Reviewerly/exHarmony huggingface dataset:
Enhancement-Registration-Homogenization (ERH): A Comprehensive Underwater Visual Reconstruction Paradigm

This paper presents a comprehensive underwater visual reconstruction paradigm that comprises three procedures, i.e., the E-procedure, the R-procedure, and the H-procedure. The E-procedure enhances original underwater images based on color compensation balance and weighted image fusion, yielding restored color, sharpened edges, and global contrast. The R-procedure registers multiple enhanced underwater images by exploiting global similarity and local deformation. The H-procedure homogenizes the registered underwater images by multi-scale composition strategy, which eliminates the inhomogeneous transition and brightness difference across overlapping regions, resulting in a reconstructed wide-field underwater image with comfortable and natural visibility. The three procedures operate in a cascade where the former procedure processes underwater images in a way that facilitates the latter one. We refer to the overall three procedures as the Enhancement-Registration-Homogenization (ERH) paradigm. Comprehensive qualitative and quantitative empirical evaluations reveal that our ERH paradigm outperforms state-of-the-art visual reconstruction methods, including the AutoStitch, APAP, SPHP, APNAP, and REW.
FedFace: Collaborative Learning of Face Recognition Model

DNN-based face recognition models require large centrally aggregated face datasets for training. However, due to the growing data privacy concerns and legal restrictions, accessing and sharing face datasets has become exceedingly difficult. We propose FedFace, a federated learning (FL) framework for collaborative learning of face recognition models in a privacy aware manner. FedFace utilizes the face images available on multiple clients to learn an accurate and generalizable face recognition model where the face images stored at each client are neither shared with other clients nor the central host and each client is a mobile device containing face images pertaining to only the owner of the device (one identity per client). Our experiments show the effectiveness of FedFace in enhancing the verification performance of pre-trained face recognition system on standard face verification benchmarks namely LFW, IJB-A and IJB-C.
An Unsupervised TinyML Approach Applied for Pavement Anomalies Detection Under the Internet of Intelligent Vehicles

Vehicles have been endowed with new technologies in the last years, mostly influenced by an increasing in the instrumentation level and availability of smart devices for embedded processing. In this scenario, which has paved the way for the construction of Internet of Intelligent Vehicles, the edge computing paradigm emerges with the primary role to promote the processing of raw data streams in their early stages, as close as possible to their origins. Therefore, this paper proposes the processing of data streams based on an unsupervised tiny machine learning approach to detect anomalies on the roads, exploiting for that a microcontroller and an embedded accelerometer on vehicles. The obtained results through real experiments were promising: the f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score mean was 0.76 for the first driver and 0.78 for the second. This indicates that the classifier model reached significant performance in the defined scenarios.
Metaheuristic task scheduling algorithms for cloud computing environments

Summary Cloud computing has the advantage of providing flexibility, high‐performance, pay‐as‐you‐use, and on‐demand service. One of the important research issues in cloud computing is task scheduling. The purpose of scheduling is to assign tasks to available resources while providing optimization on some objectives. Tasks have diversified characteristics, and resources are heterogeneous. These properties make task scheduling an NP‐complete problem. In this study, metaheuristic and hybrid metaheuristic algorithms are developed for task scheduling problems in cloud computing environments. We have developed genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA) based metaheuristic algorithms, which are also combined with a greedy approach (GR). In addition to this, we have developed hybrid metaheuristics algorithms, called DE‐SA and GA‐SA, which are also combined with a greedy approach. The proposed approaches are evaluated in terms of completion time and load balancing of virtual machines. In terms of average completion time, as the number of tasks increases, it has been observed that the DESA algorithm outperforms the solely used DE and SA algorithms. In addition, experiments show that hybrid algorithms improve both the average completion time and the average standard deviation of virtual machine loads for some task groups.
Speech Driven Talking Face Generation From a Single Image and an Emotion Condition

Visual emotion expression plays an important role in audiovisual speech communication. In this work, we propose a novel approach to rendering visual emotion expression in speech-driven talking face generation. Specifically, we design an end-to-end talking face generation system that takes a speech utterance, a single face image, and a categorical emotion label as input to render a talking face video synchronized with the speech and expressing the conditioned emotion. Objective evaluation on image quality, audiovisual synchronization, and visual emotion expression shows that the proposed system outperforms a state-of-the-art baseline system. Subjective evaluation of visual emotion expression and video realness also demonstrates the superiority of the proposed system. Furthermore, we conduct a human emotion recognition pilot study using generated videos with mismatched emotions among the audio and visual modalities. Results show that humans respond to the visual modality more significantly than the audio modality on this task.
Radar Camera Fusion via Representation Learning in Autonomous Driving

Radars and cameras are mature, cost-effective, and robust sensors and have been widely used in the perception stack of mass-produced autonomous driving systems. Due to their complementary properties, outputs from radar detection (radar pins) and camera perception (2D bounding boxes) are usually fused to generate the best perception results. The key to successful radar-camera fusion is the accurate data association. The challenges in the radar-camera association can be attributed to the complexity of driving scenes, the noisy and sparse nature of radar measurements, and the depth ambiguity from 2D bounding boxes. Traditional rule-based association methods are susceptible to performance degradation in challenging scenarios and failure in corner cases. In this study, we propose to address radar-camera association via deep representation learning, to explore feature-level interaction and global reasoning. Additionally, we design a loss sampling mechanism and an innovative ordinal loss to overcome the difficulty of imperfect labeling and to enforce critical human-like reasoning. Despite being trained with noisy labels generated by a rule-based algorithm, our proposed method achieves a performance of 92.2% F1 score, which is 11.6% higher than the rule-based teacher. Moreover, this data-driven method also lends itself to continuous improvement via corner case mining.
Deep Hashing Using Proxy Loss on Remote Sensing Image Retrieval

With the improvement of various space-satellite shooting methods, the sources, scenes, and quantities of remote sensing data are also increasing. An effective and fast remote sensing image retrieval method is necessary, and many researchers have conducted a lot of work in this direction. Nevertheless, a fast retrieval method called hashing retrieval is proposed to improve retrieval speed, while maintaining retrieval accuracy and greatly reducing memory space consumption. At the same time, proxy-based metric learning losses can reduce convergence time. Naturally, we present a proxy-based hash retrieval method, called DHPL (Deep Hashing using Proxy Loss), which combines hash code learning with proxy-based metric learning in a convolutional neural network. Specifically, we designed a novel proxy metric learning network, and we used one hash loss function to reduce the quantified losses. For the University of California Merced (UCMD) dataset, DHPL resulted in a mean average precision (mAP) of up to 98.53% on 16 hash bits, 98.83% on 32 hash bits, 99.01% on 48 hash bits, and 99.21% on 64 hash bits. For the aerial image dataset (AID), DHPL achieved an mAP of up to 93.53% on 16 hash bits, 97.36% on 32 hash bits, 98.28% on 48 hash bits, and 98.54% on 64 bits. Our experimental results on UCMD and AID datasets illustrate that DHPL could generate great results compared with other state-of-the-art hash approaches.
Estimating Human Pose Efficiently by Parallel Pyramid Networks

Good performance and high efficiency are both critical for estimating human pose in practice. Recent state-of-the-art methods have greatly boosted the pose detection accuracy through deep convolutional neural networks, however, the strong performance is typically achieved without high efficiency. In this paper, we design a novel network architecture for human pose estimation, which aims to strike a fine balance between speed and accuracy. Two essential tasks for successful pose estimation, preserving spatial location and extracting semantic information, are handled separately in the proposed architecture. Semantic knowledge of joint type is obtained through deep and wide sub-networks with low-resolution input, and high-resolution features indicating joint location are processed by shallow and narrow sub-networks. Because accurate semantic analysis mainly asks for adequate depth and width of the network and precise spatial information mostly requests preserving high-resolution features, good results can be produced by fusing the outputs of the sub-networks. Moreover, the computational cost can be considerably reduced comparing with existing networks, since the main part of the proposed network only deals with low-resolution features. We refer to the architecture as “parallel pyramid” network (PPNet), as features of different resolutions are processed at different levels of the hierarchical model. The superiority of our network is empirically demonstrated on two benchmark datasets: the MPII Human Pose dataset and the COCO keypoint detection dataset. PPNet outcompetes all recent methods by using less computation and memory to achieve better human pose estimation results.
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation

In this work, we address the issues of the missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at hand. We address the missing modality–the ground truth answers–that are not present at test time and use a privileged knowledge distillation scheme to deal with the issue of the missing modality. In order to efficiently do so, we first introduce a model, the "Big" Teacher, that takes the image/question/answer triplet as its input and out-performs the baseline, then use a combination of models to distill knowledge to a target network (student) that only takes the image/question pair as its inputs. We experiment our models on the VizWiz and VQA-V2 Answer Difference datasets and show through extensive experimentation and ablation the performance of our method and a diverse possibility for future research.
SpinQ Gemini: a desktop quantum computing platform for education and research

Abstract SpinQ Gemini is a commercial desktop quantum computing platform designed and manufactured by SpinQ Technology. It is an integrated hardware-software system. The first generation product with two qubits was launched in January 2020. The hardware is based on NMR spectrometer, with permanent magnets providing ∼1 T magnetic field. SpinQ Gemini operates under room temperature (0–30°C), highlighting its lightweight (55 kg with a volume of $70\times 40 \times 80\text{ cm}^{3}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>70</mml:mn><mml:mo>×</mml:mo><mml:mn>40</mml:mn><mml:mo>×</mml:mo><mml:mn>80</mml:mn><mml:msup><mml:mtext> cm</mml:mtext><mml:mn>3</mml:mn></mml:msup></mml:math> ), cost-effective (under 50k USD), and maintenance-free. SpinQ Gemini aims to provide real-device experience for quantum computing education for K-12 and at the college level. It also features quantum control design capabilities that benefit the researchers studying quantum control and quantum noise. Since its first launch, SpinQ Gemini has been shipped to institutions in Canada, Taiwan and Mainland China. This paper introduces the system of design of SpinQ Gemini, from hardware to software. We also demonstrate examples for performing quantum computing tasks on SpinQ Gemini, including one task for a variational quantum eigensolver of a two-qubit Heisenberg model. The next generations of SpinQ quantum computing devices will adopt models of more qubits, advanced control functions for researchers with comparable cost, as well as simplified models for much lower cost (under 5k USD) for K-12 education. We believe that low-cost portable quantum computing products will facilitate hands-on experience for teaching quantum computing at all levels, well-prepare younger generations of students and researchers for the future of quantum technologies.
Toward Unveiling How SAFe Framework Supports Agile in Global Software Development

Current research has shown that global software development (GSD) organizations rely on agile methods to guarantee better communication, coordination, and improved productivity.Moreover, as organizations continue to grow, they are facing large-scale global project challenges and complexity, leading to an urge to scale agile practices for better coordination.However, it has been surprising to find that approaches in the literature on scaling agile for large and global projects remain unclear.In addition, to identify and highlight which agile scales practices reported in studies on Agile GSD (AGSD) are related to SAFe practices, we conducted a systematic literature review.Besides, we mapped AGSD practices to the SAFe 5.0 framework to provide bases for those starting on such a journey.In our study, we found 24 articles related to the scaling of agile practices in a global environment, and 44 scaling agile practices that embrace 95 SAFe 5.0 components, including ''agile coaching,'' ''agile architecture,'' and ''system demo''.This study provides an in-depth scientific investigation on how SAFe can be associated with global teams applying GSD.The results and findings will benefit researchers and practitioners in conducting further research and provide more dedicated solutions for challenges to scaling agile in the context of GSD.
IoT Based Weapons Detection System for Surveillance and Security Using YOLOV4

Due to the increase in crime and terrorism in most parts of the world, security surveillance is becoming increasingly important. A computer vision-based system for detecting weapons for real-time security surveillance is designed in this work. For identification, detection, and notifying the appropriate authorities, the system employs the YOLO V4 (You Only Look Once) algorithm. This neural network can be trained using images, videos, and live streaming videos. This model incorporates Internet-of-Things (IoT) smart devices that are interconnected and automated in weapon detection. This model's accuracy varies depending on the quality of the images and videos used in the detection process. Here, the proposed research work has discovered that the detection process is affected by the type of hardware that has been utilized to run the algorithm, ranging from low-quality image/video detection with 70% accuracy to high-quality image/video detection with 95% accuracy.
A Machine Learning Model for Detection of Docker-based APP Overbooking on Kubernetes

Resource allocation overbooking is an approach used by cloud providers that allocates more virtual resources than available on physical hardware, which may imply service quality degradation. Docker in cloud computing environments is being increasingly used due to their fast provisioning and deployment, while the impact of overbooking of resources allocation due to multi-tenancy remains overlooked. This paper proposes a machine learning model to detect overbooking in Kubernetes environments within the docker container. The proposed model continuously monitors distributed container OS usage and application performance metrics. The collected metrics are used as input to a machine learning model that identifies multi-tenancy interference incurring in application performance degradation. Experiments performed on a Kubernetes cluster with a Docker-based Big Data processing application showed that our proposed model could detect resource overbooking with up to 98% accuracy. This implies an overbooking on a resource of up to 1.2 in the client’s domain.
Decentralized Multi-Robot Collision Avoidance in Complex Scenarios With Selective Communication

Deep reinforcement learning has been demonstrated to be an effective solution to the multi-robot collision avoidance problem. However, with existing methods, robots typically generate actions only based on local observations, sometimes augmented with global communication. Their performance deteriorates in limited bandwidth environments and complex scenarios with various obstacles and high robot density. We propose SelComm, a selective communication framework to generate cooperative and collision-free actions for robots in multi-robot navigation tasks. Specifically, we develop a decentralized message selector, enabling each robot to calculate relations with other robots using both agent-level information and sensor-level information, and select the most valuable messages to meet the bandwidth limitation. Then we introduce the attentional communication channel for efficient communication. Our experimental evaluations based on various scenarios demonstrate that SelComm learns more cooperative behaviors and outperforms state-of-the-art methods in limited bandwidth environments and complex scenarios.
A Comparison Between Deep Learning and Support Vector Regression Techniques Applied to Solar Forecast in Spain

Abstract Solar energy is one of the main renewable energy sources capable of contributing to global energy demand. However, the solar resource is intermittent, making its integration into the electrical system a difficult task. Here, we present and compare two machine learning techniques, deep learning (DL) and support vector regression (SVR), to verify their behavior for solar forecasting. Our testing from Spain showed that the mean absolute percentage error for predictions using DL and SVR is 7.9% and 8.52%, respectively. The DL achieved the best results for solar energy forecast, but it is worth mentioning that the SVR also obtained satisfactory results.
Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes

Extraction and integration of semantic connections, spatial relations and dependencies are critical in volumetric image segmentation. This is a challenging issue, especially when there are long-distance objects with close semantic relations and neighboring objects with indistinct boundaries. We propose a novel dynamic graph convolution (DGC) autoencoder with node-attribute-wise attention (NodeAttri-Attention) for relation inference and reasoning, with applications on kidney and tumor segmentation from computerized tomography (CT) volumes. We first introduce a new graph construction strategy for 3D volumetric image data, where graph node attributes and connections represent topological relations and high-level correlations. Then NodeAttri-Attention mechanism is proposed to obtain attention-enhanced node attributes by discriminating adaptive contributions of various features. Finally, the DGC strategy is designed to learn and integrate the complex and underlying correlations across image regions. Our DGC dynamically updates graph topology and node attributes as the graph convolutional layer gradually deepens. Experimental results and ablation studies demonstrated the effectiveness of each of our major innovations in NodeAttri-Attention DGC, especially when objects are with weak boundaries, irregular shapes, and various sizes. The improved segmentation results of embedding NodeAttri-Attention DGC to different segmentation backbones show the generality of DGC autoencoder.
Albireo: Energy-Efficient Acceleration of Convolutional Neural Networks via Silicon Photonics

With the end of Dennard scaling, highly-parallel and specialized hardware accelerators have been proposed to improve the throughput and energy-efficiency of deep neural network (DNN) models for various applications. However, collective data movement primitives such as multicast and broadcast that are required for multiply-and-accumulate (MAC) computation in DNN models are expensive, and require excessive energy and latency when implemented with electrical networks. This consequently limits the scalability and performance of electronic hardware accelerators. Emerging technology such as silicon photonics can inherently provide efficient implementation of multicast and broadcast operations, making photonics more amenable to exploit parallelism within DNN models. Moreover, when coupled with other unique features such as low energy consumption, high channel capacity with wavelength-division multiplexing (WDM), and high speed, silicon photonics could potentially provide a viable technology for scaling DNN acceleration.In this paper, we propose Albireo, an analog photonic architecture for scaling DNN acceleration. By characterizing photonic devices such as microring resonators (MRRs) and Mach-Zehnder modulators (MZM) using photonic simulators, we develop realistic device models and outline their capability for system level acceleration. Using the device models, we develop an efficient broadcast combined with multicast data distribution by leveraging parameter sharing through unique WDM dot product processing. We evaluate the energy and throughput performance of Albireo on DNN models such as ResNet18, MobileNet and VGG16. When compared to cur-rent state-of-the-art electronic accelerators, Albireo increases throughput by 110 X, and improves energy-delay product (EDP) by an average of 74 X with current photonic devices. Furthermore, by considering moderate and aggressive photonic scaling, the proposed Albireo design shows that EDP can be reduced by at least 229 X.
Blockchain technology as a new driver in supply chain

Processes associated with the transformation of inputs (raw materials, components) into outputs (goods, finished products) and their transport to the place of consumption are an essential part of the functioning of today's society. These processes are becoming more complex with the gradual globalization. As the complexity of the supply chain increases, so does the risk of disruption. There are problems such as a lack of information on the origin of the products, a lack of real-time information and, with that, a problem with tracking shipments. With a large number of documents, the risk of fraud and forgery also increases. Due to the large number of stakeholders in logistics chains, the level of transparency is being lost. One possible solution to eliminate the risk of supply chain complexity is to integrate blockchain technology into the supply chain. Thanks to properties such as distribution, immutability, transparency, blockchain can have great potential to solve the problems of the traditional supply chain. The aim of this paper is to describe the basic problems of the traditional supply chain, which can disrupt its course. In the next part, we will introduce the basics of blockchain technology and describe what processes of the traditional supply chain could be replaced by this technology.