Reviewerly/exHarmony huggingface dataset:
Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Abstract In some practical situations, new computational methods are required for appropriately representing systems and their variables with inaccuracies, uncertainties, or variability. The chaos numbers seem to efficiently represent a set or range of values with lower and upper bounds for variables. In this article, a new chaos‐enhanced representation scheme that is based on the notion of chaos numbers is proposed for evolutionary optimization methods. As a first application of chaos‐based new encoding type, it is integrated into the novel hybrid intelligent optimization method proposed that is adapted as numerical association rules miner for the first time. The proposed hybrid method can also be used for complex types of search and optimization problems. The method is designed as a multiobjective rule miner that simultaneously handles different conflicting objectives and finds the accurate and comprehensible rules automatically. Based on the chaotic encoding, the proposed method easily and effectively adjusts the intervals of the attributes and automatically mines the rules without any preprocess. The performance of the proposed method was tested in real quantitative data sets and results were compared with the other association rule mining methods. According to the obtained results, the proposed method seems promising with respect to different metrics.
Memory-Efficient Deep Learning Inference in Trusted Execution Environments

This study identifies and proposes techniques to alleviate two key bottlenecks to executing deep neural networks in trusted execution environments (TEEs): page thrashing during the execution of convolutional layers and the decryption of large weight matrices in fully-connected layers. For the former, we propose a novel partitioning scheme, y-plane partitioning, designed to (i) provide consistent execution time when the layer output is large compared to the TEE secure memory; and (ii) significantly reduce the memory footprint of convolutional layers. For the latter, we leverage quantization and compression. In our evaluation, the proposed optimizations incurred latency overheads ranging from 1.09X to 2X baseline for a wide range of TEE sizes; in contrast, an unmodified implementation incurred latencies of up to 26X when running inside of the TEE.
Supplement and Suppression: Both Boundary and Nonboundary Are Helpful for Salient Object Detection

Current methods aggregate multilevel features from the backbone and introduce edge information to get more refined saliency maps. However, little attention is paid to how to suppress the regions with similar saliency appearances in the background. These regions usually exist in the vicinity of salient objects and have high contrast with the background, which is easy to be misclassified as foreground. To solve this problem, we propose a gated feature interaction network (GFINet) to integrate multiple saliency features, which can utilize nonboundary features with background information to suppress pseudosalient objects and simultaneously apply boundary features to supplement edge details. Different from previous methods that only consider the complementarity between saliency and boundary, the proposed network introduces nonboundary features into the decoder to filter the pseudosalient objects. Specifically, GFINet consists of global features aggregation branch (GFAB), boundary and nonboundary features' perception branch (B&NFPB), and gated feature interaction module (GFIM). According to the global features generated by GFAB, boundary and nonboundary features produced by B&NFPB and GFIM employ a gate structure to adaptively optimize the saliency information interchange between abovementioned features and, thus, predict the final saliency maps. Besides, due to the imbalanced distribution between the boundary pixels and nonboundary ones, the binary cross-entropy (BCE) loss is difficult to predict the pixels near the boundary. Therefore, we design a border region aware (BRA) loss to further boost the quality of boundary and nonboundary, which can guide the network to focus more on the hard pixels near the boundary by assigning different weights to different positions. Compared with 12 counterparts, experimental results on five benchmark datasets show that our method has better generalization and improves the state-of-the-art approach by 4.85% averagely in terms of the regional and boundary evaluation measures. In addition, our model is more efficient with an inference speed of 50.3 FPS when processing a 320 ×320 image. Code has been made available at https://github.com/lesonly/GFINet.
Interactive Analysis of CNN Robustness

Abstract While convolutional neural networks (CNNs) have found wide adoption as state‐of‐the‐art models for image‐related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against. This paper presents Perturber, a web‐based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed. Perturber offers a large variety of scene modifications, such as camera controls, lighting and shading effects, background modifications, object morphing, as well as adversarial attacks, to facilitate the discovery of potential vulnerabilities. Fine‐tuned model versions can be directly compared for qualitative evaluation of their robustness. Case studies with machine learning experts have shown that Perturber helps users to quickly generate hypotheses about model vulnerabilities and to qualitatively compare model behavior. Using quantitative analyses, we could replicate users’ insights with other CNN architectures and input images, yielding new insights about the vulnerability of adversarially trained models.
Performance Comparison of The Artificial Intelligence Applications to Dual-Axis Tracking Controls of Solar Panels

In this paper, the efficiency of photovoltaic panels is improved by adding a solar tracking system. The solar tracking system is used to track the sun so that the photovoltaic always faces the sun. This system uses a dual axis consisting of a horizontal rotation axis and a vertical rotation axis. The motion of the horizontal axis of rotation follows the sun's azimuth angle from north to south. The movement of the vertical axis of rotation follows the sun's azimuth angle from east to west. Both types of movements are controlled using a PID controller optimized with an artificial intelligence approach, namely PID control with Particle Swarm Optimization tuning, with Firefly Algorithm tuning, Imperialist Competitive Algorithm tuning, Bat Algorithm tuning, and Ant Colony Optimization tuning. Experiments of various approaches were carried out and the corresponding performances were compared. The experimental results show that the control PID tuned by Bat Algorithm has the best performance in terms of settling time (0.073 s), undershot (1.193), and overshoot (19.734).
Cross-Modal Guidance Assisted Hierarchical Learning Based Siamese Network for MR Image Denoising

Cross-modal medical imaging techniques are predominantly being used in the clinical suite. The ensemble learning methods using cross-modal medical imaging adds reliability to several medical image analysis tasks. Motivated by the performance of deep learning in several medical imaging tasks, a deep learning-based denoising method Cross-Modality Guided Denoising Network CMGDNet for removing Rician noise in T1-weighted (T1-w) Magnetic Resonance Images (MRI) is proposed in this paper. CMGDNet uses a guidance image, which is a cross-modal (T2-w) image of better perceptual quality to guide the model in denoising its noisy T1-w counterpart. This cross-modal combination allows the network to exploit complementary information existing in both images and therefore improve the learning capability of the model. The proposed framework consists of two components: Paired Hierarchical Learning (PHL) module and Cross-Modal Assisted Reconstruction (CMAR) module. PHL module uses Siamese network to extract hierarchical features from dual images, which are then combined in a densely connected manner in the CMAR module to finally reconstruct the image. The impact of using registered guidance data is investigated in removing noise as well as retaining structural similarity with the original image. Several experiments were conducted on two publicly available brain imaging datasets available on the IXI database. The quantitative assessment using Peak Signal to noise ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) demonstrates that the proposed method exhibits 4.7% and 2.3% gain (average), respectively, in SSIM and FSIM values compared to other state-of-the-art denoising methods that do not integrate cross-modal image information in removing various levels of noise.
Few-Shot Adaptive Detection of Objects of Concern Using Generative Models with Negative Retraining

Detecting objects which we are interested in, Objects of Concern (OoC), is nowadays attracting attention. In aviation and transport, it is important to robustly detect OoC in security images. OoC are rare, differ from typical samples, and may be unknown during training. Most OoC detection methods need to be trained on large datasets to achieve good performance and have limited real-world generalization ability. A large variety of samples is needed, and it is expensive to collect and label large datasets due to the rarity of OoC. To address such limitations, we propose the negative REtraining with Few-shots Generative Adversarial Network (REFGAN) for detecting OoC. REFGAN aims at automatically identifying OoC by learning from Objects of No Concern (OoNC) and OoC. Our methodology comprises learning a prior using OoNC, and few-shot adaptation using the Few-Shot OoC (FSOoC). We propose a methodology to robustly perform few-shot adaptive detection of OoC using GANs and learned distribution boundaries. The evaluation of REFGAN on the Baggage SIXray dataset shows that when FSOoC are used, our model outperforms the prior improving OoC detection, and outperforms recent benchmarks by approximately 6.3% in mean values. REFGAN using few-shots of 80 samples shows a robust comparable performance compared to REFGAN using all the samples for retraining and model adaptation. REFGAN can detect unknown OoC and its evaluation on SIXray and CIFAR-10 shows robustness against the number of few-shot samples of OoC. REFGAN on CIFAR-10 outperforms benchmarks by approximately 16% using few-shots of 80 and of 10 samples.
Analysis of the Relationship between the Users Personality Traits and the Images They Post on Social Media

Due to the dramatic rise in the popularity of the social media, the problem of analyzing the interconnections of digital traces and the psychological characteristics of the user become urgent. The development of deep learning models has ensured the automatic image analysis and feature extraction. In this study, we propose a clustering model for digital traces of users based on VGG16 deep learning model and a K-means clustering algorithm. We use this model for predictive analytics of digital footprints in the form of arbitrary images. The scientific novelty of the research lies in the originality of the proposed methodology. This technique firstly consists in directly using the compressed representation obtained by the encoder instead of object tags. And secondly, we propose a method for obtaining representations of clusters, based on the weighting of users belonging to a given cluster. To study the correlations of the user's personality characteristics and their posted images, dataset of BIG Five traits of VK social network users was used. According to the results of the expert assessment of a specialist psychologist, the presented relations between the clusters content and the personality characteristics of the users were determined. Namely, the "faces" cluster is characterized by a high value of consciousness, the "cats" cluster is characteristic of introverted users, and low agreeableness was associated with the "landscapes" cluster. Based on the obtained similarity of cluster representations, the personality traits of users related to these clusters were found similar, and the possibility to combine these clusters was proposed.
Toward a Fully Secure Authenticated Encryption Scheme from a Pseudorandom Permutation

In this paper, we propose a new block cipher-based authenticated encryption scheme, dubbed the Synthetic Counter with Masking (\(\mathsf {SCM}\)) mode. \(\mathsf {SCM}\) follows the \(\mathsf {NSIV}\) paradigm proposed by Peyrin and Seurin (CRYPTO 2016), where a keyed hash function accepts a nonce N with associated data and a message, yielding an authentication tag T, and then the message is encrypted by a counter-like mode using both T and N. Here we move one step further by encrypting nonces; in the encryption part, the inputs to the block cipher are determined by T, counters, and an encrypted nonce, and all its outputs are also masked by an (additional) encrypted nonce, yielding keystream blocks.
IOT in Marketing: Current Applications and Future Opportunities

In the past decade, Internet of Things (IoT) has significantly transformed the world we live in. Moreover, as more organizations and people augment their life with such interconnected digital devices, the way we communicate, transact and behave is bound to change considerably. In this chapter we discuss how IoT is being leveraged by organizations, which is expected to make formidable changes in marketing and allied activities. Further, the chapter focuses on how AI and IoT can together enhance their individual capabilities and synergize the potential benefits. The chapter presents various ways and instances in which organizations are using IoT systems and the developments underway. We share various examples of how companies are developing new products and solutions driven by IoT. The chapter also describe some of the pitfalls which may have hindered the growth of IoT adoption. Finally, we present a case-let discussing how a self-drive car rental company used IoT to enhance its services and reaped benefits for itself and its customers.
Tightening polytopic constraint in MPC designs for mobile robot navigation

We introduce a new implementation of a standard linear polytopic constraint which allows not only to validate the constraint itself but also to encourage a distance from the system to the boundary (i.e. the edges of the polytope), and hence, is called tightening polytopic constraint. We also present the MPC designs for mobile robots to navigate within a polytopic working zone towards the goal, which employ the proposed tightening constraint. Extensive simulations and comparison with the potential field approach show the effectiveness and advantages of the proposed methods.
Quantum communication for sender anonymity based on single-particle with collective detection

Abstract Nowadays, identity protection has turned into a fundamental demand for online activities. Currently, the present quantum anonymous communication protocols mostly rely on multi-entanglement. In this paper, we propose an anonymous communication protocol for anonymous sender by using single-particle states. The protocol can be extended to a communication protocol where the sender and receiver are fully anonymous with the message kept secret. In terms of security, our protocol is designed to comply with the technique of collective detection. Compared to the step-by-step detection, collective detection, in which the participants perform detection only once, reduces the complexity of the protocol to some extent. Moreover, we analytically demonstrate the security of the protocol in the face of active attacks. Any active attack employed by an external or internal attacker cannot reveal any useful information about the sender’s identity. Meanwhile, any malicious behavior will be detected by honest participants.
From exergames to immersive virtual reality systems: serious games for supporting older adults

Interactive gaming systems have the potential to create enjoyable experiences by challenging older adults and transporting them into computer-generated virtual environments. The use of serious games to promote health among older adults has grown significantly over the last decade. There has been a diversity of specialized games for health with growing scientific evidence of its effectiveness in applications such as exercise promotion, physical rehabilitation, cognitive training, and different types of assessment. This chapter covers an overview of considerations regarding the design of serious games for older adults as well as evidence demonstrating their usefulness in both immersive (e.g., virtual reality) and nonimmersive (e.g., Wii, Kinect) contexts. We highlight the importance of designing for the target end-users through systematic multidisciplinary user-centered design methods to create games that are more aligned with older adult’s needs, interests, and capabilities. Several methods for evaluating the impact on players’ physical and cognitive performance as well as usability and game experience are presented. A case for adaptivity to support personalization for the gaming experience is proposed. The chapter concludes with challenges and future directions for this emerging and exciting field.
Efficiency Enhancement of Machine Learning Approaches through the Impact of Preprocessing Techniques

A prediction system is affected by numerous factors. The primary factor is the depiction and traits of instance data. In the presence of unnecessary and redundant data or boisterous and deceptive information, it becomes arduous to discover knowledge during the training phase. One of the persistent challenges in data analytics is identifying and rectifying dirty data. The outcomes will be unreliable decisions and inaccurate analytics if an individual fails to do so. It is known by a large mass of people that the machine learning model performances are affected by the quality of data. Consequently, a significant amount of time is spent by the scientists on cleaning data before model training. Data pre-processing is considered the fundamental stage of ML methods by countless researchers. Nevertheless, only a fraction of works have paid emphasis on the consequences of data processing techniques. This paper addresses the serious impact that issues of data pre-processing can have on the generality performance of an ML algorithm and here all the pre-processing techniques like removal of missing values with all the possible methods, data binning, and data normalization, etc. are discussed. After performing all the pre-processing techniques, divide the dataset into training and testing datasets and then apply machine learning algorithms. It is concluded that to achieve a reliable result or better accuracy data preprocessing plays a very important role and to build a machine learning model we must have deep knowledge of all the pre-processing techniques, also where and how to apply them.
Insulator UV Image Fault Detection Based on Deep Learning

In this paper, ultraviolet images of porcelain insulators were taken as the research object, a sample library of ultraviolet images of insulators with different insulation states was established, and a convolutional neural network model was built. The model was operated under different learning rates using AlexNet structure, and the training process was analyzed visually. The results showed that the learning rate had a great influence on the performance of the network, and the accuracy of the test set reached the optimal value under a certain learning rate. Through supervised learning of samples, Convolutional Neural Network realized autonomous feature extraction and high-precision fault evaluation for UV imaging of porcelain insulators.
Equilibrium Staking Levels in a Proof-of-Stake Blockchain

We study the equilibrium level of staking in a Proof-of-Stake blockchain when investors have different trading horizons. We find that, contrary to conventional wisdom, staking levels do not always increase in block rewards. Rather, block rewards serve as an inflationary transfer from short-horizon cryptocurrency investors to long-horizon cryptocurrency investors. Thus, increasing block rewards reduces short-horizon cryptocurrency investment which, under certain conditions, reduces the overall transfer to long-horizon cryptocurrency investors and therefore reduces long-horizon investment as well. When this is the case, increasing block rewards decreases total cryptocurrency investment which leads to a reduction in the equilibrium value of staked cryptocurrency.
UAE e-Learning Sentiment Analysis Framework

This research project predicts and infers real-time insights on public mental health relevant to education during and after the COVID-19 pandemic by modeling, deploying, and testing an end-to-end spatiotemporal sentiment analysis framework. Moreover, the project aims to analyze the sentiments and emotions of the public; from Twitter, toward the current context of the e-learning process factored by aspects and emotions. The framework consists of four predictive models based on statistical analysis and machine learning to analyze the UAE education-related Twitter dataset. The first analytics is spatiotemporal analytics, which describes an event at a specific time and specific location. Spatiotemporal analytics is used as the base for the remaining three analytics: Aspect-based Sentiment Analysis, sentiment analysis, and emotion analysis. Aspect-based Sentiment Analysis considers the words/terms related to relevant aspects and then identify the sentiment associated with them. Sentiment Analysis is used to extract the sentiment in a specific text. Emotion Analysis identifies the type of emotion felt by users in their tweets. All the analytics will be visualized into a responsive website that provides a prompt understanding of the public opinions and their feedback towards the e-learning process. As a result, a group of recommendations is generated based on the analytics’ resulting emotion to enhance the mental health.
A Pipeline for Automating Labeling to Prediction in Classification of NFRs

Non-Functional Requirements (NFRs) focus on the operational constraints of the software system. Early detection of NFRs enables their incorporation into the architectural design at an initial stage, a practice obviously preferable to expensive refactoring at a later stage. Automated identification and classification of NFRs has therefore seen numerous efforts using rule-based, machine learning and deep learning-based approaches. One of the major challenges for such an automation is the manual effort that needs to be invested into labeling of training data. This is a concern for large software vendors who typically work on a variety of applications in diverse domains. We address this challenge by designing a pipeline that facilitates classification of NFRs using only a limited amount (~ 20% of an available new dataset) of labeled data for training. We (1) employed Snorkel to automatically label a dataset comprising NFRs from various Software Requirement Specification documents, (2) trained several classifiers using it, and (3) reused these pre-trained classifiers using a Transfer Learning approach to classify NFRs in industry-specific datasets. From among the various language model classifiers, the best results have been obtained for a BERT based classifier fine-tuned to learn the linguistic intricacies of three different domain-specific datasets from real-life projects.