Reviewerly/exHarmony huggingface dataset:
Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey

Abstract Cryptocurrencies are decentralized electronic counterparts of government‐issued money. The first and best‐known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine‐learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.
Optimal site selection for photovoltaic power plants using a GIS-based multi-criteria decision making and spatial overlay with electric load

The growing adoption of photovoltaic systems as a result of government incentives and the cost-effectiveness of the technology will bring significant environmental benefits and help countries meeting their international commitments in terms of renewables share. Nevertheless, an unsuitable site location could compromise its production and lead to a poor integration. An optimal location of photovoltaic systems must account for factors such as land use restrictions, orography, environmental, climatic limitations, and proximity to infrastructure. A key aspect that needs to be further researched is the influence of the electric demand requirement and its spatial distribution on the enhancement of photovoltaic integration. This paper proposes a novel approach to define optimal sites for photovoltaic plants, connected to the medium-voltage level, using a geographic information system based multi-criteria decision making and spatial overlay with electric load. The main feature of this work is the use of high-resolution information to spatially characterize the demand and make a density analysis. The performance of the proposed method is assessed in the service area of an Ecuadorian power utility. Scenarios considering solar potential and the massive penetration of a new type of load are assessed to define the photovoltaic sites that enhance the integration of renewable sources in the case study.
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses

The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from antifatigue safe driving to intelligent route planning. However, ADSs are still plagued by increasing threats from different attacks, which could be categorized into physical attacks, cyberattacks and learning-based adversarial attacks. Inevitably, the safety and security of deep learning-based autonomous driving are severely challenged by these attacks, from which the countermeasures should be analyzed and studied comprehensively to mitigate all potential risks. This survey provides a thorough analysis of different attacks that may jeopardize ADSs, as well as the corresponding state-of-the-art defense mechanisms. The analysis is unrolled by taking an in-depth overview of each step in the ADS workflow, covering adversarial attacks for various deep learning models and attacks in both physical and cyber context. Furthermore, some promising research directions are suggested in order to improve deep learning-based autonomous driving safety, including model robustness training, model testing and verification, and anomaly detection based on cloud/edge servers.
TeachText: CrossModal Generalized Distillation for Text-Video Retrieval

In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerful video encoders. By contrast, despite the natural symmetry, the design of effective algorithms for exploiting large-scale language pretraining remains under-explored. In this work, we are the first to investigate the design of such algorithms and propose a novel generalized distillation method, TeachText, which leverages complementary cues from multiple text encoders to provide an enhanced supervisory signal to the retrieval model. Moreover, we extend our method to video side modalities and show that we can effectively reduce the number of used modalities at test time without compromising performance. Our approach advances the state of the art on several video retrieval benchmarks by a significant margin and adds no computational overhead at test time. Last but not least, we show an effective application of our method for eliminating noise from retrieval datasets. Code and data can be found at https://www.robots.ox.ac.uk/˜vgg/research/teachtext/.
Understanding learners’ acceptance of high-immersion virtual reality systems: Insights from confirmatory and exploratory PLS-SEM analyses

This article reports on a study that investigated user acceptance of a high-immersion virtual reality learning environment to learn English paragraph writing structure. 134 undergraduate university students participated in the study by using a high-immersion virtual reality system combined with an in-house developed virtual reality learning environment program for the purpose of learning paragraph structure. A post-session questionnaire was used to collect data. A partial least squares structural equation modelling (PLS-SEM) analysis was then conducted to test an extended technology acceptance model conceptualized from a priori theoretical deduction and previous empirical findings. The extended model featured the constructs of Imagination, Immersion, and Interaction as exogenous variables with Perceived Ease of Use and Perceived Usefulness in mediating roles leading to the endogenous construct of Behavioral Intention to Use. Results of the confirmatory PLS-SEM analysis revealed inherent problems with the model's application to the high-immersion virtual reality system and program used. Considering these findings, a PLS-SEM exploratory model optimization analysis was conducted which saw the variable of Immersion best relocated to be a predicator for both the Interaction and Imagination constructs. This exploratory model showed superior predictive relevance and improved significance of the relevant construct paths. Based on these results, this paper provides new theoretical reasoning that is in line with these statistical findings, namely that the construct of Immersion is best positioned as a singular external exogenous variable impacting Interaction and Imagination for the high-immersion virtual reality technology acceptance model used in this study.
A Better and Faster end-to-end Model for Streaming ASR

End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for streaming speech recognition [1] across many dimensions, including quality (as measured by word error rate (WER)) and endpointer latency [2]. However, the model still tends to delay the predictions towards the end and thus has much higher partial latency compared to a conventional ASR model. To address this issue, we look at encouraging the E2E model to emit words early, through an algorithm called FastEmit [3]. Naturally, improving on latency results in a quality degradation. To address this, we explore replacing the LSTM layers in the encoder of our E2E model with Conformer layers [4], which has shown good improvements for ASR. Secondly, we also explore running a 2nd-pass beam search to improve quality. In order to ensure the 2nd-pass completes quickly, we explore non-causal Conformer layers that feed into the same 1st-pass RNN-T decoder, an algorithm called Cascaded Encoders [5]. Overall, the Conformer RNN-T with Cascaded Encoders offers a better quality and latency tradeoff for streaming ASR.
Eliciting Tech Futures Among Black Young Adults: A Case Study of Remote Speculative Co-Design

The question of who gets to contribute to design futures and technology innovation has become a topic of conversation across HCI, CSCW, and other computing communities. This conversation has grave implications for communities that often find themselves an afterthought in technology design, and who coincidentally could benefit most from technological interventions in response to societal oppression. To explore this topic, we examined “futuring” through co-designed speculative design fictions as methods to envision utopian and dystopian futures. In a case study, we examined technology’s role in the imagined futures of youth participants of a Chicago summer design program. We highlight emerging themes and contribute an analysis of remote co-design through an Afrofuturism lens. Our analysis shows that concepts of utopian futures and technologies to support those futures are still heavily laden with dystopian realities of racism and poverty. We discuss ways that speculative design fictions and futuring can serve to address inclusivity in concept generation for new technologies, and we provide recommendations for conducting design techniques remotely with historically excluded populations.
Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.
VulDeeLocator: A Deep Learning-Based Fine-Grained Vulnerability Detector

Automatically detecting software vulnerabilities is an important problem that has attracted much attention from the academic research community. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and the locating precision that would warrant their adoption for real-world use. In this paper, we present a vulnerability detector that can simultaneously achieve a high detection capability and a high locating precision, dubbed Vulnerability Deep learning-based Locator (VulDeeLocator). In the course of designing VulDeeLocator, we encounter difficulties including how to accommodate semantic relations between the definitions of types as well as macros and their uses across files, how to accommodate accurate control flows and variable define-use relations, and how to achieve high locating precision. We solve these difficulties by using two innovative ideas: (i) leveraging intermediate code to accommodate extra semantic information, and (ii) using the notion of granularity refinement to pin down locations of vulnerabilities. When applied to 200 files randomly selected from three real-world software products, VulDeeLocator detects 18 confirmed vulnerabilities (i.e., true-positives). Among them, 16 vulnerabilities correspond to known vulnerabilities; the other two are not reported in the National Vulnerability Database (NVD) but have been "silently" patched by the vendor of Libav when releasing newer versions.
Theorizing the dark side of business-to-business relationships in the era of AI, big data, and blockchain

New technologies, such as artificial intelligence (AI), blockchain, and big data analytics, can shape how B2B relationships are managed and how dark side effects are manifested in B2B relationships. Moreover, despite several advances in the dark side of B2B relationships research, concerns have recently been raised that this stream is under theorized, and the focus has been on the manifestations of the dark side and not on the underlying processes. We address these challenges by examining how the emergence of new technologies has the potential to transform B2B relationships and lead to the emergence of dark side effects through processes that have not been hitherto examined or understood. In the process, we further illustrate how theories that have been largely neglected within this literature (i.e., social dominance, organizational inertia, organizational information processing, and role theory) can provide novel insights when exploring the dark side of B2B relationships.
Review of digital image correlation application to large-scale composite structure testing

This study reviews the recent progress in the application of digital image correlation (DIC) to testing composite structures with an order of size magnitude of meters. The DIC technique has gained wide popularity in materials and structural testing due to the merit of being full-field and non-contact compared to conventional point-wise measurement techniques. This study focuses on the DIC application to large-scale composite structure testing. Experimental setup requirements, measurement capabilities, and the associated challenges are thoroughly reviewed and analyzed. Typical applications found in the literature are from wind energy and aerospace sectors for displacement and strain measurements under static loading and operational modal analysis in dynamic structural tests. The remaining challenges along with their possible solutions are discussed and summarized in this work to shed light on future research needs on testing large-scale composite structures using the DIC technique.
Blockchain technology in maritime supply chains: applications, architecture and challenges

As an essential carrier of cross-border trade, the maritime supply chain has always been plagued by long service cycles, complex structures, and heterogeneous information from multiple sources. In this context, as a distributed database system, the emerging blockchain technology has the characteristics of decentralization, tamper-proofness, and traceability, which can be correctly applied in the maritime industry to promote its transformation and upgrading. However, blockchain application in the maritime supply chain remains notably absent, leaving this critical topic almost unexplored. Thus, we first conduct a comprehensive literature review and an industrial investigation to clarify the current status, problems, and challenges of the blockchain-based maritime supply chain system (BMSCS). We construct a new operation management mode of the maritime supply chain and propose an integrated BMSCS suitable for global economic development. Finally, based on comparison of the current research results, reasonable suggestions for the future operation and development of the BMSCS are put forward to enhance the coordination among members, accelerate the application of blockchain in the maritime industry, and gradually realise the intelligent operation of the maritime supply chain.
Federated Transfer Learning Based Cross-Domain Prediction for Smart Manufacturing

Smart manufacturing aims to support highly customizable production processes. Therefore, the associated machine intelligence needs to be quickly adaptable to new products, processes, and applications with limited training data while preserving data privacy. In this article, a new federated transfer learning framework, federated transfer learning for cross-domain prediction, is proposed to address the challenges of data scarcity and data privacy faced by most machine learning approaches in modern smart manufacturing with cross-domain applications. The framework architecture consists of a central server and several groups of smart devices, where each group handles a different application. The existing applications can share their knowledge through the central server as base models, while new applications can convert a base model to their target-domain models with limited application-specific data using a transfer learning technique. Meanwhile, the federated learning scheme is deployed within a group to further enhance the accuracy of the application-specific model. The integrated framework allows model sharing across the central server and different smart devices without exposing any raw data and, hence, protects the data privacy. Two public datasets, COCO and PETS2009, which represent the source and target applications, are employed for evaluations. The simulation results show that the proposed method outperforms two state-of-the-art machine learning approaches by achieving better learning efficiency and accuracy.
Chatbots: Security, privacy, data protection, and social aspects

Summary Chatbots are artificial communication systems becoming increasingly popular and not all their security questions are clearly solved. People use chatbots for assistance in shopping, bank communication, meal delivery, healthcare, cars, and many other actions. However, it brings an additional security risk and creates serious security challenges which have to be handled. Understanding the underlying problems requires defining the crucial steps in the techniques used to design chatbots related to security. There are many factors increasing security threats and vulnerabilities. All of them are comprehensively studied, and security practices to decrease security weaknesses are presented. Modern chatbots are no longer rule‐based models, but they employ modern natural language and machine learning techniques. Such techniques learn from a conversation, which can contain personal information. The paper discusses circumstances under which such data can be used and how chatbots treat them. Many chatbots operate on a social/messaging platform, which has their terms and conditions about data. The paper aims to present a comprehensive study of security aspects in communication with chatbots. The article could open a discussion and highlight the problems of data storage and usage obtained from the communication user—chatbot and propose some standards to protect the user.
RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss

RGBT tracking has attracted increasing attention since RGB and thermal infrared data have strong complementary advantages, which could make trackers all-day and all-weather work. However, how to effectively represent RGBT data for visual tracking remains unstudied well. Existing works usually focus on extracting modality-shared or modality-specific information, but the potentials of these two cues are not well explored and exploited in RGBT tracking. In this paper, we propose a novel multi-adapter network to jointly perform modality-shared, modality-specific and instance-aware target representation learning for RGBT tracking. To this end, we design three kinds of adapters within an end-to-end deep learning framework. In specific, we use the modified VGG-M as the generality adapter to extract the modality-shared target representations.To extract the modality-specific features while reducing the computational complexity, we design a modality adapter, which adds a small block to the generality adapter in each layer and each modality in a parallel manner. Such a design could learn multilevel modality-specific representations with a modest number of parameters as the vast majority of parameters are shared with the generality adapter. We also design instance adapter to capture the appearance properties and temporal variations of a certain target. Moreover, to enhance the shared and specific features, we employ the loss of multiple kernel maximum mean discrepancy to measure the distribution divergence of different modal features and integrate it into each layer for more robust representation learning. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against the state-of-the-art methods.
Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Further-more, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to view-changes by eliminating view-specific and task-irrelevant in-formation. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual information.