Reviewerly/exHarmony huggingface dataset:
Saliency map in image visual quality assessment and processing

Images are mainly viewed and analyzed by humans. Because of this, in the characterization of image quality and effectiveness of image processing, it is necessary to take into account the peculiarities of the human vision system and cognition that are very complex. Saliency maps as well as priority and meaning maps introduced recently are the attempts to incorporate specific features of human vision into image analysis and processing fields. Many authors that consider the aforementioned maps consider them from different viewpoints. Thus, the basic subject of this paper is the factors that influence and determine these maps. Among such factors, there are low-level features as well as social and psychological ones such as emotions, age, and life values. The main goal of this paper is to give a brief survey of these factors and to consider how maps are already used in image quality assessment and processing as well as how they can be employed in the future. The tasks of the paper are to provide a definition of saliency, priority, and meaning maps, to analyze the factors that influence these maps, and to evaluate what improvement can be obtained due to taking maps into account in the assessment of image visual quality and such image processing operations as quality assessment, denoising, and lossy compression. The main result is that, by taking saliency maps into account, image quality assessment and processing efficiency can be sufficiently improved, especially for applications oriented on image viewing and analysis by observers or customers. This can be done by the simple weighting of local estimates of a given metric with further aggregation as well as by approaches based on neural networks. Using different quantitative criteria, we show what positive results can be got due to incorporating maps into quality assessment and image processing. As conclusion, we present possible directions of future research that are mainly related to an adaptation of denoising and lossy compression parameters to peculiarities of human attention.
Impact of inclusive leadership on project success through climate for innovation: the moderating role of knowledge sharing

This research study aims to examine the impact of inclusive leadership on project success while keeping the climate for innovation as a mediator and knowledge sharing within the organisation as a moderator. Using the convenience sampling technique, data were collected from 201 participants working in project-based organisations across major cities of Pakistan. The data has been analysed using Smart PLS and SPSS. As a result of the regression analysis, it was found that inclusive leadership significantly and positively impacts project success. The findings show that inclusive leadership has a positive effect on the climate for innovation. The results revealed that the climate for innovation mediates the relationship between inclusive leadership and project success. The results showed that knowledge sharing does not moderate the relationship between inclusive leadership and project success. Limitations, future suggestions, and managerial implications have also been discussed. The study aims to address the gap on how inclusive leadership leads towards project success in the presence of climate for innovation as a mediator and knowledge sharing as a moderator in project-based organisations of Pakistan.
Blockchain for Securing Autonomous Vehicles

Autonomous Vehicles (AVs) are perceived as a revolutionary development in the worldwide transport sector and are believed to enhance safety significantly. An AV is equipped with a range of advanced technologies that allow it to communicate with other vehicles, as well as with various other systems and infrastructure such as traffic lights, road signs, and other Road-Side Units (RSUs). AVs also employ a variety of communication channels for their operation and safety. The major concerns in such communication channels are reliability, precision, and security of received and broadcasted data. These concerns directly affect the safety and security aspects of an AV which endangers the passenger safety. Thus, AVs require a secure mechanism for the exchange of crucial data. This paper explores the utilization of blockchain technology in AV communication, enabling authorized access to data, facilitating secure data sharing among vehicles and RSUs, tracking the internal status of the vehicle for In-Vehicle Network (IVN) breaches, along with the architecture of the system. Moreover, the paper outlines the implementation challenges and future research of blockchain technology in AVs.
A Secure Data Processing System in Edge Computing-Powered AIoT

Artificial Intelligent of Things (AIoT) has been utilized a lot of field since AIoT improves the overall performance of the system for the realization of smart life. As the attention about AIoT grows, the amount of data generated by IoT devices increases. Since IoT devices have low capability, it is difficult for IoT devices to store and analyze a huge amount of data. Cloud server has been introduced to handle the overgrowing data. However, cloud server centrally processes the enormous volume of data, and it leads to the problems of response delay and security. By adopting the edge server, the delay problem can be solved due to the closer communication distance. Many research also has been presented to solve the security problem in the AIoT environment. Unfortunately, it is still an ongoing issue. Although many studies have suggested countermeasures, they suffer from various physical and information security-related attacks. It causes the serious consequences such as data leakage and misuse. Thus, it is necessary to study on secure data processing and analysis in the AIoT environment. In this paper, we propose the system model for secure data processing framework by applying mutual authentication between IoT devices, edge server, and cloud server.
Data Poisoning Attack Using Hybrid Particle Swarm Optimization in Connected and Autonomous Vehicles

The development of connected and autonomous vehicles (CAV s) relies heavily on deep learning technology, which has been widely applied to perform a variety of tasks in CAYs. On the other hand, deep learning faces some security concerns. Data poisoning attack, as one of the security threats, can compromise the deep learning models by injecting poisoned training samples. The poisoned models may make more false predictions, and may cause fatal accidents of CA V s in the worst case. Therefore, the principles of poisoning attacks are worth studying in order to propose counter measures. In this work, we propose a black-box and clean-label data poisoning attack method that uses hybrid particle swarm optimization with simulated annealing to generate perturbations for poisoning. The attacking method is evaluated by experiments on the deep learning models of traffic sign recognition systems on CA V s, and the results show that the classification accuracies of the target deep learning models are obviously downgraded using our method, with a small portion of poisoned data samples in GTSRB dataset.
Rate-Adaptive Polar-Coding-Based Reconciliation for Continuous-Variable Quantum Key Distribution at Low Signal-to-Noise Ratio

Information reconciliation significantly impacts the performance of the practical continuous-variable quantum key distribution (CV QKD) system. Fixed-rate error-correction codes limit the potential applications of the CV QKD because they lead to reduced reconciliation efficiency when the signal-to-noise ratio of the quantum channel changes, further deteriorating the system's performance. Therefore, we propose a rate-adaptive polar-coding-based reconciliation scheme for practical CV QKD systems with the time-variant quantum channel. Experimental results verify that the proposed scheme can successfully extract secret keys within the range of signal-to-noise ratios from $\ensuremath{-}0.5$ to $\ensuremath{-}4.5$ dB, and the minimum frame-error rate can be less than ${10}^{\ensuremath{-}3}$. Moreover, the proposed scheme can promote the application of the CV QKD system in a realistic environment.
Using the MetaMask Crypto-Wallet

In the previous chapter, you learned how to create your own private Ethereum test network using Geth so that you can try out the various Ethereum transactions, such as transferring Ethers to different accounts and performing mining. You also learned how to create accounts so that you can hold your own Ethers. In this chapter, you will learn how to use a Chrome extension known as MetaMask. The MetaMask Chrome extension is an Ethereum crypto-wallet that allows you to hold your Ethereum account(s), and it is an essential tool to help you develop and test smart contracts in the next few chapters.
MPSoC design and implementation using microblaze soft core processor architecture for faster execution of arithmetic application

The research paper presents the design methodology with novel task distribution technique on multi-processor system on chip (MPSoC) for speeding up the execution of arithmetic application. Utilisation of multiple soft core processors on field programmable gate array (FPGA) reduces the overload of adding external hardware to a system. Parallel processing of soft core processor with proposed task distribution technique makes any application to execute at faster rate. This task distribution based speed enhancement technique for arithmetic application is very feasible and appealing to the modern applications like neural networks, fuzzy logic, algorithms of machine learning etc. Experimentation on such architecture with arithmetic application shows significant increase in speed of operation with respect to conventional design. This is implemented using Microblaze soft core processor architecture on Xilinx Virtex 5 FPGA board.
Learning Audio and Video Bitrate Selection Strategies Via Explicit Requirements

Mobile video streaming dominates today's network traffic, and adaptive bitrate (ABR) algorithms have been routinely adopted for transmitting media content across dynamic mobile networks. State-of-the-art ABR algorithms mainly alter video bitrate without considering audio bitrate as they consider the impact on the video negligible due to their small size. However, to bring users an immersive experience, recent content providers have applied high-quality audio with large sizes, like stereophonic sound. Therefore, improper audio bitrate selection will adversely affect video bitrate selection, leading to undesirable audio/video combinations (the highest video quality with the lowest audio quality, and vice versa) and frequent playback interruptions. To address these inefficiencies, we propose a Self-Play reinforcement learning-based Audio-aware ABR algorithm named SPA to learn strategies for audio and video bitrate selections. By learning from explicit goals, SPA can match the actual requirements and attain good performance. By conducting trace-driven and testbed-based experiments, we observe SPA's considerable superiority compared to existing approaches, including reducing the undesirable combinations by up to 34.17× and achieving zero stall time across 88.57% of traces. We also invite 35 volunteers to join a subjective test, and the result shows that 33/35 people consider SPA provides them with a satisfactory viewing experience.
Motion-Compensated Predictive RAHT for Dynamic Point Clouds

We study the use of predictive approaches alongside the region-adaptive hierarchical transform (RAHT) [1] in attribute compression of dynamic point clouds. The use of intra-frame prediction with RAHT was shown to improve attribute compression performance over pure RAHT [2] and represents the state-of-the-art in attribute compression of point clouds, being part of MPEG’s geometry-based test model. We studied a combination of inter-frame and intra-frame prediction for RAHT for the compression of dynamic point clouds. An adaptive zero-motion-vector (ZMV) scheme and an adaptive motion-compensated scheme are developed. The simple adaptive ZMV approach is able to achieve sizable gains over pure RAHT and over the intra-frame predictive RAHT (I-RAHT) for point clouds with little or no motion while ensuring similar compression performance to I-RAHT for point clouds with intense motion. The motion-compensated approach, more complex and more powerful, is able to achieve large gains across all of the tested dynamic point clouds.
MC-SQ: A Highly Accurate Ensemble for Multi-class Quantification

Quantification research proposes methods to estimate the class distribution in an independent sample. Many areas, such as epidemiology, sentiment analysis, political research and ecological surveillance, rely on quantification methods to estimate aggregated quantities. For instance, epidemiologists are often concerned with the dynamics of the number of disease cases across space and time. Thus, while classification predicts individual subjects, quantification is the class of methods that directly estimate the number of cases. Quantification is a thriving research area, and the community has proposed several approaches in the last decade. Nevertheless, most quantification research has focused on binary-class quantifiers, expecting these approaches to extend to multi-class using the one-versus-all (OVA) approach. However, there is enough empirical evidence indicating the performance of OVA multi-class quantifiers is subpar. This paper has two main contributions. First, we demonstrate why OVA quantifiers are doomed to underperform in multi- class settings due to a distribution shift they cannot handle. Second, we propose a new class of quantifiers based on ensemble learning that boosts the performance of the base quantifiers in the binary and, more importantly, multi-class settings. In one of the most comprehensive experimental setups ever attempted in quantification research, we show that our ensembles are the best-performing quantifiers compared with 33 state-of-the-art (single and ensemble) quantifiers and rank first in a recent quantification competition.
Matrix Profile XXVIII: Discovering Multi-Dimensional Time Series Anomalies with <i>K</i> of <i>N</i> Anomaly Detection<sup>†</sup>

In recent years there has been significant progress in univariate time series anomaly detection. However, efforts to generalize this success to the multi-dimensional case have met with limited progress. The main difficultly appears to be that in any N-dimensional time series, the anomaly will generally only manifest itself on K of the time series, with K < N. This leads to a chicken-and-egg problem. If we knew which K time series exhibited the anomaly, it would be easy to discover its location. However, we do not know this in advance, and the search space is of size 2N and not obviously amiable to greedy search. In this work we show a novel, simple algorithm that allows us to quickly find the best K of N anomaly subset for any value of K. Moreover, we show a simple metric that can rank the top anomaly subsets for all values of K from 1 to N. While our methods are mostly agnostic to the anomaly scoring model, for concreteness we use the Matrix Profile, and show that we can discover multi-dimensional anomalies that would escape detection by all current rival methods.KeywordsTime SeriesAnomaliesMatrix Profile methods
Hearing and being heard: LGBTQIA+ cross-disciplinary collection development

Purpose The purpose of this paper is to share how informal cross-disciplinary outreach methods can increase the development, awareness, investment and circulation of lesbian, gay, bisexual, transgender, queer, intersex, asexual (LGBTQIA+) collection in an academic library. The authors believe the methods shown here may be used to develop any collection, as well as increase awareness and use of a collection. Design/methodology/approach Through five different low-stakes outreach strategies, the authors connected directly with campus faculty and students across disciplines to ask for suggested items to develop an endowed LGBTQIA+ collection. Outreach was performed via email to faculty and student clubs, and by attending campus facilities, events and committees. Assessment reviewed the collection’s item count and circulation reports before and after outreach. Findings Purchasing items to develop a collection does not always need to be the result of one librarian’s research. Engaging the campus community in the expansion of the endowed LGBTQIA+ collection resulted in substantial growth of items in the collection, as well as substantial growth in the collection’s circulation. Involving faculty and students from multiple disciplines in the suggestion process is one way to build and ensure a collection is relevant to a variety of interests, with an added result of increased circulation. Originality/value This approach is a unique example of how to use low-stakes engagement to discover items to purchase for a large endowment while increasing interest and circulation of the collection. The case study also explores how to embark on collection development in ways that effectively and sustainably reach beyond one academic discipline. Yet, the authors believe this study’s examples could also be used successfully within a single discipline.
XR Technology Deployment in Value Creation

With increasing computing power and data transmission performance of information technologies, the application scenarios for Extended Reality (XR) technologies in industries are growing. Despite the ongoing scientific investigation of industrial XR applications for over 25 years, these technologies are still considered emerging. Within this paper, we present an industry- and business-process agnostic approach for classifying the deployment purposes of XR technologies in value creation. We identified two major research streams regarding the role of XR technologies in value creation: (1) the research initiatives focusing on business-process-specific use case analysis and (2) industry-oriented research reviews. This results in limited identification of suitable application scenarios for new use cases and restricted transferability of the existing use case to future deployments. First, we provide a qualitative analysis of the current research streams. Then, in the second step, by abstracting the XR technology from the existing business processes and the industry-specific context, the generic purposes for XR technologies in value creation are identified and defined. The summary of these deployment purposes results in a taxonomy that enables the identification and transfer of potential use cases of XR technologies in value creation.
PAPR: Publicly Auditable Privacy Revocation for Anonymous Credentials

We study the notion of anonymous credentials with Publicly Auditable Privacy Revocation (PAPR). PAPR credentials simultaneously provide conditional user privacy and auditable privacy revocation. The first property implies that users keep their identity private when authenticating unless and until an appointed authority requests to revoke this privacy, retroactively. The second property enforces that auditors can verify whether or not this authority has revoked privacy from an issued credential (i.e. learned the identity of the user who owns that credential), holding the authority accountable. In other words, the second property enriches conditionally anonymous credential systems with transparency by design, effectively discouraging such systems from being used for mass surveillance. In this work, we introduce the notion of a PAPR anonymous credential scheme, formalize it as an ideal functionality, and present constructions that are provably secure under standard assumptions in the Universal Composability framework. The core tool in our PAPR construction is a mechanism for randomly selecting an anonymous committee which users secret share their identity information towards, while hiding the identities of the committee members from the authority. As a consequence, in order to initiate the revocation process for a given credential, the authority is forced to post a request on a public bulletin board used as a broadcast channel to contact the anonymous committee that holds the keys needed to decrypt the identity connected to the credential. This mechanism makes the user de-anonymization publicly auditable.
New Handbook of Mathematical Psychology

The field of mathematical psychology began in the 1950s and includes both psychological theorizing, in which mathematics plays a key role, and applied mathematics motivated by substantive problems in psychology. Central to its success was the publication of the first Handbook of Mathematical Psychology in the 1960s. The psychological sciences have since expanded to include new areas of research, and significant advances have been made both in traditional psychological domains and in the applications of the computational sciences to psychology. Upholding the rigor of the original Handbook, the New Handbook of Mathematical Psychology reflects the current state of the field by exploring the mathematical and computational foundations of new developments over the last half-century. The third volume provides up-to-date, foundational chapters on early vision, psychophysics and scaling, multisensory integration, learning and memory, cognitive control, approximate Bayesian computation, and encoding models in neuroimaging.
Logo Detection Using Machine Learning Algorithm : A Survey

As more and more logos are produced, logo detection has gradually grown in popularity as study across numerous jobs and sectors. Deep learning-based solutions, which make use of numerous data sets,learning techniques, network designs, etc., have dominated recent advancements in this field. This research examines the progress made in the field of logo detection using deep learning approaches. In order to evaluate the efficacy of logo detection algorithms, which tend to be more diversified, difficult, and realistically reflective of real life, we first discuss a thorough background of the topic. The pros and disadvantages of each learning approach are then thoroughly analysed, along with the current logo detection strategies.To wrap up this study, we examine probable obstacles and provide the future directions for logo detecting development.
Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves  Propagation

With the recent rise of neural operators, scientific machine learning offers new solutions to quantify uncertainties associated with high-fidelity numerical simulations. Traditional neural networks, such as Convolutional Neural Networks (CNN) or Physics-Informed Neural Networks (PINN), are restricted to the prediction of solutions in a predefined configuration. With neural operators, one can learn the general solution of Partial Differential Equations, such as the elastic wave equation, with varying parameters. There have been very few applications of neural operators in seismology. All of them were limited to two-dimensional settings, although the importance of three-dimensional (3D) effects is well known. In this work, we apply the Fourier Neural Operator (FNO) to predict ground motion time series from a 3D geological description. We used a high-fidelity simulation code, SEM3D, to build an extensive database of ground motions generated by 30,000 different geologies. With this database, we show that the FNO can produce accurate ground motion even when the underlying geology exhibits large heterogeneities. Intensity measures at moderate and large periods are especially well reproduced. We present the first seismological application of Fourier Neural Operators in 3D. Thanks to the generalizability of our database, we believe that our model can be used to assess the influence of geological features such as sedimentary basins on ground motion, which is paramount to evaluating site effects.