Reviewerly/exHarmony huggingface dataset:
PM-MVS: PatchMatch multi-view stereo

Abstract PatchMatch Stereo is a method for generating a depth map from stereo images by repeating spatial propagation and view propagation. The concept of PatchMatch Stereo can be easily extended to Multi-View Stereo (MVS). In this paper, we present PatchMatch Multi-View Stereo (PM-MVS), which is a highly accurate 3D reconstruction method that can be used in various environments. Three techniques are introduced to PM-MVS: (i) matching score evaluation, (ii) viewpoint selection, and (iii) outlier filtering. The combination of normalized cross-correlation with bilateral weights and geometric consistency between viewpoints is used to improve the estimation accuracy of depth and normal maps at object boundaries and poor-texture regions. For each pixel, viewpoints used for stereo matching are carefully selected in order to improve robustness against disturbances such as occlusion, noise, blur, and distortion. Outliers are removed from reconstructed 3D point clouds by a weighted median filter and consistency-based filters assuming multi-view geometry. Through a set of experiments using public multi-view image datasets, we demonstrate that the proposed method exhibits efficient performance compared with conventional methods.
Memory sharing for handling memory overload on physical machines in cloud data centers

Abstract Over-committing computing resources is a widely adopted strategy for increased cluster utilization in Infrastructure as a Service (IaaS) cloud data centers. A potential consequence of over-committing computing resources is memory overload of physical machines (PMs). Memory overload occurs if memory usage exceeds a defined alarm threshold, exposing running computation tasks at a risk of being terminated by the operating system. A prevailing measure to handle memory overload of a PM is live migration of virtual machines (VMs). However, this not only consumes network bandwidth, CPU, and other resources, but also compels a temporary unavailability of the VMs being migrated. To handle memory overload, we present a memory sharing system in this paper for PMs in cloud data centers. With memory sharing, a PM automatically borrows memory from a remote PM when necessary, and releases the borrowed memory when memory overload disappears. This is implemented through swapping inactive memory pages to remote memory resource. Experimental studies conducted on InfiniBand-networked PMs show that the memory sharing system is fully functional. The measured throughput and latency are around 929 Mbps and 1.3 $$\mu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> s, respectively, on average for remote memory access. They are similar to those from accessing a local-volatile memory express solid-state drive, and thus are promising in real applications.
Short-term photovoltaic power production forecasting based on novel hybrid data-driven models

Abstract The uncertainty associated with photovoltaic (PV) systems is one of the core obstacles that hinder their seamless integration into power systems. The fluctuation, which is influenced by the weather conditions, poses significant challenges to local energy management systems. Hence, the accuracy of PV power forecasting is very important, particularly in regions with high PV penetrations. This study addresses this issue by presenting a framework of novel forecasting methodologies based on hybrid data-driven models. The proposed forecasting models hybridize Support Vector Regression (SVR) and Artificial Neural Network (ANN) with different Metaheuristic Optimization Algorithms, namely Social Spider Optimization, Particle Swarm Optimization, Cuckoo Search Optimization, and Neural Network Algorithm. These optimization algorithms are utilized to improve the predictive efficacy of SVR and ANN, where the optimal selection of their hyperparameters and architectures plays a significant role in yielding precise forecasting outcomes. In addition, the proposed methodology aims to reduce the burden of random or manual estimation of such paraments and improve the robustness of the models that are subject to under and overfitting without proper tuning. The results of this study exhibit the superiority of the proposed models. The proposed SVR models show improvements compared to the default SVR models, with Root Mean Square Error between 12.001 and 50.079%. Therefore, the outcomes of this research work can uphold and support the ongoing efforts in developing accurate data-driven models for PV forecasting.
Visual explanations prioritize functional properties at the expense of visual fidelity

Visual explanations play an integral role in communicating mechanistic knowledge about how things work. What do people think distinguishes such pictures from those that are intended to convey how things look? To explore this question, we used a drawing paradigm to elicit both visual explanations and depictions of novel machine-like objects, then conducted a detailed analysis of the semantic information conveyed in each drawing. We found that visual explanations placed greater emphasis on parts of the machines that move or interact to produce an effect, while visual depictions emphasized parts that were visually salient, even if they were static. Moreover, we found that these differences in visual emphasis impacted what information naive viewers could extract from these drawings: explanations made it easier to infer which action was needed to operate the machine, but more difficult to identify which machine it represented. Taken together, our findings suggest that people spontaneously prioritize functional information when producing visual explanations but that this strategy may be double-edged, facilitating inferences about physical mechanism at the expense of preserving visual fidelity.
Reservoir Computing Model for Human Hand Locomotion Signal Classification

Human-movement recognition is a novel challenge in soft robotics. In recent years, there have been several attempts to develop soft wearable devices for supporting human-robot interfaces. Many algorithms and programming languages are available to integrate a wearable device with a soft robot. One such promising algorithm is reservoir computing (RC), which includes of a group of recurrently and randomly connected nodes. The RC model can easily process multidimensional signal data and can handle nonlineardata and has been extensively used in robotic control. It has been reported that the RC algorithm can speed up network training and solve complex data sets. However, the main existing limitations in hand-locomotion classification are the considerable run-time and the delayed response. In this study, we figure out the best machine learning algorithms to handle three-dimensional hand-gesture data. We employ a two-part strategy: a loopback filter is included in the preprocessing of the initial dataset to support the 3-dimensional (3D) signs of each hand posture; subsequently, the training dataset is applied to the machine learning algorithm which includes an artificial neural network (ANN), convolutional neural network (CNN), long short-term memory(LSTM), and reservoir computing(RC). Each training network is optimized with various hyperparameters. Furthermore, we compare the performance of several machine-learning algorithms in classifying the three-dimensional hand-signal posture data. The results show that the classification of nonlinear hand-locomotion signals by RC requires a comparatively shorter training duration (12 minutes for training times), and that optimal accuracy 94.17, precision 94.10, and recall 93.99 is realized for time series data.
Fall Detection With Wrist-Worn Watch by Observations in Statistics of Acceleration

It is common for older people to live alone, which can have tragic consequences if they have an accident and can’t call for help in time. This is particularly acute in an aging society where falling is one of the most common accidents. According to the CDC, 1/4 of people over the age of 65 in the United States fall each year. The development of IoT and MEMS has made it possible to detect falls in time and automatically call for help. The presented fall detection system focuses on the walk-fall-still pattern, collects accelerations through the wrist-worn M5StickC-Plus watch, analyses the data locally in the watch, detects falls using an algorithm based on observations in the statistics of acceleration in one second, and then transmits the alarm signal to a remote healthcare system in real-time via WIFI. The lightweight algorithm has been proven to be 90% accurate in detecting falls, and the system can notify service staff of accidents within 1 second. The features of comfort, lightness, and timeliness make the device more practical than similar products. The low-cost, non-intrusive device can be used in care homes and is also suitable for elderly people living alone.
Exploiting Graph Matrix Duality for Efficient Graph Data Processing

Linear algebra is the backbone of modern applied data analytics. Not only does it provide the theoretical substratum for the development of analytical algorithms it also lends the technological support for extremely efficient implementations. The linear algebra ecosystem consists of remarkably fast hardware support with equally efficient software solutions leveraging the hardware capacity. Graphs are an ubiquitous modelling framework for the representation and analysis of connected data.A flipside from practical perspective is that conventional graph algorithms are challenging as they are hard to parallelize and often make too much use of non-linear data structures which do not have hardware support. In this context, this paper presents an invitation to an alternate way to look at graph processing in terms of algebraic constructs, formulations and technology. The paper presents the theoretical underpinnings of the new view and also some interesting progresses at the frontier level in this direction. Above all, an intended objective is to present a compelling invite to the academia and research community to adopt this approach in their academic and research activities.
YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s)

Can we see it all? Do we know it All? These are questions thrown to human beings in our contemporary society to evaluate our tendency to solve problems. Recent studies on the implementation of object detection models in developing and underdeveloped countries have failed to meet the demand for objectiveness and predictive accuracy. An increase in global security threats has necessitated the development of efficient approaches to tackle these issues. This paper proposes an object detection model for cyber-physical systems known as Smart Surveillance Systems (3s). A 2-phase approach was proposed, highlighting the advantages of You Only Look Once version 3 (YOLOv3) deep learning architecture in real-time and visual object detection. A transfer learning approach was implemented for this research to reduce training time and computing resources. The dataset utilized for training the model is the Microsoft COCO dataset which contains 328,000 annotated image instances. Deep learning techniques such as Pre-processing, Data pipelining, and detection was implemented to improve efficiency. Compared to other novel research models, the proposed model's results performed exceedingly well in detecting WILD objects in surveillance footages. An accuracy of 99.71% was recorded with an improved mean Average Precision (mAP) of 61.5.
A Comprehensive Review of Deep Learning-Based Real-World Image Restoration

Real-world imagery does not always exhibit good visibility and clean content, but often suffers from various kinds of degradations (e.g., noise, blur, rain drops, fog, color distortion, etc.), which severely affect vision-driven tasks (e.g., image classification, target recognition, and tracking, etc.). Thus, restoring the true scene from such degraded images is of significance. In recent years, a large body of deep learning-based image processing works has been exploited due to the advances in deep neural networks. This paper aims to make a comprehensive review of real-world image restoration algorithms and beyond. More specifically, this review provides overviews of critical benchmark datasets, image quality assessment methods, and four major categories of deep learning-based image restoration methods, i.e., based on convolutional neural network (CNN), generative adversarial network (GAN), Transformer, and multi-layer perceptron (MLP). The paper highlights the latest developments and advances in each category of network architecture to provide an up-to-date overview. Moreover, the representative state-of-the-art image restoration methods are compared visually and numerically. Finally, for real-world image restoration, the current situations are objectively assessed, challenges are discussed, and future directions and trends are presented.
Blockchain-Based Fair and Fine-Grained Data Trading With Privacy Preservation

In this article, we propose a blockchain-based fair and privacy-preserving data trading scheme that supports fine-grained data selling. First, to achieve fairness for trading participants, by incorporating attribute-based credentials, encryption, and zero-knowledge proof, we design a data trading scheme where a buyer first publishes the required data attributes on the blockchain, and a data seller can demonstrate data availability in ciphertext by only disclosing the required attributes to a data buyer and proving the authenticity of data. A data buyer transfers funds only if the correct key material is uploaded to the blockchain. Second, to guarantee fine-grained data trading and preserve identity privacy, we build a Merkle hash tree on the ciphertexts of data with a signature on its root node, which allows a data seller to split data into blocks and remove the sensitive information from the data without affecting data availability verification. The public key of the data seller is not leaked to the data buyer during the trading. Moreover, different trading transactions from the same data seller cannot be linked. We formally prove that our scheme achieves the desired security properties: fairness and privacy preservation. Simulation results demonstrate the feasibility and efficiency of the proposed scheme.
Class-incremental object detection

Deep learning architectures have shown remarkable results in the object detection task. However, they experience a critical performance drop when they are required to learn new classes incrementally without forgetting old ones. This catastrophic forgetting phenomenon impedes the deployment of artificial intelligence in real word scenarios where systems need to learn new and different representations over time. Recently, many incremental learning methods have been proposed to avoid the catastrophic forgetting problem. However, current state-of-the-art class-incremental learning strategies aim at preserving the knowledge of old classes while learning new ones sequentially, which would encounter other problems as follows: (1) In the process of preserving information of old classes, only a small portion of data in the previous tasks are kept and replayed during training, which inevitably incurs bias that is favorable for the new classes but malicious to the old classes. (2) With the knowledge of previous classes distilled into the new model, a sub-optimal solution for the new task is obtained since the preserving process of previous classes sabotages the training of new classes. To address these issues, termed as Information Asymmetry (IA), we propose a double-head framework which preserves the knowledge of old classes and learns the knowledge of new classes separately. Specifically, we transfer the knowledge of the previous model to the current learned one for overcoming the catastrophic forgetting problem. Furthermore, considering that IA would introduce impacts on the training of the new model, we propose a Non-Affection mask to distill the knowledge of the interested regions at the feature level. Comprehensive experimental results demonstrate that our proposed method significantly outperforms other state-of-the-art class-incremental object detection methods on PASCAL VOC and MS COCO datasets.
A privacy protection approach in edge-computing based on maximized dnn partition strategy with energy saving

Abstract With the development of deep neural network (DNN) techniques, applications of DNNs show state-of-art performance. In the cloud edge collaborative mode, edge devices upload the raw data, such as texts, images, and videos, to the cloud for processing. Then, the cloud returns prediction or classification results. Although edge devices take advantage of the powerful performance of DNN, there are also colossal privacy protection risks. DNN partition strategy can effectively solve the privacy problems by offload part of the DNN model to the edge, in which the encoded features are transmitted rather than original data. We explore the relationship between privacy and the intermedia result of the DNN. The more parts offloaded to the edge, the more abstract features we can have, indicating more conducive to privacy protection. We propose a privacy protection approach based on a maximum DNN partition strategy. Besides, a mix-precision quantization approach is adopted to reduce the energy use of edge devices. The experiments show that our method manages to increase at most 20% model privacy in various DNN architecture. Through the energy-aware mixed-precision quantization approach, the model’s energy consumption is reduced by at most 5x comparing to the typical edge-cloud solution.
A prediction model for healthcare time-series data with a mixture of deep mixed effect models using Gaussian processes

Healthcare outcomes such as blood pressure and heart rate are commonly tracked across time owing to technological advances in wearable devices. This advance then makes it possible to predict health risks and to practice personalized medicine. For this type of healthcare data, it is important to reflect huge variation among subjects where the subject becomes an experimental unit. The person-specific model becomes critical for accurate prediction, but it is not optimal due to the noisy nature of the data. It has been demonstrated that sharing information across subjects via a mixed effect model can improve the prediction of individual responses compared to a completely personalized model. However, sharing information across all patients can dilute signals when there are several different patterns present in the data. That is, subjects may form groups and each group behaves differently. To reflect this feature, we extend a deep mixed effect model via a mixture of deep mixed effect models. Our mixed effect model is based on Gaussian processes where the mean adopts the deep neural networks to capture flexible time trends. Our model finds a highly nonlinear trend shared among segments of patients while clustering patients with similar trends into groups. Our approach shows great performance in simulation studies as well as real data analysis, emphasizing the importance of modeling group-specific trends when making accurate predictions from healthcare time-series data.
Applications of fractional gradient descent method with adaptive momentum in BP neural networks

A novel fractional gradient descent method with adaptive momentum is presented in this paper to improve the convergence speed and stability for BP neural network training. The fractional Grünwald-Letnikov derivative is used for the fractional gradient. The coefficient of the momentum term is set as an adaptive variable, depending on the fractional gradient of the current step and the weight change of the previous step. We give a detailed convergence proof of the proposed method. Experiments on MNIST data sets and XOR problem demonstrate that the fractional gradient descent method with adaptive momentum term can effectively improve convergence speed, maintain stability of BP neural network training, help escape from local minimum points, and enlarge the selection range of the learning rate.
Exploration of the intelligent-auxiliary design of architectural space using artificial intelligence model

In order to carry out a comprehensive design description of the specific architectural model of AI, the auxiliary model of AI and architectural spatial intelligence is deeply integrated, and flexible design is carried out according to the actual situation. AI assists in the generation of architectural intention and architectural form, mainly supporting academic and working theoretical models, promoting technological innovation, and thus improving the design efficiency of the architectural design industry. AI-aided architectural design enables every designer to achieve design freedom. At the same time, with the help of AI, architectural design can complete the corresponding work faster and more efficiently. With the help of AI technology, through the adjustment and optimization of keywords, AI automatically generates a batch of architectural space design schemes. Against this background, the auxiliary model of architectural space design is established through the literature research of the AI model, the architectural space intelligent auxiliary model, and the semantic network and the internal structure analysis of architectural space. Secondly, to ensure compliance with the three-dimensional characteristics of the architectural space from the data source, based on the analysis of the overall function and structure of space design, the intelligent design of the architectural space auxiliary by Deep Learning is carried out. Finally, it takes the 3D model selected in the UrbanScene3D data set as the research object, and the auxiliary performance of AI's architectural space intelligent model is tested. The research results show that with the increasing number of network nodes, the model fitting degree on the test data set and training data set is decreasing. The fitting curve of the comprehensive model shows that the intelligent design scheme of architectural space based on AI is superior to the traditional architectural design scheme. As the number of nodes in the network connection layer increases, the intelligent score of space temperature and humidity will continue to rise. The model can achieve the optimal intelligent auxiliary effect of architectural space. The research has practical application value for promoting the intelligent and digital transformation of architectural space design.
Quantum Learning and Its Related Applications for the Future

Recent advances in high-performance computing have been rapid. On the contrary, experts also know that the Moore's Law prediction of the number of transistors on microchips that would double every 18 months is almost saturated. This calls for new techniques to enhance computational power. Quantum computing is a possible solution that uses quantum mechanical phenomena and employs quantum algorithms to improve performance (accuracy, speed). The emerging technology has many interesting potential applications, including quantum machine learning, quantum computational chemistry, post quantum cryptography, etc. The complexity of applications is ever-increasing. Quantum computing amalgamates various classical machine and reinforcement learning in multiple ways to address different challenges of many complex applications. The state-of-the-art reviews on existing works in the domain show that new learning methods can enhance the achieved performance by quantum computing. The chapter thus provides an overview of quantum learning, its applications, research challenges, and future trends.