Reviewerly/exHarmony huggingface dataset:
Agent-based cloud simulation model for resource management

Abstract Driven by the successful service model and growing demand, cloud computing has evolved from a moderate-sized data center consisting of homogeneous resources to a heterogeneous hyper-scale computing ecosystem. This evolution has made the modern cloud environment increasingly complex. Large-scale empirical studies of essential concepts such as resource allocation, virtual machine migration, and operational cost reduction have typically been conducted using simulations. This paper presents an agent-based cloud simulation model for resource management. The focus is on how service placement strategies, service migration, and server consolidation affect the overall performance of homogeneous and heterogeneous clouds, in terms of energy consumption, resource utilization, and violation of service-level agreements. The main cloud elements are modeled as autonomous agents whose properties are encapsulated. The complex relationships between components are realized through asynchronous agent-to-agent interactions. Operating states and statistics are displayed in real time. In the evaluation, the efficiency of the simulator is studied empirically. The performance of various resource management algorithms is assessed using statistical methods, and the accuracy of server energy consumption models is examined. The results show that agent-based models can accurately reflect cloud status at a fine-grained level.
Efficient Sampling of Noisy Shallow Circuits Via Monitored Unraveling

Building on the insight of unraveling noise channels into optimized measurements, a classical algorithm for sampling noisy random circuits is developed, increasing our understanding of the boundary of ``practically hard'' quantum computation in real-world noisy settings.
Mixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods

Abstract In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimization techniques with adaptive learning rate schedulers. We take advantage of the flexibility and scalability of neural network software and implement the proposed framework in mixdistreg , an software package that allows for the definition of mixtures of many different families, estimation in high-dimensional and large sample size settings and robust optimization based on TensorFlow. Numerical experiments with simulated and real-world data applications show that optimization is as reliable as estimation via classical approaches in many different settings and that results may be obtained for complicated scenarios where classical approaches consistently fail.
The power of one clean qubit in supervised machine learning

This paper explores the potential benefits of quantum coherence and quantum discord in the non-universal quantum computing model called deterministic quantum computing with one qubit (DQC1) in supervised machine learning. We show that the DQC1 model can be leveraged to develop an efficient method for estimating complex kernel functions. We demonstrate a simple relationship between coherence consumption and the kernel function, a crucial element in machine learning. The paper presents an implementation of a binary classification problem on IBM hardware using the DQC1 model and analyzes the impact of quantum coherence and hardware noise. The advantage of our proposal lies in its utilization of quantum discord, which is more resilient to noise than entanglement.
Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance.
Financial fraud detection: A comparative study of quantum machine learning models

In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of [Formula: see text] for fraud and nonfraud classes. Other models like the Variational Quantum Classifier (VQC), Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimization strategies. However, challenges exist, including the need for more efficient quantum algorithms and larger and more complex datasets. This paper provides solutions to overcome current limitations and contributes new insights to the field of QML in fraud detection, with important implications for its future development.
Robots, AI, and the Metaverse: Distinguishing the Intelligent Images in Science Fiction Movies

The metaverse represents a significant leap forward in the creation of immersive and intelligent imagery. The evolution of intelligent imagery in sci-fi movies is characterized by the emergence, development, and convergence of various technological nodes. These nodes are not isolated from one another but rather represent iterative and intersecting dimensions of a larger whole. Meaningful integration of these elements requires thoughtful consideration of the intersections between the artificial intelligence image in the material world and that which is situated within virtual digital environments such as the metaverse. Thus, the integration of these disparate elements is a crucial step towards the development of a cohesive and advanced technological landscape within the realm of sci-fi filmmaking. To fully appreciate the role of intelligent imagery in science fiction movies, it is essential to examine the artistic image of robots in-depth, which remains one of the most integral components of the genre. Furthermore, the virtual and digitized nature of the metaverse means that artificial intelligence in this realm is disembodied, existing only as intelligent programmes. This unique feature has led to the emergence of general artificial intelligence in the metaverse.
Exploring the context of use for voice user interfaces: Toward context‐dependent user experience quality testing

Abstract Voice user interface (VUI) systems, such as Alexa, Siri, and Google Assistant, are popular and widely available. Still, challenges such as privacy and the ability to have a dialog remain. In the latter example, the user expects a human‐like conversation, that is, that the VUI understands the dialog and its context. However, this VUI feature of context‐aware interaction is rather error prone. For this reason, we intend to explore the VUI context of use and its impact on interaction, that is, relevant user experience (UX). We see a demand for context‐dependent UX measurement because analyzing the context of use and UX assessment are both critical human‐centered design (HCD) methods. Therefore, we examine the VUI context of use by asking users about how, where, and for what they use VUIs, as well as their UX and improvement proposals. We interviewed people with disabilities who rely on VUIs and people without disabilities who use VUIs for convenience or fun. We identified VUI context‐of‐use categories and factors and explored their impacts on relevant UX qualities. Our result is a matrix containing these elements; thus, it provides an overview of the contextual UX of our target group's VUI interaction. We intend to develop a VUI context‐of‐use conceptual structure in the future based on this matrix, which is needed to create an automated context‐dependent UX measurement recommendation tool for VUIs. This conceptual structure could also be useful for automated UX testing in the context of VUI.
Making Solutions Visible: Facilitating Housing Equality through Interface Design

Purpose: Drawing from research on the role of digital interfaces in sociopolitical change (Selfe &amp; Selfe, 1994; Sano-Franchini, 2018; Hallinan et al., 2022), this article identifies how, in the wake of an ongoing public health crisis, one homelessness advocacy organization leveraged their website to reflect and facilitate a shift in priorities and practices. This article addresses two questions: • In what ways are organizational values emergent, mediated, and reimagined through interface design? • How can practitioners enact ethical design decisions in their work to 1) make solutions-oriented impact visible and 2) help users achieve action and social-justice oriented goals? Method: To answer these questions, we draw from an extended case study from Lex End Homelessness (LEH), a homelessness prevention and intervention initiative based out of Lexington, KY, USA. Since its launch, LEH has moved from dispelling harmful myths about homelessness to ideological conversations about homelessness causes and potential solutions (Kalodner-Martin, 2022). As a result, the website, given its public- facing nature, is being rebuilt to reflect LEH's transition. Results: Results demonstrate that the LEH's website interface reflected changes in the organizational priorities, the ideological context, and local needs, specifically regarding supporting understanding, emphasizing, and acting to end homelessness. Conclusion: As an ongoing project, we conclude our article outlining next steps in the interface redesign.
An Approach to Blood Cell Classification Based on Object Segmentation and Machine Learning

The study proposed an approach to cells classification in a blood smear image, consisting of two stages: object segmentation and application of machine learning methods to determine the cell type. To apply this approach, a dataset consisting of cell type labels and the corresponding cell area was created. Object segmentation was performed using image processing methods: noise removal, image binarization, contouring, and area determination using the Gaussian formula. The obtained data set was pre-processed: duplicate data and outliers were removed, and the data was divided into testing and training sets. Training for Gaussian Naive Bayes Classifier, Decision Tree Classifier, Random Forest, Support Vector Classifier models were performed. It was found that the accuracy of all classifiers is 97-98%. In addition, metrics were calculated to evaluate the effectiveness of the models. The Decision Tree Classifier method was chosen to apply the approach to cell classification on new images. All image processing, data collection and processing, model training and evaluation were performed using the Python programming language and relevant libraries.
Interval Type-3 Fuzzy Parameter Adaptation in Harmony Search Optimal Controller Design

Most of the currently existing metaheuristicHarmony Search algorithms have the problem of local minima and fixed parameters. Fuzzy logicFuzzy logic has contributed to solving this problem. It has been shown that the use of type-1 and type-2 fuzzy theory applied in parameter adaptationParameter Adaptation has contributed to effectively solve this problem. The advantage of utilizing fuzzy theory in parameter adaptation is the uncertainty management that offers a measurable improvement in finding solutions.
Feature Drift Aware for Intrusion Detection System Using Developed Variable Length Particle Swarm Optimization in Data Stream

Intrusion Detection Systems (IDS) serve as critical components in safeguarding network security by detecting malicious activities. Although IDS has recently been treated primarily through the lens of machine learning, challenges persist, particularly with high-dimensional data and feature drift. Feature drift pertains to the dynamic nature of feature significance, which can fluctuate over time, complicating the task of stable and effective intrusion detection. The existing Genetic Programming (GP)-combiner based ensemble classifier framework demonstrates notable efficiency in online intrusion detection, especially in accommodating concept drift. However, it does not adequately address the specific type of concept drift known as feature drift. To rectify this gap, this article proposes a refined version of GP-combiner, named Dynamic Feature Aware GP Ensemble (DFA-GPE). This advanced framework incorporates an improved variant of Variable Length Multi-Objective Particle Swarm Optimization (VLMO-PSO) to dynamically manage feature drift. The proposed VLMO-PSO employs a smart population initialization strategy based on Bernoulli distribution and symmetric uncertainty. It also utilizes a unique set of transfer functions that map the mobility equation outcomes to the decision space. To further optimize the process, the framework introduces a novel exemplar selection method, striking a balance between exploration and exploitation. DFA-GPE’s final feature selection decisions are informed by statistical analyses of feature weights, effectively addressing the challenge of dynamic feature selection as a multi-objective optimization problem that simultaneously enhances accuracy and conserves memory. Comprehensive evaluation of DFA-GPE on two benchmark datasets, namely HIKARI 2021 and TON_IoT 2020, reveals its robust performance across all metrics. From experiment results, our framework attains 99.09% and 92.64% accuracy on both datasets, respectively, while simultaneously reducing memory consumption. Hence, DFA-GPE emerges as a comprehensive framework adept at tackling the most pertinent issues related to stream data classification within IDS, notably outperforming existing methodologies.
R2GenGPT: Radiology Report Generation with Frozen LLMs

Large Language Models (LLMs) have consistently showcased remarkable generalization capa- bilities when applied to various language tasks. Nonetheless, harnessing the full potential of LLMs for Radiology Report Generation (R2Gen) still presents a challenge, stemming from the inherent disparity in modality between LLMs and the R2Gen task. To bridge this gap effectively, we propose R2GenGPT, which is a novel solution that aligns visual features with the word embedding space of LLMs using an efficient visual alignment module. This innovative approach empowers the previously static LLM to seamlessly integrate and process image information, marking a step forward in optimizing R2Gen performance. R2GenGPT offers the following benefits. First, it attains state-of-the-art (SOTA) performance by training only the lightweight visual alignment module while freezing all the parameters of LLM. Second, it exhibits high training efficiency, as it requires the training of an exceptionally minimal number of parameters while achieving rapid convergence. By employing delta tuning, our model only trains 5M parameters (which constitute just 0.07% of the total parameter count) to achieve performance close to the SOTA levels. Our code is available at https://github.com/wang-zhanyu/R2GenGPT.
Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs

Model-Based Anomaly Detection has been a successful approach to identify deviations from the expected behavior of Cyber-Physical Production Systems. Since manual creation of these models is a time-consuming process, it is advantageous to learn them from data and represent them in a generic formalism like timed automata. However, these models - and by extension, the detected anomalies - can be challenging to interpret due to a lack of additional information about the system. This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies. The authors additionally propose an ontology of the necessary concepts. The approach was validated on a five-tank mixing CPPS and was able to formally define both automata model as well as timing anomalies in automata execution.
A Study of Hand Gesture Exergame for Dementia Development Suppression

Nowadays, dementia affects more than 55 million people in the world according to World Health Organization (WHO). It becomes very important to suppress the development of dementia by providing proper exercise opportunities to such people. Then, to play a video game using hand gestures, called exergame, will be a good candidate. In this paper, we study a simple Chrome Dino exergame on a PC under this motivation where the user can control the jumping timing of the dinosaur avatar by hand gestures. For evaluations, we ask 10 students in Okayama University, Japan, to play it, where the questionnaire results confirm the validity of the proposal for the young people. The evaluation for seniors including dementia patients will be in next steps of this study.
There Is No Chilling When Your Control System Cybersecurity Is Unfulfilling

This article discusses the importance of control system cyber incident response programs as part of preparedness for critical-infrastructure protection.
CAGn: High-Order Coordinated Attention Module for Improving Fall Detection Models

In order to quickly and accurately detect the occurrence of accidental falls and save the lives of more elderly people living alone, this paper proposes a new convolutional network module Higher-order Coordinated Attention module (CAGn) based on the latest research, and uses it and the lightweight convolutional module (GSConv) to improve the YOLOv5s model to construct a new fall detection model YOLOv5s-CAGn-GSConv. In addition, a new fall detection dataset Fall-Dataset is produced, and a fall event triggering mechanism based on queue window is proposed to be applied to the fall detection system. Experimental results show that the CAGn module can capture cross-channel information and high-order spatial information at the same time, thereby effectively improving the accuracy of the detection model, and the introduction of GSConv module further improves the accuracy and greatly reduces the number of parameters of the detection model. The final YOLOv5s-CAGn-GSConv model only increases the number of parameters by 4.2% but improves the accuracy by 2.1% and the average accuracy by 0.9% (mAP@0.5) and 2.6% (mAP@0.5:0.95).
Enhanced Security against Adversarial Examples Using a Random Ensemble of Encrypted Vision Transformer Models

Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source model can fool another black-box model (target model) with a non-trivial probability. In previous studies, it was confirmed that the vision transformer (ViT) is more robust against the property of adversarial transferability than convolutional neural network (CNN) models such as ConvMixer, and moreover encrypted ViT is more robust than ViT without any encryption. In this article, we propose a random ensemble of encrypted ViT models to achieve much more robust models. In experiments, the proposed scheme is verified to be more robust against not only black-box attacks but also white-box ones than convention methods.
Clustering Re-Inference Algorithm for Deep Learning-Based Hierarchical Object Detection System

We propose the clustering re-inference algorithm (CRIA) for a deep learning-based hierarchical object detection system. CRIA uses a two-stage inference mechanism, namely a global detection unit (GDU) and a local detection unit (LDU), to detect large and small objects in aerial images, respectively, and a normalized clustering unit (NCU) to locate small objects. The input high-resolution aerial images are compressed and then inferred coarsely by the GDU to detect objects. This result is input to the NCU, which estimates the location of small objects by normalizing the vectors of small object candidates obtained from the GDU and clustering them in four dimensions. Finally, the LDU performs adaptive fine inference on the region estimated by the NCU to detect small objects. The results of an evaluation using the VisDrone dataset, which mainly consists of Full-HD aerial images, show that the proposed method improves the number of successfully detected small objects by 11.6% and the detection accuracy by 20.1% compared with the results obtained using the conventional method.
D2WFP: A Novel Protocol for Forensically Identifying, Extracting, and Analysing Deep and Dark Web Browsing Activities

The use of the unindexed web, commonly known as the deep web and dark web, to commit or facilitate criminal activity has drastically increased over the past decade. The dark web is a dangerous place where all kinds of criminal activities take place, Despite advances in web forensic techniques, tools, and methodologies, few studies have formally tackled dark and deep web forensics and the technical differences in terms of investigative techniques and artefact identification and extraction. This study proposes a novel and comprehensive protocol to guide and assist digital forensic professionals in investigating crimes committed on or via the deep and dark web. The protocol, named D2WFP, establishes a new sequential approach for performing investigative activities by observing the order of volatility and implementing a systemic approach covering all browsing-related hives and artefacts which ultimately resulted in improving the accuracy and effectiveness. Rigorous quantitative and qualitative research has been conducted by assessing the D2WFP following a scientifically sound and comprehensive process in different scenarios and the obtained results show an apparent increase in the number of artefacts recovered when adopting the D2WFP which outperforms any current industry or opensource browsing forensic tools. The second contribution of the D2WFP is the robust formulation of artefact correlation and cross-validation within the D2WFP which enables digital forensic professionals to better document and structure their analysis of host-based deep and dark web browsing artefacts.