Reviewerly/exHarmony huggingface dataset:
Blockchain technology as key contributor to the integration of maritime supply chain?

The maritime supply chain is increasingly embracing digital innovation. This development is inextricably linked to the underlying technology, and that is the so-called distributed ledger technology (DLT). At a rapid pace, blockchain initiatives are launched in the market. This chapter begins with the inefficiencies in the maritime supply chain and the barriers to further integration of the maritime supply chain through digitization. In addition, the research analyzes those initiatives that focus on data-related inefficiencies in the maritime supply chain. Within the empirical research framework, blockchain conceptual characteristics, blockchain technical design choices, and three implemented DLT solutions are examined. This research approach shows to what extent blockchain technology contributes to further integration of the maritime supply chain. Evidence is found that nontechnical barriers (e.g., resistance to change, lack of awareness, and understanding) are not to be underestimated.
M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning

UnsupervisedUnsupervised domain adaptation domain adaptationDomain adaptation techniques have been successful for a wide range of problems where supervised labels are limited. The task is to classify an unlabeled “target” dataset by leveraging a labeled “source” dataset that comes from a slightly similar distribution. We propose metric-based adversarial discriminative domain adaptationDomain adaptation (M-ADDA) which performs two main steps. First, it uses a metric learning approach to train the source model on the source dataset by optimizing the triplet lossTriplet loss function. This results in clusters where embeddings of the same label are close to each other and those with different labels are far from one another. Next, it uses the adversarial approach (as that used in ADDA (Tzeng et al. Adversarial discriminative domain adaptation, 2017, [36])) to make the extracted features from the source and target datasets indistinguishable. Simultaneously, we optimize a novel loss function that encourages the target dataset’s embeddings to form clusters. While ADDA and M-ADDA use similar architectures, we show that M-ADDA performs significantly better on the digits adaptation datasets of MNIST and USPS. This suggests that using metric learningMetric learning for domain adaptationDomain adaptation can lead to large improvements in classificationClassification accuracy for the domain adaptationDomain adaptation task. The code is available at https://github.com/IssamLaradji/M-ADDA .
Speaker Identification in Different Emotional States in Arabic and English

Speaker recognition is an important application of digital speech processing.However, a major challenge degrading the robustness of speaker-recognition systems is variation in the emotional states of speakers, such as happiness, anger, sadness, or surprise.In this paper, we propose a speaker recognition system corresponding to three states, namely emotional, neutral, and with no consideration for a speaker's state (i.e., the speaker can be in an emotional state or neutral state), for two languages: Arabic and English.Additionally, cross-language speaker recognition was applied in emotional, neutral, and (emotional + neutral) states.Convolutional neural network and long short-term memory models were used to design a convolutional recurrent neural network (CRNN) main system.We also investigated the use of linearly spaced spectrograms as speech-feature inputs.The proposed system utilizes the KSUEmotions, emotional prosody speech and transcripts, WEST POINT, and TIMIT corpora.The CRNN system exhibited accuracies as high as 97.4% and 97.18% for Arabic and English emotional speech inputs, respectively, and 99.89% and 99.4% for Arabic and English neutral speech inputs, respectively.For the cross-language program, the overall CRNN system accuracy was as high as 91.83%, 99.88%, and 95.36% for emotional, neutral, and (emotional + neutral) states, respectively.
Design of Wireless Vision Sensor Network for Smart Home

In recent years, wireless vision sensor network (WVSN) is being used to retrieve video content from different image sensors which are connected to different devices wirelessly. This information is used to do video analysis which can help to automate different tasks such as video surveillance. For such systems, power consumption during processing and communicating information has been a challenge because of limited energy sources at the node. To deal with the energy consumption problem, in this paper WVSN is proposed with its algorithm and hardware implementation for a smart home application. The computation tasks have been divided between the sensor node and the central server. While taking care of privacy issues during the transmission of data, a low complexity system has been developed for sensor nodes. For video analysis, foreground segmentation, object labeling, and object tracking have been performed. An efficient binary data compression technique has been proposed to compress the information during the labeling process. The proposed system has a high recognition rate for gesture recognition and human tracking. The system can achieve eight frames per second during processing information on Raspberry Pi board. This system can be extended further to include other smart home applications.
Towards Efficient Kyber on FPGAs: A Processor for Vector of Polynomials

Kyber is a promising candidate in post-quantum cryptography standardization process. In this paper, we propose a targeted optimization strategy and implement a processor for Kyber on FPGAs. By merging the operations, we cut off 29.4% clock cycles for Kyber512 and 33.3% for Kyber1024 compared with the textbook implementations. We utilize Gentlemen-Sande (GS) butterfly to optimize the Number-Theoretic Transform (NTT) implementation. The bottleneck of memory access is broken taking advantage of a dual-column sequential scheme. We further propose a pipeline architecture for better performance. The optimizations help the processor achieve 31684 NTT operations per second using only 477 LUTs, 237 FFs and 1 DSP. Our strategy is at least 3x more efficient than the state-of-the-art module for NTT with a similar security level.
GAN-Knowledge Distillation for One-Stage Object Detection

Convolutional neural networks (CNN) have a significant improvement in the accuracy of object detection. As networks become deeper, the precision of detection becomes obviously improved, and more floating-point calculations are also needed. Because of the great amount of calculation, it is inconvenient for mobile and embedded vision applications. Many researchers apply the knowledge distillation method to improve the precision of object detection by transferring knowledge from a deeper and larger teachers network to a small student one. Most methods of knowledge distillation are needed to design complex cost functions and mainly aim at the two-stage object detection algorithm. Therefore, we propose a clean and effective knowledge distillation method called Generative Adversarial Networks - Knowledge Distillation(GAN-KD) for the one-stage object detection. The feature maps generated by teacher network and student network are employed as true and fake samples respectively, and generating adversarial training for both of them to improve the performance of the student network in one-stage object detection. The experimental result shows that our approach achieves the performance gain of 5% mAP when compared with MobilenetV1 on COCO dataset.
High-dimensional quantum key distribution based on mutually partially unbiased bases

We propose a practical high-dimensional quantum key distribution protocol based on mutually partially unbiased bases utilizing transverse modes of light. In contrast to conventional protocols using mutually unbiased bases, our protocol uses Laguerre-Gaussian and Hermite-Gaussian modes of the same mode order as two mutually partially unbiased bases for encoding, which leads to a scheme free from mode-dependent diffraction in long-distance channels. Since only linear and passive optical elements are needed, our experimental implementation significantly simplifies qudit generation and state measurement. Since this protocol differs from conventional protocols using mutually unbiased bases, we provide a security analysis of our protocol.
Fall detection scheme based on temperature distribution with IR array sensor

In recent years, the rate of older people has been increasing with the aging of population. In such a situation, many older people are injured by falls every year. As a countermeasure, several studies have been proposed to inform others of a fall as a quick post-fall treatment. Therefore, fall detection methods using various sensors have been proposed. Assuming fall detection at places such as home or hospital room, a fall detection method without invasion of privacy and wearing anything is required. In this paper, we propose a fall detection method using IR array sensors. The method allows for fall detection that is inexpensive and capable of privacy protection in a non-wearable form. Also, we analyze temperature distributions using machine learning to enable quicker and more accurate fall detection. We evaluate multiple algorithms of machine learning to select best algorithm. Then, classifiers are created based on these algorithms. We calculate and compare the accuracy of these classifiers. One of the learning data is a series of temperature distribution data for 2 seconds. One temperature distribution is acquired every 0.1 seconds by IR array sensors installed on a ceiling. We prepare 1600 learning data (400 series of learning data each with 4 actions: fall, walking, lying, and none). Based on these data, classifiers are performed using multiple algorithms to determine accuracy. The most accurate algorithm is Voting classifier with 97.75% accuracy. Therefore, the evaluation result showed that the proposed method is possible to classify with high accuracy using IR array sensors based on these prepared learning data.
Multiobjective optimization of deep neural networks with combinations of Lp-norm cost functions for 3D medical image super-resolution

In medical imaging, the lack of high-quality images is present in many areas such as magnetic resonance (MR). Due to many acquisition impediments, the generated images have not enough resolution to carry out an adequate diagnosis. Image super-resolution (SR) is an ill-posed problem that tries to in fer information from the image to enhance its resolution. Nowadays, deep learning techniques have become a powerful tool to extract features from images and infer new information. In MR, most of the recent works are based on the minimization of the errors between the input and the output images based on the Euclidean norm. This work presents a new methodology to perform three-dimensional SR based on the combination of Lp-norms in the loss layer. Two multiobjective optimization techniques are used to combine two cost functions. The proposed loss layers were trained with the SRCNN3D and DCSRN networks and tested with two MR structural T1-weighted datasets, and then compared with the traditional Euclidean loss. Experimental results show significant differences in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Bhattacharyya Coefficient (BC), while the residual images show refined details.
A deep learning-based hybrid model for recommendation generation and ranking

A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user's point of interest, recommending products/services based on the user's latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user's point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets.
Benchmarking elasticity of FaaS platforms as a foundation for objective-driven design of serverless applications

Application providers have to solve the trade-off between performance and deployment costs by selecting the "right" amount of provisioned computing resources for their application. The high value of changing this trade-off decision at runtime fueled a decade of combined efforts by industry and research to develop elastic applications. Despite these efforts, the development of elastic applications still demands significant time and expertise from application providers.
A Large Dimensional Study of Regularized Discriminant Analysis

In this paper, we conduct a large dimensional study of regularized discriminant analysis classifiers with its two popular variants known as regularized LDA and regularized QDA. The analysis is based on the assumption that the data samples are drawn from a Gaussian mixture model with different means and covariances and relies on tools from random matrix theory (RMT). We consider the regime in which both the data dimension and training size within each class tends to infinity with fixed ratio. Under mild assumptions, we show that the probability of misclassification converges to a deterministic quantity that describes in closed form the performance of these classifiers in terms of the class statistics as well as the problem dimension. The result allows for a better understanding of the underlying classification algorithms in terms of their performances in practical large but finite dimensions. Further exploitation of the results permits to optimally tune the regularization parameter with the aim of minimizing the probability of misclassification. The analysis is validated with numerical results involving synthetic as well as real data from the USPS dataset yielding a high accuracy in predicting the performances and hence making an interesting connection between theory and practice.
BC-Mobile Device Cloud: A Blockchain-Based Decentralized Truthful Framework for Mobile Device Cloud

By exploiting the massive data generated from the numerous interconnected machines and control systems, industrial Internet-of-Things (IIoT) provides unprecedented opportunities for facilitating the intelligence and smartness of manufacturing. Timely processing the large-scaled IIoT data by the conventional computation framework, such as Cloud computing, however, is nontrivial due to its costly resource usage, intolerable delay, and unbearable backbone pressures. By leveraging the idle resources of smart objects at the edge, mobile device cloud (MDC) becomes promising for the IIoT data analysis, thanks to the flexible resource provision and nearby task offloading. However, MDC workers are mostly human-carried devices with large scale, high dynamic resource provision, and untruthful behaviors, which pose significant challenges on MDC task allocation. In this article, we propose a blockchain-based decentralized and truthful framework for MDC (BC-MDC). BC-MDC enables the decentralization and prevents dishonesty by incorporating a plasma-based blockchain into the MDC. We design four smart contracts for distributedly managing the worker registration, task posting/allocation, rewarding, and penalizing. Furthermore, MDC task allocation is formulated as a stochastic optimization problem that jointly minimizes the long-term processing cost and risk of task failing. We also design a truthful reward/penalty algorithm that stimulates workers to provide resources and enforce them to keep the promise as well. Collaborated by the extensive simulation tests, we show how our proposed scheme achieves low cost on usage and high truthfulness and outperforms state-of-the-art solutions.
Graph-based neural networks for explainable image privacy inference

With the development of social media and smartphones, people share their daily lives via a large number of images, but the convince also raises a problem of privacy leakage. Therefore, effective methods are needed to infer the privacy risk of images and identify images that may disclose privacy. Several works have tried to solve this problem with deep learning models. However, we know little about how the models infer the privacy label of an image, thus it is not easy to understand why the image may disclose privacy. Inspired by recent research on graph neural networks, we introduce prior knowledge to the deep models to make the inference more explainable. We propose the Graph-based neural networks for Image Privacy (GIP) to infer the privacy risk of images. The GIP mainly focuses on objects in an image, and the knowledge graph is extracted from the objects in the dataset without reliance on extra knowledge. Experimental results show that the GIP achieves higher performance compared with the object-based methods and comparable performance even compared with the multi-modal fusion method. The results show that the introduction of the knowledge graph not only makes the deep model more explainable but also makes better use of the information of objects provided by the images. Combing the knowledge graph with deep learning is a promising way to help protect image privacy that is worth exploring.
Effects of Presence and Challenge Variations on Emotional Engagement in Immersive Virtual Environments

Serious games and immersive virtual reality promote emotional engagement during learning tasks, mostly by providing (1) skill-adapted challenges with performance feedback (for trial and error learning) and (2) enhanced presence (further reactions to multimodal stimuli), respectively. However, it is still unclear how each of these two strategies independently influence emotional states to engage subjects to a task. This study assessed the dimensions of emotion (valence-arousal-dominance) of 87 healthy subjects in a virtual game, assigned to 2 groups that were exposed to a different set of 5 trials: Group A experienced game variations by virtual factors affecting user's presence, whereas group B experienced levels of difficulty, affecting challenge. Emotional reports and 26 features extracted from physiological signals were statistically analyzed. Results showed that presence-based experimental conditions were able to modify the sense of arousal, whereas valence and dominance responded to challenge variation, i.e. were positively correlated with game score. Arousal is likely to increase with low sense of coexistence (social presence) and decrease with low scenario realism (physical presence). Faster breathing and higher skin conductance (SC) were detected at high challenge, whereas heart rate variability and SC increased with higher arousal. The evidence from this study suggests that both strategies can be used to separately influence dimensions of emotion, pointing out the customization of presence-based factors as a promising method to adjust emotional engagement by impacting arousal. Further research should be undertaken to identify the independent effect of single presence factors on emotional states.
DECAF: Deep Case-based Policy Inference for knowledge transfer in Reinforcement Learning

Having the ability to solve increasingly complex problems using Reinforcement Learning (RL) has prompted researchers to start developing a greater interest in systematic approaches to retain and reuse knowledge over a variety of tasks. With Case-based Reasoning (CBR) there exists a general methodology that provides a framework for knowledge transfer which has been underrepresented in the RL literature so far. We formulate a terminology for the CBR framework targeted towards RL researchers with the goal of facilitating communication between the respective research communities. Based on this framework, we propose the Deep Case-based Policy Inference (DECAF) algorithm to accelerate learning by building a library of cases and reusing them if they are similar to a new task when training a new policy. DECAF guides the training by dynamically selecting and blending policies according to their usefulness for the current target task, reusing previously learned policies for a more effective exploration but still enabling the adaptation to particularities of the new task. We show an empirical evaluation in the Atari game playing domain depicting the benefits of our algorithm with regards to sample efficiency, robustness against negative transfer, and performance increase when compared to state-of-the-art methods.
EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images

Automated pathology techniques for detecting cervical cancer at the premalignant stage have advantages for women in areas with limited medical resources.This article presents EpithNet, a deep learning approach for the critical step of automated epithelium segmentation in digitized cervical histology images. EpithNet employs three regression networks of varying dimensions of image input blocks (patches) surrounding a given pixel, with all blocks at a fixed resolution, using varying network depth.The proposed model was evaluated on 311 digitized histology epithelial images and the results indicate that the technique maximizes region-based information to improve pixel-wise probability estimates. EpithNet-mc model, formed by intermediate concatenation of the convolutional layers of the three models, was observed to achieve 94% Jaccard index (intersection over union) which is 26.4% higher than the benchmark model.EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods.
Application of extreme learning machine in behavior of beam to column connections

The effectiveness and performance of beam to column connections have been highly affected by the type and properties of structures. An effective and stable design of beam to column connections is critical because in many cases, a single connection must be able to carry several types of loads at the same time. When designing these connections, it is important to consider erection, serviceability, strength, and cost-effectiveness as an efficient design that must be economic in practice. In this study, experimental results for the designed beam to column connection in concrete frames have been applied while evaluating the outcomes. Development and applying of Extreme Learning Machine (ELM) for moment prediction of beam to column connections are the objectives of this study. By understanding and integrating the relevant simulation outcomes and other statistical indicators, the reliability of the computational models has been analyzed.
Machine Learning Systems and Intelligent Applications

Machine learning techniques are useful in a wide range of contexts, but techniques alone are insufficient to solve real business problems. We introduce the intelligent applications concept, which characterizes the structure and responsibilities of contemporary machine learning systems.