Reviewerly/exHarmony huggingface dataset:
A model of smart regency framework using Meta-ethnography approach and TOGAF ADM 9.1

Abstract The concept of Smart City has now become the primary choice and trends in improving the quality of public services and is expected to be a solution to problems that occur in urban areas that are increasingly complex by offering an integrated ICT-based city governance concept. However, there are significant differences between the concepts of City and Regency in terms of government structure, area, livelihood variations, population, economy, and socio-culture. So that the idea of Smart City is not always probable and valid if applied to the District. This study aims to create a Smart Regency Framework Model that is different from the Smart City Framework Model, which has been widespread. The method used in this research is Meta-Ethnography to synthesize qualitative findings of regency building factors and TOGAF ADM 9.1 to develop an information system architecture model. The results are expected to produce a new Information Systems Architecture Model and contribute to the development of smart district service models.
QoS-Aware Dynamic Cost Management Scheme for Sensors-as-a-Service

In this article, we study the problem of quality of service (QoS)-aware cost management of sensor-cloud comprising multiple sensor-cloud service providers (SCSPs) and sensor-owners. The rapid adaptation of the wireless sensor network (WSN) and Internet-of-Things (IoT) technology led to the conceptualization of the sensor-cloud infrastructure which primarily aims to reduce the complexities associated with operating WSN-based applications by rendering Sensors-as-a-Service (Se-aaS). However, the oligopolistic market scenario of sensor-cloud involving multiple SCSPs and sensor-owners significantly impacts its profitability and QoS. Thus, there is a need to explore the dynamics of this market competition elaborately in order to maintain the usability of sensor-cloud. The existing works fail to address the aforementioned issues in sensor-cloud. Hence, in this work, we analyze the interactions among the sensor-owners and the SCSPs using a game-theoretic approach. We propose a QoS-aware dynamic cost management scheme, named QUEST, to determine the optimal strategies of the various actors in sensor-cloud market. Through simulations, we observe that, using QUEST, the price paid by the end-users decreases by 10.31-20.43 percent and the revenue of sensor-owners improves by 66.83-89.94 percent. Moreover, QUEST ensures the service satisfaction of the end-users while optimally distributing the services among the SCSPs and the sensor-owners.
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation, and Experimental Results

This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently train the TurboNet, a loss function is carefully designed to prevent tricky gradient vanishing issue. To further reduce the computational complexity and training cost of the TurboNet, we can prune it into TurboNet+. Compared with the existing black-box DL approaches, the TurboNet+ has considerable advantage in computational complexity and is conducive to significantly reducing the decoding overhead. Furthermore, we also present a simple training strategy to address the overfitting issue, which enable efficient training of the proposed TurboNet+. Simulation results demonstrate TurboNet+’s superiority in error-correction ability, signal-to-noise ratio generalization, and computational overhead. In addition, an experimental system is established for an over-the-air (OTA) test with the help of a 5G rapid prototyping system and demonstrates TurboNet’s strong learning ability and great robustness to various scenarios.
Exploring research trends and network characteristics in construction automation and robotics based on keyword network analysis

Over the past decades, automation and robotics have been widely adopted in the construction industry as a promising solution for troubleshooting issues. This field is highly interdisciplinary and rapidly changing, and new concepts and ideas are being actively introduced in association with the existing ones. To reveal new insights, the evolution of knowledge in this field needs to be characterized by identifying important knowledge elements and research trends from published data. This study investigated research trends and network characteristics in the field of construction automation and robotics by using a keyword network analysis based on published articles. We analyzed the structure of the keyword network and identified major research topics and their relationships. Notable findings of the research characteristics are as follows. 1) Popular research themes with a set of closely connected keywords continue to be selected and contribute to the evolution of the network. 2) As an interdisciplinary research area, popular keywords that actively interact with other keywords also bridge different clusters of related research concepts. This study could provide useful insights and a better understanding of the knowledge structure and research trends as well as information on future directions in the construction automation and robotics field.
Stereo Vision-Based 3D Positioning and Tracking

The evolution of technologies for the capture of human movement has been motivated by a number of potential applications across a wide variety of fields.However, capturing human motion in 3D is difficult in an outdoor environment when it is performed without controlled surroundings.In this paper, a stereo camera rig with an ultra-wide baseline distance and conventional cameras with fish-eye lenses is proposed.Its cameras provide a wide field of view (FOV) which increases the coverage area and also enables the baseline distance to be increased to cover the common area required for both cameras' views to perform as a stereo camera.We propose a passive marker-based approach to track the motion of the object.In this method, an adaptive thresholding method is applied to extract each small pink polyester marker from the video frames.As the cameras have fish-eye lenses, it is difficult to estimate the depth information using a pinhole camera model.We use a unique method to restore the 3D positions by developing a relationship between the pixel dimensions and distances in an image and real world coordinates.In this paper, occlusion detection is considered because, in the marker-based capturing of articulated human kinematics, the occlusion of a marker is one of the major challenges.The detection algorithm differentiates among types of occlusions and predicts any missing marker position where necessary.As this design is intended to be mounted on a moving carrier, such as a drone or car, a method for compensating the camera's ego-motion is proposed.The proposed 3D positioning and tracking system is tested in different situations to validate its applicability as a stereo camera rig as well as its performance for motion capture.The performance of the proposed system is compared with that of a standard motion capture system called Vicon and is shown to have the same order of accuracy while incurring less cost.
Simultaneously learning feature-wise weights and local structures for multi-view subspace clustering

Multi-view clustering integrates multiple feature sets, which usually have a complementary relationship and can reveal distinct insights of data from different angles, to improve clustering performance. It remains challenging to productively utilize complementary information across multiple views since there is always noise in real data, and their features are highly redundant. Moreover, most existing multi-view clustering approaches only aimed at exploring the consistency of all views, but overlooked the local structure of each view. However, it is necessary to take the local structure of each view into consideration, because individual views generally present different geometric structures while admitting the same cluster structure. To ease the above issues, in this paper, a novel multi-view subspace clustering method is established by concurrently assigning weights for different features and capturing local information of data in view-specific self-representation feature spaces. In particular, a common clustering assignment regularization is adopted to explore the consistency among multiple views. An alternating iteration algorithm based on the augmented Lagrangian multiplier is also developed for optimizing the associated objective. Experiments conducted on diverse multi-view datasets manifest that the proposed method achieves state-of-the-art performance. We provide the Matlab code on https://github.com/Ekin102003/JFLMSC.
Photovoltaic system monitoring for high latitude locations

Reliable monitoring of PV systems is essential to establish efficient maintenance routines that minimize the levelized cost of electricity. The existing solutions for affordable monitoring of commercial PV systems are however inadequate for climates where snow and highly varying weather result in unstable performance metrics. The aim of this work is to decrease this instability to enable more reliable monitoring solutions for PV systems installed in these climates. Different performance metrics have been tested on Norwegian installations with a total installed capacity of 3.3 MW: (i) comparison of specific yield, (ii) temperature corrected performance ratio, and (iii) power performance index based on both physical modelling and machine learning. The most influential effects leading to instability are identified as snow, low light, curtailment, and systematic irradiance differences over the system. The standard deviation of all the performance metrics is reduced when filters targeting these four effects are applied. Compared to general low irradiance or clear sky filtering, a greater reduction in the variation of the metrics is achieved, and more data remains in the useful dataset. The most suitable performance metrics are comparison of specific yield and performance index based on machine learning modelling. The analysis highlights two paths to accomplish increased reliability of PV monitoring systems without increased hardware costs. First, better reliability can be achieved by selecting a suitable performance metric. Second, the variability of the performance metric can be reduced by utilizing filters that specifically target the origin of the variability instead of using standard literature thresholds.
On decoding additive generalized twisted Gabidulin codes

Abstract In this paper, we consider an interpolation-based decoding algorithm for a large family of maximum rank distance codes, known as the additive generalized twisted Gabidulin codes, over the finite field $\mathbb {F}_{q^{n}}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mi>F</mml:mi> </mml:mrow> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>q</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> </mml:msup> </mml:mrow> </mml:msub> </mml:math> for any prime power q . This paper extends the work of the conference paper Li and Kadir (2019) presented at the International Workshop on Coding and Cryptography 2019, which decoded these codes over finite fields in characteristic two.
High-Throughput/Low-Energy MTJ-Based True Random Number Generator Using a Multi-Voltage/Current Converter

This article introduces high-throughput/low-energy true random number generators (TRNGs) based on CMOS and three-terminal magnetic tunnel junction (MTJ) devices. MTJs are fast and probabilistic switching devices, which can be used as random number sources for TRNGs. However, as the switching probability is quite sensitive to the write current given to MTJs, precise closed-loop control is necessary. Thus, a high-complexity current control circuit is required, such as high precision digital-to-analog converters (DACs), occupying large area and causing large energy dissipation. In order to address the issue, we propose a multi-voltage/current (V/I) converter capable of multilevel coarse current switching and fine adjusting within each level. The fine adjusting can be done by DACs with fewer bits, resulting in much smaller size and energy dissipation than a conventional single-V/I converter. In addition, a multiple-writing scheme for three-terminal MTJs is proposed for increasing the throughput while maintaining the write power. The proposed TRNGs are designed using TSMC 65-nm CMOS and a threeterminal MTJ model that achieves a throughput of 333 Mb/s, an energy dissipation of 0.66 pJ/bit and an area of 2040 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . This result exhibits a 5× throughput, a 93% energy reduction and an 86% area reduction in comparison with a conventional CMOS/MTJ-based TRNG.
Understanding Selective Delay as a Method for Efficient Secure Speculative Execution

Since the introduction of Meltdown and Spectre, the research community has been tirelessly working on speculative side-channel attacks and on how to shield computer systems from them. To ensure that a system is protected not only from all the currently known attacks but also from future, yet to be discovered, attacks, the solutions developed need to be general in nature, covering a wide array of system components, while at the same time keeping the performance, energy, area, and implementation complexity costs at a minimum. One such solution is our own delay-on-miss, which efficiently protects the memory hierarchy by i) selectively delaying speculative load instructions and ii) utilizing value prediction as an invisible form of speculation. In this article we dive deeper into delay-on-miss, offering insights into why and how it affects the performance of the system. We also reevaluate value prediction as an invisible form of speculation. Specifically, we focus on the implications that delaying memory loads has in the memory level parallelism of the system and how this affects the value predictor and the overall performance of the system. We present new, updated results but more importantly, we also offer deeper insight into why delay-on-miss works so well and what this means for the future of secure speculative execution.
Feasibility of Identity Authentication for IoT Based on Blockchain

With the rapid development of the Internet of Things (IoT), more and more devices are connected to the Internet. As IoT devices are resource-constrained in terms of processing, storage and network capacity, it becomes a challenging task to ensure the access of IoT devices. Identity authentication is a key technology to prevent illegal users from access resources and permissions. Based on the principle of blockchain technology, combining blockchain technology with IoT, this paper proposes a blockchain system framework for IoT identity authentication, which implements the authentication between devices and cloud servers, IoT base stations as well as devices, and then analyzes its feasibility.
Deep Reinforcement Learning based Task Scheduling in Mobile Blockchain for IoT Applications

Nowadays, the Internet of Things (IoT) has developed rapidly. To deal with the security problems in some of the IoT applications, blockchain has aroused lots of attention in both academia and industry. In this paper, we consider the mobile blockchain supporting IoT applications, and the mobile edge computing (MEC) is deployed at the Small-cell Base Station (SBS) as a supplement to enhance the computation ability of IoT devices. To encourage the participation of the SBS in the mobile blockchain networks, the long-term revenue of the SBS is considered. The task scheduling problem maximizing the long-term mining reward and minimizing the resource cost of the SBS is formulated as a Markov Decision Process (MDP). To achieve an efficient intelligent strategy, the deep reinforcement learning (DRL) based solution named policy gradient based computing tasks scheduling (PG-CTS) algorithm is proposed. The policy mapping from the system state to the task scheduling decision is represented by a deep neural network. The episodic simulations are built and the REINFORCE algorithm with baseline is used to train the policy network. According to the training results, the PG-CTS method is about 10% better than the second-best method greedy. The generalization ability of PG-CTS is proved theoretically, and the testing results also show that the PG-CTS method has better performance over the other three strategies, greedy, first-in-first-out (FIFO) and random in different environments.
Biometric-Based Blockchain EHR System (BBEHR)

In healthcare sector, the move towards Electronic Health Records (EHR) systems has been accelerating in parallel with the increased adoption of IoT and smart devices. This is driven by the anticipated advantages for patients and healthcare providers. The integration of EHR and IoT makes it highly heterogenous in terms of devices, network standard, platforms, types data, connectivity, etc., and introduces security, patient and data privacy, and trust challenges. To address such challenges, this paper proposes an architecture that combines biometric-based blockchain technology with the EHR system. This integration ensures the integrity of data to control the access to the patient's Electronic Healthcare Records (EHRs) that are synchronized and exchanged, using Blockchain Technology, between distributed healthcare providers. More specifically, this paper describes a mechanism that enables the recovery of patient's access control on their EHRs securely without compromising their privacy and identity. A biometric-based blockchain EHR system (BBEHR) is proposed to uniquely identify patients, enable them to control access to their EHRs, and ensure recoverable access to their EHRs. The system takes into account the security and privacy requirements of Health Insurance Portability and Accountability Act (HIPAA) compliance, and it overcomes the challenges of using secret keys to control access to EHRs, in the cases of lost secret keys and the need for emergency access to EHRs without the presence of secret keys.
Learning sequential and general interests via a joint neural model for session-based recommendation

Session-based recommendation is to predict the next action of a user based on his current behavior session. Recent studies have focused on designing recurrent neural networks to capture representation of users sequential behaviors from a large number of anonymous sessions. Although these neural models have greatly improved recommendation performance compared with traditional approaches, they have overemphasized the sequential information of sessions, that is, the order of items in each browsing session. We argue that besides the order of items, the set of items in each session can also reflect the general interests of current session and can also be exploited to augment recommendations. To this end, this paper proposes a joint neural model, called SGINM, for jointly learning sequential and general interests of each session for session-based recommendation. In SGINM, we design an attentive recurrent neural network which not only captures self-adaptive weighed representation of each subsequence in a session but also learns a global representation for the whole session. Furthermore, the SGINM adopts a multi-layer neural network with residual connections to learn the general interests of each session. The SGINM uses a softmax layer to jointly decode the two types of interests and output a ranking vector of recommended items for each new session. The proposed model has been extensively experimented with the benchmark datasets from the RecSys Challenge 2015 and CIKM Cup 2016. Compared with the state-of-the-art models, the proposed SGINM achieves better recommendation performance in terms of higher predicative accuracy and mean reciprocal rank in almost all experiment cases.
Nonpairwise-Trained Cycle Convolutional Neural Network for Single Remote Sensing Image Super-Resolution

Single image super-resolution (SISR) is to recover the high spatial resolution image from a single low spatial resolution one, which is a useful procedure for many remote sensing applications. Most previous convolutional neural network (CNN)-based methods adopt supervised learning. However, paired high-resolution and low-resolution remote sensing images are actually hard to acquire for supervised learning SR methods. To handle this problem, we propose a novel cycle convolutional neural network (Cycle-CNN). Our network consists of two generative CNNs for down-sampling and SR separately and can be trained with unpaired data. We perform comprehensive experiments on panchromatic and multispectral images of the GaoFen-2 satellite and the UC Merced land use data set. Experimental results indicate that our method achieves state-of-the-art CNN-based SR results and is robust against noise and blur in remote sensing images. Comprehensively considering super-resolved image quality and time costs, our proposed method outperforms the compared learning-based SISR approaches.
A Scale-Adaptive Matching Algorithm for Underwater Acoustic and Optical Images

Underwater acoustic and optical data fusion has been developed in recent decades. Matching of underwater acoustic and optical images is a fundamental and critical problem in underwater exploration because it usually acts as the key step in many applications, such as target detection, ocean observation, and joint positioning. In this study, a method of matching the same underwater object in acoustic and optical images was designed, consisting of two steps. First, an enhancement step is used to enhance the images and ensure the accuracy of the matching results based on iterative processing and estimate similarity. The acoustic and optical images are first pre-processed with the aim of eliminating the influence of contrast degradation, contour blur, and image noise. A method for image enhancement was designed based on iterative processing. In addition, a new similarity estimation method for acoustic and optical images is also proposed to provide the enhancement effect. Second, a matching step is used to accurately find the corresponding object in the acoustic images that appears in the underwater optical images. In the matching process, a correlation filter is applied to determine the correlation for matching between images. Due to the differences of angle and imaging principle between underwater optical and acoustic images, there may be major differences of size between two images of the same object. In order to eliminate the effect of these differences, we introduce the Gaussian scale-space, which is fused with multi-scale detection to determine the matching results. Therefore, the algorithm is insensitive to scale differences. Extensive experiments demonstrate the effectiveness and accuracy of our proposed method in matching acoustic and optical images.
Interactive Artificial Intelligence: Designing for the "Two Black Boxes" Problem

We present a symmetric, collaborative human-artificial intelligence (AI) model using semantic interaction as a design philosophy. We discuss challenges associated with each phase of communication between the pair of cooperatively learning entities and the benefits that emerge from combining the expertise of humans and AI.