (fast object detection) and ViT (powerful image representation capabilities) are effectively integrated into 16 classes. The proposed model demonstrates an exceptional 96.449% accuracy rate in classification.


Automatic patient-level recognition of four <i>Plasmodium</i> species on thin blood smear by a real-time detection transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation

Abstract: Malaria remains a global health problem, with 247 million cases and 619,000 deaths in 2021. Diagnosis of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis for accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a real-time detection transformer (RT-DETR) object detection algorithm to discriminate Plasmodium species on thin blood smear images. The algorithm was trained and validated on a data set consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test data set of 4,508 images from 170 smears corresponding to 358,825 labels coming from six French university hospitals. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among Plasmodium-positive smears, the global accuracy was 82.7% (91/110) with a recall of 90% (38/42), 81.8% (18/22), and 76.1% (35/46) for P. falciparum, P. malariae, and P. ovale/vivax, respectively. The RT-DETR model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas lacking microscopy experts.IMPORTANCEMalaria remains a global health problem, with 247 million cases and 619,000 deaths in 2021. Diagnosis of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis for accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a real-time detection transformer (RT-DETR) object detection algorithm to discriminate Plasmodium species on thin blood smear images. Performances were calculated with a test data set of 4,508 images from 170 smears coming from six French university hospitals. The RT-DETR model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices and could be suitable for deployment in low-resource setting areas.


Machine learning applications for biochar studies: A mini-review

Abstract: Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.


Counterfactual formulation of patient-specific root causes of disease

Abstract: Root causes of disease intuitively correspond to root vertices of a causal model that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathematical formulation needed for the development of computer algorithms designed to automatically detect root causes from data. We seek a definition of patient-specific root causes of disease that models the intuitive procedure routinely utilized by physicians to uncover root causes in the clinic.We use structural equation models, interventional counterfactuals and the recently developed mathematical formalization of backtracking counterfactuals to propose a counterfactual formulation of patient-specific root causes of disease matching clinical intuition.We introduce a definition of patient-specific root causes of disease that climbs to the third rung of Pearl's Ladder of Causation and matches clinical intuition given factual patient data and a working causal model. We then show how to assign a root causal contribution score to each variable using Shapley values from explainable artificial intelligence.The proposed counterfactual formulation of patient-specific root causes of disease accounts for noisy labels, adapts to disease prevalence and admits fast computation without the need for counterfactual simulation.


Optical ReLU-like activation function based on a semiconductor laser with optical injection

Abstract: Artificial neural networks usually consist of successive linear multiply–accumulate operations and nonlinear activation functions. However, most optical neural networks only achieve the linear operation in the optical domain, while the optical implementation of activation function remains challenging. Here we present an optical ReLU-like activation function (with 180° rotation) based on a semiconductor laser subject to the optical injection in an experiment. The ReLU-like function is achieved in a broad regime above the Hopf bifurcation of the injection-locking diagram and is operated in the continuous-wave mode. In particular, the slope of the activation function is reconfigurable by tuning the frequency difference between the master laser and the slave laser.