Reviewerly/exHarmony huggingface dataset:
Estimating Max-Stable Random Vectors with Discrete Spectral Measure  using Model-Based Clustering

Authors:
raw_author_name: Boulin, Alexis

Abstract: This study introduces a novel estimation method for the entries and structure of a matrix $A$ in the linear factor model $\textbf{X} = A\textbf{Z} + \textbf{E}$. This is applied to an observable vector $\textbf{X} \in \mathbb{R}^d$ with $\textbf{Z} \in \mathbb{R}^K$, a vector composed of independently regularly varying random variables, and light-tailed independent noise $\textbf{E} \in \mathbb{R}^d$. This leads to max-linear models treated in classical multivariate extreme value theory. The spectral measure of the limit distribution is subsequently discrete and completely characterized by the matrix $A$. Every max-stable random vector with discrete spectral measure can be written as a max-linear model. Each row of the matrix $A$ is both scaled and sparse. Additionally, the value of $K$ is not known a priori. The problem of identifying the matrix $A$ from its matrix of pairwise extremal correlation is addressed. In the presence of pure variables, which are elements of $\textbf{X}$ linked, through $A$, to a single latent factor, the matrix $A$ can be reconstructed from the extremal correlation matrix. Our proofs of identifiability are constructive and pave the way for our innovative estimation for determining the number of factors $K$ and the matrix $A$ from $n$ weakly dependent observations on $\textbf{X}$. We apply the suggested method to weekly maxima rainfall and wildfires to demonstrate its applicability.
NeuroCine: Decoding Vivid Video Sequences from Human Brain Activties

Authors:
raw_author_name: Sun, Jingyuan
raw_author_name: Li, Mingxiao
raw_author_name: Chen, Zijiao
raw_author_name: Moens, Marie-Francine

Abstract: In the pursuit to understand the intricacies of human brain's visual processing, reconstructing dynamic visual experiences from brain activities emerges as a challenging yet fascinating endeavor. While recent advancements have achieved success in reconstructing static images from non-invasive brain recordings, the domain of translating continuous brain activities into video format remains underexplored. In this work, we introduce NeuroCine, a novel dual-phase framework to targeting the inherent challenges of decoding fMRI data, such as noises, spatial redundancy and temporal lags. This framework proposes spatial masking and temporal interpolation-based augmentation for contrastive learning fMRI representations and a diffusion model enhanced by dependent prior noise for video generation. Tested on a publicly available fMRI dataset, our method shows promising results, outperforming the previous state-of-the-art models by a notable margin of ${20.97\%}$, ${31.00\%}$ and ${12.30\%}$ respectively on decoding the brain activities of three subjects in the fMRI dataset, as measured by SSIM. Additionally, our attention analysis suggests that the model aligns with existing brain structures and functions, indicating its biological plausibility and interpretability.
A GP-based Robust Motion Planning Framework for Agile Autonomous Robot  Navigation and Recovery in Unknown Environments

Authors:
raw_author_name: Mohammad, Nicholas
raw_author_name: Higgins, Jacob
raw_author_name: Bezzo, Nicola

Abstract: For autonomous mobile robots, uncertainties in the environment and system model can lead to failure in the motion planning pipeline, resulting in potential collisions. In order to achieve a high level of robust autonomy, these robots should be able to proactively predict and recover from such failures. To this end, we propose a Gaussian Process (GP) based model for proactively detecting the risk of future motion planning failure. When this risk exceeds a certain threshold, a recovery behavior is triggered that leverages the same GP model to find a safe state from which the robot may continue towards the goal. The proposed approach is trained in simulation only and can generalize to real world environments on different robotic platforms. Simulations and physical experiments demonstrate that our framework is capable of both predicting planner failures and recovering the robot to states where planner success is likely, all while producing agile motion.
L2G2G: a Scalable Local-to-Global Network Embedding with Graph  Autoencoders

Authors:
raw_author_name: Ouyang, Ruikang
raw_author_name: Limnios, Stratis
raw_author_name: Cucuringu, Mihai
raw_author_name: Reinert, Gesine

Abstract: For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representations, also called embeddings, which are obtained through minimising a loss function; these embeddings are used with a decoder for downstream tasks such as node classification and edge prediction. While GAEs tend to be fairly accurate, they suffer from scalability issues. For improved speed, a Local2Global approach, which combines graph patch embeddings based on eigenvector synchronisation, was shown to be fast and achieve good accuracy. Here we propose L2G2G, a Local2Global method which improves GAE accuracy without sacrificing scalability. This improvement is achieved by dynamically synchronising the latent node representations, while training the GAEs. It also benefits from the decoder computing an only local patch loss. Hence, aligning the local embeddings in each epoch utilises more information from the graph than a single post-training alignment does, while maintaining scalability. We illustrate on synthetic benchmarks, as well as real-world examples, that L2G2G achieves higher accuracy than the standard Local2Global approach and scales efficiently on the larger data sets. We find that for large and dense networks, it even outperforms the slow, but assumed more accurate, GAEs.
MAGDi: Structured Distillation of Multi-Agent Interaction Graphs  Improves Reasoning in Smaller Language Models

Authors:
raw_author_name: Chen, Justin Chih-Yao
raw_author_name: Saha, Swarnadeep
raw_author_name: Stengel-Eskin, Elias
raw_author_name: Bansal, Mohit

Abstract: Multi-agent interactions between Large Language Model (LLM) agents have shown major improvements on diverse reasoning tasks. However, these involve long generations from multiple models across several rounds, making them expensive. Moreover, these multi-agent approaches fail to provide a final, single model for efficient inference. To address this, we introduce MAGDi, a new method for structured distillation of the reasoning interactions between multiple LLMs into smaller LMs. MAGDi teaches smaller models by representing multi-agent interactions as graphs, augmenting a base student model with a graph encoder, and distilling knowledge using three objective functions: next-token prediction, a contrastive loss between correct and incorrect reasoning, and a graph-based objective to model the interaction structure. Experiments on seven widely-used commonsense and math reasoning benchmarks show that MAGDi improves the reasoning capabilities of smaller models, outperforming several methods that distill from a single teacher and multiple teachers. Moreover, MAGDi also demonstrates an order of magnitude higher efficiency over its teachers. We conduct extensive analyses to show that MAGDi (1) enhances the generalizability to out-of-domain tasks, (2) scales positively with the size and strength of the base student model, and (3) obtains larger improvements (via our multi-teacher training) when applying self-consistency - an inference technique that relies on model diversity.
Securing Tomorrow: The Intersection of AI, Data, and Analytics in Fraud Prevention

Authors:
raw_author_name: Pankaj Gupta
raw_affiliation_string: Discover Financial Services, Riverwoods, IL, USA.

Abstract: Aim: This research investigates the interconnections among Data Analytics, Artificial Intelligence, and other cutting-edge technologies to enhance comprehension of fraud prevention. The advantages of integrating machine learning and data analytics into artificial intelligence systems for industry-wide fraud detection and prevention are examined in this study. Study Design: My approach involved conducting an extensive examination of existing literature and analysing numerous case studies to gather information on the role of artificial intelligence, data, and analytics in fraud prevention. Place and Duration of the Study: A broad spectrum of academic, corporate, and governmental sources is utilised to supply the research study with its international scope. This study examines publications and developments from 2019 to 2023. Methodology: The research procedure incorporated an exhaustive literature review. This assessment was composed of academic journals, conference proceedings, and official publications. A qualitative analysis was conducted to assess the data, identify commonalities, and evaluate the strengths and weaknesses of AI fraud protection solutions. A more comprehensive examination of practical implementations was facilitated by case studies, which enhanced comprehension of fraud prevention strategies propelled by AI. Results: The research revealed important findings concerning the various ways in which analytics, data, and artificial intelligence can be implemented to prevent fraudulent activities. An examination of comparisons between generative AI for social engineering, credit card analytics, and cyber-physical security for Internet of Things (IoT) networks illuminated the merits and demerits of different Artificial Intelligence (AI) approaches. Conclusion: According to the findings of the study, AI, data, and analytics may alter system defences against fraud. The above-mentioned results underscore the significance of flexible fraud prevention strategies. Constant collaboration, innovative technology, and ongoing investigation are required to remain ahead of evolving fraud techniques. The paper concludes by emphasising the significance of future challenges and orientations.
Advancing Spectral Clustering for Categorical and Mixed-Type Data: Insights and Applications

Authors:
raw_author_name: Cinzia Di Nuzzo
raw_affiliation_string: Department of Economics and Business, University of Catania, Corso Italia, 55, 95129 Catania, Italy

Abstract: This study focuses on adapting spectral clustering, a numeric data-clustering technique, for categorical and mixed-type data. The method enhances spectral clustering for categorical and mixed-type data with novel kernel functions, showing improved accuracy in real-world applications. Despite achieving better clustering for datasets with mixed variables, challenges remain in identifying suitable kernel functions for categorical relationships.
Digital Death and Spectacular Death

Authors:
raw_author_name: Michael Hviid Jacobsen
raw_affiliation_string: Department of Sociology and Social Work, Aalborg University, DK-9220 Aalborg, Denmark

Abstract: Throughout human history, individuals, communities and societies have always had to confront and tackle the problem of death. Consequently, death remains a topic of social scientific relevance, highlighting the need for its study and for theorising around it. This article analyses the development of the social scientific study of death and dying, taking inspiration from Philippe Ariès’s historical stages to discuss the recent developments in the field, namely the study of digital death. The article begins with a discussion of the visibility of death in modern society in the context of spectacular death. The analysis emphasises its four dimensions: mediatisation, commercialisation, re-ritualisation and the revolution in end-of-life care. The article moves on to discuss the emergence of digital death as the current stage and reflects on its similarities to spectacular death and its transformation of public imaginaries around death in contemporary society. The article concludes with a reflection on future developments in the field, specifically the emergence and study of artificial intelligence (AI) in digitalised death culture.
Multi-object behavior recognition based on object detection for dense crowds

Authors:
raw_author_name: Min Dang
raw_affiliation_string: School of Computer Science and Technology, Xidian University, No. 266, Xinglong Section, Xifeng Road, Xi’an, 710126, Shaanxi, China
raw_author_name: Gang Liu
raw_affiliation_string: Guangzhou Institute of Technology, Xidian University, Building B5, Haisi Center, Guangzhou Knowledge City, Guangzhou, 510555, Guangdong, China; Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, No. 266, Xinglong Section, Xifeng Road, Xi’an, 710126, Shaanxi, China
raw_author_name: Ke Li
raw_affiliation_string: School of Computer Science and Technology, Xidian University, No. 266, Xinglong Section, Xifeng Road, Xi’an, 710126, Shaanxi, China
raw_author_name: Di Wang
raw_affiliation_string: Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, No. 266, Xinglong Section, Xifeng Road, Xi’an, 710126, Shaanxi, China; School of Computer Science and Technology, Xidian University, No. 266, Xinglong Section, Xifeng Road, Xi’an, 710126, Shaanxi, China
raw_author_name: Lihuo He
raw_affiliation_string: School of Electronic Engineering, Xidian University, No. 2 South Taibai Road, Xi’an, 710071, Shaanxi, China

Abstract: In recent years, research on multi-object behavior recognition methods based on pose estimation has made progress. However, in dense scenes, multi-object behavior recognition faces problems such as heavy occlusion, many small-scale objects, and an unbalanced distribution of object classes. Meanwhile, multi-object behavior recognition methods based on pose estimation are difficult to adapt to dense scenes. Therefore, we use the idea of object detection to complete the multi-object behavior recognition in dense scenes. However, compared with general object detection, the fine-grained classification and localization constrain each other due to the similarity of object classes, resulting in poor recognition performance. To solve the above problems, we propose a shallow convolutional neural networks (CNNs) module to extract task features separately in localization and classification task parallel branch structure, so that the two vision tasks avoid mutual constraints. Additionally, we propose a feature fusion mechanism to improve the detection performance of small objects by obtaining multi-scale high-level semantic information. To extract balanced and robust features, we exploit the number of objects in an image to generate dynamic class weights and difficulty weights. Experimental results on dense crowds in student classrooms demonstrated that the proposed method achieves superior performance compared to other state-of-the-art methods, with mAP50 of 88.72% and APs of 35.41%.
Linkage between Students’ Study Habits and their grades analyzed through Bayesian statistics

Authors:
raw_author_name: Muhammad Dawood
raw_affiliation_string: New Mexico State University
raw_author_name: Rachel Boren
raw_affiliation_string: New Mexico State University

Abstract: Abstract It is well documented that particular study habits and strategies, among other factors, can influence students' grades and contribute to college success, independent learning, and retention. To further explore such connections, we studied and analyzed distinctions between two groups, those reporting use of "good" study habits vs. "not so good" study habits. Results establish a link between the two groups and Exam 1 grades, coded as Pass/Fail. Logistic regression, Bayesian statistics, and Matlab programming language were utilized for analysis, indicating that study habits depicted in the group reporting use of study habits research indicates as more powerful are indeed more likely to obtain a passing grade compared with those who did not report employing the better study strategies. because Bayesian statistics are not commonly employed to analyze these effects, a brief is provided on Bayesian statistics to facilitate researchers in this field who might also wish to use Bayesian statistics in their work.
Research Experiences for Teachers Summer Program in Biologically-inspired Computing Systems

Authors:
raw_author_name: Na Gong
raw_affiliation_string: University of South Alabama

Abstract: Abstract Funded by NSF Division of Computer and Network Systems, this grant establishes a new Research Experiences for Teachers (RET) Site at the University of South Alabama (USA). In 2021 summer, eight middle school and high school teachers from two local public-school districts spent six weeks engaged with research activities on biologically-inspired computing systems. They worked on discovery-based research projects and obtain transdisciplinary research experience on biologically-inspired computing systems spanning application (cancer detection), algorithm (Spiking Neural Networks), architecture and circuit (synaptic memory design), and device (memristor). The USA faculty mentors, curriculum development specialist from school districts, Instructional Coach from Science/Mathematics faculty at USA coached participants as they designed standards-compliant curriculum modules and conduct professional development activities. The implementation details of the summer program and the evaluations results are presented in this paper.
Software Engineering and Security: Lessons Learned Creating a New Course in Security from a Software Engineering Perspective

Authors:
raw_author_name: Kevin Gary
raw_affiliation_string: Arizona State University

Abstract: Abstract Security is a rising concern for organizations hiring undergraduates out of college in computing disciplines. This is reflected in the emerging prominence of cybersecurity related courses, certificates, and degree programs, and reflected in the most recent curricular standards guidelines. Perspectives on security recognize it as both a system discipline, meaning the inclusion of hardware, software, and networking components, and a cross-cutting skill across the major phases on the software engineering lifecycle (requirements, design, verification & validation, construction, and evolution and maintenance). Our conjecture is that there are many open and available resources for the first perspective (system) but few for the second perspective (software engineering). In this paper we share experiences creating a new junior-level security course in secure software engineering as a required course in an undergraduate accredited software engineering degree program. Specifically, and aligned with the latter perspective, we share the challenges we faced when seeking curricular resources, including open courseware repositories and textbooks, to jump start the development. We reflect on these experiences by providing a map of curricular resources to cross-cutting software engineering lifecycle phases, examining popular open (and usually federally funded) courseware repositories including the SEED project from Syracuse University and the Cybersecurity Labs and Resource Knowledge-base (CLARK) initiative, as well as ad hoc resources.
Programming learners struggle as much in Python as in C++ or Java

Authors:
raw_author_name: Chelsea Gordon
raw_affiliation_string: zyBooks, A Wiley Brand
raw_author_name: Roman Lysecky
raw_affiliation_string: The University of Arizona
raw_author_name: Frank Vahid
raw_affiliation_string: University of California, Riverside

Abstract: Abstract Many computer science instructors believe switching college-level introductory programming courses to Python, versus languages like C++, Java, or C, smooths the learning of programming, due in part to Python's syntax, dynamic types, and interpreted execution. Studies providing quantitative support for that belief are rare, in part due to the challenge of creating comparative studies without extensive confounding factors. However, today one of the top textbooks in C++, Java, and Python all happen to come from the same publisher and have similar style and content, have similar off-the-shelf programming assignments that instructors can use, incorporate a cloud-based development environment and auto-grader that records every run, and each is used by tens of thousands of students at hundreds of universities. As such, a unique opportunity exists to compare student performance data across those three languages. We obtained such data from the publisher for 56 courses that met specific criteria (18 Python, 16 C++, 22 Java) totaling 6,804 students. As a potential indicator of whether Python students had a smoother learning experience, we compared median time and number of attempts on nearly-identical off-the-shelf programming assignments across the three languages. Python students exhibited no reduction in time or attempts. Manual investigation into the students' programs suggest that problem understanding and programming logic, and not language features, may be the main hurdles to learning programming.
Leveraging the power of Python, Octave and Matlab for Machine Learning

Authors:
raw_author_name: Mohammad Muqri
raw_affiliation_string: DeVry University, Pomona

Abstract: Abstract The objective of this paper is to bring awareness, instigate interest, and promote the need of using AI and machine learning algorithms for information, engineering technology and premedical students. This paper will also attempt to review some of the widely used methods for supervised and unsupervised machine learning and the key issues that students may come across, suggest some discrete resolutions so as to provide optimal results on the accuracy and validity of Train and Test methodology. Artificial intelligence is the intelligence demonstrated by machines while machine learning is the ability of a computer to learn and make decisions the same as a human. Data science is the exploration and quantitative analysis of all available structured and unstructured data to develop understanding, extract knowledge, and formulate actionable results. Machine learning process may be divided into three main steps: data cleansing, feature extraction and optimization, and train/test system modeling. Models are evaluated based on statistics about the errors, or residuals, in the predicted values. Evaluating models is challenging since there is no testing data with labels to determine the correctness. PCA is used to evaluate clustering methods. Scikit-learn is a Python library that implements the various types of machine learning algorithms, such as classification, regression, clustering, decision tree, and more. Using Scikit-learn, implementing machine learning is now simply a matter of supplying the appropriate data to a function so that you can fit and train the model. The paper will explore selected programming tools, theoretical analysis of selected machine learning algorithms, demonstrate the main ML steps with examples and also explore the use of Matlab, Octave for ML besides python ML.