Reviewerly/exHarmony huggingface dataset:
The Role of Blockchain Technologies in Construction Engineering Project Management

Due to the increasingly complex nature of large-scale structural engineering projects, there is an increasing focus on the adoption and integration of technology. However, the construction sector tends to be further behind than most when it comes to the adoption of process-based innovative technologies. On the other hand, the decentralised, peer-to-peer principles of blockchain technology has recently attracted interest from a range of industries around the globe who are attempting to improve their business practices. This paper provides insights into the performance capabilities of blockchain technology in the construction industry and the feasibility of its potential adoption through use cases. It explores smart contract applications and the feasibility of integrating the technology with building information modelling and information management. Furthermore, the paper argues that blockchain technology has the potential to significantly increase the efficiency of processes within the construction industry and eliminate issues surrounding trust, transparency and verification currently experienced in the sector.
Vehicle Detection in Remote Sensing Images Leveraging on Simultaneous Super-Resolution

Owing to the relatively small size of vehicles in remote sensing images, lacking sufficient detailed appearance to distinguish vehicles from similar objects, the detection performance is still far from satisfactory compared with the detection results on everyday images. Inspired by the positive effects of super-resolution convolutional neural network (SRCNN) for object detection and the stunning success of deep CNN techniques, we apply generative adversarial network frameworks to realize simultaneous SRCNN and vehicle detection in an end-to-end manner, and the detection loss is backpropagated into the SRCNN during training to facilitate detection. In particular, our work is unsupervised and bypasses the requirement of low-/high-resolution image pairs during the training stage, achieving increased generality and applicability. Extensive experiments on representative data sets demonstrate that our method outperforms the state-of-the-art detectors. (The source code will be made available after the review process).
The applications and effects of learning English through augmented reality: a case study of <i>Pokémon Go</i>

The rise of augmented reality hits the world of Information, Communication and Technology which brings about the booming phenomenon of mobile games. One of hot mobile game with the successful application of augmented reality is Pokémon Go, issued in August 2016. The uses of augmented reality not only give the gamers a new kind experience to learn new technology, but also provide a new way for teachers and learners to benefit the origins of English and its meanings of each pocket monster. Each pocket monster stands for a new word that is created by its founder. In each new word, the uses of prefix, root and suffix have been established and named, as the single-word utterances of the pokémon names are the sentences. Hence, this article aims at (1) the overview of augmented reality and educations, (2) the applications of augmented reality into the mobile games, (3) the uses of prefix, root and suffix in learning English and pokémon languages, and (4) the experiments. A total number of 61 students were divided into the experimental group and the control group. The questionnaires, descriptive statistics, a paired-sample t-test, one sample t-test, and ANCOVA were employed into the quantitative data. The research questions were to evaluate (a) students’ learning attitudes, (b) learning satisfactions and (c) learning achievements in order to show the differences of pre-test and post-test between the experimental group and the control group. The experimental results indicate that the experimental group had significant performances than the control group in promoting their learning attitudes, learning satisfactions and learning achievements.
PV Power Prediction Based on LSTM With Adaptive Hyperparameter Adjustment

The randomness, volatility, and intermittence of solar power generation make it difficult to achieve the desired accuracy of PV output-power prediction.Therefore, the time learning weight (TLW) proposed in this paper is used to improve the time correlation of the LSTM network.The Fusion Activation Function (FAF) is used to resolve gradient disappearance.Learning Factor Adaptation (LFA) and Momentum Resistance Weight Estimation (MRWE) are used to accelerate weight convergence and improve global search capabilities.Finally, this paper synthesizes the improvement and proposes the AHPA-LSTM model to stabilize the convergence domain.Using actual data verification, the δ MAPE indicator of the improved model is only 2.85% on a sunny day, 5.92% on a cloudy day, 7.71% on a rainy day, and only 5.8% on average.Therefore, the AHPA-LSTM model under full climate and climatic conditions has a good predictive effect which is generally applicable to the prediction of ultra-short-term PV power generation.
Assessment of eco-geo-environment quality using multivariate data: A case study in a coal mining area of Western China

Coal resource extensive development and utilization seriously threatens the region fragile ecological environment in Western China. Using the Yushenfu coal mining area as a study site, the ecological and geological environment (eco-geo-environment) factors affected by coal mining were analyzed. Spatiotemporal data extracted from remote sensing images was combined with survey data to establish an evaluation and prediction model. Integrated eco-geo-environmental quality was established by calculating the combined weight of each factor using the fuzzy delphi analytic hierarchy process method to develop a comprehensive multi-factor eco-geo-environment quality evaluation map. Results shows that the eco-geo-environment quality was divided in five grades of worse, bad, medium, good and better, the overall condition of the study region is moderate and it is apparent that regions with less intensive mining activities (Yushen sub coal mining area) are in better condition as compared to those regions where intensive mining is well established (Shenfu sub coal mining area). Comparisons with the classified eco-geo-environment categories revealed that different eco-geo-environment quality grades were affected different eco-geo-environment categories after a period of coal resources exploitation, but eco-geo-environment quality generally has been declining since the development and utilization of coal resources. The model provides a more scientific and accurate method to evaluate regional eco-geo-environmental quality, which is important for the coordinated development between coal mining and the fragile eco-environment.
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

The ability for policies to generalize to new environments is key to the broad application of RL agents. A promising approach to prevent an agent’s policy from overfitting to a limited set of training environments is to apply regularization techniques originally developed for supervised learning. However, there are stark differences between supervised learning and RL. We discuss those differences and propose modifications to existing regularization techniques in order to better adapt them to RL. In particular, we focus on regularization techniques relying on the injection of noise into the learned function, a family that includes some of the most widely used approaches such as Dropout and Batch Normalization. To adapt them to RL, we propose Selective Noise Injection (SNI), which maintains the regularizing effect the injected noise has, while mitigating the adverse effects it has on the gradient quality. Furthermore, we demonstrate that the Information Bottleneck (IB) is a particularly well suited regularization technique for RL as it is effective in the low-data regime encountered early on in training RL agents. Combining the IB with SNI, we significantly outperform current state of the art results, including on the recently proposed generalization benchmark Coinrun.
Artificial Intelligence for Law Enforcement: Challenges and Opportunities

Artificial intelligence (AI)—and particularly deep learning—is progressing rapidly from a technical perspective, but, in a number of domains, adoption is still pending over the resolution of important issues. Methods of data analysis and interpretation based on AI are becoming common among law enforcement agencies (LEAs). Typical applications include suspect profiling (e.g., on social media <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ), traffic control (automated license plate detection and vehicle identification <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), analyzing dark web money flows, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> child pornography detection, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> and anomaly detection.
Reversible data hiding in encrypted images using adaptive reversible integer transformation

Reversible data hiding in encrypted images (RDH-EI) is a technique that hides additional data into the ciphertext with a capability of recovering the original content losslessly. In recent years, many methods have been proposed on this topic. In this paper, we propose a new RDH-EI approach that can achieve a very large embedding capacity, using adaptive reversible integer transformation. We first divide the original image into non-overlapped blocks. With an algorithm of adaptive reversible integer transformation, we vacate the embedding room for accommodating the auxiliary data and the payload. After image encryption and uploading, the cloud server can hide a large amount of additional data into the ciphertext using the embedded auxiliary data. The process is reversible that the additional data can be extracted exactly and the original image can be recovered without any error. Compared with state-of-the-art RDH-EI methods, the proposed method can achieve a much higher embedding capacity and a better rate distortion performance.
Aggregating Attentional Dilated Features for Salient Object Detection

This paper presents a novel deep learning model to aggregate the attentional dilated features for salient object detection by exploring the complementary information between the global and local context in a convolutional neural network. There are two technical contributions to our network design. First, we develop an attentional dense atrous (dilated) spatial pyramid pooling (AD-ASPP) module to selectively use the local saliency cues captured by dilated convolutions with a small rate and the global saliency cues captured by dilated convolutions with a large rate. Second, taking the feature pyramid network as the backbone, we develop an aggregation network to integrate the refined features by formulating two consecutive chains of residual learning based modules: one chain from deep to shallow layers while another chain from shallow to deep layers. We evaluate our network on seven widely-used saliency detection benchmarks by comparing it against 21 state-of-the-art methods. Experimental results show that our network outperforms others on all the seven benchmark datasets.
Layer-wise learning based stochastic gradient descent method for the optimization of deep convolutional neural network

Nowadays, despite the popularity of deep convolutional neural networks (CNNs), the efficient training of network models remains challenging due to several problems. In this paper, we present a layer-wise learning based stochastic gradient descent method (LLb-SGD) for gradient-based optimization of objective functions in deep learning, which is simple and computationally efficient. By simulating the cross-media propagation mechanism of light in the natural environment, we set an adaptive learning rate for each layer of neural networks. In order to find the proper local optimum quickly, the dynamic learning sequence spanning different layers adaptively adjust the descending speed of objective function in multi-scale and multi-dimensional environment. To the best of our knowledge, this is the first attempt to introduce an adaptive layer-wise learning schedule with a certain degree of convergence guarantee. Due to its generality and robustness, the method is insensitive to hyper-parameters and therefore can be applied to various network architectures and datasets. Finally, we show promising results compared to other optimization methods on two image classification benchmarks using five standard networks.
A blockchain‐based computation offloading method for edge computing in 5G networks

Summary Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth‐generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain‐based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain‐based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA‐III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO.
Face Detection using Haar Cascades to Filter Selfie Face Image on Instagram

Instagram is one of the fastest growing social media in recent years. Instagram is a popular social media that is used to share images. An image search on Instagram can use a particular keyword or often called hashtag. There are no rules in giving hashtag when users upload pictures. It makes the hashtag given sometimes not related to the uploaded image. There are images whose contents are dominated by selfie face. It causes the background image or location of an image not to be conveyed entirely. The purpose of this study is to filter selfie face images on search results based on hashtags on Instagram by combining web data extraction technique and human face detection technique using the Haar Cascade method. The experiment was carried out by determining several hashtags as the basis for image search on Instagram. Experimental results show that the applied method produces an accuracy value of 71.48% for detecting human faces. According to the result of human face detection, Haar Cascade method can filter selfie face images that have an accuracy value of 64,6%. We use this assumption because basically, selfie face images contain a human face.
IoT-Guard: Event-Driven Fog-Based Video Surveillance System for Real-Time Security Management

In this paper, we design and implement a distributed Internet of Things (IoT) framework called IoT-guard, for an intelligent, resource-efficient, and real-time security management system. The system, consisting of edge-fog computational layers, will aid in crime prevention and predict crime events in a smart home environment (SHE). The IoT-guard will detect and confirm crime events in real-time, using Artificial Intelligence (AI) and an event-driven approach to send crime data to protective services and police units enabling immediate action while conserving resources, such as energy, bandwidth (BW), and memory and Central Processing Unit (CPU) usage. In this study, we implement an IoT-guard laboratory testbed prototype and perform evaluations on its efficiency for real-time security application. The outcomes show better performance by the proposed system in terms of resource efficiency, agility, and scalability over the traditional IoT surveillance systems and state-of-the-art (SoA) approaches.
Re-humanizing the platform: Content moderators and the logic of care

With the goal of re-humanizing discussion platform operations, this study explores the knowledge and aims of commercial content moderators by reframing their work-related ideals through the notion of the “logic of care.” In seeking to expand their professional realm by realigning users, moderators, and technical tools, moderators of discussion forums have turned to machines, ideally freeing up resources for real-time interaction between moderators and those who post. By focusing on care, the study calls for technical innovation that integrates moderators’ aims with artificial intelligence systems. Rather than acknowledging human skills and resources in terms of moderation tools and discussion culture, the current platform logic forces moderators to operate like machines. Their discontent becomes understandable within a logic that diminishes their skills and vision. The moderator is left with assessing separate posts, rather than offering a meta-perspective to the discussion, overseeing and nurturing it.
QoS Aware Transcoding for Live Streaming in Edge-Clouds Aided HetNets: An Enhanced Actor-Critic Approach

With the advances in hand-held devices (smartphones and tablets, etc.) and high speed wireless networks, users have an explosive growth demand for live streaming service. Due to the diversity of user equipments (UEs), the live streaming has to be transcoded into different versions. However, transcoding is a computationally expensive and time consuming process. Due to the dynamic characteristics of wireless network environment, providing high quality and low latency live videos for UEs is a big challenge. This study investigates user scheduling, transcoding decision, computational and wireless spectrum resources allocation problem in edge-clouds aided heterogeneous networks (HetNets). The research focuses on improving UEs' quality of service (QoS) for live-streaming services, which includes quality of video and latency requirements. Different from existing literature, to approach the real wireless environment, the available computational and wireless spectrum resources are modeled as random processes in the work. Considering dynamic characteristics of wireless networks and available resources, the above problem is modeled as a Markov decision process (MDP). Since the action space of the MDP is multi-dimensional continuous variables mixed with discrete variables, it is difficult to solve this problem by traditional learning algorithms. Therefore, an enhanced actor-critic algorithm is proposed to resolve the problem, in which both the actor part and the critic part employ eligibility traces. Extensive simulation results with different system parameters show the effectiveness of the proposed algorithm.
Entropy Analysis and Image Encryption Application Based on a New Chaotic System Crossing a Cylinder

Designing chaotic systems with specific features is a hot topic in nonlinear dynamics. In this study, a novel chaotic system is presented with a unique feature of crossing inside and outside of a cylinder repeatedly. This new system is thoroughly analyzed by the help of the bifurcation diagram, Lyapunov exponents’ spectrum, and entropy measurement. Bifurcation analysis of the proposed system with two initiation methods reveals its multistability. As an engineering application, the system’s efficiency is tested in image encryption. The complexity of the chaotic attractor of the proposed system makes it a proper choice for encryption. States of the chaotic attractor are used to shuffle the rows and columns of the image, and then the shuffled image is XORed with the states of chaotic attractor. The unpredictability of the chaotic attractor makes the encryption method very safe. The performance of the encryption method is analyzed using the histogram, correlation coefficient, Shannon entropy, and encryption quality. The results show that the encryption method using the proposed chaotic system has reliable performance.
Supply Chain Management in Agriculture Using Blockchain and IoT

Blockchains play a vital role in FARMAR to track and trace the origin of food products in food supply chain. Supply Chain Management (SCM) is an essential business process in all spheres of the economy. SCM uses specific processes to connect from producer to consumer requirement through a chain. In a BCT(Blockchain Technology) based system, “records are immutable and trusted, eliminating the need for third parties to be involved. Potential farmer-facing impacts include ensuring that farmers receive timely and complete payments through the use of smart contracts and helping farmers to capture real-time data to more effectively manage their crops and harvests (source: nextbillion.net)”. Another benefit of using BCT in FARMAR is security where hacking or tampering the existing data is impossible by any intermediary. As an add-on to this process, IoT devices (Mobile phone-based Android app) are used to update the real-time quality and transit time of the product in FARMAR. It is integrated for improved traceability and usability of the products in the supply chain. The FARMAR aims to achieve these goals by developing a web application where FARMAR creates a value chain of integrity from farm to fork by using BCT.
Deep Multi-instance Learning for Survival Prediction from Whole Slide Images

Recent image-based survival models rely on discriminative patch labeling, which are both time consuming and infeasible to extend to large scale cancer datasets. Different from the existing works on learning using key patches or clusters from WSIs, we take advantages of a deep multiple instance learning to encode all possible patterns from WSIs and consider the joint effects from different patterns for clinical outcomes prediction. We evaluate our model in its ability to predict patients’ survival risks across the Lung and Brain tumors from two large whole slide pathological images datasets. The proposed framework can improve the prediction performances compared with existing state-of-the-arts survival analysis approaches. Results also demonstrate the effectiveness of the proposed method as a recommender system to provide personalized recommendations based on an individual’s calculated risk.
Probabilistic neural networks

Probabilistic neural networks (PNNs) offer a scalable alternative to the conventional back-propagation neural networks in classification problems without the need for massive forward and backward calculations that is associated with the ordinary neural networks. In addition, they can work with smaller sets of training data. However, this advantage may come at a cost of requiring large amounts of memory as the training data get larger. This chapter takes a look at the fundamental mathematics behind the modern PNNs, their application, and approaches that address some practical issues that come with them.