Reviewerly/exHarmony huggingface dataset:
Impact of Visual Embodiment on Trust for a Self-driving Car Virtual Agent: A Survey Study and Design Recommendations

Designing trust-based in-car interfaces is critical for the adoption of self-driving cars. Indeed, latest studies revealed that a vast majority of drivers are not willing to trust this technology. Although previous research showed that visually embodying a robot can have a positive impact on the interaction with a user, the influence of this visual representation on user trust is less understood. In this study, we assessed the trustworthiness of different models of visual embodiment such as abstract, human, animal, mechanical, etc., using a survey and a trust scale. For those reasons, we considered a virtual assistant designed to support trust in automated driving and particularly in critical situations. This assistant role is to take full control of the driving task whenever the driver activates the self-driving mode, and provide a trustworthy experience. We first selected a range of visual embodiment models based on a design space for robot visual embodiment and visual representations for each of these models. Then we used a card sorting procedure (19 selected participants) in order to select the most significant visual representations for each model. Finally, we conducted a survey (146 participants) to evaluate the impact of the selected models of visual embodiment on user trust and user preferences. With our results, we attempt to provide an answer for the question of the best visual embodiment to instill trust in a virtual agent capacity to handle critical driving situations. We present possible guidelines for real-world implementation and we discuss further directions for a more ecological evaluation.
Handling Data Imbalance Using a Heterogeneous Bagging-Based Stacked Ensemble (HBSE) for Credit Card Fraud Detection

Increase in electronic payments has lured fraudsters into this domain, leading to reduced security for customers. Although banks continuously strive to provide enhanced security for the prevention of frauds, these are at times countered by the fraudsters. This has become a vicious cycle leading to the requirement of fraud detection model rather than a fraud prevention mechanism. This work presents a heterogeneous bagging-based stacked ensemble (HBSE) model for effective detection of fraud in credit card transactions. The model is composed of a bagging mechanism that aims to handle data imbalance and heterogeneous base learners that act as effective decision rule extraction mechanisms, hence effectively operating on the highly complex data. The process of creating an ensemble ensures that the model is interpretable. Experiments were conducted on benchmark data, and comparisons were performed with state-of-the-art models. Comparisons indicate highly effective performance of the proposed HBSE model.
Near-Optimal Nonbinary Index Assignment for Equiprobable Uniform Quantizers and M-PSK Transmission

In conventional communication systems, quantized source symbols are represented by distinct binary labels, which are then mapped to bandwidth-efficient modulation symbols (such as M-PSK) for transmission. Assigning quantization levels to binary labels is known as the binary index assignment (IA) and has been well studied under the assumption that binary indices are transmitted over the binary symmetric channel. It is well-known that the natural binary code gives the optimal binary IA for uniform quantizers with uniform sources. Alternatively, we can also assign quantization levels to M-ary labels to fit the M-ary transmission. This M-ary IA approach generalizes and could outperform the binary IA, and has potential applications in power-efficient transmission such as wireless sensor networks. Although there exist some significant progresses in the study of finding the optimal M-ary IA, the problem is still open. In this paper, we investigate the M-ary IA problem for equiprobable uniform scalar quantizers and M-PSK transmission. We derive a lower bound on the channel mean-squared distortion (MSD) among all possible M-ary IAs and construct a solution which is optimal for M = 3, 4. For M ≥ 5, the solution becomes near-optimal, and its MSD performance approaches the lower bound to within a small gap. In addition, under some wireless sensor network scenarios with 64-level quantization and 8-PSK, we demonstrate that the percentage energy saving of the proposed nonbinary IA solution could be up to 45% relative to the optimal binary IA approach.
The Existence of Consensus Equilibria for Data Trading under the Framework of Internet of Things (IoT) with Blockchain Ecosystems

The goal of this paper is to use a new notion called “Consensus Game (CG)” with motivation from the mechanism design of blockchain economy under the consensus incentives from Bitcoin ecosystems in financial technology (Fintech) to study the existence of equilibria for the data trading under the framework of Internet of Things (IoT). By a fact that for a given sets of sensors, sensors may take action in cooperative, or noncooperative strategies (games), we formulate s model (called “Consensus Game of IoT”) to analyze this competitive situation among the sensors with the mixture of cooperative and noncooperative strategy (game). Then we provide the analytical condition for the existence of the consensus games of IoT in general. The application shows that the concept of consensus games of IoT may play a key role for the study of data trading under the framework of IoT associated with Blockchain ecosystems.
Multilabel Feature Selection Using Mutual Information and ML-ReliefF for Multilabel Classification

Recently, multilabel classification algorithms play an increasingly significant role in data mining and machine learning. However, some existing mutual information-based algorithms ignore the influence of the proportions of labels on the correlation degree between features and label sets. Besides, the correlation degree of label sets cannot be accurately measured in most traditional ReliefF algorithms, and the repeated calculation arises from the division of heterogeneous neighbors. To overcome these shortcomings, this paper proposes a multilabel feature selection method using mutual information and improved multilabel ReliefF (ML-ReliefF). First, the proportion of each label is calculated in label space and combined with the mutual information of features and labels to construct a novel correlation degree between features and label sets to preprocess multilabel datasets, which is used to reduce runtime of ML-ReliefF. Second, the mutual information of label sets is introduced into improving accuracy of the correlation degree among label sets. Furthermore, two types of correlation degree for label sets based on ML-ReliefF are developed to divide similar and heterogeneous samples more clearly. Third, a divided method of heterogeneous neighbors is presented to effectively avoid the repeated calculation in ML-ReliefF, and a novel method of feature weighting based on ML-ReliefF is constructed to evaluate the importance of features. Finally, a multilabel feature selection algorithm based on mutual information and ML-ReliefF for multilabel classification is designed to improve the performance of multilabel classification. Experiments under fourteen multilabel datasets show the effectiveness of our algorithm and improve the classification performance for multilabel datasets.
SparTANN

While sparsity has been exploited in many inference accelerators, not much work is done for training accelerators. Exploiting sparsity in training accelerators involves multiple issues, including where to find sparsity, how to exploit sparsity, and how to create more sparsity. In this paper we present a novel sparse training architecture that can exploit sparsity in gradient tensors in both back propagation and weight update computation. We also propose a single-pass sparsification algorithm, which is a hardware-friendly version of a recently proposed sparse training algorithm, that can create additional sparsity aggressively during training. Our experimental results using large networks such as AlexNet and GoogleNet demonstrate that our sparse training architecture can accelerate convolution layer training time by 4.20~8.88× over baseline dense training without accuracy loss, and further increase the training speed by 7.30~11.87× over the baseline with minimal accuracy loss.
An Ontology Model for Interoperability and Multi-organization Data Exchange

Progress in the uptake and use of technologies such as Artificial Intelligence and the Internet of Things seems to be stalled by the colossal fragmentation of information and data standards. This complexity is compounded by issues of inter-organisational differences, hindering effective collaboration. There is a growing demand for cross-organizational integrations in regulated but decentralized environments. This paper introduces an ontology architecture where information is sliced into independent semantic layers, each focusing on a specific aspect of the data. By dissolving traditional monolithic data structures into layered, light semantic components, the necessity to maintain contextual metadata is diminished. The model proposes an ontology structure based on decentralized technologies to ensure an open environment and to avoid the flaws of centralized systems.
AN ADAPTED CONNECTED COMPONENT LABELING FOR CLUSTERING NON-PLANAR OBJECTS FROM AIRBORNE LIDAR POINT CLOUD

Abstract. Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications. A segmentation of Airborne Laser Scanning (ALS) point cloud is very important task that still interest many scientists. In this paper, the Connected Component Analysis (CCA), or Connected Component Labeling is proposed for clustering non-planar objects from Airborne Laser Scanning (ALS) LiDAR point cloud. From raw point cloud, sub-surface segmentation method is applied as preliminary filter to remove planar surfaces. Starting from unassigned points , CCA is applied on 3D data considering only neighboring distance as initial parameter. To evaluate the clustering, an interactive labeling of the resulting components is performed. Then, components are classified using Support Vector Machine, Random Forest and Decision Tree. The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany. The visualization of the results shown the potential of the proposed method to identify dormers, chimneys and ground class.
Image Preprocessing of Obstacle Avoidance for Underground Unmanned Aerial Vehicle Based on Monocular Vision

Given the inevitable existence of some static or dynamic obstacles in some local areas of the underground roadway and the characteristics of the roadway environment where the obstacles are located, this paper proposes a method of obstacle detection for underground UAV based on monocular vision. This method can extract obstacles from the complex tunnel background, and get the two-dimensional information of obstacles in the image after preprocessing and provide an important basis for the subsequent obstacle avoidance.
New Infinite Families of Perfect Quaternion Sequences and Williamson Sequences

We present new constructions for perfect and odd perfect sequences over the quaternion group Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sub> . In particular, we show for the first time that perfect and odd perfect quaternion sequences exist in all lengths 2 for t ≥ 0. In doing so we disprove the quaternionic form of Mow's conjecture that the longest perfect Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sub> -sequence that can be constructed from an orthogonal array construction is of length 64. Furthermore, we use a connection to combinatorial design theory to prove the existence of a new infinite class of Williamson sequences, showing that Williamson sequences of length 2 n exist for all t ≥ 0 when Williamson sequences of odd length n exist. Our constructions explain the abundance of Williamson sequences in lengths that are multiples of a large power of two.
Inside-out Tracking Controller for VR/AR HMD using Image Recognition with Smartphones

To smoothly operate an HMD controller in the 3D space, a variety of operation methods that are linked to the real hand movements have been proposed. Many controller recognition methods are based on outside-in tracking. Therefore, those controllers have the disadvantage that it cannot be operated if it moves into the occluded area from the sensor. In this paper, we propose an inside-out controller for HMD using a smartphone that can estimate its own position and the angle via image recognition. This is a system that transmits the controller’s 6DoF(six degrees of freedom) information to the HMD and performs raycasting based on the 6DoF information. Wherever the controller is located, the user can operate it. This method covers the disadvantages of existing HMD controllers.
Economic Causal-Chain Search Using Text Mining Technology

In this research, we extract causal information from textual data and construct a causality database in the economic field. We develop a method to produce causal chains starting from phrases representing specific events. The proposed method can offer possible ripple effects and factors of particular events or situations. Using our approach to Japanese textual data, we have implemented a prototype system that can display causal chains for user-entered words. A user can interactively edit the causal chains by selecting appropriate causalities and deleting inappropriate causalities. The economic causal-chain search algorithm can be applied to various financial information services.
A jumping mining attack and solution

Mining is the important part of the blockchain used the proof of work (PoW) on its consensus, looking for the matching block through testing a number of hash calculations. In order to attract more hash computing power, the miner who finds the proper block can obtain some rewards. Actually, these hash calculations ensure that the data of the blockchain is not easily tampered. Thus, the incentive mechanism for mining affects the security of the blockchain directly. This paper presents an approach to attack against the difficulty adjustment algorithm (abbreviated as DAA) used in blockchain mining, which has a direct impact on miners’ earnings. In this method, the attack miner jumps between different blockchains to get more benefits than the honest miner who keep mining on only one blockchain. We build a probabilistic model to simulate the time to obtain the next block at different hash computing power called hashrate. Based on this model, we analyze the DAAs of the major cryptocurrencies, including Bitcoin, Bitcoin Cash, Zcash, and Bitcoin Gold. We further verify the effectiveness of this attack called jumping mining through simulation experiments, and also get the characters for the attack in the public block data of Bitcoin Gold. Finally, we give an improved DAA scheme against this attack. Extensive experiments are provided to support the efficiency of our designed scheme.
Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes

Objective: To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. Methods: We introduce an adaptive transfer learning algorithm to classify and segment events from non-stationary multi-channel temporal data. Using a multivariate hidden Markov model (HMM) and Fisher's linear discriminant analysis (FLDA) the algorithm adaptively adjusts to shifts in distribution over time. The proposed algorithm is unsupervised and learns to label events without requiring a priori information about true event states. The procedure is illustrated on experimental data collected from a cohort in a human viral challenge (HVC) study, where certain subjects have disrupted wake and sleep patterns after exposure to an H1N1 influenza pathogen. Results: Simulations establish that the proposed adaptive algorithm significantly outperforms other event classification methods. When applied to early time points in the HVC data, the algorithm extracts sleep/wake features that are predictive of both infection and infection onset time. Conclusion: The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring. Significance: Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications.
Combining <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>T</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> estimation with randomized benchmarking and bounding the diamond distance

The characterization of errors in a quantum system is a fundamental step for two important goals. First, learning about specific sources of error is essential for optimizing experimental design and error correction methods. Second, verifying that the error is below some threshold value is required to meet the criteria of threshold theorems. We consider the case where errors are dominated by the generalized damping channel (encompassing the common intrinsic processes of amplitude damping and dephasing) but may also contain additional unknown error sources. We demonstrate the robustness of standard ${T}_{1}$ and ${T}_{2}$ estimation methods and provide expressions for the expected error in these estimates under the additional error sources. We then derive expressions that allow a comparison of the actual and expected results of fine-grained randomized benchmarking experiments based on the damping parameters. Given the results of this comparison, we provide bounds that allow robust estimation of the thresholds for fault tolerance.
Efficient Deconvolution Architecture for Heterogeneous Systems-on-Chip

Today, convolutional and deconvolutional neural network models are exceptionally popular thanks to the impressive accuracies they have been proven in several computer-vision applications. To speed up the overall tasks of these neural networks, purpose-designed accelerators are highly desirable. Unfortunately, the high computational complexity and the huge memory demand make the design of efficient hardware architectures, as well as their deployment in resource- and power-constrained embedded systems, still quite challenging. This paper presents a novel purpose-designed hardware accelerator to perform 2D deconvolutions. The proposed structure applies a hardware-oriented computational approach that overcomes the issues of traditional deconvolution methods, and it is suitable for being implemented within any virtually system-on-chip based on field-programmable gate array devices. In fact, the novel accelerator is simply scalable to comply with resources available within both high- and low-end devices by adequately scaling the adopted parallelism. As an example, when exploited to accelerate the Deep Convolutional Generative Adversarial Network model, the novel accelerator, running as a standalone unit implemented within the Xilinx Zynq XC7Z020 System-on-Chip (SoC) device, performs up to 72 GOPs. Moreover, it dissipates less than 500mW@200MHz and occupies 5.6%, 4.1%, 17%, and 96%, respectively, of the look-up tables, flip-flops, random access memory, and digital signal processors available on-chip. When accommodated within the same device, the whole embedded system equipped with the novel accelerator performs up to 54 GOPs and dissipates less than 1.8W@150MHz. Thanks to the increased parallelism exploitable, more than 900 GOPs can be executed when the high-end Virtex-7 XC7VX690T device is used as the implementation platform. Moreover, in comparison with state-of-the-art competitors implemented within the Zynq XC7Z045 device, the system proposed here reaches a computational capability up to 20% higher, and saves more than 60% and 80% of power consumption and logic resources requirement, respectively, using 5.7× fewer on-chip memory resources.
Collaborative block design task for assessing pair performance in virtual reality and reality

Collaborative problem solving is more important than ever as the problems we try to solve become increasingly complex. Meanwhile, personal and professional communication has moved from face-to-face to computer-mediated environments, but there is little understanding on how the characteristics of these environments affect the quality of interaction and joint problem solving. To develop this understanding, methods are needed for measuring success of collaboration. For this purpose, we created a collaborative block design task intended to evaluate and quantify pair performance. In this task, participants need to share information to complete visuospatial puzzles. Two versions of the task are described: a physical version and one that can be completed in virtual reality. A preliminary study was conducted with the physical version (N = 18 pairs) and the results were used to develop the task for a second study in virtual reality (N = 31 pairs). Performance measures were developed for the task, and we found that pair performance was normally distributed and positively associated with visuospatial skills, but not with other participant-specific background factors. The task specifications are released for the research community to apply and adapt in the study of computer-mediated social interaction.
RDF object description generator

This paper describes a program for generating RDF descriptions of objects of a specific type based on a given OWL ontology. We needed this kind of capability to compare the use of OWL vs. XML-based descriptions within scenarios in which capabilities of radios are matched against application needs. Most of the existing solutions for RDF dataset generation are not generic, i.e., they are locked-in to specific class models or ontologies. Existing generic solutions do not support sufficiently (or totally ignore) the OWL semantics, or rely on large numbers of real-world data. Our experimental results show that RODG performs reasonably well with generating large numbers of object descriptions, which are consistent with and have a good space coverage (variety) over the input ontology.
Sequent-Type Calculi for Systems of Nonmonotonic Paraconsistent Logics

Paraconsistent logics constitute an important class of formalisms dealing with non-trivial reasoning from inconsistent premisses. In this paper, we introduce uniform axiomatisations for a family of nonmonotonic paraconsistent logics based on minimal inconsistency in terms of sequent-type proof systems. The latter are prominent and widely-used forms of calculi well-suited for analysing proof search. In particular, we provide sequent-type calculi for Priest's three-valued minimally inconsistent logic of paradox, and for four-valued paraconsistent inference relations due to Arieli and Avron. Our calculi follow the sequent method first introduced in the context of nonmonotonic reasoning by Bonatti and Olivetti, whose distinguishing feature is the use of a so-called rejection calculus for axiomatising invalid formulas. In fact, we present a general method to obtain sequent systems for any many-valued logic based on minimal inconsistency, yielding the calculi for the logics of Priest and of Arieli and Avron as special instances.