Reviewerly/exHarmony huggingface dataset:
Characters Link Shots: Character Attention Network for Movie Scene Segmentation

Movie scene segmentation aims to automatically segment a movie into multiple story units, i.e., scenes, each of which is a series of semantically coherent and time-continual shots. Previous methods have continued efforts on shot semantic association, but few take into account the impact of different semantics on foreground characters and background scenes in movie shots. In particular, the background scene in the shot can adversely affect scene boundary classification. Motivated by the fact that it is the characters who drive the plot development of a movie scene, we build a Character Attention Network (CANet) to detect movie scene boundaries in a character-centric fashion. To eliminate the background clutter, we extract multi-view character semantics for each shot in terms of human bodies and faces. Furthermore, we equip our CANet with two stages of character attention. The first is Masked Shot Attention (MSA) through selective self-attention over similar temporal contexts from multi-view character semantics to yield an enhanced omni-view shot representation, by which the CANet can better handle the variations of characters in pose and appearance. The second is Key Character Attention (KCA) through temporal-aware attention on character reappearances for Bidirectional Long Short-Term Memory (Bi-LSTM) feature association so that linking shots can be focused on those with recurring key characters. We encourage the proposed CANet in learning boundary-discriminative shot features. Specifically, we formulate a Boundary-Aware circle Loss (BAL) to push far apart CANet-features between adjacent scenes, which is also coupled with the cross-entropy loss to drive CANet-features sensitive to scene boundaries. Experimental results on the MovieNet-SSeg and OVSD datasets show that our method achieves superior performance in temporal scene segmentation compared with state-of-the-art methods.
Explainable Stacked Ensemble Deep Learning (SEDL) Framework to Determine Cause of Death from Verbal Autopsies

Verbal autopsies (VA) are commonly used in Low- and Medium-Income Countries (LMIC) to determine cause of death (CoD) where death occurs outside clinical settings, with the most commonly used international gold standard being physician medical certification. Interviewers elicit information from relatives of the deceased, regarding circumstances and events that might have led to death. This information is stored in textual format as VA narratives. The narratives entail detailed information that can be used to determine CoD. However, this approach still remains a manual task that is costly, inconsistent, time-consuming and subjective (prone to errors), amongst many drawbacks. As such, this negatively affects the VA reporting process, despite it being vital for strengthening health priorities and informing civil registration systems. Therefore, this study seeks to close this gap by applying novel deep learning (DL) interpretable approaches for reviewing VA narratives and generate CoD prediction in a timely, easily interpretable, cost-effective and error-free way. We validate our DL models using optimisation and performance accuracy machine learning (ML) curves as a function of training samples. We report on validation with training set accuracy (LSTM = 76.11%, CNN = 76.35%, and SEDL = 82.1%), validation accuracy (LSTM = 67.05%, CNN = 66.16%, and SEDL = 82%) and test set accuracy (LSTM = 67%, CNN = 66.2%, and SEDL = 82%) for our models. Furthermore, we also present Local Interpretable Model-agnostic Explanations (LIME) for ease of interpretability of the results, thereby building trust in the use of machines in healthcare. We presented robust deep learning methods to determine CoD from VAs, with the stacked ensemble deep learning (SEDL) approaches performing optimally and better than Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). Our empirical results suggest that ensemble DL methods may be integrated in the CoD process to help experts get to a diagnosis. Ultimately, this will reduce the turnaround time needed by physicians to go through the narratives in order to be able to give an appropriate diagnosis, cut costs and minimise errors. This study was limited by the number of samples needed for training our models and the high levels of lexical variability in the words used in our textual information.
Money in the digital age: Exploring the potential of central bank digital currency with a focus on social adaptation and education

This study delves into the benefits and risks of central bank digital currency (CBDC) and examines social adaptation, and education using expert interviews and conference insights. Vital findings stress the need for targeted educational efforts to facilitate CBDC adoption. The research also emphasizes best practices, engaging external experts, and a phased introduction strategy for safe and efficient implementation. Highlighting key stages, it underscores addressing genuine market needs for successful CBDC introduction. Overall, prudent risk management, strategic education, and well-planned phased deployment are crucial for successful CBDC implementation.
Software Vulnerability Detection using Large Language Models

Software development is among the first demonstrations of using Large Language Models (LLMs) to enhance human productivity. Such a co-pilot paradigm envisions LLM working side-by-side with human developers to assist in programming tasks. Ensuring the security of software products is a critical factor for the success of such a paradigm. There have been various anecdotal reports on the success of using LLMs to detect vulnerabilities in programs. This paper reports a set of experiments applying four well-known LLMs to two widely referenced public datasets to evaluate the performance of LLMs in detecting software vulnerabilities. Our results show a significant performance gap between these LLMs and those from popular static analysis tools, primarily due to their high false positive rates. However, LLMs show great promise in identifying subtle patterns commonly associated with software vulnerabilities. This observation suggests a possible path forward by combining LLMs and other program analysis techniques to achieve better software vulnerability detection.
Performance and Comparative Analysis of Elliptic Curve Cryptography and RSA

Due to the development of computer technologies, it becomes necessary to create new or improve existing information security algorithms. Data security is one of the important issues, especially for increasing amount of transactions via the internet. Today, there are several cryptosystems for information security that are recognized as standards, including the public key cryptosystem RSA. However, Elliptic Curve Cryptography (ECC) has also recently become relevant. Performing large-key encryption quickly requires high device performance, which creates a problem. Elliptic curve cryptography handles this problem well. This paper presents a comparison of the performance and efficiency of the elliptic curve cryptographic method and the asymmetric RSA algorithm. Within the framework of the thesis, a software code was created, which was used to compare the given algorithms. Comparative analysis showed that elliptic curve cryptography is faster than RSA algorithm. The main advantage of the ECC system is that it can provide the same security as RSA with a smaller key size. There is a big difference between ECC and RSA algorithms in terms of information security. Therefore, the ECC algorithm is better for working with embedded systems.
Exploring Early Adopters' Perceptions of ChatGPT as a Code Generation Tool

ChatGPT is an artificial intelligence chatbot developed by OpenAI, able of interacting in a conversational way by taking into account successive input prompts. Among many possible uses, ChatGPT has been found to possess code generation capabilities, being able to generate code snippets and assist developers in their programming tasks. This paper performs a qualitative exploration of perceptions of early adopters regarding the use of ChatGPT for code generation, acknowledging the substantial impact this tool can have in the software development landscape. We collected a diverse set of discussions from early adopters of ChatGPT code generation capabilities and, leveraging an open card sorting methodology categorized it into relevant topics with the goal of extracting insights into the experiences, opinions, and challenges they faced. We found that early adopters (i) report their own mixed usage experiences, (ii) share suggestions for prompt engineering, (iii) debate the extent to which they can trust generated code, and (iv) discuss the impact that ChatGPT can have on the software development process. We discuss the implications of the insights we extracted from early adopters' perspectives and provide recommendations for future research.
Data-driven and echo state network-based prediction of wave propagation behavior in dam-break flood

Abstract The computational prediction of wave propagation in dam-break floods is a long-standing problem in hydrodynamics and hydrology. We show that a reservoir computing echo state network (RC-ESN) that is well-trained on a minimal amount of data can accurately predict the long-term dynamic behavior of a one-dimensional dam-break flood. We solve the de Saint-Venant equations for a one-dimensional dam-break flood scenario using the Lax–Wendroff numerical scheme and train the RC-ESN model. The results demonstrate that the RC-ESN model has good prediction ability, as it predicts wave propagation behavior 286 time-steps ahead with a root mean square error smaller than 0.01, outperforming the conventional long short-term memory (LSTM) model, which only predicts 81 time-steps ahead. We also provide a sensitivity analysis of prediction accuracy for RC-ESN's key parameters such as training set size, reservoir size, and spectral radius. Results indicate that the RC-ESN is less dependent on training set size, with a medium reservoir size of 1,200–2,600 sufficient. We confirm that the spectral radius has a complex influence on the prediction accuracy and currently recommend a smaller spectral radius. Even when the initial flow depth of the dam break is changed, the prediction horizon of RC-ESN remains greater than that of LSTM.
Cooperative online schedule of interconnected data center microgrids with shared energy storage

The rapid increasing of data centers calls for an efficient method to reduce high operation costs and carbon emissions. This paper proposes a cooperative online schedule framework for local interconnected data centers considering shared energy storage. A time-average optimization problem is built to reduce the overall operation cost with essential operational constraints. Multiple randomness and the temporal coupling constraints of energy system levels make it challenging to solve the stochastic problem. Adapting the Lyapunov optimization theory with relaxation and perturbation technique, an online algorithm is proposed to find an acceptable solution to the problem to achieve both good service coordination and efficient energy coordination. The algorithm does not require prediction of random parameters, and has a fast convergence rate. Especially, the theoretical conditions for convergence and the distance between the acceptable and real optimal solution are derived in a closed form. Simulation studies show that the algorithm can effectively reduce operation cost of data centers, carbon emissions, and dependence on the main grid.
Collision Avoidance for a Selective Compliance Assembly Robot Arm Manipulator Using Topological Path Planning

Industrial applications with robotic manipulators have grown and made production systems increasingly efficient. However, there are still some limitations that can delay production, causing losses. Several factors, such as accidents and collisions of manipulator robots with operators and other machines, can cause unforeseen stops. Thus, this work aims to develop a trajectory planning method to avoid collisions applied to a selective compliance assembly robot arm (SCARA) robotic manipulator in the context of collaborative robotics. The main contribution of this paper is a path planning method based on mathematical morphology, named topological path planning (TPP). Through some evaluation metrics such as the number of path points, computing time, distance, standard deviation of the joint acceleration, and maximum acceleration rate along the path, we show that TPP is a collision-free, deterministic, and predictable route planning. In our experiments, our proposal presented better results for applications in industrial robotic manipulators when compared to the probabilistic roadmap method (PRM) and TPP*, a particular case of TPP that is similar to the generalized Voronoi diagram (GVD).
Quantum‐Accelerated Algorithms for Generating Random Primitive Polynomials Over Finite Fields

Abstract Primitive polynomials over finite fields are crucial resources with broad applications across various domains in computer science, including classical pseudo‐random number generation, coding theory, and post‐quantum cryptography. Nevertheless, the pursuit of an efficient classical algorithm for generating random primitive polynomials over finite fields remains an ongoing challenge. In this work, it shows how this problem can be solved efficiently with the help of quantum computers. Moreover, the designs of specific quantum circuits to implement them are also presented. The research paves the way for the rapid and real‐time generation of random primitive polynomials in diverse quantum communication and computation applications.
From Global to Local: Multi-Scale Out-of-Distribution Detection

Out-of-distribution (OOD) detection aims to detect “unknown” data whose labels have not been seen during the in-distribution (ID) training process. Recent progress in representation learning gives rise to distance-based OOD detection that recognizes inputs as ID/OOD according to their relative distances to the training data of ID classes. Previous approaches calculate pairwise distances relying only on global image representations, which can be sub-optimal as the inevitable background clutter and intra-class variation may drive image-level representations from the same ID class far apart in a given representation space. In this work, we overcome this challenge by proposing Multi-scale OOD DEtection (MODE), a first framework leveraging both global visual information and local region details of images to maximally benefit OOD detection. Specifically, we first find that existing models pretrained by off-the-shelf cross-entropy or contrastive losses are incompetent to capture valuable local representations for MODE, due to the scale-discrepancy between the ID training and OOD detection processes. To mitigate this issue and encourage locally discriminative representations in ID training, we propose Attention-based Local PropAgation (ALPA), a trainable objective that exploits a cross-attention mechanism to align and highlight the local regions of the target objects for pairwise examples. During test-time OOD detection, a Cross-Scale Decision (CSD) function is further devised on the most discriminative multi-scale representations to distinguish ID/OOD data more faithfully. We demonstrate the effectiveness and flexibility of MODE on several benchmarks – on average, MODE outperforms the previous state-of-the-art by up to 19.24% in FPR, 2.77% in AUROC. Code is available at https://github.com/JimZAI/MODE-OOD.
What makes an informative and publication-worthy scientometric analysis of literature: A guide for authors, reviewers and editors

In light of the proliferation of scientometric/bibliometric software and its accessibility to a wide array of researchers, we have witnessed a substantial increase in literature analyses founded in these methods across diverse scientific domains. With these methods gaining popularity, the influx of similar papers seeking publication is expected to continue in the forthcoming years. However, persistent questions remain regarding the benchmarks for a well-justified, informative, and publication-worthy scientometric review paper. This concern resonates not only with authors but also holds significance for editors and reviewers tasked with evaluating the quality, rigour, and relevance of such contributions. In this context, this paper furnishes a concise guide elucidating the fundamental criteria underpinning a publication-worthy scientometric literature analysis. Encompassing the entire spectrum of steps integral to a scientometric review, the guide addresses the rationale and benefits of undertaking such an analysis. This includes all relevant aspects such as scoping, data procurement and search methodologies, visualisation, result delineation and interpretation. Furthermore, this guide addresses a range of prevalent misconceptions and common errors with respect to this genre of scholarly publications. It concludes by proposing a quality checklist that can be used as a standard benchmark for assessing studies that employ methodologies of scientometric literature analysis.
Powering quantum Otto engines only with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>q</mml:mi></mml:math> -deformation of the working substance

We consider a quantum Otto cycle with a q-deformed quantum oscillator working substance and classical thermal baths. We investigate the influence of the quantum statistical deformation parameter q on the work and efficiency of the cycle. In usual quantum Otto cycle, a Hamiltonian parameter is varied during the quantum adiabatic stages while the quantum statistical character of the working substance remains fixed. We point out that even if the Hamiltonian parameters are not changing, work can be harvested by quantum statistical changes of the working substance. Work extraction from thermal resources using quantum statistical mutations of the working substance makes a quantum Otto cycle without any classical analog.
Probabilistic Source Classification of Large Tephra Producing Eruptions Using Supervised Machine Learning: An Example From the Alaska‐Aleutian Arc

Abstract Alaska contains over 130 volcanoes and volcanic fields that have been active within the last 2 million years. Of these, roughly 90 have erupted during the Holocene, with many characterized by at least one large explosive eruption. These large tephra‐producing eruptions (LTPEs) generate orders of magnitude more erupted material than a “typical” arc explosive eruption and distribute ash thousands of kilometers from their source. Because LTPEs occur infrequently, and the proximal explosive deposit record in Alaska is generally limited to the Holocene, we require a method that links distal deposits to a source volcano where the correlative proximal deposits from that eruption are no longer preserved. We present a model that accurately and confidently identifies LTPE volcanic sources in the Alaska‐Aleutian arc using only in situ geochemistry. The model is a voting ensemble classifier comprised of six conceptually different machine learning algorithms trained on proximal tephra deposits that have had their source positively identified. We show that incompatible trace element ratios (e.g., Nb/U, Th/La, Rb/Sm) help produce a feature space that contains significantly more variance than one produced by major element concentrations, ultimately creating a model that can achieve high accuracy, precision, and recall on predicted volcanic sources, regardless of the perceived 2D data distribution (i.e., bimodal, uniform, normal) or composition (i.e., andesite, trachyte, rhyolite) of that source. Finally, we apply our model to unidentified distal marine tephra deposits in the region to better understand explosive volcanism in the Alaska‐Aleutian arc, specifically its pre‐Holocene spatiotemporal distribution.
Generative Artificial Intelligence (ChatGPT &amp; Bard) in Public Administration Research: A Double-Edged Sword for Street-Level Bureaucracy Studies

This manuscript critically examines the adoption of generative AI tools, notably ChatGPT and Bard, within public administration research, especially in street-level bureaucracy. While these tools offer revolutionary insights into bureaucratic behaviors and decision-making, they pose significant ethical dilemmas, including potential biases and data privacy concerns. The paper offers comprehensive recommendations designed to help researchers navigate these challenges, emphasizing the need for robust data validation, enhanced transparency, and continuous adherence to evolving ethical standards. The overarching aim is to facilitate responsible AI integration, ensuring research methodologies' efficacy and preserving ethical integrity in public administration inquiries.
Operational meanings of a generalized conditional expectation in quantum metrology

A unifying formalism of generalized conditional expectations (GCEs) for quantum mechanics has recently emerged, but its physical implications regarding the retrodiction of a quantum observable remain controversial. To address the controversy, here I offer operational meanings for a version of the GCEs in the context of quantum parameter estimation. When a quantum sensor is corrupted by decoherence, the GCE is found to relate the operator-valued optimal estimators before and after the decoherence. Furthermore, the error increase, or regret, caused by the decoherence is shown to be equal to a divergence between the two estimators. The real weak value as a special case of the GCE plays the same role in suboptimal estimation – its divergence from the optimal estimator is precisely the regret for not using the optimal measurement. For an application of the GCE, I show that it enables the use of dynamic programming for designing a controller that minimizes the estimation error. For the frequentist setting, I show that the GCE leads to a quantum Rao-Blackwell theorem, which offers significant implications for quantum metrology and thermal-light sensing in particular. These results give the GCE and the associated divergence a natural, useful, and incontrovertible role in quantum decision and control theory.
Adversarial color projection: A projector-based physical-world attack to DNNs

While deep neural networks (DNNs) have made remarkable advancements in various fields recently, the latest research indicates that DNNs are susceptible to disruptions from minor perturbations. However, conventional physical attacks employing stickers as physical perturbations to deceive classifiers encounter challenges in achieving stealthiness and are susceptible to issues such as printing quality loss. Recent advancements in physical attacks have harnessed light beams to execute attacks, producing artificial optical patterns rather than natural ones. In this study, we introduce a black-box projector-based physical attack called Adversarial Color Projection (AdvCP), which manipulates the physical parameters of color projection to execute adversarial attacks. AdvCP revolves around three pivotal criteria: effectiveness, stealthiness, and robustness. In a digital environment, our approach attains an impressive attack success rate of 97.60% on a subset of ImageNet. In the physical realm, we achieve a remarkable 100% attack success rate in indoor testing and 82.14% in outdoor testing. To underscore the stealthiness of our approach, we juxtapose the adversarial samples generated by AdvCP with baseline samples. When applied to challenge advanced and robust DNNs, our experimental results reveal that our method achieves an attack success rate exceeding 85% across most all of the models, establishing the robustness of AdvCP. Finally, we contemplate the potential threats posed by AdvCP to future vision-based systems and applications, and proffer some innovative concepts pertaining to light-based physical attacks. Our code can be accessed from the following link: https://github.com/ChengYinHu/AdvCP.git