Reviewerly/exHarmony huggingface dataset:
ZeroEGGS: Zero‐shot Example‐based Gesture Generation from Speech

Abstract We present ZeroEGGS, a neural network framework for speech‐driven gesture generation with zero‐shot style control by example. This means style can be controlled via only a short example motion clip, even for motion styles unseen during training. Our model uses a Variational framework to learn a style embedding, making it easy to modify style through latent space manipulation or blending and scaling of style embeddings. The probabilistic nature of our framework further enables the generation of a variety of outputs given the input, addressing the stochastic nature of gesture motion. In a series of experiments, we first demonstrate the flexibility and generalizability of our model to new speakers and styles. In a user study, we then show that our model outperforms previous state‐of‐the‐art techniques in naturalness of motion, appropriateness for speech, and style portrayal. Finally, we release a high‐quality dataset of full‐body gesture motion including fingers, with speech, spanning across 19 different styles. Our code and data are publicly available at https://github.com/ubisoft/ubisoft‐laforge‐ZeroEGGS .
Towards provably secure asymmetric image encryption schemes

Image encryption schemes that use substitution box (S-box) generators, and dynamic S-boxes in particular, have attracted wide research interest recently. Typically, the security of S-box generators is tested empirically, and thus encryption schemes that use them do not have provable security against linear and differential attacks. With this work, we propose an asymmetric image encryption scheme with an S-box generator that has provable security against linear attacks, which is an important first step toward developing such schemes. Our generator is based on an integer linear programming (ILP) formulation that is feasible if and only if there exists an S-box with a given level of nonlinearity. We design a pseudo random number generator based on elliptic curves (ECs) which is used in our scheme to create confusion and diffusion in the cipher-images. Our scheme is highly sensitive to the keys and the plain-images, and hence it offers high security against differential attacks. Furthermore, our scheme allows users to use any size of random numbers and ECs, which makes it more flexible than existing schemes. Finally, we confirm through computational experiments that compared to existing state-of-the-art schemes, our scheme has superior cryptographic properties with low computational complexity, and hence is a good candidate for application in real-time systems.
ABMM: Arabic BERT-Mini Model for Hate-Speech Detection on Social Media

Hate speech towards a group or an individual based on their perceived identity, such as ethnicity, religion, or nationality, is widely and rapidly spreading on social media platforms. This causes harmful impacts on users of these platforms and the quality of online shared content. Fortunately, researchers have developed different machine learning algorithms to automatically detect hate speech on social media platforms. However, most of these algorithms focus on the detection of hate speech that appears in English. There is a lack of studies on the detection of hate speech in Arabic due to the language’s complex nature. This paper aims to address this issue by proposing an effective approach for detecting Arabic hate speech on social media platforms, namely Twitter. Therefore, this paper introduces the Arabic BERT-Mini Model (ABMM) to identify hate speech on social media. More specifically, the bidirectional encoder representations from transformers (BERT) model was employed to analyze data collected from Twitter and classify the results into three categories: normal, abuse, and hate speech. In order to evaluate our model and state-of-the-art approaches, we conducted a series of experiments on Twitter data. In comparison with previous works on Arabic hate-speech detection, the ABMM model shows very promising results with an accuracy score of 0.986 compared to the other models.
A comprehensive review on blockchains for Internet of Vehicles: Challenges and directions

Internet of Vehicles (IoVs) consists of smart vehicles, Autonomous Vehicles (AVs) as well as roadside units (RSUs) that communicate wirelessly to provide enhanced transportation services such as improved traffic efficiency and reduced traffic congestion and accidents. Unfortunately, current IoV networks suffer from security, privacy, and trust issues. Blockchain technology emerged as a decentralized approach for enhanced security without depending on trusted third parties to run services. Blockchain offers the benefits of trustworthiness and immutability and mitigates the problem of a single point of failure and other attacks. In this work, we present the state-of-the-art Blockchain-enabled IoVs (BIoVs) with a particular focus on their applications, such as crowdsourcing-based applications, energy trading, traffic congestion reduction, collision, accident avoidance, infotainment, and content caching. We also present in-depth applications of federated learning (FL) for BIoVs. The key challenges of integrating Blockchain with IoV are investigated in several domains, such as edge computing, machine learning, and Federated Learning (FL). Lastly, we present several open issues, challenges, and future opportunities in AI-enabled BIoV, hardware-assisted security for BIoV, and quantum computing attacks on BIoV applications.
A Systematic Review on the Visualization of Avatars and Agents in AR &amp; VR displayed using Head-Mounted Displays

Augmented Reality (AR) and Virtual Reality (VR) are pushing from the labs towards consumers, especially with social applications. These applications require visual representations of humans and intelligent entities. However, displaying and animating photo-realistic models comes with a high technical cost while low-fidelity representations may evoke eeriness and overall could degrade an experience. Thus, it is important to carefully select what kind of avatar to display. This article investigates the effects of rendering style and visible body parts in AR and VR by adopting a systematic literature review. We analyzed 72 papers that compare various avatar representations. Our analysis includes an outline of the research published between 2015 and 2022 on the topic of avatars and agents in AR and VR displayed using head-mounted displays, covering aspects like visible body parts (e.g., hands only, hands and head, full-body) and rendering style (e.g., abstract, cartoon, realistic); an overview of collected objective and subjective measures (e.g., task performance, presence, user experience, body ownership); and a classification of tasks where avatars and agents were used into task domains (physical activity, hand interaction, communication, game-like scenarios, and education/training). We discuss and synthesize our results within the context of today's AR and VR ecosystem, provide guidelines for practitioners, and finally identify and present promising research opportunities to encourage future research of avatars and agents in AR/VR environments.
Adaptive meta-learning extreme learning machine with golden eagle optimization and logistic map for forecasting the incomplete data of solar irradiance

Solar energy has become crucial in producing electrical energy because it is inexhaustible and sustainable. However, its uncertain generation causes problems in power system operation. Therefore, solar irradiance forecasting is significant for suitable controlling power system operation, organizing the transmission expansion planning, and dispatching power system generation. Nonetheless, the forecasting performance can be decreased due to the unfitted prediction model and lacked preprocessing. To deal with mentioned issues, this paper proposes Meta-Learning Extreme Learning Machine optimized with Golden Eagle Optimization and Logistic Map (MGEL-ELM) and the Same Datetime Interval Averaged Imputation algorithm (SAME) for improving the forecasting performance of incomplete solar irradiance time series datasets. Thus, the proposed method is not only imputing incomplete forecasting data but also achieving forecasting accuracy. The experimental result of forecasting solar irradiance dataset in Thailand indicates that the proposed method can achieve the highest coefficient of determination value up to 0.9307 compared to state-of-the-art models. Furthermore, the proposed method consumes less forecasting time than the deep learning model.
High-involvement HRM practices stimulate incremental and radical innovation: The roles of knowledge sharing and market turbulence

There is a lack of study examining the relationship between high-involvement HRM practice and innovation capability as mediated by knowledge sharing. So, the motivation of this paper is to study and explore potential intermediating roles of explicit and tacit knowledge sharing in linking high-involvement HRM practice and innovation capability under the possible moderator of market turbulence. Analysis of Moment Structures was used to measure validation and test the structural model based on the questionnaire surveys of 333 participators in 119 Vietnamese manufacturing and service enterprises from May to July 2021. The collected data were inspected and analyzed using SPSS and AMOS software version 22. The result advocates the proposed hypotheses relating to the intermediating role of KS in the HRM practices-innovation relationship. It spotlights the crucial character of market turbulence in driving the domination of high-involvement HRM practice on incremental and radical innovation. This paper remarkably increases the insights of the precursor role of high-involvement HRM practice, intermediating mechanism of KS activities, and the regulating influence of market turbulence in predicting and fostering radical and incremental innovation, thereby pushing forward the theory of HRM and innovation management.
Between a rock and a hard(ening) place: Cyber insurance in the ransomware era

Cyber insurance and ransomware are two of the most studied areas within security research and practice to date, and their interplay continues to raise concerns in industry and government. This article offers substantial new insights and analysis into the complex question of whether cyber insurance can help organisations in mitigating the threat of ransomware, particularly its impacts. Having conducted an interview or workshop with 96 industry professionals spanning the cyber insurance, cyber security, ransomware negotiations, policy, and law enforcement sectors, we identify that ransomware has been a key cause of the ‘hardening’ of the cyber insurance market, which is exhibited at almost all levels of the market. Such hardening has been beneficial in raising the security standards required prior to purchase, but has also created a situation where some organisations may not be able to acquire viable cyber insurance at all. In presenting the outcomes of our thematic analysis of the interview and workshop outputs, the paper provides significant new empirical evidence to support the theory that cyber insurance can act as a form of governance for improving cyber security amongst organisations. Nonetheless, the hardening market does nothing to increase the penetration of cyber insurance. Questions were also raised as to the likelihood of unintended unethical – and potentially illegal – outcomes given the professionalisation of a remediation process that has to determine the most cost-effective solution to an organisation being held ransom. We conclude that insurance, at best, can help to mitigate the ransomware threat for those that can access it, as part of a wider basket of actions that must also come from different stakeholders.
Fintechs and the financial inclusion gender gap in Sub-Saharan African countries

This study addresses the issue of financial innovation in developing countries, focusing specifically on the role fintechs have in closing the gender gap of financial inclusion in Sub-Saharan Africa (SSA) over the period 2011–2017. The empirical evidence is based on the multilevel tobit regression model fitted to panel data. The results of this study show that fintechs reduce the financial inclusion gender gap by mitigating the gender gap in access to and use of financial services. Furthermore, the results cast doubt on the ability of fintechs development to bridge this gap on its own, and hint on the joint importance of targeted policy initiatives aimed at directly closing the gender gap to this end. These findings have important economic policy implications and provide evidence of improved economic conditions for women in terms of financial inclusion or narrowing of the gender gap.
An Adaptive Interference Removal Framework for Video Person Re-Identification

Video person re-identification (V-ReID) can leverage rich spatial-temporal information embedded in sequence data to achieve better accuracy. However, it is vulnerable to the interference of inaccurate and redundant noisy frames in each sequence as well as the background clutter and person-irrelevant pixels in each frame. To solve the above issues, this paper presents an adaptive interference removal framework (IRF) to learn discriminative feature representations by removing various interference. Our IRF mainly consists of two modules including an attention-guided adaptive interference frame removal module (IFRM) and an attention-guided adaptive interference pixel removal module (IPRM). IFRM and IPRM are designed to locate task-relevant keyframes and key pixels, respectively. IFRM adopts the attention mechanism to predict frame-wise scores to characterize the contribution of each frame to the final identification task. IPRM collaboratively utilizes camera identity classification loss, person identity classification loss, target attention loss, and person mask adversarial loss for extracting pure pedestrian representations. A progressive mask augmentation strategy is designed to restrain the data distribution of the generated person masks to further guide model training. Extensive experiments demonstrate that our models outperform state-of-the-art accuracy on seven person ReID datasets.
Phishing or Not Phishing? A Survey on the Detection of Phishing Websites

Phishing is a security threat with serious effects on individuals as well as on the targeted brands. Although this threat has been around for quite a long time, it is still very active and successful. In fact, the tactics used by attackers have been evolving continuously in the years to make the attacks more convincing and effective. In this context, phishing detection is of primary importance. The literature offers many diverse solutions that cope with this issue and in particular with the detection of phishing websites. This paper provides a broad and comprehensive review of the state of the art in this field by discussing the main challenges and findings. More specifically, the discussion is centered around three important categories of detection approaches, namely, list-based, similarity-based and machine learning-based. For each category we describe the detection methods proposed in the literature together with the datasets considered for their assessment and we discuss some research gaps that need to be filled.
Probing the psychology of AI models

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
Fast quantum circuit cutting with randomized measurements

We propose a new method to extend the size of a quantum computation beyond the number of physical qubits available on a single device. This is accomplished by randomly inserting measure-and-prepare channels to express the output state of a large circuit as a separable state across distinct devices. Our method employs randomized measurements, resulting in a sample overhead that is $\widetilde{O}(4^k / \varepsilon ^2)$, where $\varepsilon $ is the accuracy of the computation and $k$ the number of parallel wires that are "cut" to obtain smaller sub-circuits. We also show an information-theoretic lower bound of $\Omega(2^k / \varepsilon ^2)$ for any comparable procedure. We use our techniques to show that circuits in the Quantum Approximate Optimization Algorithm (QAOA) with $p$ entangling layers can be simulated by circuits on a fraction of the original number of qubits with an overhead that is roughly $2^{O(p\kappa)}$, where $\kappa$ is the size of a known balanced vertex separator of the graph which encodes the optimization problem. We obtain numerical evidence of practical speedups using our method applied to the QAOA, compared to prior work. Finally, we investigate the practical feasibility of applying the circuit cutting procedure to large-scale QAOA problems on clustered graphs by using a $30$-qubit simulator to evaluate the variational energy of a $129$-qubit problem as well as carry out a $62$-qubit optimization.
Appreciation vs. apology: Research on the influence mechanism of chatbot service recovery based on politeness theory

Failures in human-chatbot interactions are becoming inevitable such as failure of chatbots to maintain contextual awareness. Thus, an effective service recovery strategy is essential for e-commerce enterprises to restore customers and resolve their complaints. Therefore, based on politeness theory, this study explores the fundamental mechanism and boundary conditions for a chatbot politeness strategy (appreciation vs. apology) on consumers' post-recovery satisfaction using four scenario-based experiments. The results indicate that establishing a good human-chatbot relationship (appreciation) is a more effective recovery strategy than admitting the chatbot's limited competence (apology) in redressing service failures; face concern mediates the effect of the politeness strategy on post-recovery satisfaction; and time pressure plays a moderating role in the effect of the politeness strategy on face concern and post-recovery satisfaction. This study extends the research of politeness theory and face concern in the field of chatbot marketing, and provide practical guidance for e-commerce enterprises to deal effectively with chatbot failure.
Design and implementation of smart voice assistant and recognizing academic words

This paper approaches the use of a Virtual Assistant using neural networks for recognition of commonly used words. The main purpose is to facilitate the users’ daily lives by sensing the voice and interpreting it into action. Alice, which is the name of the assistant, is implemented based on four main techniques: Hot word detection, Voice to Text conversion, Intent recognition, and Text to Voice conversion. Linux is the operating system of choice, for developing and running the assistant because it is in the public domain, also, Linux has been implemented on most Single-board computers. Python is chosen as a development language due to its capabilities and compatibility with various APIs and libraries, which are deemed necessary for the project. The virtual assistant will be required to communicate with IoT devices. In addition, a speech recognition system is created in order to recognize the significant technical words. An artificial neural network (ANN) with different structure networks and training algorithms is utilized in conjunction with the Mel Frequency Cepstral Coefficient (MFCC) feature extraction technique to increase the identification rate effectively and find the optimal performance. For training purposes, the Levenberg-Marquardt (LM) and BGFS Quasi-Newton Resilient Backpropagation are compared using 10 MFCC, utilizing from 10 to 50 neurons increasing in increments of 10 similarly for 13MFCC the training is done utilizing from between 10 to 50 neurons.
A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation

Breast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast lesions. While breast biopsy is referred to as the "gold standard" in assessing both the activity and degree of breast cancer, it is an invasive and time-consuming approach.The current study's primary objective was to develop a novel deep-learning architecture based on the InceptionV3 network to classify ultrasound breast lesions. The main promotions of the proposed architecture were converting the InceptionV3 modules to residual inception ones, increasing their number, and altering the hyperparameters. In addition, we used a combination of five datasets (three public datasets and two prepared from different imaging centers) for training and evaluating the model.The dataset was split into the train (80%) and test (20%) groups. The model achieved 0.83, 0.77, 0.8, 0.81, 0.81, 0.18, and 0.77 for the precision, recall, F1 score, accuracy, AUC, Root Mean Squared Error, and Cronbach's α in the test group, respectively.This study illustrates that the improved InceptionV3 can robustly classify breast tumors, potentially reducing the need for biopsy in many cases.
Blockchain technology adoption and business performance in large enterprises: A comparison of the United States and China

Blockchain technology has been used by many companies to improve their production processes and reduce costs. However, the impacts of the adoption of these new technologies on the operating performance of large enterprises are not yet sufficiently understood. Previous studies have mostly applied technology adoption theories such as the technology, organization, and environment (TOE) framework and the technology acceptance model (TAM) and used questionnaires to collect data, which do not measure actual adoption. Therefore, this study uses the resource-based view and real data to explore whether the adoption of new technologies (using blockchain as an example) is affected by the size of the company and its investment in innovation and whether the business performance of such a company is improved by the adoption of new technologies and investment in research and development (R&D), taking country (the United States and China) as a moderator. The research sample includes US Fortune 500 and China Fortune 500 companies from 2012 to 2019. Findings show that the scale of corporate assets and R&D investment are important driving factors behind the adoption of blockchain technology, larger companies in the United States (but not China) adopt blockchain technology, enterprises' investment in innovation and their adoption of blockchain technology help improve corporate business performance, and the improvement in operating performance in the US companies using blockchain is greater than that in Chinese enterprises.