Reviewerly/exHarmony huggingface dataset:
Cross Knowledge-based Generative Zero-Shot Learning approach with Taxonomy Regularization

Although zero-shot learning (ZSL) has an inferential capability of recognizing new classes that have never been seen before, it always faces two fundamental challenges of the cross modality and cross-domain challenges. In order to alleviate these problems, we develop a generative network-based ZSL approach equipped with the proposed Cross Knowledge Learning (CKL) scheme and Taxonomy Regularization (TR). In our approach, the semantic features are taken as inputs, and the output is the synthesized visual features generated from the corresponding semantic features. CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy Regularization (TR) significantly improves the intersections with unseen images with more generalized visual features generated from generative network. Extensive experiments on several benchmark datasets (i.e., AwA1, AwA2, CUB, NAB and aPY) show that our approach is superior to these state-of-the-art methods in terms of ZSL image classification and retrieval.
An economic analysis of information security investment decision making for substitutable enterprises

As the competition among enterprises is increasingly fierce, the substitution of information assets makes the behavior of hackers and enterprises more complicated in the information security investment decision‐making process. In this paper, a game model of substitutable enterprise information security investment decision making is constructed by considering the influencing of substitution rate among enterprises, enterprise quantity, and probability of hacker invasion, and the optimal investment level is analyzed among substitutable enterprises under individual decision and joint decision based on game theory, optimization theory, and synergy theory. This research provides a new solution of information security investment decision making among substitutable enterprises.
Contourlet-CNN for SAR Image Despeckling

A multiscale and multidirectional network named the Contourlet convolutional neural network (CCNN) is proposed for synthetic aperture radar (SAR) image despeckling. SAR image resolution is not higher than that of optical images. If the network depth is increased blindly, the SAR image detail information flow will become quite weak, resulting in severe vanishing/exploding gradients. In this paper, a multiscale and multidirectional convolutional neural network is constructed, in which a single-stream structure of convolutional layers is replaced with a multiple-stream structure to extract image features with multidirectional and multiscale properties, thus significantly improving the despeckling performance. With the help of the Contourlet, the CCNN is designed with multiple independent subnetworks to respectively capture abstract features of an image in a certain frequency and direction band. The CCNN can increase the number of convolutional layers by increasing the number of subnetworks, which makes the CCNN not only have enough convolutional layers to capture the SAR image features, but also overcome the problem of vanishing/exploding gradients caused by deepening the networks. Extensive quantitative and qualitative evaluations of synthetic and real SAR images show the superiority of our proposed method over the state-of-the-art speckle reduction method.
Monocular 3D object detection using dual quadric for autonomous driving

3D object detection is an essential component of scene perception and motion prediction in autonomous driving. Previous methods represent objects as the truncated signed distance fields (3D bounding box), which can only provide the geometric constraints of point-to-line. In this work, we define the object as a more compact representation, quadric (ellipsoid) in a 3D scene and a conic (ellipse) in an image, which can provide stronger geometric constraints of surface-to-curve. Specifically, we estimate a ellipsoid from a conic fitted by a 2D bounding box to obtain 3D object localization and occupancy. We further to formulate this constraint relation as a nonlinear optimization problem in dual space, which enables us to easy recover stable and accurate 3D object parameters by adding only three additional direction-aware branches to the existing 2D detection networks. In addition, we decouple the dimensions of object and update the length and orientation of objects in our iterative algorithm when the estimations from the 2D detection networks have different deviations. The final detection results can be obtained after passing through our geometry-related refinement network. We evaluate our method on the KITTI object detection benchmark and achieve the best performance among published monocular competitors.
Finite-Time Performance of Distributed Temporal-Difference Learning with Linear Function Approximation

Finite-Time Performance of Distributed Temporal-Difference Learning with Linear Function Approximation
Communication-safe web programming in TypeScript with routed multiparty session types

Modern web programming involves coordinating interactions between browser clients and a server. Typically, the interactions in web-based distributed systems are informally described, making it hard to ensure correctness, especially communication safety, i.e. all endpoints progress without type errors or deadlocks, conforming to a specified protocol.
TripleRank: An unsupervised keyphrase extraction algorithm

Automatic keyphrase extraction algorithms aim to identify words and phrases that contain the core information in documents. As online scholarly resources have become widespread in recent years, better keyphrase extraction techniques are required to improve search efficiency. We present two features, keyphrase semantic diversity and keyphrase coverage, to overcome limitations of existing methods for unsupervised keyphrase extraction. Keyphrase semantic diversity is the degree of semantic variety in the extraction result, which is introduced to avoid extracting synonym phrases that contain the same high-score candidate. Keyphrase coverage refers to candidates’ representativeness of other words in documents. We propose an unsupervised keyphrase extraction method called TripleRank, which evaluates three features: word position (a sensitive feature for academic documents) and two innovative features mentioned above. The architecture of TripleRank includes three sub-models that score the three features and a summing model. Though involving multiple models, there is no typical iteration process in TripleRank; hence, the computational cost is relatively low. TripleRank has led the experiment results on four academic datasets compared to four state-of-the-art baseline models, which confirmed the influence of keyphrase semantic diversity and keyphrase coverage and proved the efficiency of our method.
A level set model by regularizing local fitting energy and penalty energy term for image segmentation

A novel level set model is proposed by regularizing local fitting energy to segment the intensity inhomogeneous images. In proposed model, local image fitting energy information is incorporated with penalty energy function. To segment intensity inhomogeneous images, a circular window filter is used to identify homogeneous regions. In addition, local information of homogeneous regions is constructed in form of local fitting function. A new double well potential function is formulated to regularize the contour curve and is used a penalty energy term which is added with local fitting functional in order to formulate multi-scale energy function. The experimental results show the robustness of proposed method compared to other level set models and deep learning based level set model. The comparative study of Jaccard similarity index (JSI) values and segmentation accuracy also validate the preciseness of the proposed model. Further, the proposed model yields better segmentation results compared to the other state-of-the-art models in terms of higher precision and recall values and lesser computational time. In addition, the proposed model is computationally efficient and robust to noise as well as contour initialization.
Recursive Copy and Paste GAN: Face Hallucination from Shaded Thumbnails

Existing face hallucination methods based on convolutional neural networks (CNNs) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in non-uniform illumination conditions. This paper proposes a Recursive Copy and Paste Generative Adversarial Network (Re-CPGAN) to recover authentic high-resolution (HR) face images while compensating for non-uniform illumination. To this end, we develop two key components in our Re-CPGAN: internal and recursive external Copy and Paste networks (CPnets). Our internal CPnet exploits facial self-similarity information residing in the input image to enhance facial details; while our recursive external CPnet leverages an external guided face for illumination compensation. Specifically, our recursive external CPnet stacks multiple external Copy and Paste (EX-CP) units in a compact model to learn normal illumination and enhance facial details recursively. By doing so, our method offsets illumination and upsamples facial details progressively in a coarse-to-fine fashion, thus alleviating the ambiguity of correspondences between LR inputs and external guided inputs. Furthermore, a new illumination compensation loss is developed to capture illumination from the external guided face image effectively. Extensive experiments demonstrate that our method achieves authentic HR face images in a uniform illumination condition with a 16× magnification factor and outperforms state-of-the-art methods qualitatively and quantitatively.
The role of finance in inclusive human development in Africa revisited

Purpose This study aims to investigate the direct and indirect linkages between financial development and inclusive human development in African countries. Design/methodology/approach The study employs a battery of estimation techniques, notably: two-stage least squares, fixed effects, generalized method of moments and Tobit regressions. The dependent variable is the inequality adjusted human development index. All dimensions of the Financial Development and Structure Database of the World Bank are considered. Findings The main finding is that financial dynamics of depth, activity and size improve inclusive human development, whereas the inability of banks to transform mobilized deposits into credit for financial access negatively affects inclusive human development. Practical implications Policies should be tailored to improve mechanisms by which credit facilities can be provided to both households and business operators. Surplus liquidity issues resulting from the inability of banks to transform mobilized deposits into credit can be resolved by enhancing the introduction of information sharing offices (like public credit registries and private credit bureaus) that would reduce information asymmetry between lenders and borrowers. Originality/value This study complements the extant literature by assessing the nexus between financial development and inclusive human development in Africa.
WITHDRAWN: Sentiment analysis of product feedback using natural language processing

Sentiment Analysis is characterised as contextual manipulation of the text that recognises subjective information in the source material and then extracts it and also helps many companies understand their product or service's social sentiment. Nearly all professional and non-technical companies use sentiment analysis in order to reliably interpret customer input and wishes on how the product or service is fit. Sentimental research is used by top companies in nontechnology and technology. This is more or less organisational and not available to the public. More feedback applications are based on the organisational level and a standard interface that almost any company can use in any field can be very useful to recognise feedback easily using the natural language processing algorithm, without needing a particular programme or application at the organisational level.Customer reviews are digitally gathered and analysed using the NLP algorithm for fast and accurate review. The usage for such purposes can be used by top organisations which have revealed APIs and SDK resources. In this input review method APIs from organisations such as IBM, Parallel points etc can be improved like Twitter, Facebook and WordPress.
An Augmented Reality Human-Robot Physical Collaboration Interface Design for Shared, Large-Scale, Labour-Intensive Manufacturing Tasks

This paper investigate potential use of augmented reality (AR) for physical human-robot collaboration in large-scale, labour-intensive manufacturing tasks. While it has been shown that use of AR can help increase task efficiency in teleoperative and robot programming tasks involving smaller-scale robots, its use for physical human-robot collaboration in shared workspaces and large-scale manufacturing tasks have not been well-studied. With the eventual goal of applying our AR system to collaborative aircraft body manufacturing, we compare in a user study the use of an AR interface we developed with a standard joystick for human robot collaboration in an experiment task simulating industrial carbon-fibre-reinforced-polymer manufacturing procedure. Results show that use of AR yields reduced task time and physical demand, with increased robot utilization.
L∗-based learning of Markov decision processes (extended version)

Abstract Automata learning techniques automatically generate systemmodels fromtest observations. Typically, these techniques fall into two categories: passive and active. On the one hand, passive learning assumes no interaction with the system under learning and uses a predetermined training set, e.g., system logs. On the other hand, active learning techniques collect training data by actively querying the system under learning, allowing one to steer the discovery ofmeaningful information about the systemunder learning leading to effective learning strategies. A notable example of active learning technique for regular languages is Angluin’s <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> -algorithm. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> -algorithm describes the strategy of a student who learns the minimal deterministic finite automaton of an unknown regular language <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>L</mml:mi> </mml:math> by asking a succinct number of queries to a teacher who knows <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>L</mml:mi> </mml:math> . In this work, we study <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> -based learning of deterministic Markov decision processes, a class of Markov decision processes where an observation following an action uniquely determines a successor state. For this purpose, we first assume an ideal setting with a teacher who provides perfect information to the student. Then, we relax this assumption and present a novel learning algorithm that collects information by sampling execution traces of the system via testing. Experiments performed on an implementation of our sampling-based algorithm suggest that our method achieves better accuracy than state-of-the-art passive learning techniques using the same amount of test obser vations. In contrast to existing learning algorithms which assume a predefined number of states, our algorithm learns the complete model structure including the state space.
Differentially private locality sensitive hashing based federated recommender system

Summary Recommender systems are important applications in big data analytics because accurate recommendation items or high‐valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine‐grained data for training. In the current big data era, data often exists in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy‐preserving recommender system is of paramount importance. Existing privacy‐preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we conduct privacy analysis on the existing locality sensitive hashing (LSH) approach based privacy‐preserving recommender system and show how an attacker can retrieve user's information under such a recommender system. Given such privacy risks, we propose differentially private LSH approach to build recommender system that can offer differential privacy guarantees for users. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing privacy preservation of users' data in contributing parties. Extensive experiments on real‐world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets.
Resource Management in Cloud Computing Using Machine Learning: A Survey

Efficient resource management in cloud computing research is a crucial problem because resource over-provisioning increases costs for cloud providers and cloud customers; resource under-provisioning increases the application latency, and it may violate service level agreements, which eventually makes cloud providers lose their customers and income. As a result, researchers have been striving to develop optimal resource management in cloud computing environments in different ways, such as container placement, job scheduling and multi-resource scheduling. Machine learning techniques are extensively used in this area. In this paper, we present a comprehensive survey on the projects that leveraged machine learning techniques for resource management solutions in the cloud computing environment. At the end, we provide a comparison between these projects. Furthermore, we propose some future directions that will guide researchers to advance this field.
FPGA-Based Wigner–Hough Transform System for Detection and Parameter Extraction of LPI Radar LFMCW Signals

Low probability of intercept (LPI) radars using linear frequency-modulated continuous waveforms (LFMCWs) is rapidly proliferating in the military domain and posing a serious challenge to conventional electronic support receivers (ESRs). In this article, we provide a practical approach that can be used in digital ESR systems for real-time LFMCW radar signal detection and parameter extraction under challenging SNR levels. We proposed a modified version of the Wigner-Ville distribution (WVD), namely, short-time Wigner distribution (STWD), postprocessing steps, e.g., column-based thresholding of STWD combined with the Hough transform (HT), and a field-programmable gate array (FPGA) design as an embedded solution with low computational complexity. The performance of the proposed system is tested with MATLAB and hardware simulations using a signal database generated with different signal-to-noise ratio (SNR) levels. The system is then embedded into a Virtex-7 FPGA where the performance evaluations showed satisfactory detection results down to -6-dB SNR level, and parameter extraction results down to -8-dB SNR level.
MRCC: A Practical Covert Channel Over Monero With Provable Security

Covert channels are designed to protect the communication relationship of the sender and receiver.Traditional covert channels have become insecure due to the continuous improvement of traffic analysis techniques.In this context, there is an urgent need to identify new approaches for covert channels.Blockchain is an emerging technique with characteristics of user anonymity, a flooding propagation mechanism, and tamper resistance, which make it a compelling platform for covert channels.Previous approaches applied Bitcoin as the underlying blockchain, and its pseudoanonymity may expose the communication relationship.Moreover, the reliance of these approaches on prenegotiated labels to identify transactions containing covert messages further reduced their concealment.In this work, we present a practical and secure covert channel over Monero.Compared to Bitcoin, Monero's full anonymity efficiently protects the relationship between the sender and receiver.Moreover, no labels are employed to identify special transactions.The receiver filters and extracts the covert message using his private key.In this study, we make a complete assessment of the robustness, reliability, and anti-traceability of our protocol, as these properties are regarded as desirable for a covert channel.We also formalize the definition of security for covert channels through a transaction distinguishing experiment.A rigorous proof shows that our protocol meets this definition and is secure to use.Finally, we make a detailed comparison between our protocol and the existing blockchain-based covert channels.
Feature selection and threshold method based on fuzzy joint mutual information

Improving classification performance is one of the main challenges in a variety of real-world applications. Unfortunately, classification models are sensitive to undesirable features of data such as redundant and irrelevant features. Feature selection (FS) is a powerful solution to address the negative effect of these features. Among various methods, Feature selection based on mutual information (MI) is an effective method to select the significant features and deny the undesirable ones. Although most of the existing methods can estimate the feature relevancy efficiently, they may find difficulty to estimate the feature redundancy well. This is due to the individual estimation between the candidate feature and the features of pre-selected subset. To address this limitation, this paper introduces a novel feature selection method, called Fuzzy Joint Mutual Information (FJMI). The proposed method also overcomes common limitations as dealing with continuous feature without information loss and returns the best feature subset automatically without a user-defined threshold. To evaluate the effectiveness of the proposed method, FJMI is compared with six conventional and state-of-the-art feature selection methods. The experimental results on 16 benchmark datasets, with moderate size, show a promising improvement by the proposed method in terms of classification performance, feature selection stability, and number of selected features.