Comparison of Discriminant Analysis and Support Vector Machine on Mixed Categorical and Continuous Independent Variables for COVID-19 Patients Data

Abstract: Purpose: Numerous factors can affect the duration of COVID-19 recovery. One method involves utilizing natural herbal medication. This study seeks to determine the variables influencing the duration of COVID-19 recovery and to compare discriminant analysis and support vector machine models using COVID-19 patient data from West Sumatra.Methods: Two data mining methods, Discriminant Analysis and Support Vector Machine with different types of kernels (linear, polynomial, and radial basis function), were employed to categorize the time of COVID-19 recovery in this work. The study utilized 428 data points, with 75% allocated for training data and 25% for testing data. The independent factors were evaluated by determining the selection variables' information value (IV) to gauge their influence on the dependent variable. Data resampling techniques were employed to tackle the problem of data imbalance. This study employs data resampling techniques, including undersampling, oversampling, and SMOTE. The balancing accuracy of Discriminant Analysis and Support Vector Machine was examined.Result: The Discriminant Analysis with SMOTE achieved a balanced accuracy of 66.50%, outperforming the linear kernel Support Vector Machine with SMOTE, which had a balanced accuracy of 63.20% in this dataset.Novelty: This study assessed the novelty, originality, and value by comparing Discriminant Analysis and SVM algorithms with categorical and continuous independent variables. This research explores techniques for managing imbalanced data using undersampling, oversampling, and SMOTE, with variable selection based on information value assessment.


Implementation of Feature Selection Strategies to Enhance Classification Using XGBoost and Decision Tree

Abstract: Purpose: Grades in the world of education are often a benchmark for students to be considered successful or not during the learning period. The facilities and teaching staff provided by schools with the same portion do not make student grades the same, the value gap is still found in every school. The purpose of this research is to produce a better accuracy rate by applying feature selection Information Gain (IG), Recursive Feature Elimination (RFE), Lasso, and Hybrid (RFE + Mutual Information) using XGBoost and Decision Tree models.Methods: This research was conducted using 649 Portuguese course student data that had been pre-processed according to data requirements, then, feature selection was carried out to select features that affect the target, after that all data can be classified using XGBoost and Decision tree, finally evaluating and displaying the results. Results: The results showed that feature selection Information Gain combined with the XGBoost algorithm has the best accuracy results compared to others, which is 81.53%.Novelty: The contribution of this research is to improve the classification accuracy results of previous research by using 2 traditional machine learning algorithms and some feature selection.


The Comparison of K-Nearest Neighbors and Random Forest Algorithm to Recognize Indonesian Sign Language in a Real-Time

Abstract: Purpose: Comparing 2 models or prototype programs which can recognize Indonesian Sign Language System or Sistem Isyarat Bahasa Indonesia (SIBI) fonts from hand gesture and translate it’s into writing Messages in real-time.Methods: After selecting datasets and reprocessed by the researcher into 1 dataset, which are a combination of several sign image datasets of the SIBI letters images available on the Kaggle website, the dataset is converted into landmarks. The landmarks are divided into 26 sign classes and preprocessed to a total of 19,826 rows of data, and then divided into 67% training data and 33% test data. Next, both K-NN and Random Forest algorithm are implemented into different program and get tested into 2 different tests, model evaluation and real-time. At the end, the result is compared to see the increase of accuracy level of both K-Nearest Neighbors (K-NN) and Random Forest algorithm.Result: The constructed and trained model is then evaluated and the results of Precision, Recall, Accuracy, and F1-Score are 99.88% using the Random Forest algorithm. The results of real-time program testing with the K-Nearest Neighbors algorithm get higher results, where the average accuracy value reaches 99%.Novelty: From the result shows that the model built with the Random Forest algorithm is superior, but the K-Nearest Neighbors algorithm is better in real-time testing. Therefore, image data and its diversity should be increased, in order to improve recognition accuracy. The program could be enhanced by adding a function where the program can recognize hand gesture, not only one or two hands but also can recognize a hand gesture with movements so the program can recognize static and dynamic letter (required hands movement).


Analysis Impact of Rapid Application Development Method on Development Cycle and User Satisfaction: A Case Study on Web-Based Registration Service

Abstract: Purpose: This research was conducted to respond to obstacles and inefficiencies in the new student registration system at RA Plus Rabbani. Currently, the conventional method of using physical documents for registration is vulnerable to damage and data loss. Therefore, the proposed solution is implementing a website-based online registration system using the Rapid Application Development (RAD) method. This aims to simplify the process, increase accessibility for prospective students, and reduce the costs and time required.Methods: This research commenced by identifying constraints within the conventional student registration system at RA Plus Rabbani through observations and interviews. The development, following the RAD methodology, involved testing with PHPUnit and Blackbox Testing to ensure the functionality of the system aligned with specifications. In addition, usability evaluation was conducted based on the ISO 9126 standard.Result: The research results show that testing on MVC indicated a 100% success rate for each architectural feature. Referring to expectations with a “valid” conclusion on functionality using Blackbox testing, based on ISO 9126 percentage displayed, it is known that the criterion with the most significant value is the understandability characteristic with a value of 83%. Novelty: This research makes a significant contribution by improving student registration services at RA Plus Rabbani through the implementation of various testing techniques, following the research flow offered by RAD. The study also provides substantial references for further research in web-based system development.


Indonesian Hate Speech Text Classification Using Improved K-Nearest Neighbor with TF-IDF-ICSρF

Abstract: Purpose: Freedom in social media gives rise to the possibility of disturbing users through the sentences they send, which is limited by the Electronic Information and Transactions Law (UU ITE). This research aims to find an effective method for classifying hate speech text data, especially in Indonesian, with many categories expected to minimize this case.Methods: This study used 1.000 data from Twitter with five labels, including religion, race, physical, gender and other (invective or slander). The process started with several steps of preprocessing, data transformation using TF-IDF-ICSρF term weighting and data mining using an Improved KNN algorithm. Then, the results were compared with the TF-IDF and KNN methods to evaluate the differences.Result: Using TF-IDF-ICSρF and Improved KNN algorithms gets an average accuracy value of 88.11%, 17.81% higher compared with the same data and parameters to the K-Nearest Neighbor and TF-IDF algorithms, which get results of 70.30%.Novelty: Based on the comparison results, TF-IDF-ICSρF and Improved KNN methods can effectively classify hate speech sentences that have many labels with fairly good accuracy.


Enhancing Mathematical Disposition in Geometry Instruction : The Role of E-Comics in Realistic Mathematics Education

Abstract: Success in learning mathematics can be measured through five aspects called the five strands of mathematical proficiency. These five aspects need to be taught fairly, resulting in a change in students' mathematical attitudes where this aspect is still minimally looked at and researched. Therefore, the research aims to analyze changes in students' mathematical dispositions by reviewing the role of e-comics in Realistic Mathematics Education (RME). This context-based mathematics learning approach is assumed to provide meaningful learning for students. This mixed-methods research with a sequential explanatory research design was carried out on 30 elementary school students by combining quantitative data collection and analysis techniques with qualitative data support. The research showed that using e-comics in RME learning significantly improved students' mathematical dispositions with a large effect size (Cohen’s effect size = 0,98). In a qualitative review involving data triangulation, evidence was found that using e-comics in RME learning has a role in facilitating the development of each student indicator, as evidenced in the high students’ enthusiasm or response in learning both in observations, questionnaires, and documentation studies. In this way, it is hoped that this innovation can be researched and developed further on a more diverse scale and content. In addition, this research provides further opportunities for research and teacher training related to developing RME learning designs and e-comics in learning.


Empowering Future Teachers: The Role of Student Organizations in Enhancing Public Speaking and Teaching Skills

Abstract: Recognizing the pivotal role of teachers in shaping education, extensive research has been conducted to assess their effectiveness. Among the prominent challenges faced by many educators is mastering public speaking, a crucial skill for effective classroom instruction. This study delves into whether participation in student groups can augment aspiring elementary school teachers' public speaking and teaching competencies while emphasizing the significance of public speaking skills within the teaching profession. Employing descriptive qualitative methods such as interviews, observations, and document analysis, the research elucidates the profound impact of public speaking proficiency on teaching effectiveness. The study encompasses four key stages: preparation, implementation, analysis, and reporting. Twelve students participated as respondents, focusing on those actively engaged in group activities. Additionally, six lecturers from the Primary School Teacher Education Study Program, Muhammadiyah University of Surakarta, knowledgeable about the fundamental skill requirements for aspiring teachers and the program director, served as supplementary subjects. The findings suggest that engagement in student groups can significantly enhance aspiring teachers' public speaking skills, thereby better equipping them for successful careers in education.


The Relationship Between Training Methods And Student Activity in Extracurricular Arts SMKN 5 Padang

Abstract: This research was hampered by the low student activity on extracurricular arts, which was allegedly due to the inadequacy of the practice methods used. The aim is to look at the methods of practice, student activity and see the relationship between the method of practice and student activity on extracurricular arts. The research method uses a quantitative approach to the type of correlational research. The population of this research is all that follows the activity with sampling using the Cluster Random Sampling technique. Data gathering techniques using lifting. Data analysis techniques using percentage formula and Product Moment formula.The results of the study showed that; the methods of training used by trainers in extracurricular still belong to the less appropriate category; student activity also belonged to low; and there was a significant relationship between the method of training and student activity in the extra-curricular arts.Since the extracurricular activities of the arts are very beneficial to the students, there is a need for the efforts of the school to pay attention and enhance the construction of such extracursual activities. It is recommended to the extra-curricular art trainers, to increase and maximize the use of methods of training in extracural activities so as to a high level of activity.


The Relationship Between Parenting Styles And Student Learning Discipline At MDTA Nurul Amal, Sungai Aur District, West Pasaman Regency

Abstract: The inspiration behind this study stems from the insufficient level of discipline observed in students' learning. The primary objective is to assess the implementation of democratic parenting patterns among the parents of students, evaluate the extent of students' learning discipline, and investigate the correlation between parents' parenting styles and students' learning discipline at MDTA Nurul Amal Muara Tapus, Sungai Aur District, West Pasaman Regency. Employing a quantitative approach with a correlational research design, the study encompasses a population of 47 individuals from classes III and IV enrolled as students at MDTA Nurul Amal. A sample representing 80% of the population is selected using the Stratified Random Sampling technique. Data is collected through the administration of questionnaires, and for analysis, the percentage and product-moment formulas are employed. The findings indicate a low level of democratic parenting among the parents of students, a low level of students' learning discipline, and a significant correlation between parents' democratic parenting style and students' learning discipline of santri at MDTA Nurul Amal Muara Tapus, Sungai Aur District, West Pasaman Regency. The researcher's suggestions in this study to parents are expected to increase the application of democratic parenting patterns to support children in carrying out high learning disciplineKeywords: Parenting style, Democratic, Discipline


The Relationship Between Parental Support and Learning Outcomes For Social Studies Package B At SPNF SKB Bukittinggi City

Abstract: The background to this research is the low social studies learning outcomes of residents studying package B. The aim of this research is to see a picture of the family environment of study residents, see a picture of social studies learning outcomes of residents studying package B, and see a picture of social studies learning outcomes of residents studying package B. The relationship between the family environment and Social Sciences learning results package B at SPNF SKB Bukittinggi City. This research uses a correlational quantitative approach. The population of this study was residents studying package b at SPNF SKB Bukittinggi City, totaling 45 people. Meanwhile, samples were taken from 60% of the population, namely 27 people, using a cluster random sampling technique. Data collection techniques using questionnaires. Data analysis techniques using percentage formulas and Spearman rank formulas. The research results show (1) the family environment is still not good, (2) student learning outcomes are still relatively low, (3) there is a relationship between the family environment and social studies learning outcomes package b, this relationship is declared significant because it is categorized as very strong. It is recommended that parents provide support in education in the family environment in order to optimize student learning outcomes. The SPNF SKB Institute is able to provide motivational support to learning residents and be creative in using learning media so that learning enthusiasm can increase among learning residents. Future researchers can identify characteristics or other variables that have not been examined in this research for further follow-up. Keywords: Family Environment, Learning Outcomes


Development of Uno Stacko Games-Based Learning Media Integrated with Qr Code on the Material of the Human Respiratory System in Increasing Interest and Understanding of Concepts for Grade VIII Middle School Students

Abstract: Game-based learning media is a media that can improve students' interest and conceptual understanding effectively. In this study, the research and development (RD) method was used by adopting the ADDIE model to develop a learning media based on Uno Stacko game with a focus on the topic of Respiratory System which was integrated with the QR Code. The effectiveness test that was used on the learning media was a quasi-experimental design. The media testing used three classes consisting of a trial class with a total of 25 students, as well as a control class and an experimental class, each of which consisted of 23 students. The results of the study of the students' learning interest with the Respiratory System Uno Stacko learning media along with further explanation by the teacher using the lecture method differ significantly from the learning interest of the students who only used the lecture method. The implementation of the learning media can improve learning interest. It can be seen from the result of the average percentage of the experimental class with a total of 81% which is higher than the control class with a total of 76


DESAIN PESANTREN MINHAJUSSALAM TINGKAT MTS DI KECAMATAN GUNUNG RAJA KABUPATEN TANAH LAUT

Abstract: Flood is a natural disaster that can harm the environment, especially human settlements. The potential for flooding can be seen based on environmental characteristics, one of which is a swamp. Gunung Raja, Bati-Bati District, Kab. Tanah Laut is one of the residential areas that stands on swampland. Swampy land has high tide times and can potentially flood when it encounters high rainfall. Knowing the potential for flooding, we need a building that is responsive to tides. Buildings that have the potential for disaster response are educational facilities/schools, namely the Minhajussalam Islamic Boarding School. By creating a design using the Contextual Architecture method, it is hoped that this design can create a building that is responsive to sea tides and can become an emergency facility for the surrounding color in the event of a flood.


Handwritten Digit Recognition Accuracy Comparison Using Knn,Cnn And Svm.

Abstract: This project Handwritten digit recognition is a vital area in the field of computer vision.This paper compares the performance of convolutional neural networks (CNNs), k-nearest neighbors (KNN), and support vector machines (SVMs) for digit classification using the MNIST dataset.The dataset consists of 60,000 labeled images of handwritten digits 0-9 for training and 10,000 images for testing.Experimental results demonstrate that CNNs achieve the highest test accuracy of 98.6%, outperforming KNN (96.8%) and SVM (97.1%).The multilayer feature extraction capability and depth of CNN models are better at capturing the unique visual patterns of different digit classes in the images.The study provides quantitative results that illustrate that CNN architectures yield state-of-the-art performance on MNIST digit classification compared to other common approaches.


Methods of individual educational work of curators at universities of the Ministry of the Interior in Russia

Abstract: The subject of the article is the methods of individual educational work of curators in universities of the Ministry of Internal Affairs of Russia. The relevance of the study is due to the insufficient development of the system of methods for individual educational work of curators, methodological recommendations for their use in educational organizations of the Ministry of Internal Affairs of Russia, as well as the predominant orientation of many curators to the use of forms and methods of educational work carried out with groups and teams. The effective work curators involves the integrated use of the entire arsenal of methods and means of educational work. The purpose of the study is a detailed description of the methods of individual educational work of curators in universities of the Ministry of Internal Affairs of Russia. The author lists and reveals the principles of organizing and conducting educational influence on the of cadets person in the system of individual educational work of curators, requirements for their use of methods of individual educational work. The article provides detailed methodological recommendations for the use of such methods of individual educational work as persuasion, example, competition, exercise, acceptance, criticism of actions and deeds by teachers at universities of the Ministry of Internal Affairs of Rus-sia, individual tasks and assignments, providing comradely assistance, rendering knowledge of trust, coercion. It is noted that the choice of methods of educational influence depends on the experience of the subjects of education (including the curators and the cadet), goals, objectives, content and conditions of individual educational work, personal and professional characteristics of the cadet. The curator must make the optimal choice of method (or methods) of educational influence.


THE SECOND GRADE STUDENTS' LEARNING MOTIVATION OF IMPLEMENTATION PROBLEM BASED LEARNING (PBL) ON GRAMMAR MATERIAL

Abstract: Problem based leaning become one of teaching method ofimplementation instrusction. PBL required student to active duringleaning. Problem-based learning (PBL) is a self-directed learning strategywhere student work collaboratively in small group to investigate openendedrelatable case scenarios (Fukuzawa et al., 2017). Albanese et al in(Fukuzawa et al., 2017) wroten that Problem-based learning (PBL) canincreased student’s motivation and engagement in learning activity. It ismean that student have motivation to increase and engage active inlearning throught PBL. This research focus on achievemet motivationthat student have. This reseach used qualitative research method whichcover indentification, review the literatude,collect the data, analyze thedata and evaluate the research. questionnaires and interview used tocollect the data . Thirty four students enganged as participants. Based onthe data that was analyzed, the research present six things that motivatedthe student on grammar learning throught implementation PBL. Thoseare the role of grammar in english learning. The student’s spirit learning,More change to get achievement, PBL help student to show the ability inGrammar and english learning, Grammar is difficult. And feedback thathave sustainable effect in learning.


The use of art technologies in the development of the professional position of future educational psychologists

Abstract: Relevance. The relevance of this topic stems from the fact that the Republic of Kazakhstan's modern society is experiencing rapid sociocultural development, and that the implementation of new educational practices has generated radically altered demands for teacher preparation. In order to professionally contribute to the execution of the modern educational process in the growth of the Republic of Kazakhstan's complete educational system, it is required in this regard to train educational psychologists. The growth of the professional position of future educational psychologists in the Republic of Kazakhstan is examined in this study using cutting-edge creative technology. Purpose. The purpose of this study is to evaluate and support the theoretical underpinnings of the professional positioning of aspiring educational psychologists through the use of cutting-edge technologies and to identify the prerequisites for their efficient application in the process of developing qualified specialists. Methodology. The principles on human initiative in the transformation of the psychological environment in the process of self-fulfillment, the dissemination of knowledge in the process of continuing education worldwide, student-oriented and sociocultural approaches that concentrate on the development of pedagogical systems, and pedagogical provisions of psychology on the understanding of personality in the process of artistic expression form the basis for this study's methodology. The major cognitive techniques are the theoretical examination of the research topic's scientific literature and the practical analysis of how art technologies are used in Kazakhstan's educational system. Results and Conclusions. The research on "Development of the professional position of future educational psychologists through art technologies" has practical implications that can be applied to the creation of art therapy programs and methodologies within the framework of the Republic of Kazakhstan's professional psychological and pedagogical education system.


Methods of professionally-oriented teaching of physics to students of technical universities

Abstract: Relevance. The study of technical, and especially physical, education at the secondary and higher education levels in recent years has focused more on the exploration of concepts or understanding than on the development of skills and values. The least attention was paid to the development of values. Teaching physics in lectures often emphasises concepts and their interrelation, while the laboratory class also focuses on the development of laboratory skills. Purpose. The purpose of the study is to investigate the methods of professionally-oriented teaching of physics to students of technical universities. Methodology. Systematic review can be explained as a research method and process for identifying and critically evaluating relevant studies, and for collecting and analysing data from these studies. When promoting physics among the population and encouraging students to pursue a career in physics, emphasis is usually placed on developing the ability to solve problems in physics. In the review of the value of the physics diploma, graduates of the technical university emphasised the importance of skills in their profession. Results. It is assumed that with the help of an interactive way of teaching physics, it would be possible to eliminate students' misconceptions, reduce the dropout rate of first-year students, and also increase the level of students' knowledge in introductory general physics courses, mainly in the field of mechanics. Conclusions. The concept of "knowledge in motion" combined with theories in the workplace can offer a useful conceptual union for studying the nature of professional knowledge, which is taught to students of technical specialities after vocational education and training courses. This suggests that professional knowledge, rather than as a dichotomy, is perhaps more correctly viewed as distributed and networked.


Relation-Aggregated Cross-Graph Correlation Learning for Fine-Grained Image–Text Retrieval

Abstract: Fine-grained image-text retrieval has been a hot research topic to bridge the vision and languages, and its main challenge is how to learn the semantic correspondence across different modalities. The existing methods mainly focus on learning the global semantic correspondence or intramodal relation correspondence in separate data representations, but which rarely consider the intermodal relation that interactively provide complementary hints for fine-grained semantic correlation learning. To address this issue, we propose a relation-aggregated cross-graph (RACG) model to explicitly learn the fine-grained semantic correspondence by aggregating both intramodal and intermodal relations, which can be well utilized to guide the feature correspondence learning process. More specifically, we first build semantic-embedded graph to explore both fine-grained objects and their relations of different media types, which aim not only to characterize the object appearance in each modality, but also to capture the intrinsic relation information to differentiate intramodal discrepancies. Then, a cross-graph relation encoder is newly designed to explore the intermodal relation across different modalities, which can mutually boost the cross-modal correlations to learn more precise intermodal dependencies. Besides, the feature reconstruction module and multihead similarity alignment are efficiently leveraged to optimize the node-level semantic correspondence, whereby the relation-aggregated cross-modal embeddings between image and text are discriminatively obtained to benefit various image-text retrieval tasks with high retrieval performance. Extensive experiments evaluated on benchmark datasets quantitatively and qualitatively verify the advantages of the proposed framework for fine-grained image-text retrieval and show its competitive performance with the state of the arts.


Research Ideas Discovery via Hierarchical Negative Correlation

Abstract: A new research idea may be inspired by the connections of keywords. Link prediction discovers potential nonexisting links in an existing graph and has been applied in many applications. This article explores a method of discovering new research ideas based on link prediction, which predicts the possible connections of different keywords by analyzing the topological structure of the keyword graph. The patterns of links between keywords may be diversified due to different domains and different habits of authors. Therefore, it is often difficult for a single learner to extract diverse patterns of different research domains. To address this issue, groups of learners are organized with negative correlation to encourage the diversity of sublearners. Moreover, a hierarchical negative correlation mechanism is proposed to extract subgraph features in different order subgraphs, which improves the diversity by explicitly supervising the negative correlation on each layer of sublearners. Experiments are conducted to illustrate the effectiveness of the proposed model to discover new research ideas. Under the premise of ensuring the performance of the model, the proposed method consumes less time and computational cost compared with other ensemble methods.


Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness

Abstract: In this work, we address the task of few-shot medical image segmentation (MIS) with a novel proposed framework based on the learning registration to learn segmentation (LRLS) paradigm. To cope with the limitations of lack of authenticity, diversity, and robustness in the existing LRLS frameworks, we propose the better registration better segmentation (BRBS) framework with three main contributions that are experimentally shown to have substantial practical merit. First, we improve the authenticity in the registration-based generation program and propose the knowledge consistency constraint strategy that constrains the registration network to learn according to the domain knowledge. It brings the semantic-aligned and topology-preserved registration, thus allowing the generation program to output new data with great space and style authenticity. Second, we deeply studied the diversity of the generation process and propose the space-style sampling program, which introduces the modeling of the transformation path of style and space change between few atlases and numerous unlabeled images into the generation program. Therefore, the sampling on the transformation paths provides much more diverse space and style features to the generated data effectively improving the diversity. Third, we first highlight the robustness in the learning of segmentation in the LRLS paradigm and propose the mix misalignment regularization, which simulates the misalignment distortion and constrains the network to reduce the fitting degree of misaligned regions. Therefore, it builds regularization for these regions improving the robustness of segmentation learning. Without any bells and whistles, our approach achieves a new state-of-the-art performance in few-shot MIS on two challenging tasks that outperform the existing LRLS-based few-shot methods. We believe that this novel and effective framework will provide a powerful few-shot benchmark for the field of medical image and efficiently reduce the costs of medical image research. All of our code will be made publicly available online.


Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph

Abstract: Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proven to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering performance. Although existing localized MKC algorithms exhibit improved performance compared with globally designed competitors, most of them widely adopt the KNN mechanism to localize kernel matrix by accounting for τ -nearest neighbors. However, such a coarse manner follows an unreasonable strategy that the ranking importance of different neighbors is equal, which is impractical in applications. To alleviate such problems, this article proposes a novel local sample-weighted MKC (LSWMKC) model. We first construct a consensus discriminative affinity graph in kernel space, revealing the latent local structures. Furthermore, an optimal neighborhood kernel for the learned affinity graph is output with naturally sparse property and clear block diagonal structure. Moreover, LSWMKC implicitly optimizes adaptive weights on different neighbors with corresponding samples. Experimental results demonstrate that our LSWMKC possesses better local manifold representation and outperforms existing kernel or graph-based clustering algorithms. The source code of LSWMKC can be publicly accessed from https://github.com/liliangnudt/LSWMKC.


Cybercrime and the Law: Addressing the Challenges of Digital Forensics in Criminal Investigations

Abstract: A lack of standards and regulations for handling digital evidence is impeding its admissibility in court proceedings. This article addresses the challenges of digital forensics in criminal investigations due to the rise of cybercrime. The literature existing studies on digital forensics, legal frameworks, and cybercrime is reviewed in order to find possible solutions. The results demonstrate the importance of collaboration between the legal and technological sectors in developing standardized norms and processes for digital evidence collection and processing. The findings also highlight the importance of digital forensics in criminal investigations and the need for a robust legal framework to combat cybercrime effectively. This note emphasizes the vital significance of digital forensics in criminal investigations and the need to develop standardized rules and procedures for the management of digital evidence. The recommendations presented in this article may assist policymakers and law enforcement authorities in designing legal framework capable of effectively confronting cybercrime.


DRLIE: Flexible Low-Light Image Enhancement via Disentangled Representations

Abstract: Low-light image enhancement (LIME) aims to convert images with unsatisfied lighting into desired ones. Different from existing methods that manipulate illumination in uncontrollable manners, we propose a flexible framework to take user-specified guide images as references to improve the practicability. To achieve the goal, this article models an image as the combination of two components, that is, content and exposure attribute, from an information decoupling perspective. Specifically, we first adopt a content encoder and an attribute encoder to disentangle the two components. Then, we combine the scene content information of the low-light image with the exposure attribute of the guide image to reconstruct the enhanced image through a generator. Extensive experiments on public datasets demonstrate the superiority of our approach over state-of-the-art alternatives. Particularly, the proposed method allows users to enhance images according to their preferences, by providing specific guide images. Our source code and the pretrained model are available at https://github.com/Linfeng-Tang/DRLIE.


Geometrical Interpretation and Design of Multilayer Perceptrons

Abstract: The multilayer perceptron (MLP) neural network is interpreted from the geometrical viewpoint in this work, that is, an MLP partition an input feature space into multiple nonoverlapping subspaces using a set of hyperplanes, where the great majority of samples in a subspace belongs to one object class. Based on this high-level idea, we propose a three-layer feedforward MLP (FF-MLP) architecture for its implementation. In the first layer, the input feature space is split into multiple subspaces by a set of partitioning hyperplanes and rectified linear unit (ReLU) activation, which is implemented by the classical two-class linear discriminant analysis (LDA). In the second layer, each neuron activates one of the subspaces formed by the partitioning hyperplanes with specially designed weights. In the third layer, all subspaces of the same class are connected to an output node that represents the object class. The proposed design determines all MLP parameters in a feedforward one-pass fashion analytically without backpropagation. Experiments are conducted to compare the performance of the traditional backpropagation-based MLP (BP-MLP) and the new FF-MLP. It is observed that the FF-MLP outperforms the BP-MLP in terms of design time, training time, and classification performance in several benchmarking datasets. Our source code is available at https://colab.research.google.com/drive/1Gz0L8AnT4ijrUchrhEXXsnaacrFdenn?usp = sharing.


Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation

Abstract: Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarize both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review can inspire the research community to explore solutions to this challenge and further advance the field of medical image segmentation.


Advancing the College of Engineering Strategic Goal of Becoming a National Model of Inclusivity and Collaboration

Abstract: Abstract The College of Engineering at a public research institution, with Land Grant and Sea Grant designations, has committed to becoming a community of faculty, students, and staff that is increasingly more inclusive, collaborative, diverse, and centered on student success. We are meeting this commitment in part through design and implementation of new and revised practices for recruitment, professional development, mentoring, and advancement. But changing organizational culture is a large-scale undertaking. In order to build an organizational conscience for the college and secure its transformation into a community where all members feel welcome and engaged, "top-down" policy change must be complemented by enlistment of change agents from every employment sector of the college. For this purpose, a 20-member Change Team — including a balance of tenure-track and professional faculty and staff — was assembled in AY18 and empowered to work with college leadership to envision, develop, and resource infrastructure and communication needs to engage all college employees in our culture change process. The work of the Change Team has emerged as essential to advancing our goals in relation to community. We will discuss inception and evolution of the Team, and profile five projects launched in AY18: • Increasing competency and building an organizational conscience (this project foregrounds the symbolic level of oppression through bringing two events to campus, featuring an internationally acclaimed social justice scholar, and a play that explores the dangers of racism and political correctness). • Design of modules to enhance graduate students' capacities to engage issues of inclusivity, equity and social justice. • Development of a scaffolded approach to enhancing undergraduate students' knowledge and skills supporting inclusive and socially just teaming practices. • Alignment of 'Action Plans' developed by engineering faculty and administrators who completed the NSF-supported ADVANCE seminar. • Revision of the manufacturing engineering undergraduate curriculum in order to enhance its flexibility and accessibility.


A Blockchain-Based Scheme for Secure Data Offloading in Healthcare With Deep Reinforcement Learning

Abstract: With the widespread popularity of the Internet of Things and various intelligent medical devices, the amount of medical data is rising sharply, and thus medical data processing has become increasingly challenging. Mobile edge computing technology allows computing power to be allocated at the edge closer to users, which enables efficient data offloading for healthcare systems. However, existing studies on medical data offloading seldom guarantee effective data privacy and security. Moreover, the research equipping data offloading architectures with Blockchain neglect the delay and energy consumption costs incurred in using Blockchain technology for medical data offloading. Therefore, in this paper, we propose a data offloading scheme for healthcare based on Blockchain technology, which achieves optimal medical resource allocation and simultaneously minimizes the cost of offloading tasks. Specifically, we design a smart contract to ensure secure data offloading. And, we formulate the cost problem as a Markov Decision Process, solved by a policy search-based deep reinforcement learning (Asynchronous Advantage Actor-Critic) scheme, where we jointly consider offloading decisions, allocation of computing resources and radio transmission bandwidth, and Blockchain data security audits. The security of our smart-contract-based mechanism is theoretically and empirically proved, while extensive experimental results also show that our solution can obtain superior performance gains with lower cost than other baselines.


A Training-Free, Lightweight Global Image Descriptor for Long-Term Visual Place Recognition Toward Autonomous Vehicles

Abstract: Long-term visual place recognition (VPR) has recently become a popular research topic in the field of autonomous driving. In urban scenarios, variations in scene appearance due to the change in seasons and illumination bring great challenges for scene description. Several learning-based VPR techniques can learn latent invariant descriptors for appearance variations and show excellent performance in long-term VPR tasks. However, these methods require huge datasets and computational resources (e.g., GPUs) for training and inference. Mobile platforms such as autonomous vehicles often cannot provide sufficient computing power. To address this issue, in this paper, a training-free lightweight global image descriptor named SSR-VLAD is proposed for VPR. This descriptor is able to work accurately in real-time without GPUs, even on embedded platforms. The contribution of this work has two aspects. (1) A novel semantic skeleton representation (SSR) is proposed to describe the semantic spatial distribution of scenes by using the semantic spatial context; (2) Inspired by the Vector of Locally Aggregated Descriptors (VLAD), a spatial-temporal aggregation framework for SSR features is constructed to aggregate all SSR features into one SSR-VLAD descriptor, which encodes the spatial and temporal information into a fixed-size global descriptor. SSR-VLAD shows robust performance towards the appearance variations of scenes. Specifically, on three public datasets with challenging urban scenes, experimental results show that SSR-VLAD has competitive VPR performance compared to several state-of-the-art (SoTA) VPR methods. Additionally, SSR-VLAD achieves SoTA real-time computational performance with lower RAM consumption in computationally constrained scenarios.


Deep Autoencoder-Based Z-Interference Channels With Perfect and Imperfect CSI

Abstract: A deep autoencoder (DAE)-based structure for end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The proposed structure jointly optimizes the two encoder/decoder pairs and generates interference-aware constellations that dynamically adapt their shape based on interference intensity to minimize the bit error rate (BER). An in-phase/quadrature-phase (I/Q) power allocation layer is introduced in the DAE to guarantee an average power constraint and enable the architecture to generate constellations with nonuniform shapes. This brings further gain compared to standard uniform constellations such as quadrature amplitude modulation. The proposed structure is then extended to work with imperfect channel state information (CSI). The CSI imperfection due to both the estimation and quantization errors are examined. The performance of the DAE-ZIC is compared with two baseline methods, i.e., standard and rotated constellations. The proposed structure significantly enhances the performance of the ZIC both for the perfect and imperfect CSI. Simulation results show that the improvement is achieved in all interference regimes (weak, moderate, and strong) and consistently increases with the signal-to-noise ratio (SNR). For instance, more than an order of magnitude BER reduction is obtained with respect to the most competitive conventional method at weak interference when SNR > 15dB and two bits per symbol are transmitted. The improvements reach about two orders of magnitude when quantization error exists, indicating that the DAE-ZIC is more robust to the interference compared to the conventional methods.


UTDNet: A unified triplet decoder network for multimodal salient object detection

Abstract: Image Salient Object Detection (SOD) is a fundamental research topic in the area of computer vision. Recently, the multimodal information in RGB, Depth (D), and Thermal (T) modalities has been proven to be beneficial to the SOD. However, existing methods are only designed for RGB-D or RGB-T SOD, which may limit the utilization in various modalities, or just finetuned on specific datasets, which may bring about extra computation overhead. These defects can hinder the practical deployment of SOD in real-world applications. In this paper, we propose an end-to-end Unified Triplet Decoder Network, dubbed UTDNet, for both RGB-T and RGB-D SOD tasks. The intractable challenges for the unified multimodal SOD are mainly two-fold, i.e., 1) accurately detecting and segmenting salient objects, and 2) preferably via a single network that fits both RGB-T and RGB-D SOD. First, to deal with the former challenge, we propose the multi-scale feature extraction unit to enrich the discriminative contextual information, and the efficient fusion module to explore cross-modality complementary information. Then, the multimodal features are fed to the triplet decoder, where the hierarchical deep supervision loss further enable the network to capture distinctive saliency cues. Second, as to the latter challenge, we propose a simple yet effective continual learning method to unify multimodal SOD. Concretely, we sequentially train multimodal SOD tasks by applying Elastic Weight Consolidation (EWC) regularization with the hierarchical loss function to avoid catastrophic forgetting without inducing more parameters. Critically, the triplet decoder separates task-specific and task-invariant information, making the network easily adaptable to multimodal SOD tasks. Extensive comparisons with 26 recently proposed RGB-T and RGB-D SOD methods demonstrate the superiority of the proposed UTDNet.


Indonesian Presidential Election Sentiment: Dataset of Response Public Before 2024.

Abstract: Indonesia is one of the countries that is currently entering the political year for the election of President, Regional Heads, and Members of the Legislative in 2024. This has become a hot topic on social media, especially about the Presidential Election. Twitter is one of the platforms with the largest users in Indonesia. It is interesting to see the alignment of Twitter users towards presidential candidates who already have a carrying party, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan based on a sentiment analysis approach. User feedback data about Indonesian Presidential candidates are obtained from the Twitter platform using Twitter API with Python programming language. The data obtained was 30,000 data with each candidate as many as 10,000 data. Data is pulled in April 2023 with specific keywords. The time for data withdrawal is chosen based on the announcement of Presidential Candidates carried by political parties before the schedule for determining or campaigning for Presidential candidates. Current data can potentially be used again as a comparison of analysis of presidential candidates on campaign time spans and after campaigns or actual calculation results. The data that can be accessed is in CSV format and has gone through several stages such as labelling using Language experts, removing spam Tweets & empty cells and preprocessing.


A cohesive structure of Bi-directional long-short-term memory (BiLSTM) -GRU for predicting hourly solar radiation

Abstract: Uncertain weather scenarios have an impact on the output of solar farms and therefore affect the security of the grid. It is advantageous for power system operators to forecast solar energy to balance the load generation and for optimal power scheduling. The most promising deep-learning techniques to combine weather variables with precise measurements of solar irradiance are not widely discussed. To close this research gap and produce better prediction results, this article aims to formulate and compare two distinctive deep learning algorithms for using time series forecasting approaches to predict solar irradiance. For multivariate data, the forecasting technique Bi-Directional Long Short-Term Memory (BiLSTM), and BiLSTM-GRU (Gated Recurrent Unit) Dropout, are examined in this study. The output results from the proposed model are compared with other benchmark models based on performance error measurements, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Mean Absolute Percentage Error (MAPE). It was found that the proposed hybrid method, BiLSTM-GRU with dropout outperformed the other methods in terms of solar irradiance predicting accuracy. The analysis presented the best RMSE of 1.55 and MAE of 1.13 for BiLSTM and RMSE of 1.40 and MAE of 0.91 for BiLSTM-GRU architecture using hyperparameter tuning. The comparison results show that the prediction accuracy is improved by tuning the hyperparameters.


DSCA: A Dual Semantic Correlation Alignment Method for domain adaptation object detection

Abstract: In self-driving cars, adverse weather (e.g., fog, rain, snow, and cloud) or occlusion scenarios result in domain shift being unavoidable in object detection. Researchers have recently proposed Domain Adaptive Object Detection (DAOD), i.e., aligning the source and target domains at the image and instance levels distribution by utilizing the Unsupervised Domain Adaptation (UDA) method. However, the semantic correlation information is ignored leading to the effect of aligning not good, and low detection accuracy of objects in adverse weather or occlusion scenarios. Here, we propose a Dual Semantic Correlation Alignment (DSCA) method for DAOD to address the problem. The core idea behind DSCA is to make full use of semantic correlation information including context correlation semantic information and class correlation semantic information to align object semantic information in source and target domains, which supplement and enhance the missing information for target domains. It consists of a two-level semantic alignment: (1) context correlation semantic alignment is developed to obtain the context correlation semantic information of the object to align context semantic information at the image level; (2) class correlation semantic alignment is proposed to obtain the class correlation semantic information of the object to align class semantic information at the instance level. The two-level semantic alignment can effectively decrease negative transfer and complete object information to improve the detection accuracy of objects in different domains. Experiments on four challenging benchmarks show that our proposed DSCA method outperforms state-of-the-art DAOD methods.


Using Cryogenic CMOS Control Electronics to Enable a Two-Qubit Cross-Resonance Gate

Abstract: CMOS chips comprised of a low-power processor and arbitrary waveform generator operating at 4 K are developed and tested, enabling two-qubit randomized benchmarking and highlighting the promise and challenges of cryogenic control.


4-DOF Visual Servoing of a Robotic Flexible Endoscope With a Predefined-Time Convergent and Noise-Immune Adaptive Neural Network

Abstract: Endoscopes provide views inside the patient's body during minimally invasive surgery. Although various robotic endoscopes have been developed to reduce surgeons' workload in manual endoscope operations, autonomous endoscope manipulation remains challenging due to the misorientation effect and different disturbances. In this work, we developed an intelligent endoscope system to steer the surgical view automatically. To keep the target (i.e., the tip of an instrument) at the center of the camera view with a suitable size and orientation, an image moment-based 4-degree-of-freedom (DOF) visual servoing method is implemented. We propose an error learning-based sliding mode controller to realize fast and smooth error convergence. It is specially constructed to improve convergence rate without causing undesirable system chattering. Moreover, the kinematic modeling of the endoscope results in a quadratic programming problem, which is solved by a novel adaptive noise-immune zeroing neural network accelerated to predefined-time convergence by a newly constructed activation function. The experiments show that the proposed control strategy guarantees a superior convergence rate and robustness compared with existing methods. Lab tests show the application potential of the proposed endoscope system in clinical practice.


The construction and use of cognitive maps in model-based control.

Abstract: When making decisions, we sometimes rely on habit and at other times plan toward goals. Planning requires the construction and use of an internal representation of the environment, a cognitive map. How are these maps constructed, and how do they guide goal-directed decisions? We coupled a sequential decision-making task with a behavioral representational similarity analysis approach to examine how relationships between choice options change when people build a cognitive map of the task structure. We found that participants who encoded stronger higher-order relationships among choice options showed increased planning and better performance. These higher-order relationships were more strongly encoded among objects encountered in high-reward contexts, indicating a role for motivation during cognitive map construction. In contrast, lower-order relationships such as simple visual co-occurrence of objects did not predict goal-directed planning. These results show that the construction of cognitive maps is an active process, with motivation dictating the degree to which higher-order relationships are encoded and used for planning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


Design of buoy observation network over the Arctic Ocean

Abstract: In the Arctic, near-surface temperature affects the surface energy balance and the evolution of planetary boundary layer. Particularly in summer, due to sea-ice extent variations, the uncertainties of simulated near-surface temperature in the Arctic Ocean increase. To reduce these uncertainties, high-quality near-surface temperature observations are necessary; however, in situ observations are difficult to operate in the Arctic. Although buoys can monitor various atmospheric variables in the Arctic over longer periods compared to those of other types of in situ monitoring, the number of buoy near-surface temperature observations in the Arctic has decreased since 2018. Near-surface temperature observations by buoys need to be added and the optimal buoy observation network need to be designed to reduce the uncertainties associated with estimating near-surface temperature over the Arctic Ocean in summer. In this study, buoy observation networks to monitor near-surface temperature were designed to minimize the 24-h forecast error variances of 2-m temperature in the Arctic. To design the optimal buoy observation network, an objective algorithm for the observation network design based on an ensemble sensitivity was used. The reductions of the forecast error variances of 2-m temperature were estimated quantitatively using the ensemble sensitivity. To calculate the ensemble sensitivity, ensemble members of the initial condition and response function (i.e. forecast aspect of interest) were composed of deterministic analyses and 24-h forecast errors of 2-m temperature, respectively, which were simulated by the Global Forecast System (GFS) during July, August, September, and October from 2016 to 2019. By using long-term GFS simulations, the buoy observation network was designed in a climatological perspective. The determined optimal buoy locations were consistent with regions where both the analysis variances and forecast error variances of 2-m temperature were large. Assuming that 2-m temperature buoy observations were assimilated to ensemble members from GFS simulations at the 10 optimal buoy locations, the analysis variances of 2-m temperature were reduced by 60.48% and the 24-h forecast error variances of the temperature were reduced by 2.83% compared to the respective variances before assimilating buoy observations. Thus, after efficiently using the limited buoy resources (i.e., 10 buoys) to design the observation network, the uncertainties of the 2-m temperature simulated by the numerical weather prediction (NWP) model were greatly reduced in the Arctic. The optimal buoy observation network can contribute to reducing uncertainties associated with the surface energy balance, planetary boundary layer, and sea-ice properties simulated by NWP models.


Multivariate Data Explanation by Jumping Emerging Patterns Visualization

Abstract: Multivariate or multidimensional visualization plays an essential role in exploratory data analysis by allowing users to derive insights and formulate hypotheses. Despite their popularity, it is usually users' responsibility to (visually) discover the data patterns, which can be cumbersome and time-consuming. Visual Analytics (VA) and machine learning techniques can be instrumental in mitigating this problem by automatically discovering and representing such patterns. One example is the integration of classification models with (visual) interpretability strategies, where models are used as surrogates for data patterns so that understanding a model enables understanding the phenomenon represented by the data. Although useful and inspiring, the few proposed solutions are based on visual representations of so-called black-box models, so the interpretation of the patterns captured by the models is not straightforward, requiring mechanisms to transform them into human-understandable pieces of information. This paper presents multiVariate dAta eXplanation (VAX), a new VA method to support identifying and visual interpreting patterns in multivariate datasets. Unlike the existing similar approaches, VAX uses the concept of Jumping Emerging Patterns, inherent interpretable logic statements representing class-variable relationships (patterns) derived from random Decision Trees. The potential of VAX is shown through use cases employing two real-world datasets covering different scenarios where intricate patterns are discovered and represented, something challenging to be done using usual exploratory approaches.


Are there independent effects of constraint and predictability on eye movements during reading?

Abstract: Evidence of processing costs for unexpected words presented in place of a more expected completion remains elusive in the eye-movement literature. The current study investigated whether such prediction error costs depend on the source of constraint violation provided by the prior context. Participants' eye movements were recorded as they read predictable words and unpredictable alternatives that were either semantically related or unrelated in three-sentence passages. The passages differed in whether the source of constraint originated solely from the global context provided by the first two semantically rich sentences of the passage, from the local context provided by the final sentence of the passage, from both the global and local context, or from none of the three sentences of the passage. The results revealed the expected processing advantage for predictable completions in any constraining context, although the relative contributions of the different sources of constraint varied across the time course of word processing. Unpredictable completions, however, did not yield any processing costs when the context constrained toward a different word, instead producing immediate processing benefits in the presence of any constraining context. Moreover, the initial processing of related unpredictable completions was enhanced further by the provision of a supportive global context. Predictability effects therefore do not appear to be determined by cloze probability alone but also by the nature of the prior contextual constraint especially when they encourage the construction of higher-level discourse representations. The implications of these findings for understanding existing theoretical models of predictive processing are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


Hydrological Process Modelling using RBNN - A Neural Network Computing Technique

Abstract: Severe erosion in the watersheds under Damodar Valley Corporations (DVC), Hazaribagh, Jharkhand, India has been taking place for a long time. Several soil and water conservation measures are being adopted by the Soil Conservation Department in Damodar Valley. For effective planning of soil conservation programmes, hydrologic models have been used, but with several limitations. In the present study, Nagwa watershed under DVC was selected for simulating monthly surface runoff and sediment yield using radial basis neural network (RBNN) model, which requires lesser data. Different sets of input data were employed and it was observed that only average monthly rainfall was sufficient to estimate surface runoff, while average monthly rainfall and average monthly discharge were needed for estimating sediment yield. The RBNN model was trained for the years 1991-2000 and validated for the period 2001-2007. Results indicate that coefficient of determination, R2, Nash-Sutcliffe simulation efficiency, NSE and root mean square error, RMSE (m3.s-1) values for RBNN model were 0.93, 0.92 and 1.25 during training, and 0.73, 0.73 and 0.77 during validation period, respectively. The model performed quite well for simulation of sediment yield with R2, NSE and RMSE values of 0.92, 0.92 and 1822 during training period; and 0.80, 0.70 and 2288 during validation period, respectively. It could be stated that RBNN model based on simple inputs couldbe a powerful tool for simulation of monthly surface runoff and sediment yield.


CFGN: A Lightweight Context Feature Guided Network for Image Super-Resolution

Abstract: Convolutional neural networks (CNNs) have recently been widely and successfully applied in the image computer vision community, and obtained great advances in single image super-resolution (SR). However, most of the existing SR methods focus on designing networks with deeper or wider structures for better performance and suffer from the problem of heavy computational costs. To address this problem, we propose a novel Context Feature Guided Network (CFGN), which is an efficient and effective lightweight SR method. Specifically, To capture semantic features effectively, we propose a novel block, called context feature guided convolution (CFGC), to capture more discriminative features while enlarging the receptive field. Moreover, we design a novel context feature guided group (CFGG) to exploit the multi-scale context information. Extensive experiments demonstrate the effectiveness of our method with a good trade-off between performance and computational efficiency, compared with the proposed method over the state-of-the-art lightweight SR methods.


TD-MDB: A Truth Discovery Based Multi-Dimensional Bidding Strategy for Federated Learning in Industrial IoT Systems

Abstract: As Industrial Internet of Things (IIoT) systems have superior distributed characteristics, Federated Learning (FL) has gradually become a mainstream distributed computing paradigm in recent years, promoting its intelligent development. A key factor of FL is the model accuracy, which relies on the truth of the participating Industrial Edge Device (IED)’s parameter. However, some untrusted IEDs can contaminate models by fabricating false FL parameters, thereby deceiving rewards, known as Model Poison Attack (MPA). In this paper, a Truth Discovery based Multi-Dimensional Bidding (TD-MDB) strategy is proposed to obtain truthful parameters at low cost for FL. In TD-MDB strategy, Parameter Server (PS) will randomly assign the lightweight trust evaluation model to test the trust of each IED according to the accuracy of IED’s calculation. Then, in order to resist MPA and inference attack, we use an improved clustering algorithm to divide the trusted IEDs into different clusters based on their different trust and stability. Finally, a device selection strategy is proposed to select cost-effective IEDs for model training based on Asynchronous Advantage Actor-Critic (A3C) algorithm. In TD-MDB strategy, the IEDs that provide more resources, high trust and low bid will be selected to train the model and maximize the real revenue of the system with high accuracy. We finally carry out extensive evaluations, where results demonstrate that the TD-MDB strategy can effectively provide efficient offloading strategy, resist MPA and ensure that truth computing for FL, which is better than the state-of-the-art strategies in model accuracy, system revenue and other performance indicators.


Introducing Desirable Difficulty in Engineering Mathematics with Spaced Retrieval Practice

Abstract: Abstract Spaced retrieval practice is an evidence-based, memory-enhancing instructional technique in which students are asked to practice recall multiple times with temporal delays in between [1], [2]. Spacing out practice naturally increases the difficulty of retrieval and reduces immediate performance [3], but it improves memory in the long term [BLIND], [4]. The short-term costs and long-term benefits of spaced retrieval practice can be explained by the concept of desirable difficulty, which proposes that more difficult exercises during the early stages of learning can improve long-term retention [5]. Instructional techniques that improve learning are much needed in introductory engineering courses that establish foundational knowledge and abilities for first-year students. These courses often pose barriers to success in engineering [6]–[9]. Despite the efforts of the mathematics reform movement of 30 years ago to make calculus a pump instead of a filter [10], calculus continues to pose a challenge. To improve performance and retention in engineering mathematics courses, adopting research-based teaching practices such as spaced retrieval practice has the potential to improve learning in barrier courses and increase the number of STEM graduates. Currently in the second year of a 3-year study (NSF Grant Award #BLIND), we are collecting student performance data from a spaced retrieval practice implementation in engineering mathematics, as well as 9 other undergraduate STEM barrier courses. The engineering mathematics course (Calculus I for Engineers) is a prerequisite for many engineering courses at BLIND university, and therefore it is imperative that students retain the course content. In all courses, we identified 24 target learning objectives in the first half of the semester. In 5 bi-weekly quizzes administered throughout the semester, we assigned each learning objective to either a massed (three questions on the same quiz) or spaced (three questions on three consecutive quizzes) condition. A fourth and final question for all objectives will be asked on a quiz at the end of the semester to measure learning. Our research design is within-subjects, which controls for differences in student ability and effort while giving all students experience with both massed and spaced retrieval practice. In addition, assignment of objectives to conditions is counterbalanced, which controls for differences in the difficulty of different objectives. In this ASEE 2021 poster and full-length paper, we explore the difficulty introduced by spaced retrieval practice in engineering mathematics. Our research question is: How much does spaced retrieval practice impact short-term performance in engineering mathematics? We compare student performance on questions that are asked in either a massed or spaced condition. We expect that performance will be lower on spaced questions than massed questions. Although this analysis will not reveal whether we have induced desirable difficulty, since we are not assessing long-term learning at this time, it will indicate whether we have imposed additional difficulty with the manipulation of spacing. These intermediate results are important for our understanding of the mechanism by which spaced retrieval practice may enhance learning.


Cryptanalysis and Improvement of a Blockchain-Based Certificateless Signature for IIoT Devices

Abstract: With the rapid development of the Industrial Internet of Things (IIoT), security and privacy issues have become increasingly prominent when the data from IIoT devices is shared across public networks. Very recently, Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">al. presented a blockchain-based certificateless signature (CLS) scheme for IIoT devices in order to achieve secure and lightweight communication (IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2021.3084753). Wang et al. claimed that their CLS scheme is secure under the assumption of the elliptic curve discrete logarithm problem. Unfortunately, by providing two attack methods to analyze the security of Wang et al.'s CLS scheme, we find that it is insecure against universal forgery attacks. Without knowing any information about the target user's private key, the two categories of attackers can successfully forge the valid signature of any message. Hence, Wang et al.'s CLS scheme fails to achieve the claimed security goals. To fix the security flaws, we propose an enhanced blockchain-based and pairing-free CLS scheme, and prove its security in the random oracle model. Furthermore, the analysis results demonstrate that our revised scheme has higher security while keeping the performance of the original scheme.


Preliminary Analysis of Simple Novelty Search

Abstract: Novelty search is a powerful tool for finding diverse sets of objects in complicated spaces. Recent experiments on simplified versions of novelty search introduce the idea that novelty search happens at the level of the archive space, rather than individual points. The sparseness measure and archive update criterion create a process that is driven by a two measures: 1) spread out to cover the space while trying to remain as efficiently packed as possible, and 2) metrics inspired by k Nearest Neighbor theory. In this paper, we generalize previous simplifications of novelty search to include traditional population (μ,λ) dynamics for generating new search points, where the population and the archive are updated separately. We provide some theoretical guidance regarding balancing mutation and sparseness criteria and introduce the concept of saturation as a way of talking about fully covered spaces. We show empirically that claims that novelty search is inherently objectiveless are incorrect. We leverage the understanding of novelty search as an optimizer of archive coverage suggest several ways to improve the search, and we demonstrate one simple improvement-generate some new points directly from the archive rather than the parent population.


Exploring reservoir computing: Implementation via double stochastic nanowire networks

Abstract: Neuromorphic computing, inspired by the human brain, uses memristor devices for complex tasks. Recent studies show that self-organizing random nanowires can implement neuromorphic information processing, enabling data analysis. This paper presents a model based on these nanowire networks, with an improved conductance variation profile. We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses. The nanowire network layer generates dynamic behaviors for pulse voltages, allowing time series prediction analysis. Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals, outperforming traditional reservoir computing in terms of fewer nodes, enriched dynamics and improved prediction accuracy. Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets, making neuromorphic nanowire networks promising for physical implementation of reservoir computing.


Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks

Abstract: Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.


Distributed Nonlinear Polynomial Adaptive Graph Filter Based on Diffusion Conjugate Gradient Strategy

Abstract: This brief investigates distributed and adaptive estimation of streaming data based nonlinear graph filters. To begin with, a new distributed polynomial filter for nonlinear graphs is proposed based on the Hadamard product, which not only represents the nonlinear relationship between dynamically changing graph input and output signals, but also accounts for the time dimension. A diffusion least mean square algorithm is presented to estimate the nonlinear polynomial graph filter parameters in a distributed manner. The method of diffusion hybrid conjugate gradient is leveraged to further improve the convergence rate of the graph diffusion adaptive filter. The advantages of the proposed algorithms are demonstrated in simulation studies.


Design of a Low-Cost and Device-Free Human Activity Recognition Model for Smart LED Lighting Control

Abstract: Human activity recognition (HAR) constitutes an integral part of occupant centric smart services, such as health monitoring and building energy management. In this paper, a simple and cost effective solution to HAR through passive sensing of WiFi channel state information (CSI), is proposed. WiFi CSI extracted from ESP32 was utilized to classify four different human activities, using ensemble machine learning models. A mean accuracy of 83.39% was achieved using gradient boosting classifier with Haar wavelet based denoising, in spite of using a single transmission link and in presence of coexisting wireless devices. The proposed model can be employed for the development of an IoT enabled smart LED lighting system, with minimal infrastructure changes. In this strategy, the illuminance level of LED lighting fixtures is adjusted according to the predicted occupant activity, thereby reducing power consumption, without compromising visual comfort. This method offers a potential energy savings of up to 36.42% and 29.45% per month, for a typical office and home scenario, respectively. The possibility of supplementing activity sensing with daylight harvesting, is also explored. An additional energy savings of up to 18.26% may be obtained using this method, during daytime. An evaluation of the annual electricity cost shows an estimated reduction between 29.43% and 62.13% using activity recognition and daylight harvesting.


Role of radial index in self-reconstruction of structured beams

Abstract: We demonstrate that the self-reconstruction performance of structured light is positively correlated with its radial degrees of freedom; specifically, structured beams with larger radial indices have better self-reconstruction abilities and higher tolerance to obstacles. These results were determined by quantifying the self-reconstruction degree of the Laguerre-Gauss beams. After the beam is partially blocked by an obstacle, we present convincing evidence theoretically and experimentally that an increase in the radial index of the Laguerre-Gauss beam is accompanied by a better self-reconstruction ability. We provide a physical explanation for this result in terms of non-diffraction, traveling-wave solution, and transverse momentum. Finally, we theoretically demonstrate that the contribution of the radial index to the self-reconstruction ability of the Hermite-Gauss and Ince-Gauss modes, the results are consistent. We believe that our results are applicable to all beams with complex radial structure, which provides another perspective on the self-reconstruction of structured beams: the contribution of the radial structure index.


Rational local unitary invariants of symmetrically mixed states of two qubits

Abstract: We compute the field of rational local unitary invariants for locally maximally mixed states and symmetrically mixed states of two qubits. In both cases, we prove that the field of rational invariants is purely transcendental. We also construct explicit geometric quotients and prove that they are always rational. All the results are obtained by working over the field of real numbers, employing methods from classical and geometric invariant theory over arbitrary fields of characteristic zero.


Scalable and Parallel Optimization of the Number Theoretic Transform Based on FPGA

Abstract: In lattice-based postquantum cryptography (PQC), polynomial multiplication is complex and time-consuming, which affects the overall computational efficiency. In addition, the parameters of different lattice-based algorithms require different number theoretic transform (NTT) structures, which limits the versatility of hardware design. To this end, this article proposes scalable and parallel optimization of the NTT based on a field-programmable gate array (FPGA). By analyzing the algorithm flow of the NTT, inverse NTT (INTT), and pointwise multiplication (PWM), an FPGA loosely coupled structure is designed, which can be used to place butterfly units of multiple pipelines in parallel and supports various modulo operations of a polynomial. In addition, to improve computing efficiency and scalability, key algorithm modules such as multipliers and modular reduction are deeply optimized. Moreover, the storage optimization of multiple RAM channels is carried out, and the alternate access control of data and the multiplexing of RAM resources reduce resource consumption and improve data access efficiency. For the SHA-3 algorithm, the scalable Keccak algorithm is implemented in a serial–parallel hybrid manner and supports multiple hash modes. Finally, taking the Dilithium algorithm as an example, through the parallelization of SHA-3 and NTT, the calculation cycle of key generation, signature, and verification is shortened. The experimental results and analysis show that the scheme in this article shortens the NTT calculation period while ensuring a high frequency, and the calculation time is significantly better than that of other schemes. Furthermore, it can support the optimized parallelization of multiple moduli and give full play to the computing advantages of an FPGA.


Three-dimensional reconstruction of light field based on cubic B-spline curve fitting

Abstract: Fringe projection profilometry has been widely used in three-dimensional (3D) reconstruction. In this paper, we replace the camera with a self-made light field camera to achieve structured light field 3D reconstruction. Our self-made light field camera boasts higher resolution and lower cost with the ability to adjust spatial and angular resolution as needed. A novel 3D reconstruction method based on cubic B-spline curve fitting is proposed to directly fit the relationship between phase and 3D coordinates, and the look up table method is utilized to accelerate the entire calculation process. The experimental results demonstrate that the proposed method can accomplish 3D reconstruction of the structured light field under various viewing angles. Specifically, it has respectively reduced the computation time for phase and modulation intensity by 64.03% and 41.43%, and the reconstruction time is decreased by 32.99% without compromising the reconstruction accuracy.


Sequential Learning Network With Residual Blocks: Incorporating Temporal Convolutional Information Into Recurrent Neural Networks

Abstract: Temporal convolutional networks (TCNs) have shown remarkable performance in sequence modeling and surpassed recurrent neural networks (RNNs) in a number of tasks. However, performing exceptionally on extremely long sequences remains an obstacle due to the restrained receptive field of temporal convolutions and a lack of forgetting mechanism. Although RNNs can carry state transmission down the full sequence length and latch information by forgetting mechanism, the issues of information saturation and gradient vanishing or exploding still persist, which usually occur in back-propagation due to the effect of multiplicative accumulation. To benefit from both temporal convolutions and RNNs, we propose a neural architecture that merge temporal convolutional data into recurrent networks. The temporal convolutions are employed intermittently and fused into the hidden states of RNNs with the assistance of attention for providing long-term information. With this architecture, it is not needed for convolutional networks to cover the total length of the sequence, thus gradient and saturation issues in RNNs are ameliorated since convolutions are integrated into its cells and the state is updated with convolutional information. Extensive experiments illustrate the superiority of our network against other competitive counterparts, such as TCNs and the different variants of RNNs.


Differentially Private Federated Learning With Stragglers’ Delays in Cross-Silo Settings: An Online Mirror Descent Approach

Abstract: Federated learning is a privacy-preserving machine learning paradigm to protect the data of clients against privacy breaches. A lot of work on federated learning considers the cross-device setting where the number of clients is large and the data sample size of each client is low. However, this work focuses on cross-silo settings, where clients are few and have large sample sizes. We consider a fully decentralized setting where clients communicate with their immediate time-varying neighbors without the need for a central aggregator prone to congestion and a single point of failure. Our goal is to address stragglers’ delays in cross-silo settings. Existing algorithms designed to overcome stragglers’ delays work with fixed data distributions. They cannot work in real-time settings, such as wireless communication, characterized by time-varying data distributions. Therefore, this paper proposes two online learning algorithms that work with time-varying data and address stragglers’ delays while guaranteeing differential privacy, strong convergence, and communication efficiency. Using the mirror descent technique, the first proposed algorithm addresses the case where the loss gradient is easily computed while the second proposed algorithm addresses the case where the loss gradient is difficult to compute. Simulation results show the performance of the proposed algorithms.


Totally-ordered Sequential Rules for Utility Maximization

Abstract: High-utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High-utility sequential rule mining (HUSRM) discovers high utility and high confidence sequential rules, so it can solve the issue in HUSPM. However, all existing HUSRM algorithms aim to find high-utility partially-ordered sequential rules (HUSRs), which are not consistent with reality and may generate fake HUSRs. Therefore, in this article, we formulate the problem of high-utility totally-ordered sequential rule mining and propose a novel algorithm, called TotalSR, which aims to identify all high-utility totally-ordered sequential rules (HTSRs). TotalSR introduces a left-first expansion strategy that can utilize the anti-monotonic property to use a confidence pruning strategy. TotalSR also designs a new utility upper bound: RSPEU , which is tighter than the existing upper bounds. TotalSR can drastically reduce the search space with the help of utility upper bounds pruning strategies, avoiding much more meaningless computation. To effectively compute the information, TotalSR proposes an auxiliary antecedent record table that can efficiently calculate the antecedent’s support and a utility prefix sum list that can compute the upper bound in O (1) time for a sequence. Finally, there are numerous experimental results on both real and synthetic datasets demonstrating that TotalSR is more efficient than the existing algorithms.


Reliable Event Generation With Invertible Conditional Normalizing Flow

Abstract: Event streams provide a novel paradigm to describe visual scenes by capturing intensity variations above specific thresholds along with various types of noise. Existing event generation methods usually rely on one-way mappings using hand-crafted parameters and noise rates, which may not adequately suit diverse scenarios and event cameras. To address this limitation, we propose a novel approach to learn a bidirectional mapping between the feature space of event streams and their inherent parameters, enabling the generation of reliable event streams with enhanced generalization capabilities. We first randomly generate a vast number of parameters and synthesize massive event streams using an event simulator. Subsequently, an event-based normalizing flow network is proposed to learn the invertible mapping between the representation of a synthetic event stream and its parameters. The invertible mapping is implemented by incorporating an intensity-guided conditional affine simulation mechanism, facilitating better alignment between event features and parameter spaces. Additionally, we impose constraints on event sparsity, edge distribution, and noise distribution through novel event losses, further emphasizing event priors in the bidirectional mapping. Our framework surpasses state-of-the-art methods in video reconstruction, optical flow estimation, and parameter estimation tasks on synthetic and real-world datasets, exhibiting excellent generalization across diverse scenes and cameras.


Back to Reality: Learning Data-Efficient 3D Object Detector With Shape Guidance

Abstract: In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers and categories). In order to remedy the information loss from box annotations to centers, our method makes use of synthetic 3D shapes to convert the position-level annotations into virtual scenes with box-level annotations, and in turn utilizes the fully-annotated virtual scenes to complement the real labels. Specifically, we first present a shape-guided label-enhancement method, which assembles 3D shapes into physically reasonable virtual scenes according to the coarse scene layout extracted from position-level annotations. Then we transfer the information contained in the virtual scenes back to real ones by applying a virtual-to-real domain adaptation method, which refines the annotated object centers and additionally supervises the training of detector with the virtual scenes. Since the shape-guided label enhancement method generates virtual scenes by human-heuristic physical constraints, the layout of the fixed virtual scenes may be unreasonable with varied object combinations. To address this, we further present differentiable label enhancement to optimize the virtual scenes including object scales, orientations and locations in a data-driven manner. Moreover, we further propose a label-assisted self-training strategy to fully exploit the capability of detector. By reusing the position-level annotations and virtual scenes, we fuse the information from both domains and generate box-level pseudo labels on the real scenes, which enables us to directly train a detector in fully-supervised manner. Extensive experiments on the widely used ScanNet and Matterport3D datasets show that our approach surpasses current weakly-supervised and semi-supervised methods by a large margin, and achieves comparable detection performance with some popular fully-supervised methods with less than 5% of the labeling labor.


Gray code based gradient-free optimization algorithm for parameterized quantum circuit

Abstract: A Gray code based gradient-free optimization (GCO) algorithm is proposed to update the parameters of parameterized quantum circuits (PQCs) in this work. Each parameter of PQCs is encoded as a binary string, named as a gene, and a genetic-based method is adopted to select the offsprings. The individuals in the offspring are decoded in Gray code way to keep Hamming distance, and then are evaluated to obtain the best one with the lowest cost value in each iteration. The algorithm is performed iteratively for all parameters one by one until the cost value satisfies the stop condition or the number of iterations is reached. The GCO algorithm is demonstrated for classification tasks in Iris and MNIST datasets, and their performance are compared by those with the Bayesian optimization algorithm and binary code based optimization algorithm. The simulation results show that the GCO algorithm can reach high accuracies steadily for quantum classification tasks. Importantly, the GCO algorithm has a robust performance in the noise environment.


Differentiable Image Data Augmentation and Its Applications: A Survey

Abstract: Data augmentation is an effective method to improve model robustness and generalization. Conventional data augmentation pipelines are commonly used as preprocessing modules for neural networks with predefined heuristics and restricted differentiability. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the augmentation policy searching strategies. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the searching of augmentation policy strategies. This survey provides a comprehensive and structured overview of the advances in DDA. Specifically, we focus on fundamental elements including differentiable operations, operation relaxations, and gradient estimations, then categorize existing DDA works accordingly, and investigate the utilization of DDA in selected of practical applications, specifically neural augmentation networks and differentiable augmentation search. Finally, we discuss current challenges of DDA and future research directions.


Graph embedding approach to analyze sentiments on cryptocurrency

Abstract: This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data.


Star algorithm for neural network ensembling

Abstract: Neural network ensembling is a common and robust way to increase model efficiency. In this paper, we propose a new neural network ensemble algorithm based on Audibert’s empirical star algorithm. We provide optimal theoretical minimax bound on the excess squared risk. Additionally, we empirically study this algorithm on regression and classification tasks and compare it to most popular ensembling methods.


How is a “Kitchen Chair” like a “Farm Horse”? Exploring the Representation of Noun-Noun Compound Semantics in Transformer-based Language Models

Abstract: Abstract Despite the success of Transformer-based language models in a wide variety of natural language processing tasks, our understanding of how these models process a given input in order to represent task-relevant information remains incomplete. In this work, we focus on semantic composition and examine how Transformer-based language models represent semantic information related to the meaning of English noun-noun compounds. We probe Transformer-based language models for their knowledge of the thematic relations that link the head nouns and modifier words of compounds (e.g., kitchen chair: a chair located in a kitchen). Firstly, using a dataset featuring groups of compounds with shared lexical or semantic features, we find that token representations of six Transformer-based language models distinguish between pairs of compounds based on whether they use the same thematic relation. Secondly, we utilize fine-grained vector representations of compound semantics derived from human annotations, and find that token vectors from several models elicit a strong signal of the semantic relations used in the compounds. In a novel “compositional probe” setting, where we compare the semantic relation signal in mean-pooled token vectors of compounds to mean-pooled token vectors when the two constituent words appear in separate sentences, we find that the Transformer-based language models that best represent the semantics of noun-noun compounds also do so substantially better than in the control condition where the two constituent works are processed separately. Overall, our results shed light on the ability of Transformer-based language models to support compositional semantic processes in representing the meaning of noun-noun compounds.