Reviewerly/exHarmony huggingface dataset:
Enchanted by Your Surrounding? Measuring the Effects of Immersion and Design of Virtual Environments on Decision-Making

Impaired decision-making leads to the inability to distinguish between advantageous and disadvantageous choices. The impairment of a person’s decision-making is a common goal of gambling games. Given the recent trend of gambling using immersive Virtual Reality it is crucial to investigate the effects of both immersion and the virtual environment (VE) on decision-making. In a novel user study, we measured decision-making using three virtual versions of the Iowa Gambling Task (IGT). The versions differed with regard to the degree of immersion and design of the virtual environment. While emotions affect decision-making, we further measured the positive and negative affect of participants. A higher visual angle on a stimulus leads to an increased emotional response. Thus, we kept the visual angle on the Iowa Gambling Task the same between our conditions. Our results revealed no significant impact of immersion or the VE on the IGT. We further found no significant difference between the conditions with regard to positive and negative affect. This suggests that neither the medium used nor the design of the VE causes an impairment of decision-making. However, in combination with a recent study, we provide first evidence that a higher visual angle on the IGT leads to an effect of impairment.
An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web

Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system’s feasibility, where the participants show a positive interest in its functionalities.
Quantum Metrological Power of Continuous-Variable Quantum Networks

We investigate the quantum metrological power of typical continuous-variable (CV) quantum networks. Particularly, we show that most CV quantum networks provide an entanglement to quantum states in distant nodes that enables one to achieve the Heisenberg scaling in the number of modes for distributed quantum displacement sensing, which cannot be attained using an unentangled probe state. Notably, our scheme only requires local operations and measurements after generating an entangled probe using the quantum network. In addition, we find a tolerable photon-loss rate that maintains the quantum enhancement. Finally, we numerically demonstrate that even when CV quantum networks are composed of local beam splitters, the quantum enhancement can be attained when the depth is sufficiently large.
A Low Cost and Real Time Vehicle Detection Using Enhanced YOLOv4-Tiny

Just as the accuracy of the detectors is important, so the speed of their performance is no less important, especially in applications that require real-time response processing. In addition to real-time processing, vehicle traffic systems require detection models to be lightweight and deployable to peripheral devices, which typically have limited processing and memory capacities. In this paper, we propose an enhancement to the YOLOv4-tiny detector in order to reduce memory usage and increase image processing speed. Our key improvement is on the detector backbone so that less floating-point operations are generated while still maintaining vehicle feature information, thus that detection accuracy is not compromised. On a vehicles dataset, we trained and tested the proposed model, then compared the results to the most common models in the same category. The experimental results revealed that our proposed model is 7.9% more FPS and 0.5% less memory consumption than the base model. Our proposed model outperforms the basic model in terms of speed and memory consumption, while maintaining a level of accuracy that is nearly equal to the basic model.
<scp>LEOBAT</scp>: Lightweight encryption and <scp>OTP</scp> based authentication technique for securing <scp>IoT</scp> networks

Abstract Authentication is one of the foremost pillars in the Internet of Things (IoT) security that authenticates the identity of a device or a person with the help of a unique identification number. If authentication is compromised then an intruder can gain access and launch a variety of attacks on the network. The world of IoT devices is benefiting us in many ways, but the vulnerability of becoming a victim of cybercrime is also increasing at a rapid pace. This research paper proposes an authentication‐based solution for designing a secure communication network to ensure safe access to data and stop attackers from any unauthorized access to various IoT applications using cryptography and cloud computing. The proposed work is then compared with other popular cryptosystems such as Secure Internet of Things (SIT), Data Encryption Standard (DES) and Blowfish. LEOBAT is simulated using MATLAB and proves to be a fast and efficient authentication technique.
Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels

Moving camera-based object tracking method for the intelligent transportation system (ITS) has drawn increasing attention. The unpredictability of driving environments and noise from the camera calibration, however, make conventional ground plane estimation unreliable and adversely affecting the tracking result. In this paper, we propose an object tracking system using an adaptive ground plane estimation algorithm, facilitated with constrained multiple kernel (CMK) tracking and Kalman filtering, to continuously update the location of moving objects. The proposed algorithm takes advantage of the structure from motion (SfM) to estimate the pose of moving camera, and then the estimated camera’s yaw angle is used as a feedback to improve the accuracy of the ground plane estimation. To further robustly and efficiently tracking objects under occlusion, the constrained multiple kernel tracking technique is adopted in the proposed system to track moving objects in 3D space (depth). The proposed system is evaluated on several challenging datasets, and the experimental results show the favorable performance, which not only can efficiently track on-road objects in a dashcam equipped on a free-moving vehicle but also can well handle occlusion in the tracking.
Reservoir Computing Based on Spintronics Technology

Recent developments in reservoir computing based on spintronics technology are described here. The rapid growth of brain-inspired computing has motivated researchers working in a broad range of scientific field to apply their own technologies, such as photonics, soft robotics, and quantum computing, to brain-inspired computing. A relatively new technology in condensed matter physics called spintronics is also a candidate for application to brain-inspired computing because the small size of devices (nanometer order), their low energy consumption, their rich magnetization dynamics, and so on are advantageous for realization of highly integrated network systems. In fact, several interesting functions, such as a spoken-digit recognition and an associative memory operation, achieved using spintronics technology have recently been demonstrated. Here, we describe our recent advances in the development of recurrent neural networks based on spintronics auto-oscillators, called spin-torque oscillators, such as experimental estimation of the short-term memory capacity of a vortex-type spin-torque oscillator and numerical simulation of reservoir computing using several macromagnetic oscillators. The results demonstrate the potential high performance of spintronics technology and its applicability to brain-inspired computing.
EHEFT-R: multi-objective task scheduling scheme in cloud computing

Abstract In cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) algorithm is used to ensure the global performance of the solution space. Simulation experiments verify the effectiveness and superiority of EHEFT-R.
Residual-Guided Multiscale Fusion Network for Bit-Depth Enhancement

Bit-depth enhancement (BDE) is a challenging task due to stubborn false contour artifacts and disappeared detailed information. Given the mixture of structural distortions and real edges in low bit-depth (LBD) images, both large and small receptive fields (RFs) are critical for BDE tasks. However, even powerful state-of-the-art CNN-based methods can hardly capture sufficient LBD features under multiple RFs. This paper proposes a residual-guided multiscale fusion network (RMFNet) to explore multiscale features in a residual manner. We find that the shuffling operation provides desired multiscale inputs for effectively distinguishing false contours from real edges without any loss of information. Therefore, we shuffle LBD images to multiple scales and then fully extract residual features under different RFs with corresponding subnets. To facilitate interscale guidance from the global context to the local context, we progressively transfer the encoded residual features between adjacent subnets from top to bottom. We further propose a dual-branch depthwise group fusion (DDGF) module to fully capture inter- and inner correlations of multiscale features with fewer parameters. Finally, extensive experiments show that our algorithm achieves excellent performance improvement both quantitatively and qualitatively, verifying its effectiveness.
Narrow Band Active Contour Attention Model for Medical Segmentation

Medical image segmentation is one of the most challenging tasks in medical image analysis and widely developed for many clinical applications. While deep learning-based approaches have achieved impressive performance in semantic segmentation, they are limited to pixel-wise settings with imbalanced-class data problems and weak boundary object segmentation in medical images. In this paper, we tackle those limitations by developing a new two-branch deep network architecture which takes both higher level features and lower level features into account. The first branch extracts higher level feature as region information by a common encoder-decoder network structure such as Unet and FCN, whereas the second branch focuses on lower level features as support information around the boundary and processes in parallel to the first branch. Our key contribution is the second branch named Narrow Band Active Contour (NB-AC) attention model which treats the object contour as a hyperplane and all data inside a narrow band as support information that influences the position and orientation of the hyperplane. Our proposed NB-AC attention model incorporates the contour length with the region energy involving a fixed-width band around the curve or surface. The proposed network loss contains two fitting terms: (i) a high level feature (i.e., region) fitting term from the first branch; (ii) a lower level feature (i.e., contour) fitting term from the second branch including the (ii1) length of the object contour and (ii2) regional energy functional formed by the homogeneity criterion of both the inner band and outer band neighboring the evolving curve or surface. The proposed NB-AC loss can be incorporated into both 2D and 3D deep network architectures. The proposed network has been evaluated on different challenging medical image datasets, including DRIVE, iSeg17, MRBrainS18 and Brats18. The experimental results have shown that the proposed NB-AC loss outperforms other mainstream loss functions: Cross Entropy, Dice, Focal on two common segmentation frameworks Unet and FCN. Our 3D network which is built upon the proposed NB-AC loss and 3DUnet framework achieved state-of-the-art results on multiple volumetric datasets.
Multi-Scale Low-Discriminative Feature Reactivation for Weakly Supervised Object Localization

For weakly supervised object localization (WSOL), how to avoid the network focusing only on some small discriminative parts is a main challenge needed to solve. The widely-used Class Activation Mapping (CAM) based paradigm usually employs Adversarial Learning (AL) strategy to search more object parts by constantly hiding discovered object features, but the adversarial process is difficult to control. In this paper, we propose a novel CAM-based framework with Multi-scale Low-Discriminative Feature Reactivation (mLDFR) for WSOL. The mLDFR framework reactivates the low-discriminative object parts via bottom-up continuous feature maps recalibration and multi-scale object category mapping. Compared with the AL-based methods, our method fully improves the localization power of the network without damaging the classification power and can perform multi-instance localization, which are hard to achieve under the AL-based framework. Moreover, the mLDFR framework is flexible, and can be built on the top of various classical CNN backbones. Experimental results demonstrate the superiority of our method. With VGG16 as backbone, we achieve 46.96% Cls-Loc top1 err and 66.12% CorLoc on ILSVRC2014, 38.07% Cls-Loc top1 err and 75.04% CorLoc on CUB200-2011, surpassing the state-of-the-arts by a large margin.
Entitlement-Based Access Control for Smart Cities Using Blockchain

Smart cities use the Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data to improve infrastructure, public utilities, and services. However, the true potential of smart cities cannot be leveraged without addressing many security concerns. In particular, there is a significant challenge for provisioning a reliable access control solution to share IoT data among various users across organizations. We present a novel entitlement-based blockchain-enabled access control architecture that can be used for smart cities (and for any ap-plication domains that require large-scale IoT deployments). Our proposed entitlement-based access control model is flexible as it facilitates a resource owner to safely delegate access rights to any entities beyond the trust boundary of an organization. The detailed design and implementation on Ethereum blockchain along with a qualitative evaluation of the security and access control aspects of the proposed scheme are presented in the paper. The experimental results from private Ethereum test networks demonstrate that our proposal can be easily implemented with low latency. This validates that our proposal is applicable to use in the real world IoT environments.
Mapping hydrocarbon microseepage prospect areas by integrated studies of ASTER processing, geochemistry and geophysical surveys in Assam-Arakan Fold Belt, NE India

Long-term microseepage of hydrocarbons creates a reducing environment in the near-surface soil and sediment cover inducing an array of diagenetic alterations such as bleaching of red beds and increasing abundance of ferrous, clay and carbonate minerals. Spectral enhancements of satellite images have effectively been used in microseepage-induced mineral mapping in arid regions. However, the present study aims to delineate microseepage-induced mineral alteration areas in Assam-Arakan Fold Belt (AAFB) in the north-eastern part of India, dominated by humid climate and heavy precipitation. The study uses an integrated approach consisting of advanced spaceborne thermal emission and reflection radiometer (ASTER) image processing, geochemistry, gravity and magnetic data. ASTER image processing involves specialized band ratios (BRs), false colour composite (FCC) and spectral angle mapping (SAM). Integration of remote sensing, geochemistry, gravity and magnetic anomaly data successfully delineates microseepage prospect areas in the basin. The study would provide important advantages in hydrocarbon exploration strategy in similar basins around the world.
Washington Consensus Reforms and Lessons for Economic Performance in Sub-Saharan Africa

Over three decades after market-oriented structural reforms termed “Washington Consensus” policies were first implemented, we revisit the evidence on policy adoption and the effects of these policies on socio-economic performance in sub-Saharan African countries. We focus on three key ubiquitous reform policies around privatization, fiscal discipline, and trade openness and document significant improvements in economic performance for reformers over the past two decades. Following initial declines in per capita economic growth over the 1980s and 1990s, reform adopters experienced notable increases in per capita real GDP growth in the post-2000 period. We complement aggregate analysis with four country case studies that highlight important lessons for effective reform. Notably, the ability to implement pro-poor policies alongside market-oriented reforms played a central role in successful policy performance.
XCS with Weight-based Matching in VAE Latent Space and Additional Learning of High-Dimensional Data

In this paper, we propose MVN-ELSDeCS, which is a combination of VAE, a dimensionality reduction network, and MVN-XCSR, an XCSR extended to a distributional representation. In addition, additional learning of high-dimensional data with XCS is performed to reduce the information loss in learning caused by dimensionality reduction. We applied the proposed method to the benchmark problem of 10-class classification of handwritten digit images, and the experimental results have the following implications: 1) MVN-XCSR, which is a component of MVN-ELSDeCS, not only shows higher classification performance from the early stage of training in the dimensionally compressed latent space, but also 2) the reconstructed rules generated by MVN-ELSDeCS shows higher classification performance for the original high-dimensional data. Furthermore, 3) by applying additional learning with XCS to the reconstructed rules, the classification accuracy of rules for the 10-class classification task was significantly improved.
B2SMatcher: fine-Grained version identification of open-Source software in binary files

Abstract Codes of Open Source Software (OSS) are widely reused during software development nowadays. However, reusing some specific versions of OSS introduces 1-day vulnerabilities of which details are publicly available, which may be exploited and lead to serious security issues. Existing state-of-the-art OSS reuse detection work can not identify the specific versions of reused OSS well. The features they selected are not distinguishable enough for version detection and the matching scores are only based on similarity.This paper presents B2SMatcher, a fine-grained version identification tool for OSS in commercial off-the-shelf (COTS) software. We first discuss five kinds of version-sensitive code features that are trackable in both binary and source code. We categorize these features into program-level features and function-level features and propose a two-stage version identification approach based on the two levels of code features. B2SMatcher also identifies different types of OSS version reuse based on matching scores and matched feature instances. In order to extract source code features as accurately as possible, B2SMatcher innovatively uses machine learning methods to obtain the source files involved in the compilation and uses function abstraction and normalization methods to eliminate the comparison costs on redundant functions across versions. We have evaluated B2SMatcher using 6351 candidate OSS versions and 585 binaries. The result shows that B2SMatcher achieves a high precision up to 89.2% and outperforms state-of-the-art tools. Finally, we show how B2SMatcher can be used to evaluate real-world software and find some security risks in practice.
Study on the interactive relationship between urban residents’ expenditure and energy consumption of production sectors

Urban residential expenditure describes the end demand of urban system, we construct a framework that incorporates the urban residential expenditure and sectoral energy consumption to reveal the interactive mechanism, and clarify how these indicators are influencing each other. A fuzzy cognition map that consists of 8 consumer expenditures, 19 production sectors and 1 household sector's direct energy consumption is built. Genetic algorithm is introduced to solve the weight of the fuzzy cognition map to explore the interactive relationship between urban residents' expenditure and energy consumption of production sectors. Taking the data of Beijing from 2006 to 2017 as an example, results suggest that: residents' food expenditure has a negative impact on energy consumption in various industries, especially in the agricultural sector; residential expenditure has the greatest positive impact on energy consumption in manufacturing and real estate industries; the financial sector and public manage and social organization sector, and the residents' food expenditure and traffic and telecommunications expenditure have the greatest impact on Beijing's energy consumption. It is revealed that policies regarding to controlling or changing residents' spending behavior, including food, traffic and telecommunication, will effectively contribute to reducing Beijing's sectoral energy consumption. This paper provides quantitative evidence for urban energy conservation focused policies.
Relating structure and power: Comonadic semantics for computational resources

Abstract Combinatorial games are widely used in finite model theory, constraint satisfaction, modal logic and concurrency theory to characterize logical equivalences between structures. In particular, Ehrenfeucht–Fraïssé games, pebble games and bisimulation games play a central role. We show how each of these types of games can be described in terms of an indexed family of comonads on the category of relational structures and homomorphisms. The index $k$ is a resource parameter that bounds the degree of access to the underlying structure. The coKleisli categories for these comonads can be used to give syntax-free characterizations of a wide range of important logical equivalences. Moreover, the coalgebras for these indexed comonads can be used to characterize key combinatorial parameters: tree depth for the Ehrenfeucht–Fraïssé comonad, tree width for the pebbling comonad and synchronization tree depth for the modal unfolding comonad. These results pave the way for systematic connections between two major branches of the field of logic in computer science, which hitherto have been almost disjoint: categorical semantics and finite and algorithmic model theory.