Reviewerly/exHarmony huggingface dataset:
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning

Self-supervised learning based on instance discrimination has shown remarkable progress. In particular, contrastive learning, which regards each image as well as its augmentations as an individual class and tries to distinguish them from all other images, has been verified effective for representation learning. However, conventional contrastive learning does not model the relation between semantically similar samples explicitly. In this paper, we propose a general module that considers the semantic similarity among images. This is achieved by expanding the views generated by a single image to Cross-Samples and Multi-Levels, and modeling the invariance to semantically similar images in a hierarchical way. Specifically, the cross-samples are generated by a data mixing operation, which is constrained within samples that are semantically similar, while the multi-level samples are expanded at the intermediate layers of a network. In this way, the contrastive loss is extended to allow for multiple positives per anchor, and explicitly pulling semantically similar images together at different layers of the network. Our method, termed as CSML, has the ability to integrate multi-level representations across samples in a robust way. CSML is applicable to current contrastive based methods and consistently improves the performance. Notably, using MoCo v2 as an instantiation, CSML achieves 76.6% top-1 accuracy with linear evaluation using ResNet-50 as backbone, 66.7% and 75.1% top-1 accuracy with only 1% and 10% labels, respectively. All these numbers set the new state-of-the-art. The code is available at https://github.com/haohang96/CSML.
A patent keywords extraction method using TextRank model with prior public knowledge

Abstract For large amount of patent texts, how to extract their keywords in an unsupervised way is a very important problem. In existing methods, only the own information of patent texts is analyzed. In this study, an improved TextRank model is proposed, in which prior public knowledge is effectively utilized. Specifically, two following points are first considered: (1) a TextRank network is constructed for each patent text, (2) a prior knowledge network is constructed based on public dictionary data, in which network edges represent the prior interpretation relationship among all dictionary words in dictionary entries. Then, an improved node rank value evaluation formula is designed for TextRank networks of patent texts, in which prior interpretation information in prior knowledge network are introduced. Finally, patent keywords can be extracted by finding top-k node words with higher node rank values. In our experiments, patent text clustering task is used to examine the performance of proposed method, wherein several comparison experiments are executed. Corresponding results demonstrate that, new method can markedly obtain better performance than existing methods for patent keywords extraction task in an unsupervised way.
Fog‐assisted virtual reality‐based learning framework to control panic

Abstract The biggest challenges for several universities and educational institutions throughout the COVID‐19 pandemic are online and e‐learning infrastructure availability. The incorporation of Information and Communication Technologies in the domain of disaster management is one such dimension, which focuses on the sustainability of human beings regarding the handling of unexpected pandemics. Therefore, it is important to evaluate the panic well‐being of the student in this situation. Moreover, virtual reality technology provides a virtual classroom environment that improves the skill and knowledge of the students at remote sites. In this paper, a fog‐assisted cyber physical system is proposed that deals with the various aspects of the panic well‐being of the student, including the virtual reality platform for remote learning. The proposed system utilizes the concepts of physical and cyberspace. The physical space facilitates real‐time data acquisition, and cyberspace determines and predicts the panic well‐being of the student. The performance assessment of the proposed model acknowledges the efficiency of the virtual learning system and panic well‐being determination and prediction. The proposed system also discussed a virtual learning system that provides a virtual classroom environment to the students at remote sites and reduces the panic due to stressful times during the COVID‐19 pandemic.
Event-triggered compound learning tracking control of autonomous surface vessels in the measurement network

This paper investigates the tracking control of the autonomous surface vessels (ASVs) with the time-varying disturbances. While the echo state network (ESN) accounts for the unknown dynamics in the model, a model-based event-triggered control (MBETC) scheme is presented by using the compound learning technique, which combines the learning of the ESN and the estimation of the compound disturbance. Different from the existing compound learning, the proposed scheme updates the estimates of the ESN weights and the compound disturbances in an event-triggered manner, in which two novel prediction errors are involved in their update laws. The values of prediction errors are obtained by using the online-recorded data during the inter-event time. To solve the recently proposed problem of "jumps of virtual control laws" arising in the backstepping-based event-triggered control (ETC), an event-triggered adaptive model is established to generate the continuous estimates of the states and direct the control laws. By the merit of ETC, the proposed scheme can importantly reduce the communication traffic in the measurement network compared with its continuous substitutes. By the merit of online-recorded-data-based compound learning, the proposed scheme can achieve the good understanding of synthetic uncertainties. All the errors in the closed-loop system are proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally, a numerical example corroborates the proposed scheme.
Decentralized Task and Path Planning for Multi-Robot Systems

We consider a multi-robot system with a team of collaborative robots and multiple tasks that emerges over time. We propose a fully decentralized task and path planning (DTPP) framework consisting of a task allocation module and a localized path planning module. Each task is modeled as a Markov Decision Process (MDP) or a Mixed Observed Markov Decision Process (MOMDP) depending on whether full states or partial states are observable. The task allocation module then aims at maximizing the expected pure reward (reward minus cost) of the robotic team. We fuse the Markov model into a factor graph formulation so that the task allocation can be decentrally solved using the max-sum algorithm. Each robot agent follows the optimal policy synthesized for the Markov model and we propose a localized forward dynamic programming scheme that resolves conflicts between agents and avoids collisions. The proposed framework is demonstrated with high fidelity ROS simulations and experiments with multiple ground robots.
Energy-efficient decentralized navigation of a team of solar-powered UAVs for collaborative eavesdropping on a mobile ground target in urban environments

This paper considers using a team of solar-powered UAVs to collaboratively eavesdrop on a mobile ground target in an urban environment. A practical application is that the police department uses UAVs to collect the transmitted data from a criminal suspect so that the transmitted content can be understood. Using UAVs in this task is a much more cost-effective and also less suspicious option than the conventional surveillance by a police helicopter. We focus on the online and decentralized path planning for the solar-powered UAV team. We formulate a multi-objective optimization problem which forces the UAVs to complete the uninterrupted eavesdropping task, not enter any No-fly space (NFS), keep a safe distance from each other and harvest as much energy as possible. We propose a Rapidly-exploring Random Tree (RRT) based path planning method, consisting of an initial planning phase and an online planning phase. In the online planning phase, each UAV plans its own path based on the shared information across the team. Since the UAV model can be taken into account in the planning phase, the obtained UAV paths are feasible without any later adjustment. More importantly, this method is easily implementable in real-time.
On the Scale Invariance in State of the Art CNNs Trained on ImageNet

The diffused practice of pre-training Convolutional Neural Networks (CNNs) on large natural image datasets such as ImageNet causes the automatic learning of invariance to object scale variations. This, however, can be detrimental in medical imaging, where pixel spacing has a known physical correspondence and size is crucial to the diagnosis, for example, the size of lesions, tumors or cell nuclei. In this paper, we use deep learning interpretability to identify at what intermediate layers such invariance is learned. We train and evaluate different regression models on the PASCAL-VOC (Pattern Analysis, Statistical modeling and ComputAtional Learning-Visual Object Classes) annotated data to (i) separate the effects of the closely related yet different notions of image size and object scale, (ii) quantify the presence of scale information in the CNN in terms of the layer-wise correlation between input scale and feature maps in InceptionV3 and ResNet50, and (iii) develop a pruning strategy that reduces the invariance to object scale of the learned features. Results indicate that scale information peaks at central CNN layers and drops close to the softmax, where the invariance is reached. Our pruning strategy uses this to obtain features that preserve scale information. We show that the pruning significantly improves the performance on medical tasks where scale is a relevant factor, for example for the regression of breast histology image magnification. These results show that the presence of scale information at intermediate layers legitimates transfer learning in applications that require scale covariance rather than invariance and that the performance on these tasks can be improved by pruning off the layers where the invariance is learned. All experiments are performed on publicly available data and the code is available on GitHub.
Research on local path planning based on improved RRT algorithm

In order to solve the local path planning of self-driving car in the structured road environment, an improved path planning algorithm named Regional-Sampling RRT (RS-RRT) algorithm was proposed for obstacle avoidance conditions. Gaussian distribution sampling and local biasing sampling were integrated to improve the search efficiency in the sampling phase. In the expansion phase, considering the actual size of the vehicle and obstacles, combined with the goal of safety and comfort, the separating axis theorem (SAT) method and vehicle dynamics were used to detect the collision among vehicle and surrounding obstacles in real time. In the post-processing stage, the driver’s driving consensus and path smoothing algorithm were combined to correct the planning path. In order to track the generated path, the MPC tracking algorithm was designed based on the Four-Wheel-Independent Electric Vehicle (FWIEV) model. The co-simulation software platform of CarSim and MATLAB/Simulink was employed to verify the effectiveness and feasibility of the path planning and tracking algorithm. The results show that compared with basic RRT and Goal-biasing RRT, the proposed RS-RRT algorithm has advantages in terms of number of nodes, path length and running time. The generated path can meet the FWIEV dynamics and path tracking requirements.
Seeded random walk for multi-view semi-supervised classification

Recently, multi-view learning has captured widespread attention in the machine learning area, yet it is still crucial and challenging to exploit beneficial patterns from multi-view data. Specifically, very limited work has been devoted to multi-view semi-supervised learning, where only a small number of labeled data points are available for model training. Therefore, a simple yet efficient seeded random walk scheme is proposed in this paper to address the multi-view semi-supervised classification problem, where known labeled data points serve as random seeds to be walked with certain probability. In this scheme, the semi-supervised classification indicator is obtained based primarily on an arrival probability and a reward matrix, which are computed by leveraging an initial distribution from some random seeds. Besides, theoretical analyses are then provided to indicate a connection of the proposed model with the existing manifold ranking method. Finally, comprehensive experiments on eight publicly available data sets demonstrate the superiority of the proposed model against compared state-of-the-art semi-supervised methods and fully supervised classifiers. Furthermore, experimental results also suggest that the proposed method comes with positive robustness and promising generalization capability in terms of data classification.
The spatial phase-shifting measuring profilometry based on dual-frequency grating

A real-time three-dimensional(3D) shape measuring method based on dual-frequency spatial phase-shifting measuring profilometry (SPSMP) is proposed. To combine the low-frequency and high-frequency gratings into one single composite pattern, each individual grating is modulated along orthogonal direction with a distinct carrier frequency. Since the spectrum components are staggered in the spectrum of the composite deformed pattern, the low-frequency and high-frequency deformed patterns with background light can be demodulated easily. Then two groups of spatial phase-shifting deformed sub-patterns are extracted from the corresponding deformed patterns to calculate the low-frequency wrapped phase and the high-frequency wrapped phase. According to the dual-frequency phase unwrapping algorithm, the high-frequency wrapped phase is guided by the low-frequency unwrapped phase. Compared to the existing multi-frequency single-shot fringe projection, the filtering process of the proposed method is more concise, it does not involve multiple filtering and the extraction of the single spectrum component. The static and real-time experimental results show that the proposed method inherits the high accuracy of PMP, while guaranteeing single-shot feature.
AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection

Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. Unfortunately, it has received much less attention than supervised object detection. Models that try to address this task tend to suffer from a shortage of annotated training samples. Moreover, existing methods of feature alignments are not sufficient to learn domain-invariant representations. To address these limitations, we propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training into a unified framework. An intermediate domain image generator is proposed to enhance feature alignments by domain-adversarial training with automatically generated soft domain labels. The synthetic intermediate domain images progressively bridge the domain divergence and augment the annotated source domain training data. A feature pyramid alignment is designed and the corresponding feature discriminator is used to align multi-scale convolutional features of different semantic levels. Last but not least, we introduce a region feature alignment and an instance discriminator to learn domain-invariant features for object proposals. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations. Further extensive experiments verify the effectiveness of each component and demonstrate that the proposed network can learn domain-invariant representations.
Blockchain-Based Secure Key Management for Mobile Edge Computing

Mobile edge computing (MEC) is a promising edge technology to provide high bandwidth and low latency shared services and resources to mobile users. However, the MEC infrastructure raises major security concerns when the shared resources involve sensitive and private data of users. This paper proposes a novel blockchain-based key management scheme for MEC that is essential for ensuring secure group communication among the mobile devices as they dynamically move from one subnetwork to another. In the proposed scheme, when a mobile device joins a subnetwork, it first generates lightweight key pairs for digital signature and communication, and broadcasts its public key to neighbouring peer users in the subnetwork blockchain. The blockchain miner in the subnetwork packs all the public key of mobile devices into a block that will be sent to other users in the subnetwork. This enables the mobile device to communicate with its peers in the subnetwork by encrypting the data with the public key stored in the blockchain. When the mobile device moves to another subnetwork in the tree network, all the mobile devices of the new subnetwork can quickly verify its identity by checking its record in the local or higher hierarchy subnetwork blockchain. Furthermore, when the mobile device leaves the subnetwork, it does not need to do anything and its records will remain in the blockchain which is an append-only database. Theoretical security analysis shows that the proposed scheme can defend against the 51 percent attack and malicious entities in the blockchain network utilizing Proof-of-Work consensus mechanism. Moreover, the backward and forward secrecy is also preserved. Experimental results demonstrate that the proposed scheme outperforms two baselines in terms of computation, communication and storage.
Evaluation of the Mawid mobile healthcare application in delivering services during the COVID-19 pandemic in Saudi Arabia

The purpose of this study is to evaluate MAWID mobile application developed by the Ministry of Health, Saudi Arabia, which is used for primary care hospitals appointments management and for tracking and tracing COVID-19.An online questionnaire-based survey was used for collecting data related to three major factors including Ease of Use, Satisfaction, and Benefits of MAWID application among its users. Out of total 2542 participants, 345 participants completed only a part of the survey, and 204 participants did not use the application. After removing, 549 invalid responses, a final sample of 1993 was included for the data analysis.82.1% of the participants referred MAWID as easy to use application, 79.8% were highly satisfied with the application, and majority of the participants reflected potential benefits of using the application. T-test results have revealed that significant differences existed between males and females, and young and older participants in relation to the Ease of Use and Satisfaction levels associated with MAWID application.Mobile applications can be very effective in delivering the healthcare services during pandemics. However, there is a need for regular evaluation and assessment to trach the change in users' needs and update the app according to the changing requirements.
CTF: Anomaly Detection in High-Dimensional Time Series with Coarse-to-Fine Model Transfer

Anomaly detection is indispensable in modern IT infrastructure management. However, the dimension explosion problem of the monitoring data (large-scale machines, many key performance indicators, and frequent monitoring queries) causes a scalability issue to the existing algorithms. We propose a coarse-to-fine model transfer based framework CTF to achieve a scalable and accurate data-center-scale anomaly detection. CTF pre-trains a coarse-grained model, uses the model to extract and compress per-machine features to a distribution, clusters machines according to the distribution, and conducts model transfer to fine-tune per-cluster models for high accuracy. The framework takes advantage of clustering on the per-machine latent representation distribution, reusing the pre-trained model, and partial-layer model fine-tuning to boost the whole training efficiency. We also justify design choices such as the clustering algorithm and distance algorithm to achieve the best accuracy. We prototype CTF and experiment on production data to show its scalability and accuracy. We also release a labeling tool for multivariate time series and a labeled dataset to the research community.
A Techno-Economic Comparative Study of a Grid-Connected Residential Rooftop PV Panel: The Case Study of Nahr El-Bared, Lebanon

Lebanon suffers from daily electricity shortages. The country has paid much attention to renewable energy sources, particularly solar, to gradually replace conventional energy. Installing a photovoltaic (PV) system becomes increasingly attractive for residential consumers due to the rising electricity tariff rates while it reduces the dependency on domestic power generators. No known study has dealt with the investigation of potential grid-connected rooftop PV systems with various sun-tracking modes and PV technologies in Nahr El-Bared, Lebanon. Consequently, the main objective of the current paper is to investigate the feasibility of a 5kW grid-connected PV system of various technologies (mono-crystalline silicon and poly-crystalline silicon) and sun-tracking modes including fixed tilt and 2-axis systems for rooftop households in Lebanon. The Nahr El-Bared camp was the case study was of the paper. RETScreen Expert software was used to evaluate the techno-economic performance of the proposed systems. The results show that the annual electrical energy from a fixed 5kW PV panel tilted at an optimal angle ranged from 8564.47kWh to 8776.81kWh, while the annual electrical energy from the PV tracking system was within the range of 11511.67-12100.92kWh. This amount of energy output would contribute significantly to reduce the energy shortage in the country. A typical household was selected to establish a load profile and load supply during both grid availability and outage periods. The highest energy consumption that can be covered by the PV systems was recorded during the spring and summer seasons. Also, the average energy production cost ranged from 0.0239 to 0.0243$/kWh for all the proposed systems. It was concluded that a 5kW grid-connected rooftop PV system could be economically justifiable. Finally, this study tried to increase the awareness about utilizing PV sun-tracking systems and the feasibility of small-scale grid-connected rooftop PV systems in the selected regions. The results of this research can help investors in the energy and building sectors.
It’s Not Just the Site, It’s the Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts

The website fingerprinting (or inter-domain WSF), enhanced by various machine learning techniques, has shown its power to identify websites a user has visited. To our best knowledge, a finer-grained problem of web page fingerprinting (or intra-domain WPF) has not been systematically studied by our research community. The WPF attackers, such as government agencies enforcing Internet censorship, are keen to identify the particular web pages (e.g., a political dissident’s social media page) visited by the target user.