Reviewerly/exHarmony huggingface dataset:
Large Scale Distributed Optimization using Apache Spark: Distributed Scalable Shade-Bat (DistSSB)

Large Scale Global Optimization (LSGO) is interesting for its applications in machine learning, e.g. in deep learning or knowledge graph embeddings. Evolutionary algorithms (EA) have been found efficient in solving complex optimization problems. However, the performance of conventional EAs degrades with the increasing number of decision variables due to the lack of scalability. This paper proposes a scalable, parallel, distributed and hybrid EA named Distributed Scalable Shade-BatScalable Shade Bat (DistSSB) to solve LSGO problems. DistSSB is inspired by the exploration capability of the SHADE algorithm and exploitation feature of the Bat algorithm (BA). To achieve scalability, DistSSB is implemented using the popular distributed in-memory framework, Apache Spark. DistSSB distributes its population into multiple sub-populations using the island model. Each sub-population is independently evolved using SHADE or BA. After the migration interval, the best solutions are broadcasted employing the mesh topology. We have compared the scalability, efficiency, and efficacy of DistSSB with SHADE-ILS (CEC-2018 Winner) and GL-SHADE algorithm on the CEC-2013 LSGO benchmark function suite.For most functions, DistSSB has obtained better optimization results in lesser execution time as compared to SHADE-ILS and GL-SHADE. We have tested and shown the scalability of DistSSB for up to "one million" dimensions, whereas SHADE-ILS and GLSHADE fail to scale up for larger problems.
Towards Enhancing Fault Tolerance in Neural Networks

Deep Learning Accelerators and Neuromorphic hardware, used in many real-time safety-critical applications, are prone to faults that manifest in the form of errors in Neural Networks. Fault Tolerance in Neural Networks is a critical attribute for applications that require reliable computation for long duration such as IoT and mobile devices. The inherent fault tolerance of Neural Networks can be improved with regularization, however, the current techniques exhibit a trade-off between generalization and classification accuracy. To this extent, in this work, a Neural Network is modelled as two distinct functional components: a Feature Extractor with an unsupervised learning objective and a Fully Connected Classifier with a supervised learning objective. Traditional approaches to train the entire network using a single supervised learning objective are insufficient to achieve the objectives of the individual functional goals optimally. In this work, a novel two phase framework with multi-criteria objective function combining unsupervised training of the Feature Extractor followed by supervised training of the Classifier Network is proposed. In the Phase I, the unsupervised training of the Feature Extractor is modeled using two games solved simultaneously in the presence of Neural Networks with conflicting objectives. The first game with a generative model, trains the Feature Extractor to generate robust features for the input image by minimizing a reconstruction loss between the input and reconstructed image. The second game with a binary classification network, updates the Feature Extractor to smoothen the feature space and match with a prior Gaussian distribution. In Phase II, the resultant Feature Extractor, which is strongly regularized, is combined with the Fully Connected Classifier for fine-tuning on the classification task. The proposed two phase training algorithm is evaluated on four architectures with varying model complexity on standard image classification datasets: FashionMNIST and CIFAR10. The proposed framework is scalable and independent of the network architecture that provides superior tolerance to stuck at "0" faults as compared to existing regularization functions without loss in classification accuracy.
Unsupervised Content based Image Retrieval at Different Precision Level by Combining Multiple Features

Abstract Image retrieval is a procedure of finding appropriate images in the image database. There are two types of image retrieval systems in common practice. These are the text-based image retrieval (TBIR) system and content-based image retrieval (CBIR) system. The content based system is proven to be more effective in which the visual contents of the images are extracted and described by multi-dimensional feature vectors. In this work, several models are developed by combining different image features in a combination of two and three. To begin with, three different models based on the combination of two features, viz., color with shape, shape with texture, and color with texture are designed. A three features based model is considered with color, shape, and texture in the next step. The retrieval rate of the mentioned models is assessed in terms of precisions. The results are obtained using COREL standard database. This study shows that the images can be better retrieved using three features based model in contrast to models using two features.
Variational EM Algorithm for Student-$${\varvec{t}} $$ Mixtures of Gaussian Processes

Student- $$t$$ mixture of Gaussian processes (TMGP) extends the conventional mixture of Gaussian processes (MGP) by using Student- $$t$$ mixture as the input distribution instead of Gaussian mixture for robust regression and the un $$\nu$$ -Hardcut EM algorithm has already been established for Student- $$t$$ mixtures of Gaussian processes. However, this Hardcut EM algorithm takes an approximation of the $$Q$$ -function with the maximum a posteriori estimate of the hidden variables in the E-step, and separately optimizes the degrees of freedom of Student- $$t$$ distributions and the hyperparameters of the covariance functions in the M-step. In order to get rid of these problems, we propose a variational EM (VEM) algorithm for Student- $$t$$ mixtures of Gaussian processes from the viewpoint of variational inference. It is demonstrated by the experimental results on three datasets that our proposed VEM algorithm is effective and outperforms the un $$\nu$$ -Hardcut EM algorithm.
Transformation of Buddhist Mandalas into a Virtual Reality Installation

Abstract Technology can translocate traditional art into interactive, immersive experiences. At the Asian Art Museum of San Francisco, the authors transformed Tibetan Buddhist mandalas into a 3D virtual reality mandala installation. Furthering this project, they externalized an analog of the meditative experience by recording electroencephalograms that dynamically modulated the visual scene. The use of neurofeedback allowed fluctuations in the alpha power to drive the intensity of the fog obscuring the mandala. This aimed to give a sense of clearing the fog with one’s mind in a meditation-like state. The collaboration demonstrated how technology intended for scientific use may be adapted to an artistic installation that enriches the visitor experience.
Walsh Scalar Miyaguchi Preneel Cryptography for Secure Image Compression and Storage in WANET

One type of wireless network, Wireless Ad hoc NETwork (WANET), is arranged in a disaster zone with the objective of acquiring patients' data and enhancing medical prerequisites. WANETs are poised for various compact nodes dispersed in the disaster zone. The nodes in WANETs are competent enough in sending the collected medical data and storing in Cloud instead of in a single server. The limited bandwidth with less concentric secured storage and retrieval of huge medical data necessitates secured and data storage in Cloud to maintain the service quality of WANETs. To address this issue, we propose an optimization-based medical data compression and encryption technique, which ensures data security and high performance data retrieval in Cloud via WANET. We propose a Walsh and Scalar Quantized Singular Value Decomposition technique to deliver the compressed medical data that maximize the lifetime of WANETs. Next, Miyaguchi Preneel Encryption Standards are utilized and applied to the compressed medical data that not only improve the security aspect but also ensure high performance data retrieval by a single key for both encryption and decryption. The utility of the proposed method is exhibited with simulation results. The results show that the proposed method efficiently secures the medical data by extending network lifetime and data retrieval.
Brain inspired face recognition: A computational framework

This paper presents a new proposal of an efficient computational model of face recognition which uses cues from the distributed face recognition mechanism of the brain, and by gathering engineering equivalent of these cues from existing literature. Three distinct and widely used features – Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Principal components (PCs) extracted from target images are used in a manner which is simple, and yet effective. The HOG and LBP features further undergo Principal Component Analysis (PCA) for dimensionality reduction. Our model uses multi-layer perceptrons (MLP) to classify these three features and fuse them to form a sparsely connected model. A computational theory is first developed by using concepts from the information processing mechanism of the brain. Extensive experiments are carried out using eight publicly available face datasets to validate our proposed model’s performance in recognizing faces with extreme variation of illumination, pose angle, expression, and background. We also investigate the same mechanism because of reasons discussed later, on object recognition tasks as well. Results obtained are extremely promising when compared with other face and object recognition algorithms including CNN and deep learning-based methods. This highlights that simple computational processes, if clubbed properly, can produce competing performance with best algorithms.
HOW TO MAKE ADVANCED ONLINE USER STUDIES MEANINGFUL

Abstract It is of vital importance to explore and understand future users' needs and requirements in the early phases of the product development process. However, in times of social distancing meeting users might not be possible. The project reported on in this paper has investigated the possibilities of conducting advanced user studies online. In total 30 small experimental studies have been conducted. Common digital tools that were used were e.g. Zoom, Teams, Mural, Miro, Snapchat, and Instagram. The data was analyzed in a thematic content analysis by the authors on Mural. Identified challenges were excluding not tech-savvy user groups, missing out on interpersonal interaction and observations, as well as difficulties creating participant commitment and trust. On the positive side were perceived efficiency, a more levelled power distribution between participants, and ease of engagement and data retrieval for tech-savvy users. Identified best practices included lowering social barriers through warm up activities and techniques to support open discussion during workshops. Furthermore, engagement could be supported through private social media groups, regular reminders, as well as clear communication of purpose and goal of the activities.
Sharingchain : A privacy protection scheme based on blockchain in the supply chain

With the development of the supply chain, information-sharing has received great attention recently. At the same time, partners in the supply chain improve efficiency through information sharing, but private information needs to be protected. We propose a supply chain data sharing scheme based on Quorum blockchain, namely Sharingchain. Distribute keys through private transactions to achieve access control to private information. Access rights can be revoked or authorized through key management. In addition, a public transaction data structure is designed to transmit encrypted data. Security analysis and performance analysis show that the proposed scheme is suitable for the on-demand disclosure of private data in the supply chain. Compared with the Quorum scheme, our scheme greatly reduces the communication cost of data in the interactive protocol.
An Augmented Reality based E-Learning Tool for Engineering

The importance of quality virtual education has been shown recently. However, there are certain areas of knowledge for which it is difficult to replace face-to-face education with virtual education, mainly, when laboratories are required or equipment needs to be handled. Given this, augmented reality (AR) or virtual reality (VR) applications become important tools for virtual education. In this work, we propose an application based on AR that presents different elements or devices found in engineering laboratories. This application is developed using Unity and Vuforia software and can be installed on devices running Android operating system or HoloLens. As a case study, we have considered the virtualization of some elements found in telecommunications engineering laboratories. In addition, we have tested the developed application making some random people use it. Surveys were carried out to measure the application impact on learning. A link is also provided so that the application (.apk file) can be downloaded and installed. Thus, some examples may be observed by the reader employing his own Android device.
What Matters: Attentive and Relational Feature Aggregation Network for Video-Text Retrieval

Cross-modal video-text retrieval has been an emerging task due to the rapid growth of user-generated videos on the Internet. Most existing approaches focus on extracting visual feature for the video, while audio and caption on the screen containing rich information are ignored. Recently, the aggregations of multi-modal features in videos boost the benchmark of video-text retrieval. However, since these multi-modal features are high-dimensional and heterogeneous, their intrinsically structural relations have not been attached with enough importance and are often overlooked in previous methods. To address this issue, we propose a novel Attentive and Relational Feature Aggregation Network (ARFAN). Specifically, we introduce the self-attention mechanism to make videos adaptively assign higher weights to the representative modalities. Then, the graph convolutional layers are inserted to capture the relations among the multi-modal features to combine them. Our method achieves 15% and 12.9% relative improvements on R@1 when compared with the state-of-the-art method on MSR-VTT and MSVD datasets, respectively.
AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment

Cognitive impairment has become epidemic in older adult population. The recent advent of tiny wearable and ambient devices, a.k.a Internet of Things (IoT) provides ample platforms for continuous functional and cognitive health assessment of older adults. In this paper, we design, implement and evaluate AutoCogniSys, a context-aware automated cognitive health assessment system, combining the sensing powers of wearable physiological (Electrodermal Activity, Photoplethysmography) and physical (Accelerometer, Object) sensors in conjunction with ambient sensors. We design appropriate signal processing and machine learning techniques, and develop an automatic cognitive health assessment system in a natural older adults living environment. We validate our approaches using two datasets: (i) a naturalistic sensor data streams related to Activities of Daily Living and mental arousal of 22 older adults recruited in a retirement community center, individually living in their own apartments using a customized inexpensive IoT system (IRB #HP-00064387) and (ii) a publicly available dataset for emotion detection. The performance of AutoCogniSys attests max. 93% of accuracy in assessing cognitive health of older adults.
A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter

Abstract Social media enable companies to assess consumers’ opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.
Communication-Efficient Federated Learning for Connected Vehicles with Constrained Resources

With the upcoming next generation wireless network, vehicles are expected to be empowered by artificial intelligence (AI). By connecting vehicles and cloud server via wireless communication, federated learning (FL) allows vehicles to collaboratively train deep learning models to support intelligent services, such as autonomous driving. However, the large number of vehicles and increasing size of model parameters bring challenges to FL-empowered connected vehicles. Since communication bandwidth is insufficient to upload full-precision local models from numerous vehicles, model compression is usually conducted to reduce transmitted data size. Nevertheless, conventional model compression methods may not be practical for resource-constrained vehicles due to the increasing computational overhead for FL training. The overhead for downloading global model can also be omitted by existing methods since they are originally designed for centralized learning instead of FL. In this paper, we propose a ternary quantization based model compression method on communication-efficient FL for resource-constrained connected vehicles. Specifically, we firstly propose a ternary quantization based local model training algorithm that optimizes quantization factors and parameters simultaneously. Then, we design a communication-efficient FL approach that reduces overhead for both upstream and downstream communications. Finally, simulation results validate that the proposed method demands the lowest communication and computational overheads for FL training, while maintaining desired model accuracy compared to existing model compression methods.
Analysis on Parameter Effect for Solar Radiation Prediction Modeling using NNARX

The radiant energy from the sun is defined as solar radiation. It had been discovered as a renewable energy which can provide electricity supplies using a photovoltaic system. Before developing the system, a preliminary test must be carried out to perform the analysis of solar energy potential in that specific area. This preliminary test is known as a modeling technique. The technique will use the related parameters as an input to predict the solar radiation value. Since there are multiple parameters used for solar radiation prediction model development, there had been multiple attempts on using only certain parameters to produce predictions for solar radiation value. This paper will review and further analyzed several works presented by the previous studies on developing solar radiation prediction models using various parameters with their results. With the findings, the implementation of the Neural Network Autoregressive Model with Exogenous Input (NNARX) on solar radiation prediction carried out for the different input parameter configurations. Based on the results, it shows that the solar radiation prediction model development using more input parameters produced the best prediction performance with the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.9329.
Efficient defense strategy against spam and phishing email: An evolutionary game model

Spam and phishing emails are never-ending problems, and they are increasingly distributed with the help of the botnet. Existing spam and phishing email defense depends heavily on the spam filtering technique. However, using the spam filtering technique needs many defensive resources, which are usually limited. Therefore, to achieve the maximum effect with limited defense resources, we answer the following questions in this paper: How many user nodes we should protect in a target network? What factors affect this and how? To solve these problems, we propose an evolutional game model between multiple attackers and multiple defenders and analyze the evolution process of their strategies. The optimal number of users protected is first obtained as an efficient defense strategy, which can reduce resource consumption and lower attack density. Then we discover that this defense strategy is affected by the email opening probability of users, the attack cost, and the attack loss. Our results can help email network managers deploy the spam filtering technique according to the email opening probability, of which the defender has an asymmetric information advantage. Furthermore, we study the influences of the attack cost and loss on the defense strategy. It is revealed that the attack cost has a more marked effect on the defense strategy compared to the attack loss. Finally, we experiment on a large real-world dataset of emails sent in 2-year time duration. The defense strategy is proven to be effective for maximizing defense resources.
The effect of using digital variety media on distance learning on increasing digital literacy

Abstract At present, distance learning, especially for low-grade students, is limited by the use of learning media that is not yet in accordance with the child’s cognitive stages. This paper describes the results of using digital variety media in distance learning that are adjusted to the abilities and stages of children’s digital literacy skills. This media is developed according to the easy learning stages consisting of comics, interactive videos and assessments that are integrated with learning materials. This study uses an experimental method of the relationship between the effect of using digital media on increasing children’s digital literacy. Media that is incompatible with the abilities of low grade children can be avoided by teachers in the distance learning process because low grade children need the introduction of digital literacy skills that are in accordance with the child’s cognitive thinking stages.
Integrating Interval Type-2 Fuzzy Sets into Deep Embedding Clustering to Cope with Uncertainty

Working with unlabeled data carries the burden of uncertainties especially when the data are high-dimensional. Clustering is not an exception in this aspect and it requires special treatment. In this study, we propose to cope with the uncertainties which occur during clustering high-dimensional data with Interval Type-2 (IT2) Fuzzy Sets (FSs) and Deep Learning (DL) methods. Generation of the IT2-FSs is done with different cluster similarity functions parameterized with Interval Valued Parameters (IVPs). These parameters are introduced as the representations of the uncertainty in cluster assignments. As the backbone of the proposed method, Deep Embedding Clustering (DEC) is employed. The resulting IT2 fuzzy clustering inference is integrated into DEC so that both the inference and the training of the proposed model are operational in popular DL frameworks. Therefore, for a straightforward deployment, the constraints on IT2-FSs are redefined by introducing parameterization tricks upon IVPs. The presented comparative results indicate that coping with the uncertainties through IT2-FSs is superior to their baseline type-1 counterparts.