Reviewerly/exHarmony huggingface dataset:
ResDepth: Learned Residual Stereo Reconstruction

We propose an embarrassingly simple but very effective scheme for high-quality dense stereo reconstruction: (i) generate an approximate reconstruction with your favourite stereo matcher; (ii) rewarp the input images with that approximate model; (iii) with the initial reconstruction and the warped images as input, train a deep network to enhance the reconstruction by regressing a residual correction; and (iv) if desired, iterate the refinement with the new, improved reconstruction. The strategy to only learn the residual greatly simplifies the learning problem. A standard Unet without bells and whistles is enough to reconstruct even small surface details, like dormers and roof substructures in satellite images. We also investigate residual reconstruction with less information and find that even a single image is enough to greatly improve an approximate reconstruction. Our full model reduces the mean absolute error of state-of-the-art stereo reconstruction systems by >50%, both in our target domain of satellite stereo and on stereo pairs from the ETH3D benchmark.
AT Makers: A Multidisciplinary Approach to Co-Designing Assistive Technologies by Co-Optimizing Expert Knowledge

Assistive technology (AT) improves the functional abilities of individuals with disabilities and enables them to perform activities of daily living independently. Custom-made ATs have recently received considerable attention because generic, ready-made, as-is AT solutions often do not properly address the unique and intricate issues arising from physical impairments. In this paper, we report on a series of workshops with the goal of studying how three distinct types of participants—occupational therapists (OTs), industrial designers (IDs), and an end-user with physical disabilities—co-designed do-it-yourself (DIY) ATs. We conducted a total of four co-design workshops in which we investigated the benefits of a multidisciplinary approach in designing AT solutions. We did so primarily by studying OT–ID–user knowledge exchange processes. The workshop results indicate that such collaboration empowers participants in a synergistic manner, thus giving rise to a combined expertise that is greater than the sum of their isolated skills, knowledge, and expertise. In other words, this combined expertise—comprised of clinical expertise; design expertise; and personal experiences, insights, and knowledge—resulted in the co-creation of novel and advanced AT solutions.
Signature Recognition Using Machine Learning

Signatures are popularly used as a method of personal identification and confirmation. Many certificates such as bank checks and legal activities need signature verification. Verifying the signature of a large number of documents is a very difficult and time-consuming task. As a result, explosive growth has been observed in biometric personal verification and authentication systems that relate to unique quantifiable physical properties (fingerprints, hand, and face, ear, iris, or DNA scan) or behavioral characteristics (gait, sound, etc.). Several methods are used to describe the ability of the suggested system in specifying the genuine signatures from the forgeries. This approach presents a new technique for signature verification and recognition, using a tow dataset for training the model by a siamese network.
Cloud computing model for big data processing and performance optimization of multimedia communication

In this paper, the technical aspects of big data processing of multimedia communication are deeply studied. Firstly, we propose a cloud implementation method of radial basis function neural network which is based on Map-Reduce on cloud computing cluster. Secondly, in order to meet the needs of big data processing of multimedia communication, the map-Reduce-based error back-propagation algorithm is trained to effectively map the effective mapping mechanism of multi-layer neural networks. For a cloud algorithm on a cloud computing cluster and a serial algorithm on a single processor, the time required to implement the algorithm is theoretically derived, and the cloud algorithm and performance parameters (acceleration ratio) on the cloud cluster are evaluated, the optimal number and minimum number of data nodes). Finally, the experimental results show that compared with the existing algorithms, the cloud algorithm proposed in this paper has better acceleration speed, faster convergence speed and fewer iterations.
An Improved Fuzziness based Random Vector Functional Link Network for Liver Disease Detection

There are three challenges in real-life disease detection scenarios: 1) the number of open samples is small; 2) the difficulty and cost of labeling the samples are very high; 3) The class distribution of the samples is extremely unbalanced. To solve these problems, we combine the Synthetic Minority Oversampling TEchnique (SMOTE) with the Fuzziness based Random Vector Functional Link network (F-RVFL) and propose an Improved F-RVFL algorithm (IF-RVFL) in this paper. The proposed IF-RVFL is a semi-supervised learning algorithm using the self-training strategy, which can make full use of a large number of unlabeled samples to improve the performance of the model. At the same time, the SMOTE technique enables the IFRVFL to effectively solve the class imbalanced problem. The effectiveness of the proposed IF-RVFL has been verified on a reallife liver disease data set. Extensive experimental results show that the IF-RVFL algorithm can achieve better generalization ability than the RVFL, F-RVFL, and their variants. IF-RVFL also provides a new technique with great potential for other disease detection.
Online trajectory planning and control of a MAV payload system in dynamic environments

Abstract Micro Aerial Vehicles (MAVs) can be used for aerial transportation in remote and urban spaces where portability can be exploited to reach previously inaccessible and inhospitable spaces. Current approaches for path planning of MAV swung payload system either compute conservative minimal-swing trajectories or pre-generate agile collision-free trajectories. However, these approaches have failed to address the prospect of online re-planning in uncertain and dynamic environments, which is a prerequisite for real-world deployability. This paper describes an online method for agile and closed-loop local trajectory planning and control that relies on Non-Linear Model Predictive Control and that addresses the mentioned limitations of contemporary approaches. We integrate the controller in a full system framework, and demonstrate the algorithm’s effectiveness in simulation and in experimental studies. Results show the scalability and adaptability of our method to various dynamic setups with repeatable performance over several complex tasks that include flying through a narrow opening and avoiding moving humans.
No Reference Quality Assessment for 3D Synthesized Views by Local Structure Variation and Global Naturalness Change

Depth image based rendering (DIBR) has been widely used to generate different virtual viewpoints of the same scene from the new perspective. However, DIBR tends to introduce annoying artifacts including blurring, discontinuity, blocking, and stretching, etc.. Thus, to improve DIBR performance, it is important to accurately measure the visual quality of synthesized views. In this paper, we propose a novel and effective no reference (NR) quality assessment method for 3D synthesized views by local variation and global change (LVGC). More specifically, we firstly compute the Gaussian derivatives for the input image to extract structure and chromatic features. Then, we use the local binary pattern (LBP) operator to encode the structure and chromatic feature maps, which are used to calculate quality-aware features to measure the local structural and chromatic distortion. Besides, we extract luminance features by global change to evaluate the naturalness of 3D synthesized views. With these extracted features, we utilize random forest regression (RFR) to train the quality prediction model from visual features to human ratings. Experimental results on three public benchmark databases demonstrate the effectiveness of our method on estimating visual quality of 3D synthesized views.
Headland turning algorithmization for autonomous N-trailer vehicles in agricultural scenarios

Articulated vehicles composed of a tractor and several passive trailers are commonly used for transportation purposes in agricultural applications. The increment of the number of trailers increases the payload capacity, but on the other hand, it also implies serious motion constraints, especially in turning scenarios where there is a greater risk of collisions with the crops. In this context, to reduce dangerous maneuvers during the headland turning scenarios, this paper presents a headland turning algorithmization for the N-trailers, characterized by unifying the motion planning stage with the motion control stage, in contrast to most of the available solutions which treat these stages independently. The proposed algorithmization delivers an admissible headland reference path and the location of the vehicle guidance point for the path-following task, to reduce both possible collisions with the crop and the inter-segment collisions. The proposed approach was validated by solving several illustrative problems which address various field/crop dimensions and vehicles with different number of trailers. The results showed that the proposed system can find and execute a safe maneuver in a broad range of known situations from the agricultural domain.
Certificate Injection-Based Encrypted Traffic Forensics in AI Speaker Ecosystem

AI Speakers are typical cloud-based internet of things (IoT) devices that store a variety of information regarding users on the cloud. Although analyzing encrypted traffic between these devices and the cloud, as well as the artifacts stored there, is an important research topic from the perspective of cloud-based IoT forensics, studies on directly analyzing encrypted traffic between AI Speakers and the cloud remain insufficient. In this study, we propose a forensic model that can collect and analyze encrypted traffic between an AI Speaker and the cloud based on a certificate injection. The proposed model consists of porting AI Speaker image on Android device, porting AI Speaker image using QEMU (Quick EMUlator), running exploit using the AI Speaker app vulnerability, rewriting Flash memory using H/W interface, and reworking and updating Flash memory. These five forensic methods are used to inject the certificate into AI Speakers. The proposed model shows that we can analyze encrypted traffic against various AI Speakers such as an Amazon Echo Dot, Naver Clova, SKT NUGU Candle, SKT NUGU, and KT GiGA Genie, and obtain artifacts stored on the cloud. In addition, we develop a verification tool that collects artifacts stored on KT GiGA Genie cloud.
Lateral refinement network for contour detection

Abstract There has been significant progress in contour detection with the development of convolutional neural networks. To improve the detection performance of the object contour in the complex scenes, we proposed a novel lateral refinement network (LRNet) that extracts feature information from multiple refinement modules. In addition, we modified LRNet to train an effective contour detector, called lateral refinement contour (LRC). LRNet expands the generic refinement architecture and has different refinement levels that make the refinement network deeper to extract richer convolutional features. The low-level refinement modules explicitly exploit information available along the side output of the down-sampling process, and the high-level refinement modules attempt to fuse the low-level output features. The proposed method improves the performance of image-to-image predictions by deep-stacking all the meaningful refinement levels in a holistic manner. Using the pre-trained VGG16 network and Resnet network as backbones to train LRNet, we achieved state-of-the-art performance on several available datasets. The experimental results on the BSDS dataset are ODS = 0.816 and ODS = 0.820, and the experimental results on the NYUD-v2 dataset are ODS = 0.761 and ODS = 0.760. Especially, the results on BSDS500 surpassed the human-level performance under stricter criteria.
Probabilistic inference of Bayesian neural networks with generalized expectation propagation

Deep learning plays an important role in the field of machine learning. However, deterministic methods such as neural networks cannot capture the model uncertainty. Bayesian neural network (BNN) are recently under consideration since Bayesian models provide a theoretical framework to infer model uncertainty. Since it is often difficult to find an analytical solution for BNNs, an effective and efficient approximate inference method is very important for model training and prediction. The generalized version of expectation propagation (GEP) was recently proposed and considered a powerful approximate inference method, which is based on the minimization of Kullback–Leibler (KL) divergence of the true posterior and the approximate distributions. In this paper, we further instantiate the GEP to provide an effective and efficient approximate inference method for BNNs. We assess this method on BNNs including fully connected neural networks and convolutional neural networks on multiple benchmark datasets and show a better performance than some state-of-the-art approximate inference methods.
Discriminative sampling via deep reinforcement learning for kinship verification

In this paper, we propose a discriminative sampling method to select most effective negative samples via deep reinforcement learning for kinship verification. Unlike most existing facial kinship verification methods which focus on extracting effective features with the random sampling strategy, we develop a deep reinforcement learning method to select samples which are more suitable for learning discriminative features, so that the overall performance can be improved. Specifically, our method uses two subnetworks to achieve the kinship verification task: one DQN-based sampling network to filter the negative samples, and one multi-layer convolutional network to verify the kin relationship. Experimental results on the KinFaceW-I and KinFaceW-II datasets show the superiority of our proposed approach over the state-of-the-arts.
Outlier Detection in Wireless Sensor Networks using Machine Learning Techniques: A Survey

Now-a-days, Internet of Things (IoT) based systems are developing very fast which have various type of wireless sensor networks (WSN) behind it. These networks have various applications viz., healthcare, agricultural, industrial and military applications. Anomaly or outlier detection is one of the important research problems in such applications of wireless sensor networks where a huge amount of data is collected. Anomaly detection helps to find out defective, erroneous, and noisy nodes. There are many techniques which are used to detect the anomalies. Machine learning algorithm (MLA) based approaches are very much useful and effective among them and provides better accuracy. This paper presents a brief study on such approaches.
Pipeline-Based Linear Scheduling of Big Data Streams in the Cloud

Nowadays, there is an accelerating need to efficiently and timely handle large amounts of data that arrives continuously.Streams of big data led to the emergence of several Distributed Stream Processing Systems (DSPS) that assign processing tasks to the available resources (dynamically or not) and route streaming data between them.Efficient scheduling of processing tasks can reduce application latencies and eliminate network congestions.However, the available DSPSs' in-built scheduling techniques are far from optimal.In this work, we extend our previous work, where we proposed a linear scheme for the task allocation and scheduling problem.Our scheme takes advantage of pipelines to handle efficiently applications, where there is need for heavy communication (all-to-all) between tasks assigned to pairs of components.In this work, we prove that our scheme is periodic, we provide a communication refinement algorithm and a mechanism to handle many-to-one assignments efficiently.For concreteness, our work is illustrated based on Apache Storm semantics.The performance evaluation depicts that our algorithm achieves load balance and constraints the required buffer space.For throughput testing, we compared our work to the default Storm scheduler, as well as to R-Storm.Our scheme was found to outperform both the other strategies and achieved an average of 25%-40% improvement compared to Storm's default scheduler under different scenarios, mainly as a result of reduced buffering (≈ 45% less memory).Compared to R-storm, the results indicate an average of 35%-45% improvement.
Extracting highlights of scientific articles: A supervised summarization approach

Scientific articles can be annotated with short sentences, called highlights, providing readers with an at-a-glance overview of the main findings. Highlights are usually manually specified by the authors. This paper presents a supervised approach, based on regression techniques, with the twofold aim at automatically extracting highlights of past articles with missing annotations and simplifying the process of manually annotating new articles. To this end, regression models are trained on a variety of features extracted from previously annotated articles. The proposed approach extends existing extractive approaches by predicting a similarity score, based on n-gram co-occurrences, between article sentences and highlights. The experimental results, achieved on a benchmark collection of articles ranging over heterogeneous topics, show that the proposed regression models perform better than existing methods, both supervised and not.
Bitstream-Based Model Standard for 4K/UHD: ITU-T P.1204.3 — Model Details, Evaluation, Analysis and Open Source Implementation

With the increasing requirement of users to view high-quality videos with a constrained bandwidth, typically realized using HTTP-based adaptive streaming, it becomes more and more important to determine the quality of the encoded videos accurately, to assess and possibly optimize the overall streaming quality. In this paper, we describe a bitstream-based no-reference video quality model developed as part of the latest model-development competition conducted by ITU-T Study Group 12 and the Video Quality Experts Group (VQEG), “P.NATS Phase 2”. It is now part of the new P.1204 series of Recommendations as P.1204.3. It can be applied to bitstreams encoded with H.264/AVC, HEVC and VP9, using various encoding options, including resolution, bitrate, framerate and typical encoder settings such as number of passes, rate control variants and speeds. The proposed model follows an ensemble-modelling-inspired approach with weighted parametric and machine-learning parts to efficiently leverage the performance of both approaches. The paper provides details about the general approach to modelling, the features used and the final feature aggregation. The model creates per-segment and per-second video quality scores on the 5-point Absolute Category Rating scale, and is applicable to segments of 5–10 seconds duration. It covers both PC/TV and mobile/tablet viewing scenarios. We outline the databases on which the model was trained and validated as part of the competition, and perform an additional evaluation using a total of four independently created databases, where resolutions varied from 360p to 2160p, and frame rates from 15–60fps, using realistic coding and bitrate settings. We found that the model performs well on the independent dataset, with a Pearson correlation of 0.942 and an RMSE of 0.42. We also provide an open-source reference implementation of the described P.1204.3 model, as well as the multi-codec bitstream parser required to extract the input data, which is not part of the standard.
Lightweight Online Profiling-Based Configuration Adaptation for Video Analytics System in Edge Computing

Video Analytics System has emerged as a promising technology to realize deep neural network based intelligent applications for video streams. Its objective is to maximize the video analytics performance of video streams, such as accuracy, while utilizing the limited computing resource capacity efficiently. Existing video analytics systems attempt to adapt configurations to optimize resource-accuracy tradeoffs under limited resource capacity. Especially, several works recently propose a configuration adaptation algorithm based on online profiling to overcome the inefficiency of one-time offline profiling caused by the dynamics of the configuration's impact on video analytics accuracy. However, their systems are inefficient or limited to process the video analytics of multiple video streams in a GPU-enabled edge server. Furthermore, their online profiling methods still leads to a high profiling cost. In this paper, we design a video analytics system to adapt configurations for optimizing the resource-accuracy tradeoffs of multiple video streams with respect to frame rate and resolution fully under limited resource capacity of a GPU-enable edge server. In addition, we propose a lightweight online profiling method, utilizing the underlying characteristics of the video objects. Then, based on it, we propose a configuration adaptation algorithm to find the best configuration of each video stream and minimize accuracy degradation of multiple video streams under limited resource capacity of a GPU-enable edge server. To evaluate the proposed algorithm, we use a subset of surveillance videos and annotations from VIRAT 2.0 Ground dataset. The experimental results show that, in a GPU-enabled edge server, our video analytics system achieves the optimal configuration adaptation on resource-accuracy tradeoffs and our algorithm reduce the profiling cost of existing systems significantly while achieving the video analytics performance comparable to them.
Lightweight authenticated semi-quantum key distribution protocol without trojan horse attack

A lightweight authenticated semi-quantum key distribution (LASQKD) protocol is proposed for secure communication. By pre-sharing a master key and adopting a one-way communication strategy, the proposed protocol allows a quantum user and a classical user to share secret keys without using an authenticated classical channel and any Trojan horse detection device. Security analyses show that the proposed LASQKD protocol is free from well-known attacks, and a simulation implementation proves the feasibility of the proposed protocol. Compared to existing authenticated semi-quantum key distribution protocols, the protocol proposed herein provides improved practicality, indicating its potential for use in modern quantum systems.