Reviewerly/exHarmony huggingface dataset:
Side-Blotched Lizard Algorithm: A polymorphic population approach

In metaheuristic algorithms, finding the optimal balance between exploration and exploitation is a key research topic that remains open. In the nature, a reptile called Side-Blotched Lizard has achieved an interesting dynamic balance over its population. Such lizards evolved with three morphs associated with distinctive mating strategies. The synergy between the morphs generates a polymorphic population, able to self-balance the subpopulations of each morph without depleting the weakest morph. This equilibrium is achieved as the most common morph becomes the weakest, and the smaller subpopulations increase their chances of mating. In this paper, the Side-Blotched Lizard Algorithm (SBLA) is proposed to emulate the polymorphic population of the lizard. For this purpose, three operators are used to guarantee a dynamic over the population that allows the coexistence of multiple morphs. From the computational point, SBLA uses a subpopulation managing strategy which emulates the sinusoidal distribution of the lizard population over time. Even more, the mating behavior of each morph is modeled with three concepts, defensive, expansive, and sneaky. The performance of SBLA is tested on a set of five unimodal, eighteen multimodal, four composite benchmark functions, and engineering problems like; the welded beam, FM synthesizer, and rolling element bearing. To validate the results, we compared them to ten well-established algorithms and using the Wilcoxon test and the Bonferroni correction. The examination of the experimental results exhibits the accuracy, robustness and unique problem-solving method of the proposed algorithm.
Feature Selection for Image Steganalysis Using Binary Bat Algorithm

Steganography is to hide secret information in a normal cover, so that the secret information cannot be detected. With the rapid development of steganography, it's more and more difficult to detect. Steganalysis is the counter of steganography. In order to improve the detection effect, more complex high-dimensional features are proposed for steganalysis. However, this also creates huge redundancy features, which in turn consume generous time. Feature selection is a technique that can effectively remove redundant features. In this paper, we propose a new blind image steganalysis algorithm to distinguish stego images from cover images using a nature-inspired feature selection method based on the binary bat algorithm(BBA). Meanwhile, SPAM and several classifiers have been used to improve the detection effect. Furthermore, we select the ideal feature subset using BBA from the original features and use the selected feature subset to train the several classifiers. The experimental results demonstrate that our proposed method can improve the detection effect and reduces the redundant features.
Automating App Review Response Generation

Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a template-based strategy where the templates can express appreciation for using the app or mention the company email address for users to follow up. However, reading a large number of user reviews every day is not an easy task for developers. Thus, there is a need for more automation to help developers respond to user reviews. Addressing the aforementioned need, in this work we propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4 (an accuracy measure that is widely used to evaluate dialogue response generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating relevant and accurate responses.
An efficient and provable certificate-based proxy signature scheme for IIoT environment

Recently, the deployment of sensors and actuators to collect and disseminate data in various applications such as e-healthcare, vehicular adhoc networks (VANETs) and smart factories has revolutionized several new communication technologies. The Internet of Things (IoT) is one of those emerging communication technologies. These revolutionary applications of IoT in industrial environment are termed as Industry 4.0 and it has vitalized the concept of Industrial IoT (IIoT). Being wireless communication, the authentication and integrity of data are the most important challenges. To mitigate these challenges, several digital signature schemes are proposed in the literature. However, due to identity-based or certificate-less construction, those schemes suffer from inborn key escrow and secret key distribution problems. To resolve such issues, the first certificate-based proxy signature (PFCBPS) scheme without pairing is proposed. The proposed PFCBPS scheme is provably secure in random oracle model (ROM). The performance comparison (in terms of computational costs of different phases and length of resulting delegation and signature) shows that the proposed PFCBPS scheme's total computational cost is 46.69 ms which is 52.24% of He et al. [8], 61.40% of Debiao et al. [5], 23.33% of Seo et al. [20], 28% of Hu et al. [9] and 36.84% of Verma and Singh [23]. Thus, it is more suitable to IIoT environment than existing competitive schemes.
Accountable Cross-Border Data Sharing Using Blockchain Under Relaxed Trust Assumption

Cross-border data sharing for knowledge generation is a challenging research direction since an application may access personal data stored in countries different from the one where the application is accessed from. In this article, we propose a cross-border data sharing platform where a global cloud is built atop multiple security gateways that are set up in different countries. Once an application requests access to data from a particular country or region, the global cloud collects the data stored in local data hubs through that region's security gateway. While transferring the data to the global cloud, the security gateway records this transfer information on a blockchain maintained by the global cloud. When an application reports any misbehavior (e.g., providing wrong data type or incorrect data) against a security gateway, the global cloud verifies the claim by auditing the blockchain and punishes the misbehaving security gateway if the claim is true. In the case of false misbehavior report, the application itself will be punished by the global cloud. Thus, our platform provides an accountable data sharing function using blockchain that relies on a relaxed trust assumption on the data providers. We include five algorithms to handle data access request, data sharing, blockchain transaction, detecting, and punishing misbehaving entities. In the algorithms, we also introduce how the transaction takes place in the platform. Thus, the proposed platform is able to handle misbehaving data sender, data receiver, or any entity participating in the platform. We analyze our platform empirically by showing different graphs, which have been generated by a number of experiments on blockchain environment. We also delineate how the multilayer of signature (Elliptic Curve Digital Signature Algorithm) acts in our platform.
Discriminative distribution alignment: A unified framework for heterogeneous domain adaptation

Heterogeneous domain adaptation (HDA) aims to leverage knowledge from a source domain for helping learn an accurate model in a heterogeneous target domain. HDA is exceedingly challenging since the feature spaces of domains are distinct. To tackle this issue, we propose a unified learning framework called Discriminative Distribution Alignment (DDA) for deriving a domain-invariant subspace. The proposed DDA can simultaneously match the discriminative directions of domains, align the distributions across domains, and enhance the separability of data during adaptation. To achieve this, DDA trains an adaptive classifier by both reducing the distribution divergence and enlarging distances between class centroids. Based on the proposed DDA framework, we further develop two methods, by embedding the cross-entropy loss and squared loss into this framework, respectively. We conduct experiments on the tasks of categorization across domains and modalities. Experimental results clearly demonstrate that the proposed DDA outperforms several state-of-the-art models.
Improved spotted hyena optimizer with space transformational search for training pi‐sigma higher order neural network

Abstract Spotted hyena optimizer (SHO) is a recently developed swarm‐based algorithm in the field of metaheuristic research, for solving realistic engineering design constraint and unconstrained difficulties. To resolve complicated nonlinear physical world tasks, at times, SHO reveals deprived performance concerning to explorative strength. So, to enhance the explorative strength along with exploitation in the search region, an attempt has been made by proposing the enhanced version of classical SHO. The suggested method is designated as space transformation search (STS)‐SHO. In STS‐SHO, a new evolutionary technique named as STS technique has been incorporated with original SHO. The suggested method has been assessed by IEEE CEC 2017 benchmark problems. The efficacy of the said method has been proven by using standard measures such as given performance metrics in CEC 2017, complexity analysis, convergence analysis, and statistical implications. Further as real‐world application, the said algorithm has been applied to train pi‐sigma neural network by means of 13 benchmark datasets considered from UCI depository. From the article it can be concluded that the suggested method STS‐SHO is an effective and trustworthy algorithm, which has the ability to resolve real‐life optimization complications.
An Architecture for Biometric Electronic Identification Document System Based on Blockchain †

This paper proposes an architecture for biometric electronic identification document (e-ID) system based on Blockchain for citizens identity verification in transactions corresponding to the notary, registration, tax declaration and payment, basic health services and registration of economic activities, among others. To validate the user authentication, a biometric e-ID system is used to avoid spoofing and related attacks. Also, to validate the document a digital certificate is used with the corresponding public and private key for each citizen by using a user’s PIN. The proposed transaction validation process was implemented on a Blockchain system in order to record and verify the transactions made by all citizens registered in the electoral census, which guarantees security, integrity, scalability, traceability, and no-ambiguity. Additionally, a Blockchain network architecture is presented in a distributed and decentralized way including all the nodes of the network, database and government entities such as national register and notary offices. The results of the application of a new consensus algorithm to our Blockchain network are also presented showing mining time, memory and CPU usage when the number of transactions scales up.
Integrating reinforcement learning and skyline computing for adaptive service composition

In service computing, combining multiple services through service composition to address complex user requirements has become a popular research topic. QoS-aware service composition aims to find the optimal composition scheme with the QoS attributes that best match user requirements. However, certain QoS attributes may continuously change in a dynamic service environment, so service composition methods need to be adaptive. Furthermore, the large number of candidate services poses a key challenge for service composition, where existing service composition approaches based on reinforcement learning (RL) suffer from low efficiency. To deal with the problems above, in this paper, a new service composition approach is proposed which combines RL with skyline computing where the latter is used for reducing the search space and computational complexity. A WSC-MDP model is proposed to solve the large-scale service composition within a dynamically changing environment. To verify the proposed method, a series of comparative experiments are conducted, and the experimental results demonstrate the effectiveness, scalability and adaptability of the proposed approach.
Extreme Low-Light Image Enhancement for Surveillance Cameras Using Attention U-Net

Low-light image enhancement is one of the most challenging tasks in computer vision, and it is actively researched and used to solve various problems. Most of the time, image processing achieves significant performance under normal lighting conditions. However, under low-light conditions, an image turns out to be noisy and dark, which makes subsequent computer vision tasks difficult. To make buried details more visible, and reduce blur and noise in a low-light captured image, a low-light image enhancement task is necessary. A lot of research has been applied to many different techniques. However, most of these approaches require much effort or expensive equipment to perform low-light image enhancement. For example, the image has to be captured in a raw camera file in order to be processed, and the addressing method does not perform well under extreme low-light conditions. In this paper, we propose a new convolutional network, Attention U-net (the integration of an attention gate and a U-net network), which is able to work on common file types (.PNG, .JPEG, .JPG, etc.) with primary support from deep learning to solve the problem of surveillance camera security in smart city inducements without requiring the raw image file from the camera, and it can perform under the most extreme low-light conditions.
Comparative Study of Model Optimization Techniques in Fine-Tuned CNN Models

Deep learning & especially Convolutional Neural Networks (CNNs) are taking a different shape in the area of Image Recognition & Classification. Number of different CNN architectures are found in the literature. Performance of any CNN model depends on various parameters such as size of the dataset, number of classes, weights of the model, hypermeters and optimizers so on. Transfer learning or Fine tuning a pretrained model has become very common during present time because of its advantages. Several model optimization techniques have been discussed in the literature. Stochastic Gradient Descent (SGD), Adam & RMS Prop model optimizers are more commonly used in CNN model optimization. This paper focusses on the effect of above three optimizers on well-known CNN models namely, ResNet50 and InceptionV3. Above optimizers are used to train the Fine-tuned CNN models for 15 Epochs on cat vs dog dataset created by hand picking hundreds of images of cats & dogs from Kaggle cat vs dog dataset. For this experimentation, learning rate of 0.001 is used, Categorical Cross Entropy is used to calculate the training & validation loss. Comparative analysis is made between optimizers by plotting training loss vs epochs & training accuracy vs epochs. Results showed that, SGD optimizer outperforms other two for ResNet50. Training accuracy of approximately 99% is observed for ResNet50 with 500 training & 100 validation images.
SARL: Deep Reinforcement Learning based Human-Aware Navigation for Mobile Robot in Indoor Environments

In a human-robot coexisting environment, reaching the goal position safely and efficiently is essential for a mobile service robot. In this paper, we present an advanced version of the Socially Attentive Reinforcement Learning (SARL) algorithm, namely SARL*, to achieve human-aware navigation in indoor environments. Recently, deep reinforcement learning has achieved great success in generating human-aware navigation policies. However, there exist some limitations in the real-world implementations: the learned navigation policies are limited to certain distances associated with the training process, and the simplification of the environment neglects obstacles other than humans. In this work, we improve the state-of-the-art SARL algorithm by introducing a dynamic local goal setting mechanism and a map-based safe action space to tackle the above problems. Real-world experimental results demonstrate that the proposed algorithm outperforms the original SARL algorithm in both time cost and path length in the human-aware navigation tasks in the indoor environment.
Traffic Fingerprinting Attacks on Internet of Things Using Machine Learning

The Internet of Things (IoT) concept has been widely adopted and Internet connected devices enter more and more areas of our everyday lives. However, their limited security measures raise increasing concerns, especially in terms of users' privacy. That is why, in this paper, privacy risks, focusing primarily on information leakage exposed by traffic fingerprinting attacks, on IoT devices are investigated. The considered attacks take advantage of the statistical network flows' features and application of machine learning (ML) methods and can be utilized by a passive traffic observer. In this perspective, the first part of the research presented in this paper analyzes the feasibility of identifying individual devices in a victim's home network. It considers smart environment setups of different scales and conditions, and it also includes a performance comparison of the different ML models applied. The second part introduces and validates a method for the devices' state detection based on pattern recognition with ML. Finally, recommendations for mitigating the discussed privacy risks are also enclosed.
Hybrid RRT: A Semi-Dual-Tree RRT-Based Motion Planner

Rapidly-exploring Random Trees (RRTs) have been widely used for motion planning problems due to their ability to efficiently find solutions. Informed RRT* is an optimized version of RRT, which not only implements the rewiring process to optimize the tree but also limits the search area to a subset of the state space to return near-optimal solutions faster. However, limiting the state space is a function of the obtained shortest path so that before a solution is found, the planner cannot limit the state space to a subset. Moreover, unidirectional RRTs such as Informed RRT* take more time to find initial solutions in comparison to the bidirectional RRTs. In this paper, we propose Hybrid RRT, which divides the planning process into three parts: finding initial solutions by a dual-tree search, combining two trees into one, and optimizing the solution. In order to obtain an initial solution, Hybrid RRT implements a dual-tree search, which helps it find solutions faster than unidirectional searches. Then, it combines the start tree and the goal tree of the dual-tree search into one so as to implement informed sampling for a single tree to optimize the current solution. The simulation carried out in Open Motion Planning Library (OMPL), which shows that Hybrid RRT achieved outstanding improvement over RRT* and Informed RRT*.
Evolving clustering algorithm based on mixture of typicalities for stream data mining

Many applications have been producing streaming data nowadays, which motivates techniques to extract knowledge from such sources. In this sense, the development of data stream clustering algorithms has gained an increasing interest. However, the application of these algorithms in real systems remains a challenge, since data streams often come from non-stationary environments, which can affect the choice of a proper set of model parameters for fitting the data or finding a correct number of clusters. This work proposes an evolving clustering algorithm based on a mixture of typicalities. It is based on the TEDA framework and divide the clustering problem into two subproblems: micro-clusters and macro-clusters. Experimental results with benchmarking data sets showed that the proposed methodology can provide good results for clustering data and estimating its density even in the presence of events that can affect data distribution parameters, such as concept drifts. In addition, the model parameters were robust in relation to the state-of-the-art algorithms.
Microsoft Uses Machine Learning and Optimization to Reduce E-Commerce Fraud

The authors discuss Microsoft’s development of a fraud-management system that uses customized long-term and short-term sequential machine learning models to detect both historical and emerging fraud patterns. It also makes rapid real-time optimal decisions using a dynamic programming approach to optimize long-term profit by taking into account decisions made by multiple parties (e.g., banks issuing credit cards).
Zero-shot learning for action recognition using synthesized features

The major disadvantage of supervised methods for action recognition is the need for a large amount of annotated data, where the data is matched to its label accurately. To address this issue, Zero-Shot Learning (ZSL) is introduced. Zero short learning primarily uses data that is synthesized to compensate for lack of training examples. In this paper, two different approaches are proposed for the synthesis of artificial examples for novel classes; namely, inverse autoregressive flow (IAF) based generative model and bi-directional adversarial GAN(Bi-dir GAN). A consequence of the proposed approach is a transductive setting using a semi-supervised variational autoencoder, where the unlabelled data from unseen classes are used to train the model. This enables the generation of novel class examples from textual descriptions. The proposed models perform well in the following settings, namely, i) Standard setting(ZSL), where the test data comes only from unseen classes, and ii) Generalized setting(GZSL), where the test data comes from both seen and unseen classes. In the case of the generalized setting, examples with pseudo labels are generated for unseen classes. Experiments are performed on three baseline datasets, UCF101, HMDB51, and Olympic. In comparison with state-of-the-art approaches, both the proposed models, IAF based generative model and Bi-dir GAN model outperform in UCF101, and Olympic datasets in all the settings and achieve comparative results in HMDB51.