Reviewerly/exHarmony huggingface dataset:
Learning Explicit Smoothing Kernels for Joint Image Filtering

Abstract Smoothing noises while preserving strong edges in images is an important problem in image processing. Image smoothing filters can be either explicit (based on local weighted average) or implicit (based on global optimization). Implicit methods are usually time‐consuming and cannot be applied to joint image filtering tasks, i.e., leveraging the structural information of a guidance image to filter a target image. Previous deep learning based image smoothing filters are all implicit and unavailable for joint filtering. In this paper, we propose to learn explicit guidance feature maps as well as offset maps from the guidance image and smoothing parameter that can be utilized to smooth the input itself or to filter images in other target domains. We design a deep convolutional neural network consisting of a fully‐convolution block for guidance and offset maps extraction together with a stacked spatially varying deformable convolution block for joint image filtering. Our models can approximate several representative image smoothing filters with high accuracy comparable to state‐of‐the‐art methods, and serve as general tools for other joint image filtering tasks, such as color interpolation, depth map upsampling, saliency map upsampling, flash/non‐flash image denoising and RGB/NIR image denoising.
Credit Card Fraud Detection Using Various Classification and Sampling Techniques: A Comparative Study

With an ascent in the development of web-based business, the utilization of credit cards for internet shopping has expanded significantly. This, in turn, has brought about a great deal of credit card fakes. However, once in a while. Consequently, the execution of effective fraud detection frameworks has turned out to be fundamental for all banks to limit their misfortunes as far as credit card transactions are concerned. Numerous advanced systems have been created to monitor different credit card exchanges in literature. In this way, individuals have been attempting their best to identify the extortion in credit card exchanges as much as they can. Various machine learning techniques have been applied to predict whether a particular transaction is fraudulent or not. The biggest challenge with the techniques is the unavailability of the balanced dataset. Which is due to the nature of the transaction: the fraud transactions are too less when compared to genuine transactions. This work handles the challenge by balancing the dataset. Five machine learning techniques: Random forest, Naive Bayes, Support Vector Machine, K-Nearest Neighbor and Logistic regression were applied on the balanced dataset with different sampling techniques such as Oversampling, Undersampling, Both sampling, ROSE and SMOTE. The performance metric AUC – ROC suggests that logistic regression performs with an accuracy of 97.04 % and precision of 99.99%.
A modified scheme for liver tumor segmentation based on cascaded FCNs

With the development of artificial intelligence technology, more and more researchers use deep neural network to solve various difficulties in segmenting liver and liver tumors from abdominal CT images. Recently, researchers propose to use cascaded FCNs strategy to segment both liver and liver tumors automatically. The cascaded FCNs trains the first FCN to segment liver region, and uses the output of the first FCN as the input of the second FCN to train the tumor segmentation network. However, in the cascaded FCNs structure, inaccurate segmentation of liver in the first FCN will directly affect the segmentation of tumors at the edge of liver and the second FCN is not conductive to segment small tumors. To address these issues, this paper proposed a modified scheme based on cascaded FCNs: firstly, we modified the first FCN by adopting variable pooling kernel to provide a better liver region for the later segmentation of tumors. Secondly, we replaced the pooling and convolution layer of the second FCN with dilated convolution to improve global feature extraction of small tumors. We extensively evaluated our method on 3D-IRCADb-01 and 2017 LiTS dataset. The DICE coefficient of tumors reached 85.71% and 82.43% respectively, 3.71% and 3.61% higher than the original cascaded FCNs. Experimental results indicate that our method achieved very competitive performance and outperformed other state-of-the-arts on the liver tumor segmentation accuracy.
Sentiment Classification of Indian Banks' Customer Complaints

Sentiment Analysis of the customer complaints of Indian banks is an interesting task and critical for appropriate business functionality and improvement. Applying machine learning (ML) techniques on these raw textual data is increasingly gaining traction. Towards pre-processing the raw textual data, we employed techniques like document term matrix (DTM) driven by Term Frequency - Inverse Document Frequency (TF-IDF), embedding model likeWord2Vec and psycho-linguistic method like Linguistic Inquiry and Word Count (LIWC). For the purpose of classification, the raw textual data of complaints is labeled as “moderate” or “extreme” by the three human annotators. Results indicate that the LIWC in combination with Random Forest and Naïve Bayes techniques performed the best in three banks datasets. The results were statistically corroborated with a t-test.
A distributed Kalman filter with symbolic zonotopes and unique symbols provider for robust state estimation in CPS

Robust state estimation is addressed in a noisy environment and within a distributed and networked architecture. Both bounded disturbances and random noises are considered. A Distributed Zonotopic and Gaussian Kalman Filter (DZG-KF) is proposed where each network node implements a local state estimator using symbolic Zonotopes and Gaussian noise Mergers (s-ZGM), a class of Set-membership and Probabilistic Mergers (SPM). Each network node communicates its own state information only to its neighbours. The proposed system includes a dedicated service called Unique Symbols Provider (USP) giving unique identifiers. It also includes Matrices with Labelled Columns (MLC) featuring column-wise sparsity, and symbolic zonotopes (s-zonotopes). This significantly enhances the propagation of uncertainties and preserves global dependencies that would otherwise be lost (or impeded) by the peer-to-peer communication through the network. A number of other network-related constraints can be managed within this framework. Numerical simulations show significant improvements compared to a non-symbolic approach.
Angle Multiplexing Optical Image Encryption in the Fresnel Transform Domain Using Phase-Only Computer-Generated Hologram

We propose an angle multiplexing method for optics-based image encryption using a phase-only computer-generated hologram (POCGH) in the tilted Fresnel transform (TFrT) domain. Modified Gerchberg-Saxton algorithms, based on the three types of rotation manipulation in both the hologram and reconstruction planes, are used with their corresponding TFrT parameters to extract the phase-only functions (POFs) of the target images. All the extracted POFs are then phase-modulated and summed to obtain the final POCGH, which is capable of multiplexing and avoiding overlap in the reconstructed images. The computer simulation results show that the images corresponding to the various rotation manipulations at the hologram and image reconstruction planes can be successfully restored with high correlation coefficients. Due to the encrypted nature of the multiplexed images, a higher system security level can be achieved, as the images can only be correctly displayed when all the required parameters in the TFrT are available. The angle sensitivity on the image quality for each manipulation is also investigated.
Traceable and Authenticated Key Negotiations via Blockchain for Vehicular Communications

While key negotiation schemes, such as those based on Diffie–Hellman, have been the subject of ongoing research, designing an efficient and security scheme remains challenging. In this paper, we propose a novel key negotiation scheme based on blockchain, which can be deployed in blockchain-enabled contexts such as data sharing or facilitating electric transactions between vehicles (e.g., unmanned vehicles). We propose three candidates for flexible selection, namely, key exchanges via transaction currency values through value channels (such as the amount in transactions), automated key exchanges through static scripts,and dynamic scripts, which can not only guarantee key availability with timeliness but also defend against MITM (man-in-the-middle) attacks, packet-dropping attacks, and decryption failure attacks.
Dual-channel interferometer for vibration-resistant optical measurement

A dual-channel interferometer (DCI) is proposed for vibration-resistant optical measurement. The DCI employs a dual-channel interference optical path design, wherein an assistant channel is used to generate dense fringe patterns by introducing a spatial carrier frequency, and the coefficients of the vibration tilt phase plane are then extracted. The sparse fringe patterns of the main channel are combined with the coefficients of the tilt phase plane to calculate the measured phase distribution. This study reports the optical path and principles of the interference system. Simulations are performed using the proposed DCI phase extraction algorithm. To verify the DCI performance, phase measurements are carried out in a vibrating environment, and the results obtained using the proposed method are compared with those obtained using spatial phase-shifting interferometry (SPSI). The experimental results demonstrate that the DCI can effectively overcome the effect of vibration. Unlike spatial carrier frequency interferometry, although the spatial carrier frequency is also introduced in the assistant channel in the proposed DCI method, the dual-channel information extraction algorithm helps eliminate the calibration process of the retrace error before measurement, making this a calibration-free measurement. Moreover, the DCI utilizes environmental vibration to generate a ‘phase shift’, thus not only eliminating the need for a vibration isolation platform but also the need for a precise phase shifting device, with advantages such as structural simplicity, ease of operation, and low cost.
Ontology-Driven Security Requirements Recommendation for APT Attack

Advanced Persistent Threat (APT) is one of the cyber threats that continuously attack specific targets exfiltrate information or destroy the system [1]. Because the attackers use various tools and methods according to the target, it is difficult to describe APT attack in a single pattern. Therefore, APT attacks are difficult to defend against with general countermeasures. In these days, systems consist of various components and related stakeholders, which makes it difficult to consider all the security concerns. In this paper, we propose an ontology knowledge base and its design process to recommend security requirements based on APT attack cases and system domain knowledge. The proposed knowledge base is divided into three parts; APT ontology, general security knowledge ontology, and domain-specific knowledge ontology. Each ontology can help to understand the security concerns in their knowledge. While integrating three ontologies into the problem domain ontology, the appropriate security requirements can be derived with the security requirements recommendation process. The proposed knowledge base and process can help to derive the security requirements while considering both real attacks and systems.
SIRLC: Secure Information Retrieval using Lightweight Cryptography in HIoT

Advances in new Communication and Information innovations has led to a new paradigm known as Internet of Things (IoT). Healthcare environment uses IoT technologies for Patients care which can be used in various medical applications. Patient information is encrypted consistently to maintain the access of therapeutic records by authoritative entities. Healthcare Internet of Things (HIoT) facilitate the access of Patient files immediately in emergency situations. In the proposed system, the Patient directly provides the Key to the Doctor in normal care access. In Emergency care, a Patient shares an Attribute based Key with a set of Emergency Supporting Representatives (ESRs) and access permission to the Doctor for utilizing Emergency key from ESR. The Doctor decrypts the medical records by using Attribute based key and Emergency key to save the Patient's life. The proposed model Secure Information Retrieval using Lightweight Cryptography (SIRLC) reduces the secret key generation time and cipher text size. The performance evaluation indicates that SIRLC is a better option to utilize in Healthcare IoT than Lightweight Break-glass Access Control(LiBAC) with enhanced security and reduced computational complexity.
Single-image raindrop removal using concurrent channel-spatial attention and long-short skip connections

Image raindrop removal refers to removing raindrops from the images taken through glass under rainy weather. It is a challenging problem due to the large variations of raindrop appearances as well as the nonlinear visual distortions caused by raindrops. This problem becomes much more difficult when only a single image is provided. This paper aims at tackling the single-image raindrop removal problem by leveraging the power of deep learning. We proposed an end-to-end approach using the deep neural network. To address the challenges of raindrop removal, we introduce a concurrent channel and spatial attention mechanism implemented by Squeeze & Excitation into the network. The channel attention mechanism allows individually selecting useful features in the network for handling the raindrops of different appearances and restoring different image patterns. The spatial attention enables different treatments to the image regions with different degrees of distortion and with different local image patterns. In addition, long-short skip connections are added for utilizing the intermediate features of the network for better restoration. The experimental results show that the proposed approach outperformed the existing ones.
Vehicle Count System based on Time Interval Image Capture Method and Deep Learning Mask R-CNN

Traffic congestion is an undesirable problem for big cities especially in third world countries. Better policy planning and decision-making from the authority comes from well-conducted practical research. In this study, a Vehicle Count System (VCS) using deep learning Mask R-CNN is developed to classify and count vehicles passing in a target street. A novel time interval image capture (TIIC) system is employed to the VCS instead of the typical real-time video streaming to avoid big data storage cost. To determine the effectiveness of the developed VCS, its output is compared to that of conventional method from manual recording of the passing vehicles. Four vehicle types - cars, motorbikes, trucks and buses are present in the 1800 real traffic images gathered from an actual field. As an initial stage, the developed tool performs satisfactorily in classifying and counting car-type vehicle with 97.62% accuracy in a 10-hour testing. However, it fails to recognize motorbikes probably due to its relatively smaller pixel size compared to other vehicle types. The presence of jeeps confused the VCS. The real image dataset can be used as basis for further development. The newly developed TIIC system can also be used in future research as a promising tool to replace real-time video streaming.
A Markov Model of Users’ Interactive Behavior in Scatterplots

Recently, Wall et al. proposed a set of computational metrics for quantifying cognitive bias based on user interaction sequences. The metrics rely on a Markov model to predict a user’s next interaction based on the current interaction. The metrics characterize how a user’s actual interactive behavior deviates from a theoretical baseline, where "unbiased behavior" was previously defined to be equal probabilities of all interactions. In this paper, we analyze the assumptions made of these metrics. We conduct a study in which participants, subject to anchoring bias, interact with a scatterplot to complete a categorization task. Our results indicate that, rather than equal probabilities of all interactions, unbiased behavior across both bias conditions can be better approximated by proximity of data points.
Active one-shot learning by a deep Q-network strategy

One-shot learning has recently attracted growing attention to produce models which can classify significant events from a few or even no labeled examples. In this paper, we introduce a deep Q-network strategy into one-shot learning (OL-DQN) to design a more intelligent learner to infer whether to label a sample automatically or request the true label for the active-learning set-up. Then we conducted experiments in the ALOI dataset for the classification of objects recorded under various imaging circumstances and a dataset for handwriting recognition composed of both characters and digits to have a performance evaluation and application analysis of the proposed model respectively, and the obtained results demonstrate that our model can achieve a better trade-off between prediction accuracy and the need of label requests compared with a purely supervised task, a prior work AOL, and a conventional active learning algorithm QBC.
SEC-BlockEdge: Security Threats in Blockchain-Edge based Industrial IoT Networks

Internet of Things (IoT), together with fifth-generation (5G) mobile systems and related enabling communication technologies, constantly contributing for the enhancement in various industrial applications and driving towards the vision of complete industrial automation. In this context, both Blockchain and Edge computing are considered as promising technologies to fulfill the vision and requirements for the future Industry 4.0. The integration of these technologies together with massive scale Industrial IoT (IIoT) applications would be vital in addressing several key challenges related to e.g. latency, scalability and security of data processing and sharing, among others. However, such complex IIoT systems are constantly exposed to security threats from various sources. This paper focuses on identifying the major security challenges in the IIoT blockchain-edge related networks. In addition, some potential security solutions are presented in order to overcome these challenges.
Understanding the social evolution of the Java community in Stack Overflow: A 10-year study of developer interactions

Today, Social Media is a key information source for a wide range of domains as a means to gain a better understanding of information flows and user communities. This work introduces a methodology combining machine learning and graph mining approaches to address relevant aspects of quality of service and user intrinsic motivation from the user’s perspective. The focus of the present analysis is set on the social interactions among software developers via Stack Overflow. Over the last 10 years, software developers have become intensively involved in knowledge sharing and platforms such as Stack Overflow have accumulated a lot of development data and knowledge. The proposed methodology is applied to explore the social dynamics of the Java programming language community and bring forward relevant, non-trivial knowledge about developer interests, information flows and user engagement and reputation. The ultimate aim is to improve question preparation towards better question routing and voting outcome.
Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction

This article embarks a study on multiagent transfer learning (TL) for addressing the specific challenges that arise in complex multiagent systems where agents have different or even competing objectives. Specifically, beyond the essential backbone of a state-of-the-art evolutionary TL framework (eTL), this article presents the novel TL framework with prediction (eTL-P) as an upgrade over existing eTL to endow agents with abilities to interact with their opponents effectively by building candidate models and accordingly predicting their behavioral strategies. To reduce the complexity of candidate models, eTL-P constructs a monotone submodular function, which facilitates to select Top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> models from all available candidate models based on their representativeness in terms of behavioral coverage as well as reward diversity. eTL-P also integrates social selection mechanisms for agents to identify their better-performing partners, thus improving their learning performance and reducing the complexity of behavior prediction by reusing useful knowledge with respect to their partners’ mind universes. Experiments based on a partner-opponent minefield navigation task (PO-MNT) have shown that eTL-P exhibits the superiority in achieving higher learning capability and efficiency of multiple agents when compared to the state-of-the-art multiagent TL approaches.
Toward a Decentralized Marketplace for Self-Maintaining Machines

Industrial procurement processes are inefficient, expensive, and error-prone as they require the agreement of numerous participants, audit-proof documentation, and are characterized by a high number of manual tasks. Blockchain technology has the potential to counter these challenges by combining peer-to-peer networks, cryptography, and consensus algorithms. A blockchain is a decentralized database that stores transactions and data transparently, chronologically, and tamper-proof in a distributed network. In this paper, we present a blockchain-based industrial marketplace, where machines have an identity and account for selecting, ordering, and paying materials in an automated manner using smart contracts. This allows manufacturers with a low real-net-output-ratio, such as car manufacturers that depend on numerous suppliers, to optimize their purchasing processes. Using our case study, we demonstrate prototypical implementation of the decentralized marketplace and its applications in the area of industrial maintenance. Furthermore, we conducted a semi-structured interview with procurement and information technology experts from the automotive industry. The results indicate that our prototype offers a promising approach streamlining procurement processes within the next two to five years.