Reviewerly/exHarmony huggingface dataset:
VideoDP: A Flexible Platform for Video Analytics with Differential Privacy

Abstract Massive amounts of videos are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps). Unfortunately, privacy leakage in the blurred video cannot be effectively bounded, especially against unknown background knowledge. In this paper, to our best knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Finally, we conduct experiments on real videos, and the experimental results demonstrate that VideoDP can generate accurate results for video analytics.
Evolution of a Cognitive Architecture for Social Robots: Integrating Behaviors and Symbolic Knowledge

This paper presents the evolution of a robotic architecture intended for controlling autonomous social robots. The first instance of this architecture was originally designed according to behavior-based principles. The building blocks of this architecture were behaviors designed as a finite state machine and organized in an ethological inspired way. However, the need of managing explicit symbolic knowledge in human–robot interaction required the integration of planning capabilities into the architecture and a symbolic representation of the environment and the internal state of the robot. A major contribution of this paper is the description of the working memory that integrates these two approaches. This working memory has been implemented as a distributed graph. Another contribution is the use of behavior trees instead of state machine for implementing the behavior-based part of the architecture. This late version of the architecture has been tested in robotic competitions (RoboCup or European Robotics League, among others), whose performance is also discussed in this paper.
Trend-Based Categories Recommendations and Age-Gender Prediction for Pinterest and Twitter Users

Category suggestions or recommendations for customers or users have become an essential feature for commerce or leisure websites. This is a growing topic that follows users’ activity in social networks generating a huge quantity of information about their interests, contacts, among many others. These data are usually collected to analyze people’s behavior, trends, and integrate a complete user profile. In this sense, we analyze a dataset collected from Pinterest to predict the gender and age by processing input images using a Convolutional Neural Network. Our method is based on the meaning of the image rather than the visual content. Additionally, we propose a heuristic-based approach for text analysis to predict users’ age and gender from Twitter. Both of the classifiers are based on text and images and they are compared with various similar approaches in the state of the art. Suggested categories are based on association rules conformed by the activity of thousands of users in order to estimate trends. Computer simulations showed that our approach can recommend interesting categories for a user analyzing his current interest and comparing this interest with similar users’ profiles or trends and, therefore, achieve an improved user profile. The proposed method is capable of predicting the user’s age with high accuracy, and at the same time, it is able to predict gender and category information from the user. The certainty that one or more suggested categories be interesting to people is higher for those users with a large number of publications.
xMatcher: Matching Extensible Markup Language Schemas using Semantic-based Techniques

Schema matching is a critical step in data integration systems.Most recent schema matching systems require a manual double-check of the matching results to add missed matches and remove incorrect matches.Manual correction is labor-intensive and time-consuming, however without it the results accuracy is significantly lower.In this paper, we present xMatcher, an approach to automatically match XML schemas.Given two schemas S1 and S2, xMatcher identifies semantically similar schema elements between S1 and S2.To obtain correct matches, xMatcher first transforms S1 and S2 into sets of words; then, it uses a context-based measure to identify the meanings of words in their contexts; next, it captures semantic relatedness between sets of words in different schemas; finally, it uses WordNet information to calculate the similarity values between semantically related sets and matches the pairs of sets whose similarity values are greater than or equal to 0.8.The results show that xMatcher provides superior matching accuracy compared to the state of the art matching systems.Overall, our proposal can be a stepping stone towards decreasing human assistance and overcoming the weaknesses of current matching initiatives in terms of matching accuracy.
Privacy-Enhancing Preferential LBS Query for Mobile Social Network Users

While social networking sites gain massive popularity for their friendship networks, user privacy issues arise due to the incorporation of location-based services (LBS) into the system. Preferential LBS takes a user’s social profile along with their location to generate personalized recommender systems. With the availability of the user’s profile and location history, we often reveal sensitive information to unwanted parties. Hence, providing location privacy to such preferential LBS requests has become crucial. However, the current technologies focus on anonymizing the location through granularity generalization. Such systems, although provides the required privacy, come at the cost of losing accurate recommendations. Hence, in this paper, we propose a novel location privacy-preserving mechanism that provides location privacy through <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>k</mml:mi></mml:math>-anonymity and provides the most accurate results. Experimental results that focus on mobile users and context-aware LBS requests prove that the proposed method performs superior to the existing methods.
Comparing the Performance of Regional Earthquake Early Warning Algorithms in Europe

Several earthquake early warning (EEW) algorithms have been developed worldwide for rapidly estimating real-time information (i.e., location, magnitude, ground shaking, and/or potential consequences) about ongoing seismic events. This study quantitatively compares the operational performance of two popular regional EEW algorithms for European conditions of seismicity and network configurations. We specifically test PRobabilistic and Evolutionary early warning SysTem (PRESTo) and the implementation of the Virtual Seismologist magnitude component within SeisComP, VS(SC), which we use jointly with the SeisComP scanloc module for locating events. We first evaluate the timeliness and accuracy of the location and magnitude estimates computed by both algorithms in real-time simulation mode, accounting for the continuous streaming of data and effective processing times. Then, we focus on the alert-triggering (decision-making) phase of EEW and investigate both algorithms’ ability to yield accurate ground-motion predictions at the various temporal instances that provide a range of warning times at target sites. We find that the two algorithms show comparable performances in terms of source parameters. In addition, PRESTo produces better rapid estimates of ground motion (i.e., those that facilitate the largest lead times); therefore, we conclude that PRESTo may have a greater risk-mitigation potential than VS(SC) in general. However, VS(SC) is the optimal choice of EEW algorithm if shorter warning times are permissible. The findings of this study can be used to inform current and future implementations of EEW systems in Europe.
Observation of Intensity Squeezing in Resonance Fluorescence from a Solid-State Device

Intensity squeezing—i.e., photon number fluctuations below the shot-noise limit—is a fundamental aspect of quantum optics and has wide applications in quantum metrology. It was predicted in 1979 that intensity squeezing could be observed in resonance fluorescence from a two-level quantum system. However, its experimental observation in solid states was hindered by inefficiencies in generating, collecting, and detecting resonance fluorescence. Here, we report the intensity squeezing in a single-mode fiber-coupled resonance fluorescence single-photon source based on a quantum dot–micropillar system. We detect pulsed single-photon streams with 22.6% system efficiency, which show sub-shot-noise intensity fluctuation with an intensity squeezing of 0.59 dB. We estimate a corrected squeezing of 3.29 dB at the first lens. The observed intensity squeezing provides the last piece of the fundamental picture of resonance fluorescence, which can be used as a new standard for optical radiation and in scalable quantum metrology with indistinguishable single photons.Received 27 June 2020Accepted 2 September 2020DOI:https://doi.org/10.1103/PhysRevLett.125.153601© 2020 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum fluctuations & noiseQuantum opticsSingle photon sourcesSqueezing of quantum noiseAtomic, Molecular & Optical
DHPV: a distributed algorithm for large-scale graph partitioning

Abstract Big graphs are part of the movement of “Not Only SQL” databases (also called NoSQL) focusing on the relationships between data, rather than the values themselves. The data is stored in vertices while the edges model the interactions or relationships between these data. They offer flexibility in handling data that is strongly connected to each other. The analysis of a big graph generally involves exploring all of its vertices. Thus, this operation is costly in time and resources because big graphs are generally composed of millions of vertices connected through billions of edges. Consequently, the graph algorithms are expansive compared to the size of the big graph, and are therefore ineffective for data exploration. Thus, partitioning the graph stands out as an efficient and less expensive alternative for exploring a big graph. This technique consists in partitioning the graph into a set of k sub-graphs in order to reduce the complexity of the queries. Nevertheless, it presents many challenges because it is an NP-complete problem. In this article, we present DPHV (Distributed Placement of Hub-Vertices) an efficient parallel and distributed heuristic for large-scale graph partitioning. An application on a real-world graphs demonstrates the feasibility and reliability of our method. The experiments carried on a 10-nodes Spark cluster proved that the proposed methodology achieves significant gain in term of time and outperforms JA-BE-JA, Greedy, DFEP.
Short- and long-term energy flux prediction using Multi-Task Evolutionary Artificial Neural Networks

This paper presents a novel approach to tackle simultaneously short- and long-term energy flux prediction (specifically, at 6h, 12h, 24h and 48h time horizons). The methodology proposed is based on the Multi-Task Learning paradigm in order to solve the four problems with a single model. We consider Multi-Task Evolutionary Artificial Neural Networks (MTEANN) with four outputs, one for each time prediction horizon. For this purpose, three buoys located at the Gulf of Alaska are considered. Measurements collected by these buoys are used to obtain the target values of energy flux, whereas, only reanalysis data are used as input values, allowing the applicability to other locations. The performance of three different basis functions (Sigmoidal Unit, Radial Basis Function and Product Unit) are compared against some popular state-of-the-art approaches such as Extreme Learning Machines and Support Vector Regressors. The results show that MTEANN methodology using Sigmoidal Units in the hidden layer and a linear output achieves the best performance. In this way, the multi-task methodology is an excellent and lower-complexity approach for energy flux prediction at both short- and long-term prediction time horizons. Furthermore, the results also confirm that reanalysis data is enough for describing well the problem tackled.
A New Challenge: Path Planning for Autonomous Truck of Open-Pit Mines in The Last Transport Section

During the operation of open-pit mining, the loading position of a haulage truck often changes, bringing a new challenge concerning how to plan an optimal truck transportation path considering the terrain factors. This paper proposes a path planning method based on a high-precision digital map. It contains two parts: (1) constructing a high-precision digital map of the cutting zone and (2) planning the optimal path based on the modified Hybrid A* algorithm. Firstly, we process the high-precision map based on different terrain feature factors to generate the obstacle cost map and surface roughness cost map of the cutting zone. Then, we fuse the two cost maps to generate the final cost map for path planning. Finally, we incorporate the contact cost between tire and ground to improve the node extension and path smoothing part of the Hybrid A* algorithm and further enhance the algorithm’s capability of avoiding the roughness. We use real elevation data with different terrain resolutions to perform random tests and the results show that, compared with the path without considering the terrain factors, the total transportation cost of the optimal path is reduced by 10%–20%. Moreover, the methods demonstrate robustness.
Wire-Suspended Device Control Based on Wireless Communication With Multirotor for Long Reach-Aerial Manipulation

In this study, we propose a long-reach aerial manipulation system with a wire-suspended device that allows the airframe to maintain its position at the desired distance away from the target.We implemented a feedback communication between the multirotor and wire-suspended device to maintain the position of the wire-suspended device directly below the multirotor while maintaining a constant distance from the target.The multirotor communicates its velocity and relative position to the wire-suspended device to control the ducted fans, whereas the wire-suspended device transmits the attitude and distance from the ground to the multirotor to control the winch.The designed controller for the ducted fans allows the wire-suspended device to behave like a long rigid robotic arm.The performance of the designed controller was verified by navigating the multirotor over stepped surfaces while the wire-suspended device was being lowered.
Cross-Modal Deep Networks For Document Image Classification

As a fundamental step of document related tasks, document classification has been widely adopted to various document image processing applications. Unlike the general image classification problem in the computer vision field, text document images contain both the visual cues and the corresponding text within the image. However, how to bridge these two different modalities and leverage textual and visual features to classify text document images remains challenging. In this paper, we present a cross-modal deep network that enables to capture both the textual content and the visual information included in document images. Thanks to the efficient jointly learning of text and image features, the proposed cross-modal approach shows its superiority to the state-of-the-art single-modal methods. In this paper, we propose to use NASNet-Large and Bert to extract image and text features respectively. Experimental results demonstrate that the proposed cross-modal approach achieves new state-of-the-art results for text document image classification on the benchmark Tobacco-3482 dataset, outperforming the current state-of-the-art method by 3.91% of classification accuracy.
Computer Vision-Assisted Instant Alerts in 5G

This paper introduces an innovative model which incorporates vehicle On-Board Unit (OBU) data and roadside video information to provide instant alert messages to drivers. We apply computer vision techniques to perform real-time danger event detection and to identify specific surrounding vehicles that should be alerted. Different from traditional broadcast-based alerting, we propose to send these instant alert messages to the target vehicles by unicast and geocast. To do so, an accurate method is required to analyze the spatial relation of vehicles. Also, to confine our alert messages to only those target vehicles, we rely on roadside cameras and apply a sensor fusion technique that can link a video object with its communication MAC address. Through this innovative idea, we integrate computer vision with 5G networks and enable transmitting instant alerts to precise vehicles without interfering irrelevant vehicles. How to incorporate our system with 3GPP V2X by setting proper transmission parameters is also addressed. To validate our idea, we present four common road danger events and show how our model works. To the best of our knowledge, this is the first work bringing computer vision to instant messaging.
The EPI Framework: A Dynamic Data Sharing Framework for Healthcare Use Cases

To support the current trend of personalised medicine, a collaboration between different healthcare providers is increasingly vital. The main element is the ability to share data among all parties while abiding by a data sharing policy. The EPI (Enabling Personalised Intervention) project addresses the problem of personalised diagnosis by developing real-time monitoring services and digital health twins. The EPI services run over adaptive computing infrastructures which provide more flexibility to accommodate the different requests. This paper proposes the EPI framework to support these novel health services over programmable infrastructure. The framework works on aligning the parties' ability to share data with the policy defined beforehand. We explain the approach by introducing the framework's data sharing logic model. We define the formalism of the logic model to deduce feasible data movements between and possibly satisfy a data collaboration request. We reinforce the framework's logic model by introducing the algorithms running on this federated system to simulate its workflow. We provide three healthcare use cases running on a typical EPI infrastructure. We evaluated our model according to three relevant parameters, performance, feasibility, and aggregation power, and we can conclude that our framework supports the required interoperability between the EPI partners.
FCNNLib: An Efficient and Flexible Convolution Algorithm Library on FPGAs

Convolutions can be implemented with different algorithms, which are diverse in arithmetic complexity, resource requirement, etc. Multiple algorithms can share the FPGA resources spatially as well as temporally, introducing either reconfiguration overhead or resource underutilization. In this paper, we propose an efficient library FCNNLib to coordinate multiple convolution algorithms on FPGAs. We develop three scheduling techniques: spatial, temporal, and hybrid, which exhibit different trade-offs in latency and throughput. We also expose a set of interfaces to arm the users. Experiments using modern CNNs demonstrate FCNNLib achieves up to 1.315X latency improvement compared with dedicated accelerators and 1.755X energy efficiency improvement compared with cuDNN.
PQA-CNN: Towards Perceptual Quality Assured Single-Image Super-Resolution in Remote Sensing

Recent advances in remote sensing open up unprecedented opportunities to obtain a rich set of visual features of objects on the earth's surface. In this paper, we focus on a single-image super-resolution (SISR) problem in remote sensing, where the objective is to generate a reconstructed satellite image of high quality (i.e., a high spatial resolution) from a satellite image of relatively low quality. This problem is motivated by the lack of high quality satellite images in many remote sensing applications (e.g., due to the cost of high resolution sensors, communication bandwidth constraints, and historic hardware limitations). Two important challenges exist in solving our problem: i) it is not a trivial task to reconstruct a satellite image of high quality that meets the human perceptual requirement from a single low quality image; ii) it is challenging to rigorously quantify the uncertainty of the results of an SISR scheme in the absence of ground truth data. To address the above challenges, we develop PQA-CNN, a perceptual quality-assured conventional neural network framework, to reconstruct a high quality satellite image from a low quality one by designing novel uncertainty-driven neural network architectures and integrating an uncertainty quantification model with the framework. We evaluate PQA-CNN on a real-world remote sensing application on land usage classifications. The results show that PQA-CNN significantly outperforms the state-of-the-art super-resolution baselines in terms of accurately reconstructing high-resolution satellite images under various evaluation scenarios.
Parameter clustering in Bayesian functional principal component analysis of neuroscientific data

The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatiotemporal data sets. To reduce oversimplifications, new models have been developed to be able to identify meaningful patterns and new insights within a highly demanding data environment. To this extent, we propose a new model called parameter clustering functional principal component analysis (PCl‐fPCA) that merges ideas from functional data analysis and Bayesian nonparametrics to obtain a flexible and computationally feasible signal reconstruction and exploration of spatiotemporal neuroscientific data. In particular, we use a Dirichlet process Gaussian mixture model to cluster functional principal component scores within the standard Bayesian functional PCA framework. This approach captures the spatial dependence structure among smoothed time series (curves) and its interaction with the time domain without imposing a prior spatial structure on the data. Moreover, by moving the mixture from data to functional principal component scores, we obtain a more general clustering procedure, thus allowing a higher level of intricate insight and understanding of the data. We present results from a simulation study showing improvements in curve and correlation reconstruction compared with different Bayesian and frequentist fPCA models and we apply our method to functional magnetic resonance imaging and electroencephalogram data analyses providing a rich exploration of the spatiotemporal dependence in brain time series.