Reviewerly/exHarmony huggingface dataset:
How majority-vote crossover and estimation-of-distribution algorithms cope with fitness valleys

The benefits of using crossover in crossing fitness gaps have been studied extensively in evolutionary computation. Recent runtime results show that majority-vote crossover is particularly efficient at optimizing the well-known Jump benchmark function that includes a fitness gap next to the global optimum. Also estimation-of-distribution algorithms (EDAs), which use an implicit crossover, are much more efficient on Jump than typical mutation-based algorithms. However, the allowed gap size for polynomial runtimes with EDAs is at most logarithmic in the problem dimension n. In this paper, we investigate variants of the Jump function where the gap is shifted and appears in the middle of the typical search trajectory. Such gaps can still be overcome efficiently in time O(nlog⁡n) by majority-vote crossover and an estimation-of-distribution algorithm, even for gap sizes almost n. However, if the global optimum is located in the gap instead of the usual all-ones string, majority-vote crossover would nevertheless approach the all-ones string and be highly inefficient. In sharp contrast, an EDA can still find such a shifted optimum efficiently. Thanks to a general property called fair sampling, the EDA will with high probability sample from almost every fitness level of the function, including levels in the gap, and sample the global optimum even though the overall search trajectory points towards the all-ones string. Finally, we derive limits on the gap size allowing efficient runtimes for the EDA.
Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models

The rapid and wide proliferation of mobile phones has led to accelerated demand for mobile applications (apps). Consequently, a large number of mobile apps have been developed and deployed on the Google and Apple Play stores. Calling apps hold special importance in this regard by offering the services of sharing messages, making video calls, and sending audio messages, free of cost. Although each app has its own set of features, different apps can provide higher levels of satisfaction for the user, and aspect analysis is often overlooked by existing studies. This study presents an aspect-level analysis of IMO, Skype, Telegram, WeChat, and WhatsApp regarding the services offered for the account, app, call, message, update, video, and working features. A large collected dataset from the Google Play store is utilized for aspect extraction and analysis using the Latent Dirichlet Allocation (LDA) model. Apps are analyzed using LDA-extracted aspects and recommended regarding users’ priorities of call, message, and video requirements. Sentiment analysis is adopted to analyze user sentiments regarding apps as well as to aid in the aspect analysis. For sentiment analysis, a novel ensemble model of a gated recurrent unit and convolutional neural network is presented, which obtains a 94% accuracy score.
A hybrid deep-Q-network and model predictive control for point stabilization of visual servoing systems

Major difficulties and challenges of visual servoing point stabilization focus on how to design controllers to efficiently complete the task while satisfying various constraints. This paper designs a hybrid control scheme that combines reinforcement learning and model predictive control to handle the point stabilization problem for image-based visual servoing systems of wheeled mobile robots with the visibility and input constraint. The image plane is divided into two parts: the deep-Q-network (DQN) part and the model predictive control (MPC) part. In the DQN part, a DQN controller incorporating two deep networks is designed and activated to pull the image coordinates of the feature into the MPC part. In the MPC part, a simplified-dual-neural-network-based MPC scheme (SDN-MPC) is proposed to precisely guide the robot to the desired position. The asymptotic stability of the SDN-MPC is proven for the point stabilization task. Finally, simulations and experiments are provided to verify the effectiveness, efficiency and robustness of the proposed hybrid controller by comparing with the MPC-only controller and DQN-only controller.
A Multimodal Framework for Video Caption Generation

Video captioning is a highly challenging computer vision task that automatically describes the video clips using natural language sentences with a clear understanding of the embedded semantics. In this work, a video caption generation framework consisting of discrete wavelet convolutional neural architecture along with multimodal feature attention is proposed. Here global, contextual and temporal features in the video frames are taken into account and separate attention networks are integrated in the visual attention predictor network to capture multiple attentions from these features. These attended features with textual attention are employed in the visual-to-text translator for caption generation. The experiments are conducted on two benchmark video captioning datasets - MSVD and MSR-VTT. The results prove an improved performance of the method with a CIDEr score of 91.7 and 52.2, for the aforementioned datasets, respectively.
An I/O Virtualization Framework With I/O-Related Memory Contention Control for Real-Time Systems

Modern applications are often characterized by a tight interaction with I/O devices. At the same time, many application domains are also facing a shift toward an integrated approach where multiple applications with mixed levels of safety and security need to co-exist on top of a shared hardware platform, which is typically managed by a hypervisor. This gives rise to the need for a predictable mechanism allowing multiple virtual machines to share I/O devices, while at the same time controlling contention delays when they access global memory. To deal with these shortcomings, this article proposes an I/O virtualization framework providing support for controlling the I/O-related memory contention by leveraging the ARM QoS-400 regulators. Extensive experiments are performed to compare the proposed solution with the Xen hypervisor, showing improvements up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> when controlling the I/O-related memory contention.
On the Implementation of Fuzzy Inference Engines on Quantum Computers

Quantum computers can be a revolutionary tool to implement inference engines for fuzzy rule-based systems. In fact, the use of quantum mechanical principles can enable parallel execution of fuzzy rules and allow them to be used efficiently in complex contexts such as distributed and big data environments. This article introduces the very first quantum-based fuzzy inference engine that is capable of providing exponential acceleration in fuzzy rule execution compared to its classical counterpart, and allows a quantum computer to be programmed by fuzzy linguistic rules. The proposed inference engine was implemented using a quantum algorithm design scheme based on the oracle notion. This scheme allows the modeling of a fuzzy rule-based system as a Boolean function, the oracle, which is able to reconstruct the relationships between the antecedent and consequent parts of fuzzy rules, and can be efficiently computed on a quantum computer. The suitability of the proposed quantum algorithm for use as a fuzzy inference engine was tested in a typical benchmark scenario, such as that provided by inverted pendulum control.
MKE-GCN: Multi-Modal Knowledge Embedded Graph Convolutional Network for Skeleton-Based Action Recognition in the Wild

The graph convolutional networks (GCNs), which model human body skeletons as several spatial-temporal graphs, have been widely used and become a key to representative feature extraction. However, existing methods have limitations in recognizing action in the wild, where human body skeletons are captured from real-world scenes with diversified view-points, obvious motion blurs, complex interactions and fast varying resolutions of the human body. In this paper, we propose a Multi-modal Knowledge Embedded Graph Convolutional Network (MKE-GCN), which is a conceptually simple yet effective method for skeleton-based action recognition in the wild. In the proposed framework, we address two main problems: 1) how to design a simple yet effective pipeline for modeling multi-modal body skeletons; and 2) how to equip this pipeline with the ability of handling “in the wild”. To tackle these problems, in MKE-GCN, we first build an adaptive multi-modal aggregation (AMA) module and add it to traditional GCNs for multi-modal representation learning. Then, we further enhance the GCN model by a multi-modal knowledge distillation (MKD) strategy, where the proposed MKE-GCN mines action recognition knowledge from various multi-modal models. We discover that aside from the multi-modal representation, the MKD is of particular importance for improving the accuracy of skeleton-based action recognition “in the wild”. Notably, the proposed method is light-weight, which can be applied to any GCN based method. Furthermore, extensive experiments on three challenging benchmarks, e.g., UAV-Human, NTU-RGB+D 60 and NTU-RGB+D 120, demonstrate that our approach sets a new record for skeleton-based action recognition. Our anonymous code and models are also released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Multi-Biometric Unified Network for Cloth-Changing Person Re-Identification

Person re-identification (re-ID) aims at matching the same person across different cameras. Most of the existing meth-ods for re- ID assume that people wear the same clothes on different cameras. However, Cloth-Changing re- ID is a quite challenging problem since people are likely to change clothes as the time span increases. To tackle this problem, a Multi-Biometric Unified Network (MBUNet) is proposed to ex-ploit clothing-unrelated cues. We firstly introduce a multi-biological feature branch that aims at extracting a variety of biological features, such as the head, neck, and shoulders to resist clothing changes. To extract discriminative fine-grained biological features, we embed a differential feature attention module (DFAM) for it. Besides, we adopt differ-ential recombination on max pooling (DRMP) and apply a direction-adaptive graph convolutional layer to extract more robust global features and pose features. Extensive experi-ments on three Cloth-Changing re-ID datasets show the ad-vantages of our proposed MBUNet.
DSE-Net: Artistic Font Image Synthesis via Disentangled Style Encoding

Recently, the artistic font generation has made significant progress. However, existing methods typically treat the style of artistic font as a whole. Their performance is usually limited to the artistic fonts with complex style elements in glyph and text effect. To solve these problems, this paper presents a disentangled style encoding network, termed DSE-Net, to synthesize artistic fonts. In order to obtain the disentangled text effect features, we introduce a perspective transformation network. We propose a cross-layer fusion mechanism to improve the artistic fonts' structure and texture according to their different representations in CNN. Notably, encoding different style elements for artistic font generation is a new task, so there is no publicly-accessible dataset. Therefore, a new dataset, termed SSAF, has been constructed. Extensive experiments demonstrate that our model significantly outperforms the state-of-the-art methods, with more fine-grained text effect and accurate stroke details.
Cross-Modal Attention Networks with Modality Disentanglement for Scene-Text VQA

Understanding the texts in visual scenes is essential for reasoning tasks when texts carry key information. Therefore, it is a common way to learn multi-modal representation from visual scenes to support Scene-Text Visual Question&Answering. Different modalities, such as text and image, are embedded into a joint semantic space where attention mechanism is widely applied. This paper regards Scene-Text VQA as a kind of cross-modal task where the joint embedding between a scene image and its text answer is a semantic bridge to share the strong semantic clues with the two modalities. To this end, our proposed framework firstly divides the scene recognition features into two types: visual and textual features. And then external cross-modal pre-trained features are introduced to simultaneously guide the representation of visual and text features through cross-modal attention net-works. Finally, Transformer is applied as a decoder to output answers. Conducted on a public Scene-Text VQA dataset, experimental results show that our proposed framework out-performs existing approaches in terms of accuracy.
An analytical comparison of datasets of Real-World and simulated falls intended for the evaluation of wearable fall alerting systems

Automatic fall detection is one of the most promising applications of wearables in the field of mobile health. The characterization of the effectiveness of wearable fall detectors is hampered by the inherent difficulty of testing these devices with real-world falls. In fact, practically all the proposals in the literature assess the detection algorithms with ‘scripted’ falls that are simulated in a controlled laboratory environment by a group of volunteers (normally young and healthy participants). Aiming at appraising the adequacy of this method, this work systematically compares the statistical characteristics of the acceleration signals from two databases with real falls and those computed from the simulated falls provided by 18 well-known repositories commonly employed by the related works. The results show noteworthy differences between the dynamics of emulated and real-life falls, which undermines the testing procedures followed to date and forces to rethink the strategies for evaluating wearable fall detectors.
Blockchain-driven authorized data access mechanism for digital healthcare

The existing traditional medical database system is a centralized system in which data manipulation is easy, privacy is critical, and there is a lack of easy information exchange and adequate data management. Although blockchain mechanisms have the potential to use and easily maintained on the distributed medical architecture around the globe to address the bottleneck situation. In this paper, we propose a blockchain-based authenticated access control framework. We use blockchain technology to defend patients’ rights to licensed medical material distribution systems. The proposed mechanism’s security has been demonstrated against adversary. The analysis of performance and comparative study is also performed, which shows that it is a reliable authenticated access control architecture for e-healthcare in terms of efficiency and security.
MIRNet: A Robust RGBT Tracking Jointly with Multi-Modal Interaction and Refinement

RGBT tracking attempts to design a robust all-weather tracker by integrating the complementary features of visible and thermal spectrums. To explore the latent interdependencies across modalities, we propose a novel real-time tracker named MIR-Net, which contains a multi-modal interaction module (MIM) and a refinement mechanism (RM), thereby adaptively merging multi-modal features and achieving precise scale estimation. Specifically, to enhance instance representation in low-quality modality, the MIM reinforces discriminative features from one modality to another in a bidirectional way. Considering the negative effects of unreliable modality, we further introduce a gate function in MIM to filter redundancy. To address the problem of random drifting and estimate the precise scale in the online tracking, we present a well-designed RM that combines optical flow and refinement network. Comprehensive experiments on two public RGBT benchmarks validate that our tracker outperforms the state-of-the-art methods.
A Blockchain-Based and SGX-Enabled Access Control Framework for IoT

With the rapid development of physical networks, tens of billions of Internet of Things (IoT) devices have been deployed worldwide. Access control is essential in the IoT system, which manages user access to vital IoT data. However, access control for the IoT is mainly based on centralized trusted servers, which face problems such as a single point of failure and data leakage. To tackle these challenges, we propose an access control framework for the IoT by combining blockchain and Intel software guard extension (SGX) technology. A blockchain validates both IoT devices and edge servers added to the network. The access control contract is deployed on the blockchain, which can manage attribute-based access control policies in a fine-grained manner and make access control decisions flexibly. SGX technology is introduced into the edge computing server to realize the confidentiality of data processing. Finally, we implemented the prototype of the framework on Quorum and conducted extensive experiments and theoretical analyses on the performance of the blockchain. The results of the experimental tests and theoretical analyses show that our framework has more advantages in computing costs and on-chain storage costs.
KDE-Based Ensemble Learning for Imbalanced Data

Imbalanced class distribution affects many applications in machine learning, including medical diagnostics, text classification, intrusion detection and many others. In this paper, we propose a novel ensemble classification method designed to deal with imbalanced data. The proposed method trains each tree in the ensemble using uniquely generated synthetically balanced data. The data balancing is carried out via kernel density estimation, which offers a natural and effective approach to generating new sample points. We show that the proposed method results in a lower variance of the model estimator. The proposed method is tested against benchmark classifiers on a range of simulated and real-life data. The results of experiments show that the proposed classifier significantly outperforms the benchmark methods.
Uncertainty-aware report generation for chest X-rays by variational topic inference

Automating report generation for medical imaging promises to minimize labor and aid diagnosis in clinical practice. Deep learning algorithms have recently been shown to be capable of captioning natural photos. However, doing a similar thing for medical data, is difficult due to the variety in reports written by different radiologists with fluctuating levels of knowledge and experience. Current methods for automatic report generation tend to merely copy one of the training samples in the created report. To tackle this issue, we propose variational topic inference, a probabilistic approach for automatic chest X-ray report generation. Specifically, we introduce a probabilistic latent variable model where a latent variable defines a single topic. The topics are inferred in a conditional variational inference framework by aligning vision and language modalities in a latent space, with each topic governing the generation of one sentence in the report. We further adopt a visual attention module that enables the model to attend to different locations in the image while generating the descriptions. We conduct extensive experiments on two benchmarks, namely Indiana U. Chest X-rays and MIMIC-CXR. The results demonstrate that our proposed variational topic inference method can generate reports with novel sentence structure, rather than mere copies of reports used in training, while still achieving comparable performance to state-of-the-art methods in terms of standard language generation criteria.
Smart Contract for Academic Certificate Verification Using Ethereum

A certificate is a document that a person receives after completing a course. This certificate can be used for recruitment purpose and getting admission in a college or institution. There is no system in place for institutes/universities to share information place to detect counterfeit credentials in the admissions process. As a result, a blockchain-based approach for storing certificates is needed that will prevent admittance forgeries via fake certifications. The proposed system is based on blockchain technology, which boosts privacy, security, and openness efficiency while lowering costs. To combat this issue, the proposed system must be both immutable and transparent. This article gives an overview of step-by-step development methodology to build a blockchain-based academic certificate verification system. A smart contract is a critical component of this system. Using Sublime Text 3 as a text editor with Remix Ethereum as an integrated development environment, a smart contract called AcadCerti was created for holding certificates. Using this AcadCerti smart contract, certificates can be saved on the blockchain that can be utilised by the company’s or admissions team’s during recruitment or admissions process. Because blockchain technology has a transparency aspect, the same certificate can be viewed by the employer, admissions staff, and student. Thay all have access to this information at the same time.
Novel Spatio-Temporal Continuous Sign Language Recognition Using an Attentive Multi-Feature Network

Given video streams, we aim to correctly detect unsegmented signs related to continuous sign language recognition (CSLR). Despite the increase in proposed deep learning methods in this area, most of them mainly focus on using only an RGB feature, either the full-frame image or details of hands and face. The scarcity of information for the CSLR training process heavily constrains the capability to learn multiple features using the video input frames. Moreover, exploiting all frames in a video for the CSLR task could lead to suboptimal performance since each frame contains a different level of information, including main features in the inferencing of noise. Therefore, we propose novel spatio-temporal continuous sign language recognition using the attentive multi-feature network to enhance CSLR by providing extra keypoint features. In addition, we exploit the attention layer in the spatial and temporal modules to simultaneously emphasize multiple important features. Experimental results from both CSLR datasets demonstrate that the proposed method achieves superior performance in comparison with current state-of-the-art methods by 0.76 and 20.56 for the WER score on CSL and PHOENIX datasets, respectively.
A Systematic Mapping of Quality Models for AI Systems, Software and Components

Recently, there has been a significant increase in the number of Artificial Intelligence (AI) systems, software, and components. As a result, it is crucial to evaluate their quality. Quality models for AI have in fact been proposed, but there is a lack of Systematic Mapping Studies (SMS) for quality models in AI systems, software, and components. The goal of this paper is to understand, classify, and critically evaluate existing quality models for AI systems, software, and components. This study conducts an SMS to investigate quality models proposed by various authors in the past. The study only found quality models for AI systems and software. So far, the SMS has revealed no work on AI software component quality models. Finally, the limitations of the quality models and the implications for future research and development efforts are discussed.