Reviewerly/exHarmony huggingface dataset:
User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition on Federated Learning

Many existing privacy-enhanced speech emotion recognition (SER) frameworks focus on perturbing the original speech data through adversarial training within a centralized machine learning setup. However, this privacy protection scheme can fail since the adversary can still access the perturbed data. In recent years, distributed learning algorithms, especially federated learning (FL), have gained popularity to protect privacy in machine learning applications. While FL provides good intuition to safeguard privacy by keeping the data on local devices, prior work has shown that privacy attacks, such as attribute inference attacks, are achievable for SER systems trained using FL. In this work, we propose to evaluate the user-level differential privacy (UDP) in mitigating the privacy leaks of the SER system in FL. UDP provides theoretical privacy guarantees with privacy parameters $\epsilon$ and $\delta$. Our results show that the UDP can effectively decrease attribute information leakage while keeping the utility of the SER system with the adversary accessing one model update. However, the efficacy of the UDP suffers when the FL system leaks more model updates to the adversary. We make the code publicly available to reproduce the results in https://github.com/usc-sail/fed-ser-leakage.
On Fractional Tikhonov Regularization: Application to the Adaptive Network-Based Fuzzy Inference System for Regression Problems

In this article, we introduce a variant of the adaptive network-based fuzzy inference system (ANFIS). The proposed variant does not use backpropagation and grid partitioning, but the least-squares method with fractional Tikhonov regularization. The fractional regularization is a generalization of the standard regularization and is applied here to the learning process of the ANFIS scheme for the first time. This results in a simpler rule base, with a low number of rules, allowing to handle problems with many input variables with relatively low computational time while keeping high accuracy. We present new theoretical results on the fractional Tikhonov regularization. Such results are the basis for a formal discussion on how much the choice of a different architecture, resulting in a different matrix in the least-squares minimization, could affect the accuracy. We perform several numerical experiments on benchmark examples, first to assess the impact of the fractional regularization on the accuracy and then to compare our results against the most recent ones reported in the literature by other ANFIS-like or neuro-fuzzy systems. The numerical results show the good performance of the proposed approach.
Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels

Training the multi-label image recognition models with partial labels, in which merely some labels are known while others are unknown for each image, is a considerably challenging and practical task. To address this task, current algorithms mainly depend on pre-training classification or similarity models to generate pseudo labels for the unknown labels. However, these algorithms depend on sufficient multi-label annotations to train the models, leading to poor performance especially with low known label proportion. In this work, we propose to blend category-specific representation across different images to transfer information of known labels to complement unknown labels, which can get rid of pre-training models and thus does not depend on sufficient annotations. To this end, we design a unified semantic-aware representation blending (SARB) framework that exploits instance-level and prototype-level semantic representation to complement unknown labels by two complementary modules: 1) an instance-level representation blending (ILRB) module blends the representations of the known labels in an image to the representations of the unknown labels in another image to complement these unknown labels. 2) a prototype-level representation blending (PLRB) module learns more stable representation prototypes for each category and blends the representation of unknown labels with the prototypes of corresponding labels to complement these labels. Extensive experiments on the MS-COCO, Visual Genome, Pascal VOC 2007 datasets show that the proposed SARB framework obtains superior performance over current leading competitors on all known label proportion settings, i.e., with the mAP improvement of 4.6%, 4.6%, 2.2% on these three datasets when the known label proportion is 10%. Codes are available at https://github.com/HCPLab-SYSU/HCP-MLR-PL.
Experimentally feasible computational advantage from quantum superposition of gate orders

In an ordinary quantum algorithm the gates are applied in a fixed order on the systems. The introduction of indefinite causal structures allows to relax this constraint and control the order of the gates with an additional quantum state. It is known that this quantum-controlled ordering of gates can reduce the query complexity in deciding a property of black-box unitaries with respect to the best algorithm in which the gates are applied in a fixed order. However, all tasks explicitly found so far require unitaries that either act on unbounded dimensional quantum systems in the asymptotic limit (the limiting case of a large number of black-box gates) or act on qubits, but then involve only a few unitaries. Here we introduce tasks (1) for which there is a provable computational advantage of a quantum-controlled ordering of gates in the asymptotic case and (2) that require only qubit gates and are therefore suitable to demonstrate this advantage experimentally. We study their solutions with the quantum-$n$-switch and within the quantum circuit model and find that while the $n$-switch requires to call each gate only once, a causal algorithm has to call at least $2n-1$ gates. Furthermore, the best known solution with a fixed gate ordering calls $O(n\log_2{(n)})$ gates.
PDGAN: Phishing Detection With Generative Adversarial Networks

Phishing is a harmful online attack that could lead to identity theft and financial damages.The demand for high-accuracy phishing detection tools has risen due to the increase of online electronic services and payment systems.Most phishing detection techniques depend on features related to webpage content, which necessitates crawling the webpage and relying on third-party services.Relying on features related to webpage content could not provide high detection accuracy and leads to high false detection rates.Recently, deep learning has become a popular approach for detecting phishing websites.However, limited attention has been given to the generative adversarial network (GAN).This paper proposes a phishing detection model called PDGAN that depends only on a website's uniform resource locator (URL) to achieve reliable performance.We use a long short-term memory network (LSTM) network as a generator of synthetic phishing URLs and a convolutional neural network (CNN) as a discriminator to decide whether the URLs are phishing or legitimate.We use a dataset containing nearly two million phishing and legitimate URLs obtained through PhishTank and DomCop.The experimental results show that the PDGAN achieves a detection accuracy of 97.58% and a precision of 98.02% without depending on third-party services and with greater accuracy than the state-of-the-art models.
Examining the effect of organisational innovation on employee creativity and firm performance: moderating role of knowledge sharing between employee creativity and employee performance

This study sets out to investigate the influence of organisational innovation (innovation speed, innovation quality and innovation quantity), extrinsic rewards, and intrinsic motivation on employee creativity and firm performance. We advance the body of knowledge on this subject and examined the moderating role of knowledge sharing between employee creativity and employee performance. Data was collected from employees working in Saudi public sector. For empirical findings structural equation modelling (SEM) was applied. The results show that employee creativity was jointly predicted by organisational innovation, extrinsic rewards and intrinsic rewards and explained R2 66.5% variance in employee creativity. Therefore, firm performance was predicted by employee creativity and showed R2 60.5% variance in firm performance. Furthermore, this study confirmed the moderating role of knowledge sharing between employee creativity and employee performance such that the positive relationship between employee creativity and employee performance will be stronger when knowledge sharing is higher.
Cryptocurrency

A cryptocurrency system can be understood as a system intended for the issuance of tokens which are intended to be used as a general or limited-purpose medium-of-exchange, and which are accounted for using an often collectively-maintained digital ledger making use of cryptography to replace trust in institutions to varying extents.Against such a backdrop, the singular term cryptocurrency can mean a token, intended to be used as a general or limitedpurpose medium-of-exchange, issued via a cryptocurrency system. Issue 2This article belongs to the Glossary of decentralised technosocial systems, a special section of Internet Policy Review.1.An online search on Google Trends and Google Ngrams indicated that the term cryptocurrency was not used before the inception of Bitcoin.
How integrated are cryptocurrencies

The present study focuses on five cryptocurrencies co-movements physiognomies both in time and frequency domain.The present study highlighted several interesting facts related to cryptocurrencies co-movements both in time and frequency domain that have high policy and investment implications.Overall wavelet coherence diagrams clearly indicate about the very short and long contagion effect among the cryptocurrency pairs for the whole study period.The contagion effect is different at different time scales.Finally wavelet clustering diagram indicates that by investing only in XBP and BitCoin cryptocurrencies investors are not going to get any benefit from diversification.This predictable co-movements pattern among the cryptocurrencies could be the basic investment strategies to gain maximum profit by diversifying the risk in cryptocurrency investments.
Zero Trust Architecture

The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation's measurement and standards infrastructure.ITL develops tests, test methods, reference data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology.ITL's responsibilities include the
Image Processing in Intelligent Traffic Management

Traffic monitoring and traffic control have always been challenging tasks. Intelligent Transportation Systems (ITS) based on wide range of technologies have certain practical challenges in their application and implementation. Video surveillance has proven advantageous over traditional systems based on inductive loops sensors and detectors for traffic monitoring. Accurate traffic density estimation which is basic to tackling traffic congestions requires detection of vehicles, assessing their speed, and tracking vehicles passing through surveillance zones. Image processing techniques require processing of large number of image frames for real-time applications in traffic management. More efficient and less costly image processing techniques for accurate vehicle detection and density determination are required for developing more effective traffic management systems. There is a need for developing algorithms with robust performance under heavy traffic loads and varied environmental conditions. Developments in artificial intelligence offer new vistas in image processing for regulation and management of traffic by signal control mechanisms and creation of neural networks for unhindered traffic flow.
Bat Algorithm

The bat algorithm (BA) is a population-based algorithm and it typically has good convergence for solving many optimization problems. This chapter first introduces the original bat algorithm with detailed descriptions of the main steps, followed by demo codes in both Matlab and C++. Discussions of main parameters and their settings are also carried out.
Federated Learning-Based Secure Electronic Health Record Sharing Scheme in Medical Informatics

Medical Cyber-Physical Systems support the mobility of electronic health records data for clinical research to accelerate new scientific discoveries. Artificial Intelligence improves medical informatics, but current centralized data training and insecure data storage management techniques expose private medical data to unauthorized foreign entities. In this paper, a Federated Learning-based Electronic Health Record sharing scheme is proposed for Medical Informatics to preserve patient data privacy. A decentralized Federated Learning-based Convolutional Neural Network model trains data locally in the hospital and stores results in a private InterPlanetary File System. A secondary global model is trained at the research center using the local models. Private IPFS secures all medical data stored locally in the hospital. The novelty of this study resides in securing valuable hospital biomedical data useful for clinical research organizations. Blockchain and smart contracts enable patients to negotiate with external entities for rewards in exchange for their data. Evaluation results demonstrate that the decentralized CNN model performs better in accuracy, sensitivity, and specificity, similar to the traditional centralized model. The performance of the Private IPFS exceeds the Blockchain-based IPFS based on file upload and download time. The scheme is suitable for promoting a secure and privacy-friendly environment for sharing data with clinical research centers for biomedical research.
A Taxonomy of Small Markovian Errors

Errors in quantum logic gates are usually modeled by quantum process matrices (CPTP maps). But process matrices can be opaque, and unwieldy. We show how to transform a gate's process matrix into an error generator that represents the same information more usefully. We construct a basis of simple and physically intuitive elementary error generators, classify them, and show how to represent any gate's error generator as a mixture of elementary error generators with various rates. Finally, we show how to build a large variety of reduced models for gate errors by combining elementary error generators and/or entire subsectors of generator space. We conclude with a few examples of reduced models, including one with just $9N^2$ parameters that describes almost all commonly predicted errors on an N-qubit processor.
MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data

Numerical reasoning over hybrid data containing both textual and tabular content (e.g., financial reports) has recently attracted much attention in the NLP community.However, existing question answering (QA) benchmarks over hybrid data only include a single flat table in each document and thus lack examples of multistep numerical reasoning across multiple hierarchical tables.To facilitate data analytical progress, we construct a new large-scale benchmark, MULTIHIERTT, with QA pairs over Multi Hierarchical Tabular and Textual data.MULTIHIERTT is built from a wealth of financial reports and has the following unique characteristics: 1) each document contain multiple tables and longer unstructured texts; 2) most of tables contained are hierarchical; 3) the reasoning process required for each question is more complex and challenging than existing benchmarks; and 4) fine-grained annotations of reasoning processes and supporting facts are provided to reveal complex numerical reasoning.We further introduce a novel QA model termed MT2Net, which first applies facts retrieving to extract relevant supporting facts from both tables and text and then uses a reasoning module to perform symbolic reasoning over retrieved facts.We conduct comprehensive experiments on various baselines.The experimental results show that MULTIHIERTT presents a strong challenge for existing baselines whose results lag far behind the performance of human experts.The dataset and code are publicly available at https://github.com/psunlpgroup/MultiHiertt.
A Study of Factors Influencing the Continuance Intention to the Usage of Augmented Reality in Museums

Augmented reality (AR) technology has rapidly developed in recent years. This technology is widely used in various fields, including museum exhibitions, where people use it to experience art in a new way. While AR aims to realize the interaction between the virtual world and the real world, museums use AR to develop new digital artwork from artifacts. When text descriptions are no longer attractive to the audience, museums need to add more sound effects to images and video dynamics to develop a sustainable way for the industry’s future. For the continued use of such technology and the better development of the museum industry, this study used a structural equation model to explore the influences on the continuance intention of museum AR technology through experiments and questionnaires. Furthermore, it established a model with six dimensions: interaction quality, information quality, information richness, satisfaction, perceived playfulness, and continuance intention. Moreover, the results of this study can serve as a reference for managers to promote the extensive application of AR technology in museum construction, thereby providing visitors with better experiences and satisfying their needs.
Time-overlapping structured-light projection: high performance on 3D shape measurement for complex dynamic scenes

High-speed three-dimensional (3D) shape measurement has been continuously researched due to the demand for analyzing dynamic behavior in transient scenes. In this work, a time-overlapping structured-light 3D shape measuring technique is proposed to realize high-speed and high-performance measurement on complex dynamic scenes. Time-overlapping structured-light projection is presented to maximumly reduce the information redundancy in temporal sequences and improve the measuring efficiency; generalized tripartite phase unwrapping (Tri-PU) is used to ensure the measuring robustness; fringe period extension is achieved by improving overlapping rate to further double the encoding fringe periods for higher measuring accuracy. Based on the proposed measuring technique, one new pixel-to-pixel and unambiguous 3D reconstruction result can be updated with three newly required patterns at a reconstruction rate of 3174 fps. Three transient scenes including collapsing wood blocks struck by a flying arrow, free-falling foam snowflakes and flying water balloon towards metal grids were measured to verify the high performance of the proposed method in various complex dynamic scenes.
Optimal generation scheduling and operating reserve management for PV generation using RNN-based forecasting models for stand-alone microgrids

Photovoltaic (PV) generation can drop sharply under reductions in solar irradiance due to cloud cover, resulting in adverse impacts on the performance of stand-alone microgrids. In addition, due to the stochastic nature of solar irradiance, it has remained challenging to forecast PV generation accurately. Therefore, a sufficient amount of operating reserve must be allocated for the variability and uncertainty of PV generation. However, excess operating reserve can decrease the economic efficiency of microgrids. This paper proposes an optimal generation scheduling and operating reserve management scheme for stand-alone PV-integrated microgrids, which comprises three core models. First, a two-stage recurrent neural network-based model is proposed for solar irradiance and cloud cover forecasting. Second, the operating reserve required for PV is managed more effectively based on weather conditions. Finally, an optimal generation scheduling model is applied based on the forecasting and operating reserve management models. The proposed scheme is applied to representative case studies to validate its advantages. The forecasting accuracy of the proposed model outperforms a conventional model. Under clear-sky conditions, the operating reserve for PV can be reduced down to 30% of PV output. Subsequently, the optimal operation scheduled by the proposed scheme can produce substantial savings of more than 5.5%.
A Review on the Adoption of AI, BC, and IoT in Sustainability Research

The rise of artificial intelligence (AI), blockchain (BC), and the internet of things (IoT) has had significant applications in the advancement of sustainability research. This review examines how these digital transformations drive natural and human systems, as well as which industry sectors have been applying them to advance sustainability. We adopted qualitative research methods, including a bibliometric analysis, in which we screened 960 publications to identify the leading sectors that apply AI/BC/IoT, and a content analysis to identify how each sector uses AI/BC/IoT to advance sustainability. We identified “smart city”, “energy system”, and “supply chain” as key leading sectors. Of these technologies, IoT received the most real-world applications in the “smart city” sector under the dimensions of “smart environment” and “smart mobility” and provided applications resolving energy consumption in the “energy system” sector. AI effectively resolved scheduling, prediction, and monitoring for both the “smart city” and “energy system” sectors. BC remained highly theoretical for “supply chain”, with limited applications. The technological integration of AI and IoT is a research trend for the “smart city” and “energy system” sectors, while BC and IoT is proposed for the “supply chain”. We observed a surge in AI/BC/IoT sustainability research since 2016 and a new research trend—technological integration—since 2020. Collectively, six of the United Nation’s seventeen sustainable development goals (i.e., 6, 7, 9, 11, 12, 13) have been the most widely involved with these technologies.
Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*): Asymmetric bidirectional sampling-based path planning

Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their search with problem-specific knowledge expressed as heuristics and can be orders of magnitude more efficient than uninformed algorithms. Heuristics are most effective when they are both accurate and computationally inexpensive to evaluate, but these are often conflicting characteristics. This makes the selection of appropriate heuristics difficult for many problems. This paper presents two almost-surely asymptotically optimal sampling-based path planning algorithms to address this challenge, Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*). These algorithms use an asymmetric bidirectional search in which both searches continuously inform each other. This allows AIT* and EIT* to improve planning performance by simultaneously calculating and exploiting increasingly accurate, problem-specific heuristics. The benefits of AIT* and EIT* relative to other sampling-based algorithms are demonstrated on 12 problems in abstract, robotic, and biomedical domains optimizing path length and obstacle clearance. The experiments show that AIT* and EIT* outperform other algorithms on problems optimizing obstacle clearance, where a priori cost heuristics are often ineffective, and still perform well on problems minimizing path length, where such heuristics are often effective.