Reviewerly/exHarmony huggingface dataset:
Video Hashing with DCT and NMF

Abstract Video hashing is a novel technique of multimedia processing and finds applications in video retrieval, video copy detection, anti-piracy search and video authentication. In this paper, we propose a robust video hashing based on discrete cosine transform (DCT) and non-negative matrix decomposition (NMF). The proposed video hashing extracts secure features from a normalized video via random partition and dominant DCT coefficients, and exploits NMF to learn a compact representation from the secure features. Experiments with 2050 videos are carried out to validate efficiency of the proposed video hashing. The results show that the proposed video hashing is robust to many digital operations and reaches good discrimination. Receiver operating characteristic (ROC) curve comparisons illustrate that the proposed video hashing outperforms some state-of-the-art algorithms in classification between robustness and discrimination.
Constrained discounted Markov decision processes with Borel state spaces

We study discrete-time discounted constrained Markov decision processes (CMDPs) with Borel state and action spaces. These CMDPs satisfy either weak (W) continuity conditions, that is, the transition probability is weakly continuous and the reward function is upper semicontinuous in state–action pairs, or setwise (S) continuity conditions, that is, the transition probability is setwise continuous and the reward function is upper semicontinuous in actions. Our main goal is to study models with unbounded reward functions, which are often encountered in applications, e.g., in consumption/investment problems. We provide some general assumptions under which the optimization problems in CMDPs are solvable in the class of randomized stationary policies and in the class of chattering policies introduced in this paper. If the initial distribution and transition probabilities are atomless, then using a general "purification result" of Feinberg and Piunovskiy we show the existence of a deterministic (stationary) optimal policy. Our main results are illustrated by examples.
Use Case Diagram Similarity Measurement: A New Approach

Building a UML diagram from scratch usually requires a lot of resources of software developer. UML reuse is the solution for speeding up software development process. Previous researches utilized diagram similarity measurement for finding reusable diagrams. Many researchers focus on class and sequence diagrams. This research introduces a new approach for measuring use case diagram similarity. This approach combines structural information and lexical information for measuring the similarity of two diagrams. It used to measure structural similarity, i.e. relationship among and between actors and use cases. It also uses Wordnet, WuPalmer, and Cosine Similarity to measure the lexical information semantically, i.e. and text extracted from actors and use cases. The initial result shows that the semantic and structural information could be used as parameters to measure similarity between two use case diagrams.
From Design to Development to Evaluation of a Pregnancy App for Low-Income Women in a Community-Based Setting

Due to the increasing rates of mobile phone adoption in low-income communities in the United States, mobile apps can be used to increase access to prenatal care in this population. But, design and evaluation studies in this area are rare. Using existing guidelines and needs assessment results, we developed, MomLink, that aligned with the needs of the target population and their community health workers. Nine women from a low-income community evaluated the app and provided suggestions to improve its design. After making suggested changes, we deployed MomLink and its corresponding provider app for a pre-pilot evaluation. The evaluation met an unexpected barrier resulting in low usage of the system by both women and providers. Based on our findings, we discuss opportunities to improve mHealth apps for the target population and better ways to collaborate with community-based partners.
DNN-based Acoustic-to-Articulatory Inversion using Ultrasound Tongue Imaging

Speech sounds are produced as the coordinated movement of the speaking organs. There are several available methods to model the relation of articulatory movements and the resulting speech signal. The reverse problem is often called as acoustic-to-articulatory inversion (AAI). In this paper we have implemented several different Deep Neural Networks (DNNs) to estimate the articulatory information from the acoustic signal. There are several previous works related to performing this task, but most of them are using ElectroMagnetic Articulography (EMA) for tracking the articulatory movement. Compared to EMA, Ultrasound Tongue Imaging (UTI) is a technique of higher cost-benefit if we take into account equipment cost, portability, safety and visualized structures. Seeing that, our goal is to train a DNN to obtain UT images, when using speech as input. We also test two approaches to represent the articulatory information: 1) the EigenTongue space and 2) the raw ultrasound image. As an objective quality measure for the reconstructed UT images, we use MSE, Structural Similarity Index (SSIM) and Complex- Wavelet SSIM (CW-SSIM). Our experimental results show that CW-SSIM is the most useful error measure in the UTI context. We tested three different system configurations: a) simple DNN composed of 2 hidden layers with 64x64 pixels of an UTI file as target; b) the same simple DNN but with ultrasound images projected to the EigenTongue space as the target; c) and a more complex DNN composed of 5 hidden layers with UTI files projected to the EigenTongue space. In a subjective experiment the subjects found that the neural networks with two hidden layers were more suitable for this inversion task.
Hindsight Experience Replay With Experience Ranking

Reinforcement Learning (RL) algorithms face difficulties when dealing with robotic tasks in sparse reward settings and as a result, they often require millions of interactions with the environment to learn successfully. A recent algorithm Hindsight Experience Replay (HER) was introduced to tackle this difficulty by adding virtual goals and therefore increase significantly the sample-efficiency by learning in transitions when the robot does not achieve the original goal. However, these additional goals are sampled randomly from each episode batch of transitions, which might have no relationship with the original goal. This might make learning with the original goal slower due to the bad influence of irrelevant virtual goals. In this paper, we address this issue by applying experience ranking (ER) to these additional goals. We first compare each sampled virtual goal and the original goal and then compare the difference with a threshold. Transitions in which the robot achieves a virtual goal that is not close to the original goal are filtered out, and the remaining are used for training the policy. The improvement in learning performance is validated in four simulated robotic tasks. The experiment results show significant improvement in terms of the learning speed and robustness.
Recent Trends in Context Exploitation for Information Fusion and AI

AI is related to information fusion (IF). Many methods in AI that use perception and reasoning align to the functionalities of high‐level IF (HLIF) operations that estimate situational and impact states. To achieve HLIF sensor, user, and mission management operations, AI elements of planning, control, and knowledge representation are needed. Both AI reasoning and IF inferencing and estimation exploit context as a basis for achieving deeper levels of understanding of complex world conditions. Open challenges for AI researchers include achieving concept generalization, response adaptation, and situation assessment. This article presents a brief survey of recent and current research on the exploitation of context in IF and discusses the interplay and similarities between IF, context exploitation, and AI. In addition, it highlights the role that contextual information can provide in the next generation of adaptive intelligent systems based on explainable AI. The article describes terminology, addresses notional processing concepts, and lists references for readers to follow up and explore ideas offered herein.
Learning disentangling and fusing networks for face completion under structured occlusions

Face completion aims to generate semantically new pixels for missing facial components. It is a challenging generative task due to large variations of face appearance. This paper studies generative face completion under structured occlusions. We treat the face completion and corruption as disentangling and fusing processes of clean faces and occlusions, and propose a jointly disentangling and fusing Generative Adversarial Network (DF-GAN). First, three domains are constructed, corresponding to the distributions of occluded faces, clean faces and structured occlusions. The disentangling and fusing processes are formulated as the transformations between the three domains. Then the disentangling and fusing networks are built to learn the transformations from unpaired data, where the encoder-decoder structure is adopted and allows DF-GAN to simulate structure occlusions by modifying the latent representations. Finally, the disentangling and fusing processes are unified into a dual learning framework along with an adversarial strategy. The proposed method is evaluated on Meshface verification problem. Experimental results on four Meshface databases demonstrate the effectiveness of our proposed method for the face completion under structured occlusions.
Improving Skin Condition Classification with a Visual Symptom Checker Trained Using Reinforcement Learning

We present a visual symptom checker that combines a pre-trained Convolutional Neural Network (CNN) with a Reinforcement Learning (RL) agent as a Question Answering (QA) model. This method increases the classification confidence and accuracy of the visual symptom checker, and decreases the average number of questions asked to narrow down the differential diagnosis. A Deep Q-Network (DQN)-based RL agent learns how to ask the patient about the presence of symptoms in order to maximize the probability of correctly identifying the underlying condition. The RL agent uses the visual information provided by CNN in addition to the answers to the asked questions to guide the QA system. We demonstrate that the RL-based approach increases the accuracy more than 20% compared to the CNN-only approach, which only uses the visual information to predict the condition. Moreover, the increased accuracy is up to 10% compared to the approach that uses the visual information provided by CNN along with a conventional decision tree-based QA system. We finally show that the RL-based approach not only outperforms the decision tree-based approach, but also narrows down the diagnosis faster in terms of the average number of asked questions.
Bias-Compensated Diffusion Pseudolinear Kalman Filter Algorithm for Censored Bearings-Only Target Tracking

This letter proposes a novel bias-compensated diffusion pseudolinear Kalman filter algorithm for censored bearings-only target tracking. The proposed algorithm considers two biases caused by the censored bearing angle measurements and the correlation between the measurement vector and the pseudolinear noise. First, the inverse Mills ratio is used to rebuild the uncensored measurements, which can efficiently compensate the bias arising from the censored measurements. Then, we present a bias analysis for the censored diffusion pseudolinear Kalman filter to develop a bias compensation method, leading to the BC-DPLKF-C algorithm. Simulation results show the improved performance of the proposed Kalman filter compared with existing algorithms.
How Many Weights Can a Cyclic Code Have?

Upper and lower bounds on the largest number of weights in a cyclic code of given length, dimension and alphabet are given. An application to irreducible cyclic codes is considered. Sharper upper bounds are given for the special cyclic codes (called here strongly cyclic), {whose nonzero codewords have period equal to the length of the code}. Asymptotics are derived on the function $Γ(k,q),$ {that is defined as} the largest number of nonzero weights a cyclic code of dimension $k$ over $\F_q$ can have, and an algorithm to compute it is sketched. The nonzero weights in some infinite families of Reed-Muller codes, either binary or $q$-ary, as well as in the $q$-ary Hamming code are determined, two difficult results of independent interest.
Strain determination based on strain gauge-guided radial basis function and digital image correlation

Two methods are proposed to determine structure surface strain fields in the presence of discontinuities. A partition inverse sequence calculation (PISC) method is presented to calculate displacement based on image matching from the second-last to first image, sequentially. The final deformed image is selected as a reference image, and several areas of interest are identified along the crack boundary prior to calculation. A strain gauge-guided partial derivative of a radial basis function (SPD-RBF) method is introduced to obtain strain distributions. Experimental data from strain gauges that adhered to specimen surfaces are applied to estimate shape parameter and obtain guided values, followed by strain calculation based on PD-RBF. Two typical experiments are conducted to verify the validity and accuracy of the PISC and SPD-RBF. Moreover, the proposed methods are successfully applied to evaluate the strain distributions at the crack tip of a cast iron beam. The experimental results further validate the applicability of the proposed methods in handling deformation with discontinuities.
A Closer Look at Seagrass Meadows: Semantic Segmentation for Visual Coverage Estimation

Underwater imaging enables marine researchers to collect large datasets of seagrass images. These images can be used to monitor the health state of underwater meadows by estimating the area that is covered by seagrass and how this area changes over time. Since the manual analysis of such images is slow and error-prone, we follow the path of deep learning for automatic image analysis.Our contribution is the investigation of deep semantic segmentation for the specific task of seagrass coverage estimation. We evaluated multiple Deep Neural Network Architectures including the DeepLabv3Plus Network which performs best, with a mean intersection over union of 87.78%. The qualitative results in our experiments indicate that the Deep Learning approach is not only more accurate than a human but also multiple times faster in annotating underwater meadows. Our code is available on GitHub: https://enviewfulda.github.io/LookingForSeagrassSemanticSegmentation/.
Fundamentals of fully homomorphic encryption

research-article Share on Fundamentals of fully homomorphic encryption Author: Zvika Brakerski View Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 543–563https://doi.org/10.1145/3335741.3335762Published:04 October 2019Publication History 11citation218DownloadsMetricsTotal Citations11Total Downloads218Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Bangla Handwritten Basic Character Recognition Using Deep Convolutional Neural Network

Bangla is one of the mostly spoken languages all over the world, nonetheless, efforts on Bangla handwritten character recognition is not adequate. The use of Deep Convolutional Neural Network (DCNN) based classifiers has become a triumph over the state of art machine learning techniques. Using DCNN model to classify Bangla isolated basic characters can provide better output because of its ability to detect many hidden features from an image. In this paper, a new DCNN model, namely, BBCNet-15 is proposed for Bangla handwritten basic character recognition. the proposed model consists of 6 convolution layers, 6 max pooling layers, 2 fully connected layers followed by the softmax output unit. To avoid overfitting dropout regularization technique is used. The implementation of the proposed DCNN model is evaluated on a benchmark dataset, CMATERdb 3.1.2 which contains 50 character classes including 39 consonants and 11 vowels. The experimental evaluation of BBCNet-15 on the dataset provides a recognition accuracy of 96.40% which outperforms some prominent techniques in existence.
A Decade of Featured Transition Systems

Variability-intensive systems (VIS) form a large and heterogeneous class of systems whose behaviour can be modified by enabling or disabling predefined features. Variability mechanisms allows the adaptation of software to the needs of their users and the environment. However, VIS verification and validation (V&V) is challenging: the combinatorial explosion of the number of possible behaviours and undesired feature interactions are amongst such challenges. To tackle them, Featured Transitions Systems (FTS) were proposed a decade ago to model and verify the behaviours of VIS. In an FTS, each transition is annotated with a combination of features determining which variants can execute it. An FTS can model all possible behaviours of a given VIS. This compact model enabled us to create efficient V&V algorithms taking advantage of the behaviours shared amongst features resulting in a reduction of the V&V effort by several orders of magnitude. In this paper, we will cover the formalism, its applications and sketch promising research directions.
Texture-Based Classification of Significant Stenosis in CCTA Multi-view Images of Coronary Arteries

A stenosis is a coronary artery disease (CAD) that poses a high risk to the patient's life by narrowing or blocking completely the vessel. Critical luminal narrowings (i.e. significant stenoses) require urgent intervention, and thus, detecting these cases among all stenoses is vitally important. The performance of previous methods for automatically classifying significant stenosis is limited by the use of hand-crafted features or generic features that cannot represent properly the characteristics of this CAD. In this paper, we present a novel method for automatic classification of significant stenosis from coronary CT angiography scans (CCTA). Our method leverages a state-of-the-art feature extractor for texture classification that describe effectively the appearance of significant stenosis. We extract features from curved planar reformation (CPR) views of the coronary arteries: axial, sagittal, coronal, and two orthogonal diagonal-views. The final decision is made by an ensemble of the classification probabilities of each view, similar to the procedure radiologists follow in the diagnosis of significant stenosis. We evaluate our method using a CCTA-CPR dataset of 57 patients with ground truth annotations provided by three experienced experts (significant stenosis if luminal narrowing $$\ge $$ $$50\%$$ ). The results of our cross-validated experiments show state-of-the-art classification performance.
Low-Complexity Distributed-Arithmetic-Based Pipelined Architecture for an LSTM Network

Long short-term memory (LSTM) networks have addressed the shortcomings of recurrent neural networks, such as vanishing gradients and the lack of ability in developing connections across discontinuous parts of sequences. However, the implementations of state-of-the-art LSTM networks face the computational bottleneck of having multiple high-order matrix-vector multiplications (MVMs). This article presents a generalized approach to accelerate a circulant MVM (C-MVM), and hence, it is applicable to many neural networks. The proposed scheme presents a novel low-complexity distributed arithmetic (DA) architecture for optimizing C-MVMs. Unlike conventional offset binary coding-based DA (OBC-DA), it is based on separate generation and selection of partial products. Only one partial product generator (PPG) with several partial product selectors (PPSs) is required. The complexity of PPSs is reduced by sharing the minterms across Boolean expressions. Fine-grained pipelining is employed to achieve approximately one adder delay. From the implementation results, the proposed design with 512 × 512 LSTM layer occupies 74.54% less core area, consumes 68.66% less core power, offers 2.61 times more throughput, and 3.89 times more hardware efficiency over the best existing design.
DScGANS: Integrate Domain Knowledge in Training Dual-Path Semi-supervised Conditional Generative Adversarial Networks and S3VM for Ultrasonography Thyroid Nodules Classification

Semi-supervised learning can reduce the burden of manual label data and improve classification performance by learning with unlabelled data. However, due to the absence of label constraints, unlabelled data is usually ambiguous, which typically results in requiring large datasets to learn the correct feature space distribution. The inherently small sample characteristics of medical image datasets may make semi-supervised learning unstable, which may lead to mixed results and even degrade performance. The domain knowledge (DK) of the physician is of great value for disease diagnosis. In this paper, we propose to promote semi-supervised learning with DK and develop a DScGANS model (DScGAN (dual-path semi-supervised conditional generative adversarial networks) and S3VM (semi-supervised support vector machine)) to diagnose ultrasound thyroid nodules. DScGAN uses DK as a condition and multimodal ultrasound data for training. We concatenate the image representation of DScGAN learning and use it as the input of S3VM. DK will be used as a condition to constrain S3VM for thyroid nodule classification. The experimental results show that our proposed model can effectively avoid mixed results that may occur in semi-supervised learning with a small medical dataset with insufficient labels. Additionally, our model provides stable and advanced diagnostic performance and is potentially integrated into the thyroid ultrasound system.
Automatic Power Quality Events Recognition Using Modes Decomposition Based Online P-Norm Adaptive Extreme Learning Machine

This article presents an automatic recognition of power quality events (PQEs) by integrating variational mode decomposition (VMD) with Hilbert transform (HT) and the proposed online P-norm adaptive extreme learning machine (OPAELM). The robust parameters estimation capability from the highly nonstationary PQE patterns is presented using VMDHT method and a novel mode selection scheme is introduced based on the correlation coefficient. Three most efficient power quality indices are extracted and fed as an input to train and test the OPAELM classifier with a few existing advanced classifiers. The distinctive modes extraction, low computational burden, robust antinoise performance, short event recognition time, and outstanding recognition capability are the prime superiority expediencies of the VMDHT-OPAELM method. Finally, the proposed method is developed in Xilinx integrated synthesis environment (ISE) Design Suite 14.5 configured with MATLAB/Simulink software environment and implemented in a high-speed field-programmable gate array digital circuitry hardware platform to validate the cogency in real time.