Reviewerly/exHarmony huggingface dataset:
Study of Dependency on number of LSTM units for Character based Text Generation models

Long Short Term Memory cells (LSTMs) are used to make character-based generation models. Single dimensional Convolutional LSTM networks are also used in sequential data processing. There is a significant effect of number of LSTM cells on the quality of the generation as the amount of overfitting and underfitting depends upon the number of units. We study the effect of the number of LSTM units and we find that increased number of LSTM cells initially improves the prediction. However increasing the number of LSTM cells above a certain number causes poorer results. The vocabulary of the corpus produces better results if the size is reduced.
Blockchain for Organizing Effective Grass-Roots Actions on a Global Commons: Saving the Planet

An overwhelming majority of experts has been flagging for decades that "Saving the Planet" requires immediate, persistent and drastic action to curb a variety of catastrophic risks over the 21st century. However, despite compelling evidence and a range of suggested solutions, transnational coordination of effective measures to protect our biosphere continues to fall short. To remedy, we propose a novel platform for addressing the central issue of affording trust, transparency and truth while minimizing administrative overheads. This will empower an even loosely organised, global grass-roots community to coordinate a large-scale project on a shared goal ("Commons") spanning the digital and real world. The Web3 concept is based on the swiftly emerging "Blockchain" and related cryptographic, distributed and permissionless technologies. "Wisdom of the crowds" mechanisms involving competitive parallelisation and prediction markets are enabled by formalised reputation and staking to incentivise high-quality work, fair validation and best management practice. While these mechanisms have been (mostly separately) applied to science, business, governance, web, sensor, information and communication technologies (ICT), our integrative approach around Blockchain-enabled 'operating principles and protocols' sets the basis for designing novel forms of potentially crowdfunded Decentralised Autonomous Organisations (DAOs).
3D Inception U‐net with Asymmetric Loss for Cancer Detection in Automated Breast Ultrasound

Purpose Breast cancer is the most common cancer and the leading cause of cancer‐related deaths for women all over the world. Recently, automated breast ultrasound (ABUS) has become a new and promising screening modality for whole breast examination. However, reviewing volumetric ABUS is time‐consuming and lesions could be missed during the examination. Therefore, computer‐aided cancer detection in ABUS volume is extremely expected to help clinician for the breast cancer screening. Methods We develop a novel end‐to‐end 3D convolutional network for automated cancer detection in ABUS volume, in order to accelerate reviewing and meanwhile to provide high detection sensitivity with low false positives (FPs). Specifically, an efficient 3D Inception Unet‐style architecture with fusion deep supervision mechanism is proposed to attain decent detection performance. In addition, a novel asymmetric loss is designed to help the network balancing false positive and false negative regions, thus improving detection sensitivity for small cancerous lesions. Results The efficacy of our network was extensively validated on a dataset including 196 patients with 661 cancer regions. Our network obtained a detection sensitivity of 95.1% with 3.0 FPs per ABUS volume. Furthermore, the average inference time of the network was 0.1 second per volume, which largely shortens the conventional reviewing time. Conclusions The proposed network provides efficient and accurate cancer detection scheme using ABUS volume, and may assist clinicians for more efficient breast cancer screening.
SP-GAN: Self-Growing and Pruning Generative Adversarial Networks

This article presents a new Self-growing and Pruning Generative Adversarial Network (SP-GAN) for realistic image generation. In contrast to traditional GAN models, our SP-GAN is able to dynamically adjust the size and architecture of a network in the training stage by using the proposed self-growing and pruning mechanisms. To be more specific, we first train two seed networks as the generator and discriminator; each contains a small number of convolution kernels. Such small-scale networks are much easier and faster to train than large-capacity networks. Second, in the self-growing step, we replicate the convolution kernels of each seed network to augment the scale of the network, followed by fine-tuning the augmented/expanded network. More importantly, to prevent the excessive growth of each seed network in the self-growing stage, we propose a pruning strategy that reduces the redundancy of an augmented network, yielding the optimal scale of the network. Finally, we design a new adaptive loss function that is treated as a variable loss computational process for the training of the proposed SP-GAN model. By design, the hyperparameters of the loss function can dynamically adapt to different training stages. Experimental results obtained on a set of data sets demonstrate the merits of the proposed method, especially in terms of the stability and efficiency of network training. The source code of the proposed SP-GAN method is publicly available at https://github.com/Lambert-chen/SPGAN.git.
Sentiment Classification for Chinese Text Based on Interactive Multitask Learning

In this paper, an interactive multitask learning method for Chinese text sentiment classification is proposed.Here, the classic BiLSTM + attention + CRF model is used to obtain full use of the interaction relationship between tasks, and it simultaneously solves the two tasks of emotional dictionary expansion and sentiment classification.The proposed method divides text sentiment classification and emotional dictionary expansion into primary task and subtask, and it adopts the Enhanced Language Representation with Informative Entities (ERNIE) model as the text representation learning model for the primary task.Then, through the maximum pooling layer and the fully connected layer, the text sentiment classification task is completed.Meanwhile, the classical BiLSTM + attention + CRF model is used to extract emotional words from the text in the subtask.In addition, the multitask information interaction mechanism is used, and the prediction information on the autonomous subtask is fed back into the potential representation of the two tasks.After iterative training, the performance of the two tasks is further optimized.Micro-blogs with COVID-19 are used here as the subject to form the experimental data set.The results demonstrate the superiority of the proposed method over other approaches, and they further verify the superiority of ERNIE over BERT, RoBERTa and XLNet for the sentiment classification of Chinese text.
Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet

Malaria is a life-threatening mosquito (Anopheles)-borne blood disease caused by the plasmodium parasite. Microscopic examination of peripheral blood smears by experts helps to identify parasites precisely. The manual assessment technique is a tedious and time-consuming process. The present study focuses on developing a hybrid screening algorithm for automated identification and classification of malaria parasite-infected red blood cells (RBCs). Initially, a semantic blood cell segmentation method is adopted where a supervised classifier-regulated pixel-based segmentation is adopted to segment individual RBC present in an image. In pixel-based classification, foreground (RBCs) and background regions are considered, a pixel-based large feature dataset is generated, and an artificial neural network (ANN) classifier is trained. The trained model generates a probability map of an image which is later post-processed by Graph-cut and Marker-controlled Watershed method for developing cropped RBC image set. The proposed segmentation method achieves 99.1% accuracy. Finally, a trained modified Capsule Network (CapsNet) model is used for classification of segmented blood cells to identify the species and stages of the parasites. Here, two specific parasite species viz., Plasmodium vivax and Plasmodium falciparum with stages are considered for classification. The performance of the proposed two-steps hybrid malaria screening is promising and the training and testing on local and benchmark dataset with respect to ground truth yield 98.7% accuracy.
Fast and efficient implementation of image filtering using a side window convolutional neural network

Convolutional neural networks (CNNs) designed for object recognition have been successfully applied to low-level tasks such as image filtering. However, these networks are usually very large which occupy large memory space and demand very high computational capacity. This makes them unsuitable for real time low-level applications on smart and portable devices with limited memory and computational capacities. In this paper, we specifically design a novel CNN, side window convolutional neural network (SW-CNN), for the fast and efficient implementation of image filtering. In SW-CNN, a new convolutional strategy, called side kernel convolution (SKC) is proposed which aligns the side or corner of the convolutional window with the pixels under processing to preserve edges during convolution. By combining SKC and the representational power of CNNs, SW-CNN can learn various image-filtering tasks very effectively. Compared to the state-of-the-art networks, the superiority of SW-CNN includes three aspects. First, the number of learnable parameters is reduced by 96%. Second, the memory consumption is reduced to 50%. Third, the running time is decreased to 50%. Results of extensive experiments demonstrate that SW-CNN not only has good performance on implementing various edge-preserving filters, but also has the adaptability and flexibility on other low-level image processing applications.
Multi-scale enhancement fusion for underwater sea cucumber images based on human visual system modelling

The vision computing techniques are widely used in the marine industry. In order to improve the detection and recognition accuracy of artificial intelligence-robotics, we propose a novel underwater image enhancement algorithm, using a multi-scale fusion approach based on the properties of the human visual system. Our method fuses the results of underwater image enhancement algorithms that deal with the color-casting, sharpness and contrast degradation. The method is further weighted by a human visual system-based image structure map that combines Michaelson-like contrast map, saliency map, dark channel map and exposed map. The multi-scale fusion strategy is used to avoid the artifacts of sharpness blending based on Laplacian image representation. The results show that our algorithm can recover more detail information of dark regions and improve the overall visual quality of underwater images.
EM Fault Injection on ARM and RISC-V

Recently Electro-Magnetic Fault Injection (EMFI) techniques have been found to have significant implications on the security of embedded devices. Unfortunately, there is still a lack of understanding of EM faults and countermeasures for embedded processors. For the first time, this paper empirically shows that EMFI can cause skipping/faulting of more than one instruction on a 320MHz RISC-V processor, thus making it susceptible to a wider range of attacks. Additionally, empirical results on ARM Cortex M0 and RISC-V embedded processors show that EMFI is more susceptible at lower supply voltages and higher clock frequencies. Exception codes are also shown to be useful in understanding details of injected faults, providing further evidence that instructions have been corrupted in many cases. This research aims to enhance the understanding of faults, in order to better design countermeasures for embedded processors resistant to fault injection attacks.
High-Resolution Solar Climate Atlas for Greece under Climate Change Using the Weather Research and Forecasting (WRF) Model

In the context of climate change and growing energy demand, solar technologies are considered promising solutions to mitigate Greenhouse Gas (GHG) emissions and support sustainable adaptation. In Greece, solar power is the second major renewable energy, constituting an increasingly important component of the future low-carbon energy portfolio. In this work, we propose the use of a high-resolution regional climate model (Weather Research and Forecasting model, WRF) to generate a solar climate atlas for the near-term climatological future under the Representative Concentration Pathway (RCPs) 4.5 and 8.5 scenarios. The model is set up with a 5 × 5 km2 spatial resolution, forced by the ERA-INTERIM for the historic (1980–2004) period and by the EC-EARTH General Circulation Models (GCM) for the future (2020–2044). Results reaffirm the high quality of solar energy potential in Greece and highlight the ability of the WRF model to produce a highly reliable future climate solar atlas. Projected changes between the annual historic and future RCPs scenarios indicate changes of the annual Global Horizontal Irradiance (GHI) in the range of ±5.0%. Seasonal analysis of the GHI values indicates percentage changes in the range of ±12% for both scenarios, with winter exhibiting the highest seasonal increases in the order of 10%, and autumn the largest decreases. Clear-sky fraction fclear projects increases in the range of ±4.0% in eastern and north continental Greece in the future, while most of the Greek marine areas might expect above 220 clear-sky days per year.
A natural human language framework for digital forensic readiness in the public cloud

Currently, about half of all global enterprises are adopting and using some form of cloud computing services. In cloud computing, potential digital evidence is distributed across multiple isolated virtual machine instances. Investigating deleted or inactive virtual instances of a cloud is a challenge to digital forensics, and the traditional methods of digital forensics are inadequate to address such digital forensic investigation. Users of the public cloud (whether a potential victim of a cyberattack, a cybercriminal or a digital forensic investigator) inherently communicate using natural human language in the form of sentences and semantics in document messaging such as texts, emails or instant messages. Consequently, natural human language interaction provides a unique identifier for cloud users. This study leverages the natural human language as an identifier to develop a novel digital forensic readiness (DFR) framework for cloud computing to detect cybercrime. The DFR framework comprises the integration of natural language processing techniques in designing a process that mimics a near real-time approach towards cybercrime detection in a cloud environment. Natural language understanding techniques are used to analyse textdata of users in the public cloud and textdata of reported cybercrimes to develop a DFR framework. In the preliminary formation of the DFR framework, the output shows that cybercrime attacks that are in progress in the form of textdata such as online documents, instant messages or emails within an organizational cloud domain can be identified, and potentially investigated swiftly, using the unique signature of users as identifiers. When adopted, the proposed DFR framework can minimize the time lapses in incident identification and reduce the subsequent investigation time of cybercrimes in the public cloud domain.
DPF-ECC: Accelerating Elliptic Curve Cryptography with Floating-Point Computing Power of GPUs

Driven by artificial intelligence (AI) and computer vision industries, Graphics Processing Units (GPUs) are now rapidly achieving extraordinary computing power. In particular, the floating-point computing power, which is heavily relied on by graphics rendering and AI computation workload, is developing much faster in GPUs. Meanwhile, in many fields such as ecommerce and online finance, the demand for cryptographic operations for secure communications and authentication is also expanding.In this contribution, targeting the important cryptographic primitives widely used in TLS 1.3, etc., we implement Curve25519 and Edwards25519 with GPUs' floating-point computing power, where various performance optimization methods are customized for the target platform, including novel big-number representations combined with a new floating-point-based computing algorithm, efficient merged reduction strategies, and curve-level acceleration. This paper reports record-setting performance for the elliptic-curve method: on TITAN V, we respectively achieve 7.21 and 77.30 million operations per second of unknown and known point multiplication of Edwards25519, and 13.55 million operations per second of point multiplication of Curve25519. To the best of our knowledge, this contribution is the first to show that floating-point-based ECC implementations can outperform the integer-based ones by a huge margin. The experimental result in Tesla P100 achieves over double performance of the existing fastest integer work on the same platform, and the result in TITAN V sets a record for the throughput which is 4.43 times better than the second.
On the Benefits of Populations for the Exploitation Speed of Standard Steady-State Genetic Algorithms

Abstract It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems. Indeed, several theoretical results are available showing such advantages over single-trajectory search heuristics. In this paper we provide evidence that evolving populations via crossover and mutation may also benefit the optimisation time for hillclimbing unimodal functions. In particular, we prove bounds on the expected runtime of the standard ( $$\mu +1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> ) GA for OneMax that are lower than its unary black box complexity and decrease in the leading constant with the population size up to $$\mu =o\left( \sqrt{\log n}\right) $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>=</mml:mo> <mml:mi>o</mml:mi> <mml:mfenced> <mml:msqrt> <mml:mrow> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:msqrt> </mml:mfenced> </mml:mrow> </mml:math> . Our analysis suggests that the optimal mutation strategy is to flip two bits most of the time. To achieve the results we provide two interesting contributions to the theory of randomised search heuristics: (1) A novel application of drift analysis which compares absorption times of different Markov chains without defining an explicit potential function. (2) The inversion of fundamental matrices to calculate the absorption times of the Markov chains. The latter strategy was previously proposed in the literature but to the best of our knowledge this is the first time is has been used to show non-trivial bounds on expected runtimes.
Instance-Level Heterogeneous Domain Adaptation for Limited-Labeled Sketch-to-Photo Retrieval

Although sketch-to-photo retrieval has a wide range of applications, it is costly to obtain paired and rich-labeled ground truth. Differently, photo retrieval data is easier to acquire. Therefore, previous works pre-train their models on rich-labeled photo retrieval data (i.e., source domain) and then fine-tune them on the limited-labeled sketch-to-photo retrieval data (i.e., target domain). However, without co-training source and target data, source domain knowledge might be forgotten during the fine-tuning process, while simply co-training them may cause negative transfer due to domain gaps. Moreover, identity label spaces of source data and target data are generally disjoint and therefore conventional category-level Domain Adaptation (DA) is not directly applicable. To address these issues, we propose an Instance-level Heterogeneous Domain Adaptation (IHDA) framework. We apply the fine-tuning strategy for identity label learning, aiming to transfer the instance-level knowledge in an inductive transfer manner. Meanwhile, labeled attributes from the source data are selected to form a shared label space for source and target domains. Guided by shared attributes, DA is utilized to bridge cross-dataset domain gaps and heterogeneous domain gaps, which transfers instance-level knowledge in a transductive transfer manner. Experiments show that our method has set a new state of the art on three sketch-to-photo image retrieval benchmarks without extra annotations, which opens the door to train more effective models on limited-labeled heterogeneous image retrieval tasks.
Handwritten Digit Recognition using Machine and Deep Learning Algorithms

The reliance of humans over machines has never been so high such that from object classification in photographs to adding sound to silent movies everything can be performed with the help of deep learning and machine learning algorithms.Likewise, Handwritten text recognition is one of the significant areas of research and development with a streaming number of possibilities that could be attained.Handwriting recognition (HWR), also known as Handwritten Text Recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices [1].Apparently, this paper illustrates handwritten digit recognition with the help of MNIST datasets using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), and Convolution Neural Network (CNN) models.The main objective of this paper is to compare the accuracy of the models stated above along with their execution time to get the best possible model for digit recognition.
Natural Steganography in JPEG Domain With a Linear Development Pipeline

published or not.The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte
Cross-scale generative adversarial network for crowd density estimation from images

This research develops a cross-scale convolutional spatial generative adversarial network (CSGAN), in order to estimate the crowd density from images accurately. It consists of two similar generators, one for the whole feature extraction, and the other for patch scale feature extraction. An encoder–decoder structure is employed to generate density maps from input images or patches. Additionally, a new objective function for crowd counting called cross-scale consistency pursuit containing an adversarial loss, L2 loss, perceptual loss, and consistency loss, is developed to make the generated density maps more realistic and closer to the ground truth. The effectiveness of the proposed CSGAN is verified in two public datasets. Results indicate that the new objective function is able to reach the most satisfying value of evaluation metrics in both the low-density and high-density crowd scenes when it is compared with other state-of-the-art methods on the test datasets. Moreover, the proposed CSGAN is more practical and flexible due to the smaller computational complexity. Its estimation capability will be significantly improved even in a small size of training data. Overall, this research contributes to the development of a novel computer vision approach together with a new objective function to generate density maps from cross-scale crowd images, enabling the counting process more accurately and efficiently.