Reviewerly/exHarmony huggingface dataset:
An Efficient ECC-Based CP-ABE Scheme for Power IoT

The rapid development of the power Internet of Things (IoT) has greatly enhanced the level of security, quality and efficiency in energy production, energy consumption, and related fields. However, it also puts forward higher requirements for the security and privacy of data. Ciphertext-policy attribute-based encryption (CP-ABE) is considered a suitable method to solve this issue and can implement fine-grained access control. However, its internal bilinear pairing operation is too expensive, which is not suitable for power IoT with limited computing resources. Hence, in this paper, a novel CP-ABE scheme based on elliptic curve cryptography (ECC) is proposed, which replaces the bilinear pairing operation with simple scalar multiplication and outsources most of the decryption work to edge devices. In addition, time and location attributes are combined in the proposed scheme, allowing the data users to access only within the range of time and locations set by the data owners to achieve a more fine-grained access control function. Simultaneously, the scheme uses multiple authorities to manage attributes, thereby solving the performance bottleneck of having a single authority. A performance analysis demonstrates that the proposed scheme is effective and suitable for power IoT.
Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes

Generative adversarial networks (GANs) learn to map noise latent vectors to high-fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond to human interpretable image transformations. However, these methods either rely on explicit scores of attributes (e.g., memorability) or are restricted to binary ones (e.g., gender), which largely limits the applicability of editing tasks, especially for free-form artistic tasks like style/anime editing. In this paper, we propose an adversarial method, AdvStyle, for discovering interpretable directions in the absence of well-labeled scores or binary attributes. In particular, the proposed adversarial method simultaneously optimizes the discovered directions and the attribute assessor using the target attribute data as positive samples, while the generated ones being negative. In this way, arbitrary attributes can be edited by collecting positive data only, and the proposed method learns a controllable representation enabling manipulation of non-binary attributes like anime styles and facial characteristics. Moreover, the proposed learning strategy attenuates the entanglement between attributes, such that multi-attribute manipulation can be easily achieved without any additional constraint. Furthermore, we reveal several interesting semantics with the involuntarily learned negative directions. Extensive experiments on 9 anime attributes and 7 human attributes demonstrate the effectiveness of our adversarial approach qualitatively and quantitatively. Code is available at https://github.com/BERYLSHEEP/AdvStyle.
A self-adaptive fuzzy learning system for streaming data prediction

In this paper, a novel self-adaptive fuzzy learning (SAFL) system is proposed for streaming data prediction. SAFL self-learns from data streams a predictive model composed of a set of prototype-based fuzzy rules, with each of which representing a certain local data distribution, and continuously self-evolves to follow the changing data patterns in non-stationary environments. Unlike conventional evolving fuzzy systems, both the fuzzy inference and consequent parameter learning schemes utilised by SAFL are simplified so that only a small number of selected fuzzy rules within the rule base are involved in system output generation and parameter updating during a learning cycle. Such simplification not only significantly reduces the system's computational complexity but also increases its prediction precision. In addition, both theoretical and empirical investigations guarantee the stability of the resulting SAFL. Comparative experimental studies on a wide variety of benchmark and real-world problems demonstrate that SAFL is able to learn from streaming data in a highly efficient manner and to make predictions with a great accuracy, revealing the effectiveness and validity of the proposed approach.
Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization

The crux of self-supervised video representation learning is to build general features from unlabeled videos. However, most recent works have mainly focused on high-level semantics and neglected lower-level representations and their temporal relationship which are crucial for general video understanding. To address these challenges, this paper proposes a multi-level feature optimization framework to improve the generalization and temporal modeling ability of learned video representations. Concretely, high-level features obtained from naive and prototypical contrastive learning are utilized to build distribution graphs, guiding the process of low-level and mid-level feature learning. We also devise a simple temporal modeling module from multi-level features to enhance motion pattern learning. Experiments demonstrate that multi-level feature optimization with the graph constraint and temporal modeling can greatly improve the representation ability in video understanding. Code is available$here$.
COVID‐19 frauds: An exploratory study of victimization during a global crisis

The COVID-19 pandemic threated public health and safety and led to a number of virus-related fraud schemes. We surveyed over 2,200 American adults to investigate their experiences with COVID-19-related frauds. Our goals were to better understand fraud targeting and victimization, as well as the impacts of fraud on victims. Over a quarter of our sample reported purchasing either a COVID-19-related product or a service, yet 42.5% reported feeling targeted for fraud. Being a target of COVID-19 frauds is significantly linked to one's routine activities, however it is one's level of self-control that more strongly predicts victimization. COVID-19 anxieties mediate the impact of self-control on purchasing.Legal interventions and increased regulations surrounding advertising are a potential mechanism for protecting consumers, yet "soft" interventions that interrupt routine activities might be more useful and applicable. The use of white-lists and publicly available websites that allow e-commerce sites and sellers to be verified would help enable higher levels of self-guardianship. It is also important to provide continuous and clear messaging about what is being done to protect consumers.
A diversified group teaching optimization algorithm with segment-based fitness strategy for unmanned aerial vehicle route planning

The complexity and diversity of the flight environment pose great challenges to unmanned aerial vehicle route planning, which demands feasible flight strategies and efficient route planning algorithms. To address the issue, this paper constructs a 3-D flight environment model with multiple obstacles, and designs a novel diversified group teaching optimization algorithm for the generation of flight routes of unmanned aerial vehicles. In the environment model, a variety of obstacles are taken into consideration to make the flying scenarios more realistic, including mountain, cuboid, cylinder and triangular prism, and corresponding strategies are presented for unmanned aerial vehicles to safely avoid these obstacles. In the proposed algorithm, three novel teaching methods are introduced to balance the exploitation and exploration phases. Besides, a novel constrained optimization strategy is adopted, in which constraints are incrementally added to the fitness function to avoid the premature phenomenon in the initial iteration stage of algorithm. The experimental results show that compared with several state-of-the-art optimization algorithms, the proposed algorithm is significantly superior and can always generate the optimal flight route in complicated environments.
DarkDetect: Darknet Traffic Detection and Categorization Using Modified Convolution-Long Short-Term Memory

Darknet is commonly known as the epicenter of illegal online activities.An analysis of darknet traffic is essential to monitor real-time applications and activities running over the Darknet.Recognizing network traffic bound to unused Internet addresses has become undeniably significant for identifying and examining malicious activities on the internet.Since there are no authentic hosts or devices in an unused address block, any observed network traffic must be the aftereffect of misconfiguration from spoofed source addressed and other frameworks that monitor unused address space.However, the recent advancements in artificial intelligence allow digital systems to detect and identify darknet traffic autonomously.In this paper, we propose a generalized approach for darknet traffic detection and categorization using Deep Learning.We examine the state-of-the-art complex dataset, which provides excessive information about the darknet traffic and perform data preprocessing.Next, we analyze diverse feature selection techniques to select optimal features for darknet traffic detection and categorization.We apply fine-tuned machine learning (ML) algorithms which include Decision Tree (DT), Gradient Boosting (GB), Random Forest Regressor (RFR), and Extreme Gradient Boosting (XGB) on selected features and compare the performance.Next, we apply modified Convolution-Long Short-Term Memory (CNN-LSTM) and Convolution-Gradient Recurrent Unit (CNN-GRU) deep learning techniques to recognize the network traffic more accurately.The results demonstrate that the proposed approach outperforms the existing approaches by yielding the maximum accuracy of 96% of darknet traffic detection and 89% of darknet traffic categorization through XGB as a feature selection approach and CNN-LSTM a recognition model.
Objective Quality Assessment of Lenslet Light Field Image Based on Focus Stack

The large amount of complex scene information recorded by light field imaging has the potential for immersive media applications. Compression and reconstruction algorithms are crucial for the transmission, storage, and display of such massive data. Most of the existing quality evaluation indexes do not effectively account for light field characteristics. To accurately evaluate the distortions caused by compression and reconstruction algorithms, it is necessary to construct an image evaluation index that reflects the angular-spatial characteristics of the light field. This work proposes a full-reference light field image quality evaluation index that attempts to extract less information from the focus stack to accurately evaluate the entire light field quality. The proposed framework includes three specific steps. First, we construct a key refocused image extraction framework by the maximal spatial information contrast and the minimal angular information variation. Specifically, the gradient and phase congruency operators are used in the extraction framework. Second, a novel light field quality evaluation index is built based on the angular-spatial characteristics of the key refocused images. In detail, the features used in the key refocused image extraction framework and the chrominance feature are combined to construct the union feature. Third, the similarity of the union feature is pooled by the relevant visual saliency map to obtain the predicted score. Finally, the overall quality of the light field is measured by applying the proposed index to the key refocused images. The high efficiency and precision of the proposed method are shown by extensive comparison experiments.
Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes

Identification of patient subtypes from retrospective Electronic Health Record (EHR) data is fraught with inherent modeling issues, such as missing data and variable length time intervals, and the results obtained are highly dependent on data pre-processing strategies. As we move towards personalized medicine, assessing accurate patient subtypes will be a key factor in creating patient specific treatment plans. Partitioning longitudinal trajectories from irregularly spaced and variable length time intervals is a well-established, but open problem. In this work, we present and compare k-means approaches for subtyping opioid use trajectories from EHR data. We then interpret the resulting subtypes using decision trees, examining how each subtype is influenced by opioid medication features and patient diagnoses, procedures, and demographics. Finally, we discuss how the subtypes can be incorporated in static machine learning models as features in predicting opioid overdose and adverse events. The proposed methods are general, and can be extended to other EHR prescription dosage trajectories.
Analysis of sentiment in tweets addressed to a single domain-specific Twitter account: Comparison of model performance and explainability of predictions

Many institutions and companies find it valuable to know how people feel about their ventures; hence, scientific research in sentiment analysis has been intensely developed over time. Automated sentiment analysis can be considered as a machine learning (ML) prediction task, with classes representing human affective states. Due to the rapid development of ML and deep learning (DL), improvements in automatic sentiment analysis performance are achieved almost every year. Since 2013, Semantic Evaluation (SemEval) has hosted a worldwide community-acknowledged competition that allows for comparisons of recent innovations. The sentiment analysis tasks focus on assessing sentiment in Twitter posts authored by various publishers and addressing multiple subjects. Our study aimed to compare selected popular and recent natural language processing methods using a new data set of Twitter posts sent to a single Twitter account. For improved comparability of our experiments with SemEval, we adopted their metrics and also deployed our models on data published for SemEval-2017. In addition, we investigated if an unsupervised ML technique applied for the detection of topics in tweets can be leveraged to improve the predictive performance of a selected transformer model. We also demonstrated how a recent explainable artificial intelligence technique can be used in Twitter sentiment analysis to gain a deeper understanding of the models' predictions. Our results show that the most recent DL language modeling approach provides the highest quality; however, this quality comes at reduced model transparency.
Virtual reality as a communication medium: a comparative study of forced compliance in virtual reality versus physical world

There are reasons to consider virtual reality (VR) as a newly arrived communication medium that ought to be differentiated from all other forms of mediated communication, since it is the first and only medium with the potential to enable incorporation of the full spectrum of both verbal and non-verbal cues. The present paper is part of a broader scheme in investigating potential differentiations in interpersonal communication between the physical world and VR. Our experimental design builds upon the existing knowledge base of forced compliance experiments; the set-up involved a comparative study of two groups (N = 46) performing tasks under the authoritative influence of a researcher who applied persuasion techniques. Results indicate that VR-mediated communication is as intricate as face to face, since subjects were equally or more compliant, with the nature of information exchanged (e.g. fact-based, morality-based, etc.) being a contributing factor, whilst exemplifying under-development and future applications of VR collaborative environments.
Ecosystem Readiness: Blockchain Adoption is Driven Externally

Blockchain technology has been extensively tested, implemented, used, or even abandoned by organizations. Whether organizations adopt blockchain technology depends on many factors. Previous literature has identified a range of potential factors affecting the adoption of blockchain technology, but most studies have focused solely on a subset of specific factors. As a result, these studies can provide only limited explanations for the varying degrees of blockchain adoption. By relying on the well-established Technology-Organization-Environment (TOE) framework, we propose an extended Blockchain Adoption Model (BAM). We derive a rich set of factors from the current state of research on blockchain adoption and the business ecosystem literature and extend them by conceptual reasoning. We then develop testable hypotheses for each of the identified factors. The resulting 14 hypotheses are tested by conducting a survey ( n = 350) within the DACH-region (Germany, Austria and Switzerland). We quantitatively analyze our BAM with a binary logistic regression analysis and identify six constructs impacting the adoption (relative advantage, observability, organizational age, external stakeholder pressure, regulatory uncertainty, and scope of business ecosystem). Based on our findings we introduce the novel idea of “ecosystem readiness” as the most important factor for the adoption of blockchain. Ecosystem readiness is characterized by the following attributes: 1) a large ecosystem scope, 2) stakeholders that are not yet collaborating in a trustful and regulated environment, and 3) a powerful organization leading the ecosystem. This powerful organization further intentionally promotes innovation by 4) making the benefits of this new technology observable for others, by 5) putting pressure on the other ecosystem participants to adopt the new technology, and eventually by 6) striving for regulatory certainty in the application and use of blockchain.
A Unified Framework for High Fidelity Face Swap and Expression Reenactment

Face manipulation techniques improve fast with the development of powerful image generation models. Two particular face manipulation methods, namely face swap and expression reenactment attract much attention for their flexibility and ease to generate high quality synthesis results. Recently, these two subjects are actively studied. However, most existing methods treat the two tasks separately, ignoring their underlying similarity. In this paper, we propose to tackle the two problems within a unified framework that achieves high quality synthesis results. The enabling component for our unified framework is the clean disentanglement of 3D pose, shape, and expression factors and then recombining them for different tasks accordingly. We then use the same set of 2D representations for face swap and expression reenactment tasks that are input to a common image translation model to directly generate the final synthetic images. Once trained, the proposed model can accomplish both face swap and expression reenactment tasks for previously unseen subjects. Comprehensive experiments and comparisons show that the proposed method achieves high fidelity results in multiple aspects, and it is especially good at faithfully preserving source facial shape in the face swap task, and accurately transferring facial movements in the expression reenactment task.
Ensemble slice sampling

Slice Sampling has emerged as a powerful Markov Chain Monte Carlo algorithm that adapts to the characteristics of the target distribution with minimal hand-tuning. However, Slice Sampling's performance is highly sensitive to the user-specified initial length scale hyperparameter and the method generally struggles with poorly scaled or strongly correlated distributions. This paper introduces Ensemble Slice Sampling (ESS), a new class of algorithms that bypasses such difficulties by adaptively tuning the initial length scale and utilising an ensemble of parallel walkers in order to efficiently handle strong correlations between parameters. These affine-invariant algorithms are trivial to construct, require no hand-tuning, and can easily be implemented in parallel computing environments. Empirical tests show that Ensemble Slice Sampling can improve efficiency by more than an order of magnitude compared to conventional MCMC methods on a broad range of highly correlated target distributions. In cases of strongly multimodal target distributions, Ensemble Slice Sampling can sample efficiently even in high dimensions. We argue that the parallel, black-box and gradient-free nature of the method renders it ideal for use in scientific fields such as physics, astrophysics and cosmology which are dominated by a wide variety of computationally expensive and non-differentiable models.
Proposing a task-oriented chatbot system for EFL learners speaking practice

Many learners of English as a foreign language often feel that learning spoken English is frustrating and quite difficult, especially when they have to talk to English-speaking foreigners. In general, because they are unfamiliar with the spoken mode of English and are worried about making grammatical errors, they often feel very scared to speak English. Furthermore, spoken language and reading differ in many ways. For instance, when seeing text containing new words, learners can stop and look up words in a dictionary. Additionally, a passage can be read multiple times by learners to understand it. Conversely, spoken language must be understood immediately in order to communicate effectively. The purpose of this study is to propose an interactive chatbot system named TPBOT which stands for “TOEIC Practice Chatbot” for EFL learners to eliminate their fear of speaking English and enable them to chat with online chatbots to practice spoken English at any time. This TPBOT would be very helpful to eliminate learners’ anxiety about speaking a foreign language with foreigners.Participants in this study are Taiwanese students whose oral scores on the TOEIC® test are below 100. They hope to improve their oral English ability after participating in the four-month experiment. The results have shown that students are satisfied with using this TPBOT and believe that it has indeed helped them improve their English speaking skills. Obviously, the TPBOT was very effective for the intended purpose. For educators, the TPBOT is very useful to build learning content in the TPBOT, provide learners with interactive exercises and improve learning effects.