Reviewerly/exHarmony huggingface dataset:
Herding behaviour and price convergence clubs in cryptocurrencies during bull and bear markets

This paper sets out to explore whether convergence and herding phenomena exist for digital currencies. Daily data cover a large spectrum of cryptocurrencies in separate bull and bear periods. Empirical estimations for detecting club convergence and clustering are performed by the methodology proposed by Phillips and Shu (2007, 2009). Econometric outcomes reveal preliminary evidence of powerful herding behaviour. The lowest of large-cap digital currencies attract mainly cryptocurrencies which are about in the middle of the large-cap and medium-cap categories whereas the highest-cap cryptocurrencies are parts of convergence clubs with mostly large-cap or purely medium-cap digital currencies during bear markets. Notably, segmentation is higher during bear markets as clusters are formed around more numerous cryptocurrencies than during bull markets. Convergence is stronger during flourishing periods. Secondary herding is also realized among pairs of clubs. Our findings enable investors to better diversify their portfolios and ameliorate their risk-return trade-off during extreme events.
AlignSeg: Feature-Aligned Segmentation Networks

Aggregating features in terms of different convolutional blocks or contextual embeddings has been proven to be an effective way to strengthen feature representations for semantic segmentation. However, most of the current popular network architectures tend to ignore the misalignment issues during the feature aggregation process caused by step-by-step downsampling operations and indiscriminate contextual information fusion. In this paper, we explore the principles in addressing such feature misalignment issues and inventively propose Feature-Aligned Segmentation Networks (AlignSeg). AlignSeg consists of two primary modules, i.e., the Aligned Feature Aggregation (AlignFA) module and the Aligned Context Modeling (AlignCM) module. First, AlignFA adopts a simple learnable interpolation strategy to learn transformation offsets of pixels, which can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation. Second, with the contextual embeddings in hand, AlignCM enables each pixel to choose private custom contextual information adaptively, making the contextual embeddings be better aligned. We validate the effectiveness of our AlignSeg network with extensive experiments on Cityscapes and ADE20K, achieving new state-of-the-art mIoU scores of 82.6 and 45.95 percent, respectively. Our source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/speedinghzl/AlignSeg</uri> .
Identifying Visible Parts via Pose Estimation for Occluded Person Re-Identification

We focus on the occlusion problem in person re-identification (re-id), which is one of the main challenges in real-world person retrieval scenarios. Previous methods on the occluded re-id problem usually assume that only the probes are occluded, thereby removing occlusions by manually cropping. However, this may not always hold in practice. This article relaxes this assumption and investigates a more general occlusion problem, where both the probe and gallery images could be occluded. The key to this challenging problem is depressing the noise information by identifying bodies and occlusions. We propose to incorporate the pose information into the re-id framework, which benefits the model in three aspects. First, it provides the location of the body. We then design a Pose-Masked Feature Branch to make our model focus on the body region only and filter those noise features brought by occlusions. Second, the estimated pose reveals which body parts are visible, giving us a hint to construct more informative person features. We propose a Pose-Embedded Feature Branch to adaptively re-calibrate channel-wise feature responses based on the visible body parts. Third, in testing, the estimated pose indicates which regions are informative and reliable for both probe and gallery images. Then we explicitly split the extracted spatial feature into parts. Only part features from those commonly visible parts are utilized in the retrieval. To better evaluate the performances of the occluded re-id, we also propose a large-scale data set for the occluded re-id with more than 35 000 images, namely Occluded-DukeMTMC. Extensive experiments show our approach surpasses previous methods on the occluded, partial, and non-occluded re-id data sets.
Developing Cost-Effective Blockchain-Powered Applications

Ethereum is a blockchain platform that hosts and executes smart contracts. Executing a function of a smart contract burns a certain amount of gas units (a.k.a., gas usage). The total gas usage depends on how much computing power is necessary to carry out the execution of the function. Ethereum follows a free-market policy for deciding the transaction fee for executing a transaction. More specifically, transaction issuers choose how much they are willing to pay for each unit of gas (a.k.a., gas price). The final transaction fee corresponds to the gas price times the gas usage. Miners process transactions to gain mining rewards, which come directly from these transaction fees. The flexibility and the inherent complexity of the gas system pose challenges to the development of blockchain-powered applications. Developers of blockchain-powered applications need to translate requests received in the frontend of their application into one or more smart contract transactions. Yet, it is unclear how developers should set the gas parameters of these transactions given that (i) miners are free to prioritize transactions whichever way they wish and (ii) the gas usage of a contract transaction is only known after the transaction is processed and included in a new block. In this article, we analyze the gas usage of Ethereum transactions that were processed between Oct. 2017 and Feb. 2019 (the Byzantium era). We discover that (i) most miners prioritize transactions based on their gas price only, (ii) 25% of the functions that received at least 10 transactions have an unstable gas usage (coefficient of variation = 19%), and (iii) a simple prediction model that operates on the recent gas usage of a function achieves an R-Squared of 0.76 and a median absolute percentage error of 3.3%. We conclude that (i) blockchain-powered application developers should be aware that transaction prioritization in Ethereum is frequently done based solely on the gas price of transactions (e.g., a higher transaction fee does not necessarily imply a higher transaction priority) and act accordingly and (ii) blockchain-powered application developers can leverage gas usage prediction models similar to ours to make more informed decisions to set the gas price of their transactions. Lastly, based on our findings, we list and discuss promising avenues for future research.
Bi-GISIS KE: Modified key exchange protocol with reusable keys for IoT security

We propose a new bilateral generalization inhomogeneous short integer solution (Bi-GISIS)-based key exchange protocol with reusable key feature for post-quantum IoT security. It is aimed to reduce the time consumption in the key generation of key exchange protocols to be used in IoT devices. To obtain reusable key, we define modified bilateral pasteurization in the random oracle model. By ensuring reusable keys, the same key becomes available in several executions of the proposed protocol. This feature allows efficient usage of reusable keys in resource-constrained IoT architectures. The proposed scheme is suitable for quantum secure key exchange in D2D-aided fog computing environment. A key exchange protocol with improved key management process is constructed for D2D.
Mobile money adoption and response to idiosyncratic shocks: Empirics from five selected countries in sub-Saharan Africa

In sub-Saharan Africa, financial inclusion remains low, with households being more vulnerable to idiosyncratic shocks. Mobile money has been identified as having the potential to boost financial inclusion while closing the related gender- and rural-gaps in the process. Nonetheless, little is known about how mobile money facilitates the sending and receipt of financial support in times of idiosyncratic shocks. This study examines the link between mobile money adoption and response to idiosyncratic shocks from the perspectives of senders and receivers using comprehensive household data across five countries. Employing the number of mobile money agents in respondent's neighbourhood as instrument in an instrumental variable probit procedure, we find that adoption of mobile money is associated with an increase in the probability to send (receive) financial support to (from) families, friends, relatives, co-workers and acquaintances in times of idiosyncratic shocks but the sending effect outweighs that of the receiving. Overall, female-headed and rural-located households end up being the net beneficiaries of mobile money adoption when it comes to idiosyncratic shocks. Mobile money regulators and standard setting authorities are urged to engage with telecommunications companies and other stakeholders to deepen the digital financial ecosystem.
An Asynchronous and Real-Time Update Paradigm of Federated Learning for Fault Diagnosis

The federated learning (FL) method based on model aggregation can balance data and protect data privacy, but the existing method is difficult to achieve the same effectiveness as the centralized learning method under data sharing. In addition, it is difficult for the existing federated model to realize the real-time update of the clients' network parameters, because it inhibits the optimal performance of the client. Therefore, this article proposes an asynchronous update paradigm of FL with real-time identification of the client's network parameters to tackle the shortcomings. First, we adopt the linear fusion method based on sequential filtering and fuse the parameters of federated center asynchronously considering communication delay, which can approach the diagnostic accuracy based on the centralized learning. Second, we establish the real-time identification method for the clients based on linear filtering with the new labeled samples obtained at nonequal intervals, which expects the client to acquire better performance. Finally, we test the fault classification ability of the proposed method based on the actual collected fault dataset and the test platform of bearing fault dataset.
Light Field Super-Resolution via Adaptive Feature Remixing

A novel light field super-resolution algorithm to improve the spatial and angular resolutions of light field images is proposed in this work. We develop spatial and angular super-resolution (SR) networks, which can faithfully interpolate images in the spatial and angular domains regardless of the angular coordinates. For each input image, we feed adjacent images into the SR networks to extract multi-view features using a trainable disparity estimator. We concatenate the multi-view features and remix them through the proposed adaptive feature remixing (AFR) module, which performs channel-wise pooling. Finally, the remixed feature is used to augment the spatial or angular resolution. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms on various light field datasets. The source codes and pre-trained models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/keunsoo-ko/</uri> LFSR-AFR
Toward personalized XAI: A case study in intelligent tutoring systems

Our research is a step toward ascertaining the need for personalization in XAI, and we do so in the context of investigating the value of explanations of AI-driven hints and feedback in Intelligent Tutoring Systems (ITS). We added an explanation functionality to the Adaptive CSP (ACSP) applet, an interactive simulation that helps students learn an algorithm for constraint satisfaction problems by providing AI-driven hints adapted to their predicted level of learning. We present the design of the explanation functionality and the results of a controlled study to evaluate its impact on students' learning and perception of the ACPS hints. The study includes an analysis of how these outcomes are modulated by several user characteristics such as personality traits and cognitive abilities, to asses if explanations should be personalized to these characteristics. Our results indicate that providing explanations increase students' trust in the ACPS hints, perceived usefulness of the hints, and intention to use them again. In addition, we show that students' access of the ACSP explanation and learning gains are modulated by three user characteristics, Need for Cognition, Contentiousness and Reading Proficiency, providing insights on how to personalize the ACSP explanations to these traits, as well as initial evidence on the potential value of personalized Explainable AI (XAI) for ITS.
NM-GAN: Noise-modulated generative adversarial network for video anomaly detection

As an important and challenging task for intelligent video surveillance systems, video anomaly detection is generally referred to as automatic recognition of video frames that contain abnormal targets, behavior or events. Although it has been widely applied in real scenes, anomaly detection remains a challenging task because of the vague definition of anomaly and the lack of the anomaly samples. Inspired by the widespread application of Generative Adversarial Network (GAN), we propose an end-to-end pipeline called NM-GAN which assembles an encode-decoder reconstruction network and a CNN-based discrimination network in a GAN-like architecture. The generalization ability of the reconstruction network is properly modulated via the adversarial learning around reconstruction error maps and noise maps. Meanwhile, the discrimination network is trained to distinguish anomaly samples from normal samples based on the reconstruction error maps. Finally, the output of the discrimination network is transferred to evaluate anomaly score of the input frame. The thorough proof-of-principle experiments and ablation tests on several popular datasets reveal that the proposed model enhance the generalization ability of the reconstruction network and the distinguishability of the discrimination network significantly. The comparison with the state-of-the-art shows that the proposed NM-GAN model outperforms most competing models in precision and stability.
A Simulated Annealing Algorithm and Grid Map-Based UAV Coverage Path Planning Method for 3D Reconstruction

With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of a target area and to collect high-resolution images while considering the overlap ratio of the collected images and energy consumption of clustered UAVs. The overlap ratio of the collected image set is guaranteed through a map decomposition method, which can ensure that the reconstruction results will not get affected by model breaking. In consideration of the small battery capacity of common commercial quadrotor UAVs, ray-scan-based area division was adopted to segment the target area, and near-optimized paths in subareas were calculated by a simulated annealing algorithm to find near-optimized paths, which can achieve balanced task assignment for UAV formations and minimum energy consumption for each UAV. The proposed system was validated through a site experiment and achieved a reduction in path length of approximately 12.6% compared to the traditional zigzag path.
I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI

Unintended consequences of deployed AI systems fueled the call for more interpretability in AI systems. Often explainable AI (XAI) systems provide users with simplifying local explanations for individual predictions but leave it up to them to construct a global understanding of the model behavior. In this work, we examine if non-technical users of XAI fall for an illusion of explanatory depth when interpreting additive local explanations. We applied a mixed methods approach consisting of a moderated study with 40 participants and an unmoderated study with 107 crowd workers using a spreadsheet-like explanation interface based on the SHAP framework. We observed what non-technical users do to form their mental models of global AI model behavior from local explanations and how their perception of understanding decreases when it is examined.
Augmented reality technology adoption: Case of a mobile application in Turkey

With the increasing use of smart devices, augmented reality (AR) technology has become widespread in mobile devices. As with user interaction technologies, there are factors affecting the use of applications in mobile augmented reality (MAR) applications. In this study, the factors affecting the use of mobile augmented reality in Turkey are investigated. Although AR and MAR are generally investigated during the research period, “Augment”, the application, was used in the survey and interview parts of the research study. The interview consists of three different parts in addition to a quantitative experimental study. More than hundred variables were obtained from articles and interviews which 22 of them were selected. The results showed that the two most important factors that influence usage of MAR applications are security and privacy. These two are followed by ease of learning, visual quality of the application 3D model, and ease of use in importance, respectively. It is recommended that designers and application developers consider these five variables when designing or developing a MAR application.
Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots.This paper reviews the methods based on motion-planning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment.The conventional methods of DRL are categorized to value-based, policy-based and actor-critic-based algorithms, and the corresponding theories and applications are surveyed.Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems.According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.
A Secure and Decentralized Blockchain Based EV Energy Trading Model Using Smart Contract in V2G Network

In this work, a secure and decentralized Blockchain based energy trading model for electric vehicles (EVs) using Smart contract that achieves Peer-to-Peer (P2P) transactions between EVs in Vehicle to Grid networks is designed. The traditional energy trading model is a centralized structure based on trusted third parties, and there may an issue of single-point failure and leakage of privacy. In this way, a blockchain-based framework offers a secure, efficient and transparent trading model. Initially, the participating EVs and aggregator in the trading process should register at the trusted authority. Once the registration is successfully completed, both EVs and aggregator authenticate each other mutually in an anonymous manner. Moreover, only authorized EVs (charging and discharging EVs) participate in the contrary auction mechanism to exchange power/money based on their demand. Simulation conducted for the proposed scheme shows that our scheme has high speed (i.e., less computational time and execution time) which improves the market efficiency. In-addition, the transactions are non tamperable, when compared to the conventional scheme.
MIRIA: A Mixed Reality Toolkit for the In-Situ Visualization and Analysis of Spatio-Temporal Interaction Data

In this paper, we present MIRIA, a Mixed Reality Interaction Analysis toolkit designed to support the in-situ visual analysis of user interaction in mixed reality and multi-display environments. So far, there are few options to effectively explore and analyze interaction patterns in such novel computing systems. With MIRIA, we address this gap by supporting the analysis of user movement, spatial interaction, and event data by multiple, co-located users directly in the original environment. Based on our own experiences and an analysis of the typical data, tasks, and visualizations used in existing approaches, we identify requirements for our system. We report on the design and prototypical implementation of MIRIA, which is informed by these requirements and offers various visualizations such as 3D movement trajectories, position heatmaps, and scatterplots. To demonstrate the value of MIRIA for real-world analysis tasks, we conducted expert feedback sessions using several use cases with authentic study data.
Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics' average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors' regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation's efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).
On the Unification of Schemes and Software for Wavelets on the Interval

Abstract We revisit the construction of wavelets on the interval with various degrees of polynomial exactness, and explain how existing schemes for orthogonal- and Spline wavelets can be extended to compactly supported delay-normalized wavelets. The contribution differs substantially from previous ones in how results are stated and deduced: linear algebra notation is exploited more heavily, and the use of sums and complicated index notation is reduced. This extended use of linear algebra eases translation to software, and a general open source implementation, which uses the deductions in this paper as a reference, has been developed. Key features of this implementation is its flexibility w.r.t. the length of the input, as well as its generality regarding the wavelet transform.