Reviewerly/exHarmony huggingface dataset:
What can explain the price, volatility and trading volume of Bitcoin?

We study which variables can explain and predict the return, volatility and trading volume of Bitcoin. The considered variables are return, volatility, trading volume, transaction volume, change in the number of unique Bitcoin addresses, the VIX index and Google searches for "Bitcoin". We use realized volatility calculated from high-frequency data and find that the heterogeneous autoregressive model is suitable for Bitcoin volatility. Trading volume further improves this volatility model. The trading volume of Bitcoin can be predicted from Google searches for "Bitcoin". However, none of the considered variables can predict Bitcoin returns.
The Foldable Drone: A Morphing Quadrotor That Can Squeeze and Fly

The recent advances in state estimation, perception, and navigation algorithms have significantly contributed to the ubiquitous use of quadrotors for inspection, mapping, and aerial imaging. To further increase the versatility of quadrotors, recent works investigated the use of an adaptive morphology, which consists of modifying the shape of the vehicle during flight to suit a specific task or environment. However, these works either increase the complexity of the platform or decrease its controllability. In this letter, we propose a novel, simpler, yet effective morphing design for quadrotors consisting of a frame with four independently rotating arms that fold around the main frame. To guarantee stable flight at all times, we exploit an optimal control strategy that adapts on the fly to the drone morphology. We demonstrate the versatility of the proposed adaptive morphology in different tasks, such as negotiation of narrow gaps, close inspection of vertical surfaces, and object grasping and transportation. The experiments are performed on an actual, fully autonomous quadrotor relying solely on onboard visual-inertial sensors and compute. No external motion tracking systems and computers are used. This is the first work showing stable flight without requiring any symmetry of the morphology.
Spread of Hate Speech in Online Social Media

Hate speech is considered to be one of the major issues currently plaguing the online social media. With online hate speech culminating in gruesome scenarios like the Rohingya genocide in Myanmar, anti-Muslim mob violence in Sri Lanka, and the Pittsburgh synagogue shooting, there is a dire need to understand the dynamics of user interaction that facilitate the spread of such hateful content. In this paper, we perform the first study that looks into the diffusion dynamics of the posts made by hateful and non-hateful users on Gab (Gab.com). We collect a massive dataset of 341K users with 21M posts and investigate the diffusion of the posts generated by hateful and non-hateful users. We observe that the content generated by the hateful users tend to spread faster, farther and reach a much wider audience as compared to the content generated by normal users. We further analyze the hateful and non-hateful users on the basis of their account and network characteristics. An important finding is that the hateful users are far more densely connected among themselves. Overall, our study provides the first cross-sectional view of how hateful users diffuse hate content in online social media.
Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing

Background: Data sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier. Methods and Results: Using pairs of deep neural networks, we generated simulated, synthetic participants that closely resemble participants of the SPRINT trial (Systolic Blood Pressure Trial). We showed that such paired networks can be trained with differential privacy, a formal privacy framework that limits the likelihood that queries of the synthetic participants’ data could identify a real a participant in the trial. Machine learning predictors built on the synthetic population generalize to the original data set. This finding suggests that the synthetic data can be shared with others, enabling them to perform hypothesis-generating analyses as though they had the original trial data. Conclusions: Deep neural networks that generate synthetic participants facilitate secondary analyses and reproducible investigation of clinical data sets by enhancing data sharing while preserving participant privacy.
Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty

This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multitude of applications depend on the awareness of their surroundings, and use depth cues to reason and react accordingly. On the one hand, monocular depth prediction methods fail to generate absolute and precise depth maps. On the other hand, stereoscopic approaches are still significantly outperformed by LiDAR based approaches. The goal of the depth completion task is to generate dense depth predictions from sparse and irregular point clouds which are mapped to a 2D plane. We propose a new framework which extracts both global and local information in order to produce proper depth maps. We argue that simple depth completion does not require a deep network. However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input. This improves the accuracy significantly. Moreover, confidence masks are exploited in order to take into account the uncertainty in the depth predictions from each modality. This fusion method outperforms the state-of-the-art and ranks first on the KITTI depth completion benchmark [21]. Our code with visualizations is available at https://github.com/wvangansbeke/Sparse-Depth-Completion.
Simba

Package-level integration using multi-chip-modules (MCMs) is a promising approach for building large-scale systems. Compared to a large monolithic die, an MCM combines many smaller chiplets into a larger system, substantially reducing fabrication and design costs. Current MCMs typically only contain a handful of coarse-grained large chiplets due to the high area, performance, and energy overheads associated with inter-chiplet communication. This work investigates and quantifies the costs and benefits of using MCMs with fine-grained chiplets for deep learning inference, an application area with large compute and on-chip storage requirements. To evaluate the approach, we architected, implemented, fabricated, and tested Simba, a 36-chiplet prototype MCM system for deep-learning inference. Each chiplet achieves 4 TOPS peak performance, and the 36-chiplet MCM package achieves up to 128 TOPS and up to 6.1 TOPS/W. The MCM is configurable to support a flexible mapping of DNN layers to the distributed compute and storage units. To mitigate inter-chiplet communication overheads, we introduce three tiling optimizations that improve data locality. These optimizations achieve up to 16% speedup compared to the baseline layer mapping. Our evaluation shows that Simba can process 1988 images/s running ResNet-50 with batch size of one, delivering inference latency of 0.50 ms.
Deepfake Video Detection through Optical Flow Based CNN

Recent advances in visual media technology have led to new tools for processing and, above all, generating multimedia contents. In particular, modern AI-based technologies have provided easy-to-use tools to create extremely realistic manipulated videos. Such synthetic videos, named Deep Fakes, may constitute a serious threat to attack the reputation of public subjects or to address the general opinion on a certain event. According to this, being able to individuate this kind of fake information becomes fundamental. In this work, a new forensic technique able to discern between fake and original video sequences is given; unlike other state-of-the-art methods which resorts at single video frames, we propose the adoption of optical flow fields to exploit possible inter-frame dissimilarities. Such a clue is then used as feature to be learned by CNN classifiers. Preliminary results obtained on FaceForensics++ dataset highlight very promising performances.
A research agenda for augmented and virtual reality in architecture, engineering and construction

This paper presents a study on the usage landscape of augmented reality (AR) and virtual reality (VR) in the architecture, engineering and construction sectors, and proposes a research agenda to address the existing gaps in required capabilities. A series of exploratory workshops and questionnaires were conducted with the participation of 54 experts from 36 organisations from industry and academia. Based on the data collected from the workshops, six AR and VR use-cases were defined: stakeholder engagement, design support, design review, construction support, operations and management support, and training. Three main research categories for a future research agenda have been proposed, i.e.: (i) engineering-grade devices, which encompasses research that enables robust devices that can be used in practice, e.g. the rough and complex conditions of construction sites; (ii) workflow and data management; to effectively manage data and processes required by AR and VR technologies; and (iii) new capabilities; which includes new research required that will add new features that are necessary for the specific construction industry demands. This study provides essential information for practitioners to inform adoption decisions. To researchers, it provides a research road map to inform their future research efforts. This is a foundational study that formalises and categorises the existing usage of AR and VR in the construction industry and provides a roadmap to guide future research efforts.
Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis

Bankruptcy prediction is a crucial application in financial fields to aid in accurate decision making for business enterprises. Many models may stagnate to low-accuracy results due to the uninformed choice of parameters. This paper presents a forward-thinking bankruptcy prediction model based on kernel extreme learning machine (KELM), which proposes a new efficient version of a fruit fly optimization (FOA) algorithm called LSEOFOA, to evolve and harmonize the penalty and the kernel parameter in KELM. The upgraded version of FOA is conceptualized based on three reorganizations. The first attempt is to include Levy's flight for improving exploration inclinations, and the second is based on slime mould algorithm (SMA) for avoiding premature convergence and enhancing the stability of the exploration and exploitation patterns. As the last modification, we utilized the elite opposition-based learning for accelerating the convergence. The algorithmic trends of this optimizer are verified, and then, it is verified on a bankruptcy prediction module. Therefore, to further demonstrate the superiority of the LSEOFOA method, comparison studies are performed using the conventional FOA and other variants of FOA and a set of advanced algorithms including EBOwithCMAR. Experimental results for every optimization task demonstrate that LSEOFOA can provide a high-performance and self-assured tradeoff between exploration and exploitation. Also, the developed KELM classifier is utilized for bankruptcy prediction, and its optimal parameters set are revealed by the proposed FOA. The effectiveness of the LSEOFOA-KELM model is rigorously evaluated using a financial dataset and comparison with KELM-based models with other competitive optimizers such as LSHADE-RSP. Overall research findings show that the proposed model is superior in terms of classification accuracy, Matthews correlation coefficient, sensitivity, and specificity. Towards more evolutionary and efficient prediction models, the proposed LSEOFOA-KELM prediction model can be regarded as a promising warning tool for financial decision making, with successful performance in bankruptcy prediction. Interested readers to the idea and related material of LSEOFOA-KELM can find the designed public web service at https://aliasgharheidari.com. Also, the info and source codes of the slime mould algorithm (SMA) in python, matlab and other languages are shared publicly at https://aliasgharheidari.com/SMA.html.
An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools

Natural Language Processing (NLP) is a branch of artificial intelligence that involves the design and implementation of systems and algorithms able to interact through human language. Thanks to the recent advances of deep learning, NLP applications have received an unprecedented boost in performance. In this paper, we present a survey of the application of deep learning techniques in NLP, with a focus on the various tasks where deep learning is demonstrating stronger impact. Additionally, we explore, describe, and revise the main resources in NLP research, including software, hardware, and popular corpora. Finally, we emphasize the main limits of deep learning in NLP and current research directions.
Blame the Bot: Anthropomorphism and Anger in Customer–Chatbot Interactions

Chatbots have become common in digital customer service contexts across many industries. While many companies choose to humanize their customer service chatbots (e.g., giving them names and avatars), little is known about how anthropomorphism influences customer responses to chatbots in service settings. Across five studies, including an analysis of a large real-world data set from an international telecommunications company and four experiments, the authors find that when customers enter a chatbot-led service interaction in an angry emotional state, chatbot anthropomorphism has a negative effect on customer satisfaction, overall firm evaluation, and subsequent purchase intentions. However, this is not the case for customers in nonangry emotional states. The authors uncover the underlying mechanism driving this negative effect (expectancy violations caused by inflated pre-encounter expectations of chatbot efficacy) and offer practical implications for managers. These findings suggest that it is important to both carefully design chatbots and consider the emotional context in which they are used, particularly in customer service interactions that involve resolving problems or handling complaints.
Video Transformer Network

This paper presents VTN, a transformer-based framework for video recognition. Inspired by recent developments in vision transformers, we ditch the standard approach in video action recognition that relies on 3D ConvNets and introduce a method that classifies actions by attending to the entire video sequence information. Our approach is generic and builds on top of any given 2D spatial network. In terms of wall runtime, it trains 16.1× faster and runs 5.1× faster during inference while maintaining competitive accuracy compared to other state-of-the-art methods. It enables whole video analysis, via a single end-to-end pass, while requiring 1.5× fewer GFLOPs. We report competitive results on Kinetics-400 and Moments in Time benchmarks and present an ablation study of VTN properties and the trade-off between accuracy and inference speed. We hope our approach will serve as a new baseline and start a fresh line of research in the video recognition domain. Code and models are available at: https://github.com/bomri/SlowFast/blob/master/projects/vtn/README.md.
Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond

In the Industry 4.0 era, automation and data analytics emerge as the major forces to enhance efficiency in operations management (OM). Disruptive technologies, such as artificial intelligence, robotics, blockchain, 3D printing, 5G, Internet‐of‐Thing, digital twins, and augmented reality, are widely applied. They potentially will bring a radical change to real world operations. In this study, we first explore several major disruptive technologies, examine the corresponding OM studies, and highlight their current applications in the industry. Then, we discuss the pros and cons associated with the use of these technologies and uncover the potential human–machine conflicting areas. After that, we propose measures which may be able to achieve human–machine reconciles in the coming Industry 5.0 era. A concept of “sustainable social welfare” which includes worker welfare, privacy, etc. is proposed and the roles played by policy makers are also discussed. Finally, a future research agenda, which covers topics in both the Industry 4.0 and Industry 5.0 eras, is established.
Fast universal quantum gate above the fault-tolerance threshold in silicon

Fault-tolerant quantum computers that can solve hard problems rely on quantum error correction1. One of the most promising error correction codes is the surface code2, which requires universal gate fidelities exceeding an error correction threshold of 99 per cent3. Among the many qubit platforms, only superconducting circuits4, trapped ions5 and nitrogen-vacancy centres in diamond6 have delivered this requirement. Electron spin qubits in silicon7-15 are particularly promising for a large-scale quantum computer owing to their nanofabrication capability, but the two-qubit gate fidelity has been limited to 98 per cent owing to the slow operation16. Here we demonstrate a two-qubit gate fidelity of 99.5 per cent, along with single-qubit gate fidelities of 99.8 per cent, in silicon spin qubits by fast electrical control using a micromagnet-induced gradient field and a tunable two-qubit coupling. We identify the qubit rotation speed and coupling strength where we robustly achieve high-fidelity gates. We realize Deutsch-Jozsa and Grover search algorithms with high success rates using our universal gate set. Our results demonstrate universal gate fidelity beyond the fault-tolerance threshold and may enable scalable silicon quantum computers.
Making Big Data Open in Edges: A Resource-Efficient Blockchain-Based Approach

The emergence of edge computing has witnessed a fast-growing volume of data on edge devices belonging to different stakeholders which, however, cannot be shared among them due to the lack of the trust. By exploiting blockchain's non-repudiation and non-tampering properties that enable trust, we develop a blockchain-based big data sharing framework to support various applications across resource-limited edges. In particular, we devise a number of novel resource-efficient techniques for the framework: (1) the PoC (Proof-of-Collaboration) based consensus mechanism with low computation complexity which is especially beneficial to the edge devices with low computation capacity, (2) the blockchain transaction filtering and offloading scheme that can significantly reduce the storage overhead, and (3) new types of blockchain transaction (i.e., Express Transaction) and block (i.e., Hollow Block) to enhance the communication efficiency. Extensive experiments are conducted and the results demonstrate the superior performance of our proposal.
Ensemble strategies for population-based optimization algorithms – A survey

In population-based optimization algorithms (POAs), given an optimization problem, the quality of the solutions depends heavily on the selection of algorithms, strategies and associated parameter combinations, constraint handling method, local search method, surrogate model, niching method, etc. In the literature, there exist several alternatives corresponding to each aspect of configuring a population-based algorithm such as one-point/two-points/uniform crossover operators, tournament/ranking/stochastic uniform sampling selection methods, Gaussian/Levy/Cauchy mutation operators, clearing/crowding/sharing based niching algorithms, adaptive penalty/epsilon/superiority of feasible constraint handling approaches, associated parameter values and so on. In POA literature, No Free Lunch (NFL) theorem has been well-documented and therefore, to effectively solve a given optimization problem, an appropriate configuration is necessary. But, the trial and error approach for the appropriate configuration may be impractical because at different stages of evolution, the most appropriate configurations could be different depending on the characteristics of the current search region for a given problem. Recently, the concept of incorporating ensemble strategies into POAs has become popular so that the process of configuring an optimization algorithm can benefit from both the availability of diverse approaches at different stages and alleviate the computationally intensive offline tuning. In addition, algorithmic components of different advantages could support one another during the optimization process, such that the ensemble of them could potentially result in a versatile POA. This paper provides a survey on the use of ensemble strategies in POAs. In addition, we also provide an overview of similar methods in the literature such as hyper-heuristics, island models, adaptive operator selection, etc. and compare them with the ensemble strategies in the context of POAs.
Caffeine: Toward Uniformed Representation and Acceleration for Deep Convolutional Neural Networks

With the recent advancement of multilayer convolutional neural networks (CNNs) and fully connected networks (FCNs), deep learning has achieved amazing success in many areas, especially in visual content understanding and classification. To improve the performance and energy efficiency of the computation-demanding CNN, the FPGA-based acceleration emerges as one of the most attractive alternatives. In this paper, we design and implement Caffeine, a hardware/software co-designed library to efficiently accelerate the entire CNN and FCN on FPGAs. First, we propose a uniformed convolutional matrix-multiplication representation for both computation-bound convolutional layers and communication-bound FCN layers. Based on this representation, we optimize the accelerator microarchitecture and maximize the underlying FPGA computing and bandwidth resource utilization based on a revised roofline model. Moreover, we design an automation flow to directly compile highlevel network definitions to the final FPGA accelerator. As a case study, we integrate Caffeine into the industry-standard software deep learning framework Caffe. We evaluate Caffeine and its integration with Caffe by implementing VGG16 and AlexNet networks on multiple FPGA platforms. Caffeine achieves a peak performance of 1460 giga fixed point operations per second on a medium-sized Xilinx KU060 FPGA board; to our knowledge, this is the best published result. It achieves more than 100× speedup on FCN layers over prior FPGA accelerators. An end-to-end evaluation with Caffe integration shows up to 29× and 150× performance and energy gains over Caffe on a 12-core Xeon server, and 5.7× better energy efficiency over the GPU implementation. Performance projections for a system with a high-end FPGA (Virtex7 690t) show even higher gains.
Solving the optimal path planning of a mobile robot using improved Q-learning

Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path planning recently, due to its self-learning ability without requiring a priori model of the environment. Yet, despite such advantage, Q-learning exhibits slow convergence to the optimal solution. In order to address this limitation, the concept of partially guided Q-learning is introduced wherein, the flower pollination algorithm (FPA) is utilized to improve the initialization of Q-learning. Experimental evaluation of the proposed improved Q-learning under the challenging environment with a different layout of obstacles shows that the convergence of Q-learning can be accelerated when Q-values are initialized appropriately using the FPA. Additionally, the effectiveness of the proposed algorithm is validated in a real-world experiment using a three-wheeled mobile robot.