Reviewerly/exHarmony huggingface dataset:
Application of Soft Data in Nodule Resource Estimation

Abstract Minerals and metals are of uttermost importance in our society, and mineral resources on and beneath the deep ocean floor represent a huge potential. Deciding whether mining from the deep ocean floor is financially, environmentally and technologically feasible requires information. Due to great depths and harsh conditions, this information is expensive and time and resource consuming to obtain. It is therefore important to use every piece of data in an optimum way. In this study, data retrieved from images and expert knowledge were used to estimate minimum and maximum nodule abundances at image locations from an area in the Clarion-Clipperton-Zone of the equatorial North East Pacific. From the minimum and maximum values, box cores and the spatial correlation quantified through variogram, a conditional expectation and associated uncertainty were obtained through the Gibbs sampler. The conditional expectation and the uncertainty were used with the assumed certain abundance data from the box cores in a kriging exercise to obtain better informed estimates of the block by block abundance. The quality assessment of the estimations was done based on distance criterion and on kriging quality indicators like the slope of regression and the weight of the mean. From the original image locations, alternative image configurations were tested, and it was shown that such alternatives produce better estimates, without extra costs. Future improvements will focus on improving the estimation of the minimum and the maximum values at image locations.
Role of Cryptography in Network Security

Network administrators employ several security mechanisms to protect data in the network from unauthorized access and various threats. The security mechanisms enhance the usability and integrity of the network. The design aspects of the network security mechanism involve both hardware and software technologies. The application domains of security mechanisms cover both public and private computer networks which are used in everyday jobs for conducting transactions and communications among business partners, government agencies, enterprises and individuals. The network security schemes vary depending on the types of the network, that is, public or private, wired or wireless. Data security includes encryption, tokenization, and key management practices in protecting data across all applications and platforms. The antivirus and antimalware software are also part of network security for protection from malware such as spyware, ransomware, trojans, worms, and viruses. Cryptography is an automated mathematical tool that plays a vital role in network security. It assures the confidentiality and integrity of data as well as provides authentication and non-repudiation to the users. This chapter primarily focuses on cryptography techniques and their role in preserving the network security. The cryptography technique consists of encryption and decryption algorithms. The encryption algorithms perform scrambling of ordinary text and generate an unreadable format for the third party known as ciphertext. The original data is restructured by the intended receiver using decryption algorithms. The cryptographic techniques are broadly classified into three categories namely symmetric-key cryptography, asymmetric-key cryptography and authentication. The cryptographic algorithms that are widely accepted are outlined with their relative advantages and disadvantages. Moreover, recent proficient cryptographic algorithms specific to cloud computing, wireless sensor networks and on-chip-networks are thoroughly discussed that provide a clear view about acquiring secure communication in the network using cryptography.
An Improved Algorithm for Searching Maze Based on Depth-First Search

In this paper, we propose an algorithm based on depth-first search to search for unknown mazes and construct the maps. In order to obtain a complete map of the maze, we use stacks to store the coordinates and directions of the agents that have not been searched, and we must leave the stack empty to end the search. In order to speed up the search, we improve the depth-first search method, using two agents to search in the maze at the same time, and exchange the searched maze information when the two agents met. After searching, we can find the shortest path between any two points in the searched maze.
Quantum algorithms for hedging and the learning of Ising models

A paradigmatic algorithm for online learning is the Hedge algorithm by Freund and Schapire. An allocation into different strategies is chosen for multiple rounds and each round incurs corresponding losses for each strategy. The algorithm obtains a favorable guarantee for the total losses even in an adversarial situation. This work presents quantum algorithms for such online learning in an oracular setting. For $T$ time steps and $N$ strategies, we exhibit run times of about $O\left(\mathrm{poly}(T)\sqrt{N}\right)$ for estimating the losses and for betting on individual strategies by sampling. In addition, we discuss a quantum analog of the Sparsitron, a machine learning algorithm based on the Hedge algorithm. The quantum algorithm inherits the provable learning guarantees from the classical algorithm and exhibits polynomial speedups. The speedups may find relevance in finance, for example, for hedging risks, and machine learning, for example, for learning generalized linear models or Ising models.
MDLdroidLite

Mobile Deep Learning (MDL) has emerged as a privacy-preserving learning paradigm for mobile devices. This paradigm offers unique features such as privacy preservation, continual learning and low-latency inference to the building of personal mobile sensing applications. However, squeezing Deep Learning to mobile devices is extremely challenging due to resource constraint. Traditional Deep Neural Networks (DNNs) are usually over-parametered, hence incurring huge resource overhead for on-device learning. In this paper, we present a novel on-device deep learning framework named MDLdroidLite that transforms traditional DNNs into resource-efficient model structures for on-device learning. To minimize resource overhead, we propose a novel Release-and-Inhibit Control (RIC) approach based on Model Predictive Control theory to efficiently grow DNNs from tiny to backbone. We also design a gate-based fast adaptation mechanism for channel-level knowledge transformation to quickly adapt new-born neurons with existing neurons, enabling safe parameter adaptation and fast convergence for on-device training. Our evaluations show that MDLdroidLite boosts on-device training on various PMS datasets with 28x to 50x less model parameters, 4x to 10x less floating number operations than the state-of-the-art model structures while keeping the same accuracy level.
Study of Data Imbalance and Asynchronous Aggregation Algorithm on Federated Learning System

As the use of machine learning techniques are becoming more widespread, the need for more elaborate dataset is becoming more prevalent. This is usually done with data collection methods that pay little to no attention to the data owner's privacy and consent. Federated learning is an approach that tries to solve this problem, where such system can train a machine learning model without centrally storing the needed data. But one weakness of the current implementation is that they have a slow convergence time, despite the fact that they distribute the task on many nodes. This is mainly caused by the synchronous nature of the current algorithm. In this paper, we observe the effect that asynchronous aggregation algorithm has on convergence time and test the two factors that might affect it - staleness and data imbalance - on various levels. We implement the asynchronous aggregation algorithm by adapting the Stale Synchronous Parallel algorithm. We test our system on MNIST dataset and found that asynchronous aggregation algorithm improves convergence time in a federated learning system that has large inequality in server-wise update frequency and has a relatively balanced data distribution.
A Renewable Energy-Based Task Consolidation Algorithm for Cloud Computing

Cloud computing is gaining popularity in view of its accessibility, availability, flexibility, cost-effectiveness and many more. Accordingly, several firms are hosting cloud-oriented applications for fulfilling their business requirements. Consequently, cloud service providers (CSPs) are facing various challenges and striving for cost-effective solutions. One resembling challenge is energy consumption by datacenters as it substantially affects our environment by generating carbon footprints. To abate this challenge, renewable energy (RE) sources, such as solar, wind and hydropower, play an integral role. It also curtails the usage of non-renewable energy (NRE) sources, such as oil, gas, coal and hydroelectricity. Recent research has shown that the user requests are assigned to the datacenters based on the highest available RE resources, cost, static ordering and many others without any emphasis on utilization. In this paper, we propose a RE-based task consolidation (TC) algorithm, called minimum utilization (MinUtil), to assign the user requests to the datacenters by considering the utilization of user requests and resources. Here, TC is used to improve the utilization of resources. The utilization of NRE resources is restricted to a pre-determined threshold in order to reduce the generation of carbon footprints. We simulate the proposed algorithm using MATLAB and perform the simulation runs on four generated datasets concerning the findings in terms of energy consumption (EC), overall cost (OC) and number of used RE (|URE|) resources. We compare the outcomes with two existing algorithms, called round robin (RR) and random, to show the efficacy of the proposed algorithm.
Household perceptions of factors that affect food consumption in grassland areas: a case study in the Xilin Gol Grassland, China

Food consumption acts as an intermediary that connects ecosystems with human systems in grassland areas. In this study, we used fuzzy cognitive mapping to quantitatively assess the factors that affect food consumption in the Xilin Gol Grassland, China, from the perspective of local rural households. We found that household perceptions of the factors that affect food consumption differed among parts of the grassland transects in both the number and the strength of these factors. Livestock numbers, household income, regional economic development, consumption habits, age, and infrastructure were the most important factors mentioned by the farmers and herders, but herders were more sensitive to ecological and economic factors, whereas farmers focused more on personal and social factors. The differences in the main factors between study areas revealed the key economic, social, and ecological dimensions. Our results provide a reference for policymakers to develop improved policies to encourage regional sustainable development.
Asymmetric excitable phase triggering in an optically injected semiconductor laser

One of the defining characteristics of excitability is the existence of an excitable threshold: the minimum perturbation amplitude necessary to produce an excitable response. We analyze an optically injected dual state quantum dot laser, previously shown to display a dual state stochastic excitable dynamic. We show that deterministic triggering of this dynamic can be achieved via optical phase perturbations. Further, we demonstrate that there are in fact two asymmetric excitable thresholds in this system corresponding to the two possible directions of optical phase perturbations. For fast enough perturbations, an excitable interval arises, and there is a limit to the perturbation amplitude, above which excitations no longer arise, a phenomenon heretofore unobserved in studies of excitability.
Bayesian multivariate models for case assessment in dynamic signature cases

Dynamic signatures are recordings of signatures made on digitizing devices such as tablet PCs. These handwritten signatures contain both dynamic and spatial information on every data point collected during the signature movement and can therefore be described in the form of multivariate data. The management of dynamic signatures represents a challenge for the forensic science community through its novelty and the volume of data available. Much as for static signatures, the authenticity of dynamic signatures may be doubted, which leads to a forensic examination of the unknown source signature. The Bayes’ factor, as measure of evidential support, can be assigned with statistical models to discriminate between competing propositions. In this respect, the limitations of existing probabilistic solutions to deal with dynamic signature evidence is pointed out and explained in detail. In particular, the necessity to remove the independence assumption between questioned and reference material is emphasized.
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning

We present a novel technique for self-supervised video representation learning by: (a) decoupling the learning objective into two contrastive subtasks respectively emphasizing spatial and temporal features, and (b) performing it hierarchically to encourage multi-scale understanding. Motivated by their effectiveness in supervised learning, we first introduce spatial-temporal feature learning decoupling and hierarchical learning to the context of unsupervised video learning. We show by experiments that augmentations can be manipulated as regularization to guide the network to learn desired semantics in contrastive learning, and we propose a way for the model to separately capture spatial and temporal features at multiple scales. We also introduce an approach to overcome the problem of divergent levels of instance invariance at different hierarchies by modeling the invariance as loss weights for objective re-weighting. Experiments on downstream action recognition benchmarks on UCF101 and HMDB51 show that our proposed Hierarchically Decoupled Spatial-Temporal Contrast (HDC) makes substantial improvements over directly learning spatial-temporal features as a whole and achieves competitive performance when compared with other state-of-the-art unsupervised methods. Code will be made available.
Effect of financial sector development and institutions on economic growth in SSA. Does the peculiarities of regional blocs matter?

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Efficient Residue Number System Based Winograd Convolution

Prior research has shown that Winograd algorithm can reduce the computational complexity of convolutional neural networks (CNN) with weights and activations represented in floating point. However it is difficult to apply the scheme to the inference of low-precision quantized (e.g. INT8) networks. Our work extends the Winograd algorithm to Residue Number System (RNS). The minimal complexity convolution is computed precisely over large transformation tile (e.g. $$10 \times 10$$ to $$16 \times 16$$ ) of filters and activation patches using the Winograd transformation and low cost (e.g. 8-bit) arithmetic without degrading the prediction accuracy of the networks during inference. The arithmetic complexity reduction is up to $$7.03\times $$ while the performance improvement is up to $$2.30\times $$ to $$4.69\times $$ for $$3\times 3$$ and $$5 \times 5$$ filters respectively.
AE-OT-GAN: Training GANs from Data Specific Latent Distribution

Though generative adversarial networks (GANs) are prominent models to generate realistic and crisp images, they are unstable to train and suffer from the mode collapse problem. The problems of GANs come from approximating the intrinsic discontinuous distribution transform map with continuous DNNs. The recently proposed AE-OT model addresses the discontinuity problem by explicitly computing the discontinuous optimal transform map in the latent space of the autoencoder. Though have no mode collapse, the generated images by AE-OT are blurry. In this paper, we propose the AE-OT-GAN model to utilize the advantages of the both models: generate high quality images and at the same time overcome the mode collapse problems. Specifically, we firstly embed the low dimensional image manifold into the latent space by autoencoder (AE). Then the extended semi-discrete optimal transport (SDOT) map is used to generate new latent codes. Finally, our GAN model is trained to generate high quality images from the latent distribution induced by the extended SDOT map. The distribution transform map from this dataset related latent distribution to the data distribution will be continuous, and thus can be well approximated by the continuous DNNs. Additionally, the paired data between the latent codes and the real images gives us further restriction about the generator and stabilizes the training process. Experiments on simple MNIST dataset and complex datasets like CIFAR10 and CelebA show the advantages of the proposed method.
Unsupervised Deep Learning for Handwritten Page Segmentation

Segmenting handwritten document images into regions with homogeneous patterns is an important pre-processing step for many document images analysis tasks. Hand-labeling data to train a deep learning model for layout analysis requires significant human effort. In this paper, we present an unsupervised deep learning method for page segmentation, which revokes the need for annotated images. A siamese neural network is trained to differentiate between patches using their measurable properties such as number of foreground pixels, and average component height and width. The network is trained that spatially nearby patches are similar. The network's learned features are used for page segmentation, where patches are classified as main and side text based on the extracted features. We tested the method on a dataset of handwritten document images with quite complex layouts. Our experiments show that the proposed unsupervised method is as effective as typical supervised methods.
Huffman Coding Based Encoding Techniques for Fast Distributed Deep Learning

Distributed stochastic algorithms, equipped with gradient compression techniques, such as codebook quantization, are becoming increasingly popular and considered state-of-the-art in training large deep neural network (DNN) models. However, communicating the quantized gradients in a network requires efficient encoding techniques. For this, practitioners generally use Elias encoding-based techniques without considering their computational overhead or data-volume. In this paper, based on Huffman coding, we propose several lossless encoding techniques that exploit different characteristics of the quantized gradients during distributed DNN training. Then, we show their effectiveness on 5 different DNN models across three different data-sets, and compare them with classic state-of-the-art Elias-based encoding techniques. Our results show that the proposed Huffman-based encoders (i.e., RLH, SH, and SHS) can reduce the encoded data-volume by up to 5.1×, 4.32×, and 3.8×, respectively, compared to the Elias-based encoders.
Secure Exchange of Medical Data Using a Novel Real-Time Biometric-Based Protection and Recognition Method

Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, images, and documents) become more vulnerable to breaching patients’ privacy when shared with an external organization in the current arena of the internet of medical things (IoMT). Various watermarking techniques were introduced in the medical field to secure patients’ data. Most of the existing techniques focus on an image or document’s imperceptibility without considering the watermark(logo). In this research, a novel technique of watermarking is introduced, which supersedes the shortcomings of existing approaches. It guarantees the imperceptibility of the image/document and takes care of watermark(biometric), which is further passed through a process of recognition for claiming ownership. It extracts suitable frequencies from the transform domain using specialized filters to increase the robustness level. The extracted frequencies are modified by adding the biomedical information while considering the strength factor according to the human visual system. The watermarked frequencies are further decomposed through a singular value decomposition technique to increase payload capacity up to (256 × 256). Experimental results over a variety of medical and official images demonstrate the average peak signal-to-noise ratio (PSNR 54.43), and the normal correlation (N.C.) value is 1. PSNR and N.C. of the watermark were calculated after attacks. The proposed technique is working in real-time for embedding, extraction, and recognition of biometrics over the internet, and its uses can be realized in various platforms of IoMT technologies.
Human‐like evaluation method for object motion detection algorithms

This study proposes a new method to evaluate the performance of algorithms for moving object detection (MODA) in video sequences. The proposed method is based on human performance metric intervals, instead of ideal metric values (0 or 1) which are commonly used in the literature. These intervals are proposed to establish a more reliable evaluation and comparison, and to identify areas of improvement in the evaluation of MODA. The contribution of the study includes the determination of human segmentation performance metric intervals and their comparison with state‐of‐the‐art MODA, and the evaluation of their segmentation results in a tracking task to establish the impact between performance and practical utility. Results show that human participants had difficulty with achieving a perfect segmentation score. Deep learning algorithms achieved performance above the human average, while other techniques achieved a performance between 88 and 92%. Furthermore, the authors demonstrate that algorithms not ranked at the top of the quantitative metrics worked satisfactorily in a tracking experiment; and therefore, should not be discarded for real applications.
Attention Augmented Convolutional Recurrent Network for Handwritten Japanese Text Recognition

Handwritten Japanese text recognition is still a big challenging task due to the large character set, diversity of writing styles, and multiple-touches between characters. In this paper, we propose a model of Attention Augmented Convolutional Recurrent Network (AACRN) for recognizing handwritten Japanese text lines. The AACRN model has three main parts: a convolutional feature extractor, a self-attention based encoder, and a CTC-decoder. The whole model can be trained end-to-end. In the experiment, we evaluate the performance of the AACRN model on the TUAT Kondate dataset and the Kuzushiji dataset. The results of the experiments show that the proposed model achieves higher performance than the state-of-the-art recognition accuracies on the test set of TUAT Kondate and the Kuzushiji dataset.