Reviewerly/exHarmony huggingface dataset:
Never Ending Story: Authentication and Access Control Design Flaws in Shared IoT Devices

Internet-of-Things (IoT) devices implement weak authentication and access control schemes. The on-demand nature of IoT devices requires a responsive communications channel, which is often at odds with thorough authentication and access control. This paper seeks to better understand IoT device security by examining the design of authentication and access control schemes. In this work, we explore the challenge of propagating credential revocation and access control list modifications in a shared IoT ecosystem. We evaluate the vulnerability of 19 popular security cameras and doorbells against a straightforward user-interface bound adversary attack. Our results demonstrate that 16 of 19 surveyed devices suffer from flaws that enable unauthorized access after credential modification or revocation. We conclude by discussing these findings and propose a means for balancing authentication and access control schemes while still offering responsive communications channels.
Location-Aware and Budget-Constrained Application Replication and Deployment in Multi-Cloud Environment

To gain technical and economic benefits, enterprise application providers are increasingly moving their workloads to the cloud. With the increasing number of cloud resources from multiple cloud providers at different locations with differentiated prices, application providers face the challenge to select proper cloud resources to replicate and deploy applications to maintain low response time and high quality of user experience without running into the risk of over-spending. In this paper, we study the global-wide cloud application replication and deployment problem considering the application average response time, including particularly application execution time and network latency, subject to the budgetary control. To address the problem, we propose a GA-based approach with domain-tailored solution representation, fitness measurement, and population initialization. Extensive experiments using the real-world datasets demonstrate that our proposed GA-based approach significantly outperforms common application placement strategies, i.e., NearData and NearUsers, and our recently proposed hybrid GA approach.
Traffic Data Classification using Machine Learning Algorithms in SDN Networks

As an efficient approach to proactively monitoring network dynamics, automatically analyzing network data, and predicting network usage, machine learning has been widely deployed. This enables the networks to be efficiently and autonomously coped with in SDN/NFV environment. In particular, network intelligent technology can be adopted into the infrastructure management, network operations, and service assurance. In this paper, we study the automatic network data classification based on machine learning, where several machine learning algorithms are deployed to automatically classify real network traffic data collected from ONOS (Open Network Operating System) platform. From the experiment results with simple network topology, we conclude that machine learning algorithms can effectively classify the network traffic data. However, it is also observed machine algorithms may only show a limited performance in practice if they are blindly deployed. This is because there exists not only the data that needs to be delivered to the receivers but also the data required for network maintenance in a real network system. Therefore, it is essential to develop machine learning algorithms that explicitly consider the characteristics of real network traffic data in target network scenarios.
Long-Range Gesture Recognition Using Millimeter Wave Radar

Millimeter wave (mmWave) based gesture recognition technology provides a good human computer interaction (HCI) experience. Prior works focus on the close-range gesture recognition, but fall short in range extension, i.e., they are unable to recognize gestures more than one meter away from considerable noise motions. In this paper, we design a long-range gesture recognition model which utilizes a novel data processing method and a customized artificial Convolutional Neural Network (CNN). Firstly, we break down gestures into multiple reflection points and extract their spatial-temporal features which depict gesture details. Secondly, we design a CNN to learn changing patterns of extracted features respectively and output the recognition result. We thoroughly evaluate our proposed system by implementing on a commodity mmWave radar. Besides, we also provide more extensive assessments to demonstrate that the proposed system is practical in several real-world scenarios.
MV2Flow

In video action recognition, motion is a very crucial clue, which is usually represented by optical flow. However, optical flow is computationally expensive to obtain, which becomes the bottleneck for the efficiency of traditional action recognition algorithms. In this article, we propose a network called MV2Flow to learn motion representation efficiently from the signals in the compressed domain. To learn the network, three losses are defined. First, we select the classical TV-L1 flow as proxy ground truth to guide the learning. Besides, an unsupervised image reconstruction loss is proposed to further refine it. Moreover, toward the task of action recognition, the above two losses are combined with a motion content loss. To evaluate our approach, extensive experiments on two benchmark datasets UCF-101 and HMDB-51 are conducted. The motion representation generated with our MV2Flow has shown comparable classification performance on action recognition with TV-L1 flow, while operating at an over 200× faster speed. Based on our MV2Flow and 2D-CNN-based network, we have achieved state-of-the-art performance in the compressed domain. With 3D-CNN-based network, we also achieve comparable accuracy with higher inference speed than methods in the decoded domain setting.
FORTNIoT

Ubiquitous environments, such as smart homes, are becoming more intelligent and autonomous. As a result, their behavior becomes harder to grasp and unintended behavior becomes more likely. Researchers have contributed tools to better understand and validate an environments' past behavior (e.g. logs, end-user debugging), and to prevent unintended behavior. There is, however, a lack of tools that help users understand the future behavior of such an environment. Information about the actions it will perform, and why it will perform them, remains concealed. In this paper, we contribute FORTNIoT, a well-defined approach that combines self-sustaining predictions (e.g. weather forecasts) and simulations of trigger-condition-action rules to deduce when these rules will trigger in the future and what state changes they will cause to connected smart home entities. We implemented a proof-of-concept of this approach, as well as a visual demonstrator that shows such predictions, including causes and effects, in an overview of a smart home's behavior. A between-subject evaluation with 42 participants indicates that FORTNIoT predictions lead to a more accurate understanding of the future behavior, more confidence in that understanding, and more appropriate trust in what the system will (not) do. We envision a wide variety of situations where predictions about the future are beneficial to inhabitants of smart homes, such as debugging unintended behavior and managing conflicts by exception, and hope to spark a new generation of intelligible tools for ubiquitous environments.
RETRACTED: Action recognition of dance video learning based on embedded system and computer vision image

Extraction and unfettered online / offline video sequence to identify complex human activity is computer vision a challenging task. To presents the classification of Indian classical dance moves using the powerful features of embedded system tools: Field Programmable Gate Array (FPGA). In this work, the Indian classical dance video for human action recognition is, YouTube data from offline and online control audio and video recordings of live performances carried out. Handprint create offline data with ten different themes familiar dance of 200 m / from various Indian classical dance forms in the context of a variety of poses. Online data collection dance ten different subjects from YouTube. Each dance posture is occupied 60 or video in both cases. FPGA training and 8 different sample dimensions, each performed by a plurality of sets of subject. The remaining two samples for testing the trained FPGA. Different FPGA architecture design, and with our test data in order to obtain better recognition accuracy. Compared the report on the same data set and other classification model to achieve a 90% recognition rate.
Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail

Future of sustainable fashion lies in adoption of AI for a better understanding of consumer shopping behaviour and using this understanding to further optimize product design, development and sourcing to finally reduce the probability of overproducing inventory. Explainability and interpretability are highly effective in increasing the adoption of AI based tools in creative domains like fashion. In a fashion house, stakeholders like buyers, merchandisers and financial planners have a more quantitative approach towards decision making with primary goals of high sales and reduced dead inventory. Whereas, designers have a more intuitive approach based on observing market trends, social media and runways shows. Our goal is to build an explainable new product forecasting tool with capabilities of interventional analysis such that all the stakeholders (with competing goals) can participate in collaborative decision making process of new product design, development and launch.
Spectral and Chemical Characterization of Copiapite and Rozenite from Padinjarathara in Wayanad, Southern India: Implications for Mars Exploration

Copiapite (Fe2+Fe3+4(SO4)6(OH)2 ∙ 20 H2O) and rozenite (Fe2+(SO4) ∙ 4H2O) are secondary hydrous sulfate minerals, mostly formed because of oxygenation and weathering events of primary sulfides on Earth. Acidophiles, mainly archaea and bacteria, and specifically anaerobic methanogens (methane-producing microorganisms) have been detected in regions where alteration of primary sulfide phases to secondary sulfates are predominant. These secondary sulfate minerals are significant in defining past geological environments on Earth and other rocky planets such as Mars. We discuss here the results of chemical and spectral analyses of copiapite and rozenite found in massive sulfide zones associated with banded iron formations (BIFs) in the Wayanad region in Kerala, southern India. The results from different spectroscopic techniques — VNIR reflectance, TIR (emitted radiation), FTIR, Laser Raman, and X-ray diffraction — were recorded and matched with existing literatures and databases. Different matching algorithms like Spectral Feature Fitting (SFF), Spectral Similarity Value (SSV), Modified Spectral Angle Similarity (MSAS), and Pearsonian Correlation Coefficient (PCC) were utilized in order to quantitatively match the reflectance spectra with the reference spectra (USGS and RELAB). The genesis of copiapite and rozenite in the area is through seasonal and temporary development of an acidic environment, with the high elevations of the Wayanad plateau providing the necessary low-humidity condition during the dry season. Copiapite and rozenite were detected as alteration products in different regions on Mars, which can provide insights into environmental conditions that prevailed during the biogeochemical evolution of Mars. The study of copiapite and rozenite formation in a terrestrial environment will therefore enhance our understanding of similar mineral precipitation, genesis, and fluvial processes on Mars. Future missions to Mars could also use the data obtained from the present study to calibrate similar instruments to detect these secondary sulfate minerals.
Dynamic Graph-Based Anomaly Detection in the Electrical Grid

Given sensor readings over time from a power grid, how can we accurately detect when an anomaly occurs? A key part of achieving this goal is to use the network of power grid sensors to quickly detect, in real-time, when any unusual events, whether natural faults or malicious, occur on the power grid. Existing bad-data detectors in the industry lack the sophistication to robustly detect broad types of anomalies, especially those due to emerging cyber-attacks, since they operate on a single measurement snapshot of the grid at a time. New ML methods are more widely applicable, but generally do not consider the impact of topology change on sensor measurements and thus cannot accommodate regular topology adjustments in historical data. Hence, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DynWatch</small> , a domain knowledge based and topology-aware algorithm for anomaly detection using sensors placed on a dynamic grid. Our approach is accurate, outperforming existing approaches by 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> or more (F-measure) in experiments; and fast, averaging less than 1.7 ms per time tick per sensor on a 60K+ branch case using a laptop computer, and scaling linearly with the size of the graph.
Hierarchical Action Classification with Network Pruning

Research on human action classification has made significant progresses in the past few years. Most deep learning methods focus on improving performance by adding more network components. We propose, however, to better utilize auxiliary mechanisms, including hierarchical classification, network pruning, and skeleton-based preprocessing, to boost the model robustness and performance. We test the effectiveness of our method on three commonly used testing datasets: NTU RGB+D 60, NTU RGB+D 120, and Northwestern-UCLA Multiview Action 3D. Our experiments show that our method can achieve either comparable or better performance than state-of-the-art methods on all three datasets. In particular, our method sets up a new baseline for NTU 120, the largest dataset among the three. We also analyze our method with extensive comparisons and ablation studies.
Small Blob Detector Using Bi-Threshold Constrained Adaptive Scales

Recent advances in medical imaging technology bring great promises for medicine practices. Imaging biomarkers are discovered to inform disease diagnosis, prognosis, and treatment assessment. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. The challenges of detecting objects in images, particularly small objects known as blobs, include low image resolution, image noise and overlap among the blobs. This research proposes a Bi-Threshold Constrained Adaptive Scale (BTCAS) blob detector to uncover the relationship between the U-Net threshold and the Difference of Gaussian (DoG) scale to derive a multi-threshold, multi-scale small blob detector. With lower and upper bounds on the probability thresholds from U-Net, two binarized maps of the distance are rendered between blob centers. Each blob is transformed to a DoG space with an adaptively identified local optimum scale. A Hessian convexity map is rendered using the adaptive scale, and the under-segmentation typical of the U-Net is resolved. To validate the performance of the proposed BTCAS, a 3D simulated dataset (n = 20) of blobs, a 3D MRI dataset of human kidneys and a 3D MRI dataset of mouse kidneys, are studied. BTCAS is compared against four state-of-the-art methods: HDoG, U-Net with standard thresholding, U-Net with optimal thresholding, and UH-DoG using precision, recall, F-score, Dice and IoU. We conclude that BTCAS statistically outperforms the compared detectors.
Provably Safe and Scalable Multivehicle Trajectory Planning

The Hamilton–Jacobi (HJ) reachability is a promising tool for guaranteeing goal satisfaction and safety for multivehicle systems. However, a direct application of HJ reachability in most cases becomes intractable due to its exponentially scaling computational complexity with respect to the number of vehicles. In the work by Chen <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2018), the sequential trajectory planning (STP) method was proposed, which allows safe, multiple-vehicle trajectory planning to be done with a computation complexity that scales linearly with the number of vehicles. However, the STP computation is still not tractable for large-scale systems using the currently available tools. In this work, we introduce BEACLS, a C++-based reachability toolbox, that can leverage GPU parallelization to improve the computation speed of HJ reachability by nearly 100 times compared with the existing MATLAB implementations. We then combine BEACLS with STP for the safe, large-scale multiple-unmanned aerial vehicle (UAV) planning in a city environment and a multicity environment. We show that intuitive multilane structures naturally emerge, and the size of disturbances and the vehicle density are the primary factors determining the number and width of lanes. We also extend the STP method to safely account for an adversarial intruder during trajectory planning. In the proposed formulation, the number of vehicles that need to replan is a design parameter that can be chosen based on the computational resources available during run time. The proposed formulation along with BEACLS provides both an algorithm and an efficient computational tool for resilient, large-scale multiple-vehicle trajectory planning.
Learning the Global Descriptor for 3-D Object Recognition Based on Multiple Views Decomposition

The key point of view based strategies for the analysis of 3D object is to obtain a global descriptor from a collection of its rendered views on 2D images. The views are always redundantly sampled as to ensure the completeness of the information. In this paper, we bring new insight into the study of multi-view object recognition, which models an object as a View Mixture Model (VMM). We argue that each object represented by the multiple views can be decomposed into just a few latent views. Based on the VMM, we introduce a decomposition module to mine the representations of these latent views for the construction of a compact and comprehensive descriptor. After that, we further propose a view alignment module to ensure the descriptor is robust to the variation of view permutation. We evaluate our method on the ModelNet-40, ModelNet-10 and ShapeNetCore55 datasets. The experimental results show that our method can learn efficient and comprehensive representation for 3D objects, and achieves state-of-the-art performance on both the 3D object classification and retrieval tasks. Lastly, experiments are conducted for benchmarking various popular CNN backbones on the 3D object recognition task, with a view to achieving fair comparisons and promoting the future research in this area. Codes for our paper are released: &#x201C;<uri>https://github.com/hjjpku/multi_view_sort</uri> &#x201D;.
LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB

While short range 3D pedestrian detection is sufficient for emergency breaking, long range detections are required for smooth breaking and gaining trust in autonomous vehicles. The current state-of-the-art on the KITTI benchmark performs suboptimal in detecting the position of pedestrians at long range. Thus, we propose an approach specifically targeting long range 3D pedestrian detection (LRPD), leveraging the density of RGB and the precision of LiDAR. Therefore, for proposals, RGB instance segmentation and LiDAR point based proposal generation are combined, followed by a second stage using both sensor modalities symmetrically. This leads to a significant improvement in mAP on long range compared to the current state-of-the art. The evaluation of our LRPD approach was done on the pedestrians from the KITTI benchmark.
Classifier chains for positive unlabelled multi-label learning

In traditional multi-label setting it is assumed that all relevant labels are assigned to the given instance. In positive unlabelled setting, only some of relevant labels are assigned. The appearance of a label means that the instance is really associated with this label, while the absence of the label does not imply that this label is not proper for the instance. For example, when predicting multiple diseases in one patient, some diseases can be undiagnosed however it does not mean that the patient does not have these diseases. Classifier chains are one of the most popular and successful methods used in standard multi-label classification, mainly due to their simplicity and high predictive power. However, it turns out that adaptation of classifier chains to positive unlabelled framework is not straightforward, due to the fact that the true target variables are observed only partially and therefore they cannot be used directly to train the models in the chain. The partial observability concerns not only the current target variable in the chain but also the feature space, which additionally increases the difficulty of the problem. In this paper we investigate the possibility of using classifier chains in positive unlabelled setting. We propose two methods in which we modify classifiers in the chain in order to take into account partial data observability. In the first method (called CCPU) we scale the output probabilities of the consecutive classifiers in the chain. In the second method (called CCPUW) we minimize weighted empirical risk, with weights depending on prior probabilities of the target variables. Moreover, both methods use modified feature spaces. The predictive performance of the proposed methods is studied on real multi-label datasets for different positive unlabelled settings.
Light extinction spectroscopy applied to polystyrene colloids: Sensitivity to complex refractive index uncertainties and to noise

The measurement of particle size distribution (PSD) in colloids and nanofluids presents many challenges, especially when it requires to be conducted in-situ and real-time. Our work aims to assess the capabilities of Light Extinction Spectroscopy (LES) technique to determine concentration and volumetric PSD (vPSD) of colloids and nanofluids. Numerical simulations are performed to verify robustness of LES data inversion algorithm and to identify the most relevant uncertainty sources. Experiments are carried out on well-dispersed water based colloids containing Polystyrene particles with median diameters at 120 nm and 300 nm. LES results are compared with supplier’s data, 3D-DLS measurements, and SEM images. Via sensitivity analyses, LES sensitivity to particle complex refractive index spectrum and to system noise level are examined. To improve accuracy, we limit LES data inversion at shorter wavelengths where high SNR is acquired. The noise is experimentally characterized and embedded into numerical simulations to better mimic the real conditions. We show that while noise level affects mostly vPSDs’ peak height, small inaccuracies in complex refractive index spectrum deteriorate the stability of the inversion, especially for smaller vPSDs. To overcome the latter, we carry out a preliminary in-situ calibration on particle complex refractive index spectrum. Accordingly, results are substantially improved with maximal discrepancies smaller than 3% for median and volume mean diameters, and lower than 13% for number concentrations. Besides, the retrieval of bi-modal distribution is promising. In conclusion, our methodology can be considered as a guideline to evaluate the applicability and accuracy of the LES technique in colloids.