Reviewerly/exHarmony huggingface dataset:
SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems

The railway transport system is critical infrastructure that is exposed to numerous man-made and natural threats, thus protecting this physical asset is imperative. Cyber security, privacy, and dependability (SPD) are also important, as the railway operation relies on cyber-physical systems (CPS) systems. This work presents SPD-Safe—an administration framework for railway CPS, leveraging artificial intelligence for monitoring and managing the system in real-time. The network layer protections integrated provide the core security properties of confidentiality, integrity, and authentication, along with energy-aware secure routing and authorization. The effectiveness in mitigating attacks and the efficiency under normal operation are assessed through simulations with the average delay in real equipment being 0.2–0.6 s. SPD metrics are incorporated together with safety semantics for the application environment. Considering an intelligent transportation scenario, SPD-Safe is deployed on railway critical infrastructure, safeguarding one outdoor setting on the railway’s tracks and one in-carriage setting on a freight train that contains dangerous cargo. As demonstrated, SPD-Safe provides higher security and scalability, while enhancing safety response procedures. Nonetheless, emergence response operations require a seamless interoperation of the railway system with emergency authorities’ equipment (e.g., drones). Therefore, a secure integration with external systems is considered as future work.
A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.
Living Donor-Recipient Pair Matching for Liver Transplant via Ternary Tree Representation With Cascade Incremental Learning

Visual understanding of liver vessels anatomy between the living donor-recipient (LDR) pair can assist surgeons to optimize transplant planning by avoiding non-targeted arteries which can cause severe complications. We propose to visually analyze the anatomical variants of the liver vessels anatomy to maximize similarity for finding a suitable Living Donor-Recipient (LDR) pair. Liver vessels are segmented from computed tomography angiography (CTA) volumes by employing a cascade incremental learning (CIL) model. Our CIL architecture is able to find optimal solutions, which we use to update the model with liver vessel CTA images. A novel ternary tree based algorithm is proposed to map all the possible liver vessel variants into their respective tree topologies. The tree topologies of the recipient's and donor's liver vessels are then used for an appropriate matching. The proposed algorithm utilizes a set of defined vessel tree variants which are updated to maintain the maximum matching options by leveraging the accurate segmentation results of the vessels derived from the incremental learning ability of the CIL. We introduce a novel concept of in-order digital string based comparison to match the geometry of two anatomically varied trees. Experiments through visual illustrations and quantitative analysis demonstrated the effectiveness of our approach compared to state-of-the-art.
Ecological Approach for Object Relationship Extraction in Elderly Care Robot

This paper proposes an ecological approach for object relationship extraction. First, we explain the ecological approach used to develop our previous works in the perception of a partner robot. Second, we developed a framework for finding the relation of objects based on scenarios of daily human activities. We represent the relationships among objects in a graph consisting of nodes and edges. Graph Convolutional Networks (GCN) is used to train the graph and perform semi-supervised classification. Third, we generate and prepare the datasets and then train the GCN. Finally, we present training visualization in two different scenarios. The proposed methods allow the system to identify the object based on its relationship. This approach is then used as a starting point to implement the ecological factors in elderly care robot applications.
Multi-Objective Optimization for Dynamic Resource Provisioning in a Multi-Cloud Environment using Lion Optimization Algorithm

Cloud computing offers surfeit of services like storage, computing power, run-time environment, network etc. that everyone is accustomed to use it in day-to-day lives. In cloud computing, resources need to be dynamically provisioned on a metered basis. Quality of Services(QoS) is promised like performance, scalability, efficiency, fault tolerance, availability, reliability, throughput, and so on. Several meta-heuristic nature-inspired optimization algorithms like Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO) etc. deployed to meet Service Level Agreement parameters like minimum downtime and low latency, but still have challenges in dynamically allocating resources. To overcome the above stated challenges, a new dynamic resource provisioning technique in a multi-cloud environment is proposed that uses Lion Optimization Algorithm(LOA) wherein characteristics of nomad and pride lion groups are taken into account. The multi-cloud environment provides the organization or customer to choose a provider that meets the specific requirements. As compared to PSO, this approach achieved better results while optimizing multiple objectives like completion time, average response time, makespan, cost, and average resource utilization. This study proves that the completion time and cost for LOA has outperformed when compared to PSO for a given number of tasks. The makespan and average response time for LOA improves slowly with more number of tasks as compared to PSO.
Lemmatization Algorithm Development for Bangla Natural Language Processing

Natural language processing (NLP) finds enormous applications in autonomous communication, while lemmatization is an essential preprocessing technique for simplification of a word to its origin-word in NLP. However, there is scarcity of effective algorithms in Bangla NLP. This leads us to develop a useful Bangla language lemmatization tool. Usually, some rule base stemming processes play the vital role of lemmatization in Bangla language processing as there is lack of Bangla lemmatization tool. In this paper, we propose a Bangla lemmatization framework using three effective lemmatization techniques based on data structures and dynamic programming. We have used Trie algorithm and developed a mapping algorithm named “Dictionary Based Search by Removing Affix (DBSRA)” based on data structure. We have applied both Trie and DBSRA lemmatization and selected the better one by considering the Levenshtein distance between the lemma and the original word. Eventually, we have experimented with Bangla language lemmatization among all three techniques and the framework. Among the three proposed techniques, the DBSRA performed better compared to others with an accuracy of 93.1 percent. The framework, developed by fusing three algorithms, came out with the highest efficiency of 95.89 percent. Contribution-This paper presents the development of three lemmatization algorithms and their fusion to develop a framework for Bangla Natural Language Processing.
Fake visual content detection using two-stream convolutional neural networks

Rapid progress in adversarial learning has enabled the generation of realistic-looking fake visual content. To distinguish between fake and real visual content, several detection techniques have been proposed. The performance of most of these techniques however drops off significantly if the test and the training data are sampled from different distributions. This motivates efforts towards improving the generalization of fake detectors. Since current fake content generation techniques do not accurately model the frequency spectrum of the natural images, we observe that the frequency spectrum of the fake visual data contains discriminative characteristics that can be used to detect fake content. We also observe that the information captured in the frequency spectrum is different from that of the spatial domain. Using these insights, we propose to complement frequency and spatial domain features using a two-stream convolutional neural network architecture called TwoStreamNet. We demonstrate the improved generalization of the proposed two-stream network to several unseen generation architectures, datasets, and techniques. The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors with the fusing of frequency and spatial domain streams also improving the generalization of the detector.
Deep Preset: Blending and Retouching Photos with Color Style Transfer

End-users, without knowledge in photography, desire to beautify their photos to have a similar color style as a well-retouched reference. However, recent works in image style transfer are overused. They usually synthesize undesirable results due to transferring exact colors to the wrong destination. It becomes even worse in sensitive cases such as portraits. In this work, we concentrate on learning low-level image transformation, especially color-shifting methods, rather than contextual features matching, then present a novel supervised approach for color style transfer. Furthermore, we propose a color style transfer named Deep Preset designed to 1) generalize the features representing the color transformation from content with natural colors to retouched reference, then blend it into the contextual features of content, 2) predict hyper-parameters (settings or preset) of the applied low-level color transformation methods, 3) stylize content image to have a similar color style as reference. We script Lightroom, a powerful tool in editing photos, to generate 600,000 training samples using 1,200 images from the Flick2K dataset and 500 user-generated presets with 69 settings. Experimental results show that our Deep Preset outperforms the previous works in color style transfer quantitatively and qualitatively. Our work is available at https://minhmanho.github.io/deep_preset/.
Evaluation of Chinese Sentiment Analysis APIs Based on Online Reviews

Over the last decade, online reviews on social media platforms have become one of the most influential information sources. With the help of sentiment analysis tools, this unstructured information can be converted into structured data and analyzed to extract consumers' opinions on products and services to assist important business decision-makings. Due to its great practical value, sentiment analysis using technologies like artificial intelligence has attracted attentions of researchers and practitioners. Many major Internet companies have developed their application program interface (API) to provide sentiment analysis services for individuals and businesses. This study aims to test and evaluate current mainstream Chinese sentiment analysis applications. First, authentic online reviews are collected from Ctrip.com, a major online travel Agency in China, using a crawler. These reviews are then analyzed using major Chinese sentiment analysis APIs. Meanwhile, some interviewees were asked to classify and rate these reviews as positive, neutral or negative as well using a survey. After that, human-API discrepancy is compared and evaluated. Our results revealed that classification errors of these APIs are mainly caused by incorrect word segmentation and fail to integrate the context into semantic interpretation.
Sense the pen: Classification of online handwritten sequences (text, mathematical expression, plot/graph)

This paper has a threefold contribution. First, it presents a novel online handwriting database captured using a digital/sensor pen (Apple pencil) and digital/sensor screen (iPad). The captured data are continuous streams of multi-dimensional points, analyzed and processed to classify handwritten sequences into plain text, mathematical expressions, and plots/graphs. Second, a new feature set for online handwritten sequence classification is proposed. The said feature set is used to establish a benchmark for the proposed dataset using various machine-learning classifiers. Third, an ablation study is performed to look into the performance of the proposed feature set compared to the existing feature sets. Here, the proposed feature set has outperformed all the existing feature sets in various evaluation metrics. Furthermore, the proposed dataset and the feature set have been made publicly available along with the benchmark evaluation to enable further research in the field.
PrivacyEye: A Privacy-Preserving and Computationally Efficient Deep Learning-Based Mobile Video Analytics System

Large volumes of video data recorded by the increasing mobile devices and embedded sensors can be leveraged to answer queries of our lives, physical world and our evolving society. Especially, the rapid development of convolutional neural networks (CNNs) in the past few years offers the great advantage for multiple tasks in video analysis. However, adopting running CNNs directly on mobile devices and embedded sensors for video analytics brings heavy burden due to their limited capacity, especially for learning a large volume of data. A promising approach is to outsource the computation-intensive part of CNN to cloud. However, the reveal of data to cloud may cause privacy leakage. In addition, the cloud-assisted approach may also bring some communication efficiency challenges for large volume of data. To address both privacy and efficiency issues, we design a privacy-preserving and computationally efficient framework for mobile video analytics. To protect the private information, we split the CNN model into two subnetworks, and first part is used as a feature extractor deployed in the mobile side and the second part is utilized as a classifier deployed in the cloud side. A specific-designed adversarial training process is adopted in order to extract features for normal task classification while hiding the features for sensitive task. In addition, to improve video process efficiency, we design a two-stage framework. The first stage is to extract key frames and necessary intermediate frames, while skipping redundant ones. The second stage is to extract the features of key frames by CNN-based feature extractor but apply optical-flow-based feature propagation algorithm to obtain the features of intermediate frames. Extensive experiments demonstrate our proposed system PrivacyEye can effectively protect private information while keep the accuracy of the normal tasks with less than 2 percent drop, and it saves up to 82.9 percent execution time and 78.8 percent energy consumption.
Quality of Experience Comparison of Stereoscopic 3D Videos in Different Projection Devices: Flat Screen, Panoramic Screen and Virtual Reality Headset

The use of Stereoscopic 3D (S3D) videos has been popular in commercial markets with ongoing developments in the field of visual entertainment in recent years.A wide variety of projection methods of 3D video content is currently available, such as projection to a panoramic screen and projection of omnidirectional video content from head mounted displays using Virtual Reality (VR) technology.This article investigates the Quality of Experience (QoE) and associated Visually Induced Motion Sickness (VIMS) caused by the viewing of S3D videos.The investigations used three different projection screens: a 3D flat screen, a 3D panoramic screen in a hemispherical shaped room and a VR headset.Several assessment methods including a Simulator Sickness Questionnaire (SSQ), ElectroEncephaloGraphy (EEG), and measurement tools for eye blink rate detection were applied to measure the QoE experienced by viewers.The SSQ scores were also compared with the behavioral data such as attention and meditation levels and enjoyment ratings acquired from different video content and projection screens.The results indicate that the projection screen is a key factor affecting the level of visual fatigue, VIMS and QoE assessments, which are discussed in-depth in the article.
Uncertainty Class Activation Map (U-CAM) Using Gradient Certainty Method

Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions. The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a tool for obtaining improved certainty estimates and explanations for deep learning models. We provide detailed empirical analysis for the visual question answering task on all standard benchmarks and comparison with state of the art methods.
Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation

Unsupervised Domain adaptation (UDA) attempts to recognize the unlabeled target samples by building a learning model from a differently-distributed labeled source domain. Conventional UDA concentrates on extracting domain-invariant features through deep adversarial networks. However, most of them seek to match the different domain feature distributions, without considering the task-specific decision boundaries across various classes. In this paper, we propose a novel Adversarial Dual Distinct Classifiers Network (AD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CN) to align the source and target domain data distribution simultaneously with matching task-specific category boundaries. To be specific, a domain-invariant feature generator is exploited to embed the source and target data into a latent common space with the guidance of discriminative cross-domain alignment. Moreover, we naturally design two different structure classifiers to identify the unlabeled target samples over the supervision of the labeled source domain data. Such dual distinct classifiers with various architectures can capture diverse knowledge of the target data structure from different perspectives. Extensive experimental results on several cross-domain visual benchmarks prove the model's effectiveness by comparing it with other state-of-the-art UDA.
Utilizing Every Image Object for Semi-supervised Phrase Grounding

Phrase grounding models localize an object in the image given a referring expression. The annotated language queries available during training are limited, which also limits the variations of language combinations that a model can see during training. In this paper, we study the case applying objects without labeled queries for training the semi-supervised phrase grounding. We propose to use learned location and subject embedding predictors (LSEP) to generate the corresponding language embeddings for objects lacking annotated queries in the training set. With the assistance of the detector, we also apply LSEP to train a grounding model on images without any annotation. We evaluate our method based on MAttNet on three public datasets: RefCOCO, RefCOCO+, and RefCOCOg. We show that our predictors allow the grounding system to learn from the objects without labeled queries and improve accuracy by 34.9% relatively with the detection results.
Incremental Skeptical Preferred Acceptance in Dynamic Argumentation Frameworks

In this article, we tackle the problem of efficiently recomputing the skeptical acceptance of dynamic argumentation frameworks under the well-known preferred semantics. We discuss an incremental algorithmic solution whose main idea is that of exploiting the initial knowledge (including the update to perform) in order to identify a potentially small portion of the argumentation framework. Such a portion is sufficient to compute the skeptical preferred acceptance of a goal argument w.r.t. the whole updated framework. We also discuss on how similar ideas extend to more general argumentation frameworks.
Online force-directed algorithms for visualization of dynamic graphs

Force-directed (FD) algorithms can be used to explore relationships in social networks, visualize money markets, and analyze transaction networks. However, FD algorithms are mainly designed for visualizing static graphs in which the topology of the networks remains constant throughout the calculation. In contrast to static graphs, nodes and edges in dynamic graphs can be added or removed as time progresses. In these situations, existing FD algorithms do not scale well, since any changes in the topology will trigger these algorithms to completely restart the entire computation. To alleviate this problem, we propose a design and implementation of five online FD algorithms to visualize dynamic graphs while maintaining their native force models. The online FD algorithms developed in this paper are able to reuse the force models of existing FD algorithms without significant modifications. To evaluate the effectiveness of the proposed approach, online FD algorithms are compared against static FD algorithms for visualizing dynamic graphs. Experimental results show that among the five algorithms evaluated, the online FD algorithm achieves the best number of edge crossings and the standard deviation of edge lengths for visualizing dynamic graphs.
siaNMS: Non-Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection

The rapid development of embedded hardware in autonomous vehicles broadens their computational capabilities, thus bringing the possibility to mount more complete sensor setups able to handle driving scenarios of higher complexity. As a result, new challenges such as multiple detections of the same object have to be addressed. In this work, a siamese network is integrated into the pipeline of a well-known 3D object detector approach to suppress duplicate proposals coming from different cameras via re-identification. Additionally, associations are exploited to enhance the 3D box regression of the object by aggregating their corresponding LiDAR frustums. The experimental evaluation on the nuScenes dataset shows that the proposed method outperforms traditional NMS approaches.