Reviewerly/exHarmony huggingface dataset:
A Multifeature Extraction Method Using Deep Residual Network for MR Image Denoising

In order to improve the resolution of magnetic resonance (MR) image and reduce the interference of noise, a multifeature extraction denoising algorithm based on a deep residual network is proposed. First, the feature extraction layer is constructed by combining three different sizes of convolution kernels, which are used to obtain multiple shallow features for fusion and increase the network’s multiscale perception ability. Then, it combines batch normalization and residual learning technology to accelerate and optimize the deep network, while solving the problem of internal covariate transfer in deep learning. Finally, the joint loss function is defined by combining the perceptual loss and the traditional mean square error loss. When the network is trained, it can not only be compared at the pixel level but also be learned at a higher level of semantic features to generate a clearer target image. Based on the MATLAB simulation platform, the TCGA-GBM and CH-GBM datasets are used to experimentally demonstrate the proposed algorithm. The results show that when the image size is set to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>190</mn> <mo>×</mo> <mn>215</mn> </math> and the optimization algorithm is Adam, the performance of the proposed algorithm is the best, and its denoising effect is significantly better than other comparison algorithms. Especially under high-intensity noise levels, the denoising advantage is more prominent.
Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data

Recently, machine learning techniques are often applied in real world scenarios where learning signals are provided as a stream of data points, and models need to be adapted online according to the current information. A severe problem of such settings consists in the fact that the underlying data distribution might change over time and concept drift or change of the feature characteristics have to be dealt with. In addition, data are often imbalanced because training signals for rare classes are particularly sparse. In the last years, a number of learning technologies have been proposed, which can reliably learn in the presence of drift, whereby non-parametric approaches such as the recent model SAM-kNN [] can deal particularly well with heterogeneous or priorly unknown types of drift. Yet these methods share the deficiencies of the underlying vanilla-kNN classifier when dealing with imbalanced classes. In this contribution, we propose intuitive extensions of SAM-kNN, which incorporate successful balancing techniques for kNN, namely SMOTE-sampling [] and kENN [], respectively, into the online learning scenario. Besides, we propose a new method, Informed Downsampling, for solving class imbalance in non-stationary settings with underlying drift, and demonstrate its superiority in a number of benchmarks.
Medical Blockchain of Privacy Data Sharing Model Based on Ring Signature

With the progress of information development of medical industry, the medical field has got into the era of big data. However, it still faces many challenges in data heterogeneity, interoperability, and privacy protection. In response to the above problems, a decentralized medical big data sharing model based on ring signature is constructed. The model maintains a reliable medical ledger database based on the decoupling of blockchain, ensuring that the privacy data cannot be tampered. It constructs a private data storage protocol based on ring signature, which guarantees medical data and patient identity privacy with its full anonymity. We proposed a medical information strict access control management mechanism based on smart contract automation execution of preset instructions. The authority and confidentiality of medical privacy data are guaranteed by clearly dividing the access rights of medical privacy information. The safety test results show that the model has better real-time and robustness than the traditional medical big data sharing model.
SeqPoint: Identifying Representative Iterations of Sequence-Based Neural Networks

The ubiquity of deep neural networks (DNNs) continues to rise, making them a crucial application class for hardware optimizations. However, detailed profiling and characterization of DNN training remains difficult as these applications often run for hours to days on real hardware. Prior works have exploited the iterative nature of DNNs to profile a few training iterations to represent the entire training run. While such a strategy is sound for networks like convolutional neural networks (CNNs), where the nature of the computation is largely input independent, we observe in this work that this approach is sub-optimal for sequence-based neural networks (SQNNs) such as recurrent neural networks (RNNs). The amount and nature of computations in SQNNs can vary for each input, resulting in heterogeneity across iterations. Thus, arbitrarily selecting a few iterations is insufficient to accurately summarize the behavior of the entire training run. To tackle this challenge, we carefully study the factors that impact SQNN training iterations and identify input sequence length as the key determining factor for variations across iterations. We then use this observation to characterize all iterations of an SQNN training run (requiring no profiling or simulation of the application) and select representative iterations, which we term SeqPoints. We analyze two state-of-the-art SQNNs, DeepSpeech2 and Google's Neural Machine Translation (GNMT), and show that SeqPoints can represent their entire training runs accurately, resulting in geomean errors of only 0.11% and 0.53%, respectively, when projecting overall runtime and 0.13% and 1.50% when projecting speedups due to architectural changes. This high accuracy is achieved while reducing the time needed for profiling by 345x and 214x for the two networks compared to full training runs. As a result, SeqPoint can enable analysis of SQNN training runs in mere minutes instead of hours or days.
An Accelerated Edge Cloud System for Energy Data Stream Processing Based on Adaptive Incremental Deep Learning Scheme

As smart metering technology evolves, power suppliers can make low-cost, low-risk estimation of customer-side power consumption by analyzing energy demand data collected in real-time. With advances network infrastructure, smart sensors, and various monitoring technologies, a standardized energy metering infrastructure, called advanced metering infrastructure (AMI), has been introduced and deployed to urban households to allow them to develop efficient power generation plans. Compared to traditional stochastic approaches for time-series data analysis, deep-learning methods have shown superior accuracy on many prediction applications. Because smart meters and infrastructure monitors produce a series of measurements over time, a large amount of data is accumulated, creating a large data stream, which takes much time from data generation to deployment of deep-learning model training. In this article, we propose an accelerated computing system that considers time-variant properties for accurate prediction of energy demand by processing the AMI stream data. The proposed system is a real-time training/inference system that deploys AMI data over a distributed edge cloud. It comprises two core components: an adaptive incremental learning solver and deep-learning acceleration with FPGA-GPU resource scheduling. An adaptive incremental learning scheme adjusts the batch/epoch in training iteration to reduce the time delay of the latest trained model, while trying to prevent biased-training due to the sub-optimal optimizer of incremental learning. In addition, a resource scheduling scheme manages various accelerator resources for accelerated deep-learning processing while minimizing the computational cost. The experimental results demonstrated that our method achieved good performance for adaptive batch size and epoch for incremental learning while guaranteeing a low inference error, a high model score, and queue stability with cost efficient processing.
The Bottleneck Simulator: A Model-Based Deep Reinforcement Learning Approach

Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete (bottleneck) state, which increases sample efficiency by reducing the number of model parameters and by exploiting structural properties of the environment. We provide a mathematical analysis of the Bottleneck Simulator in terms of fixed points of the learned policy, which reveals how performance is affected by four distinct sources of error: an error related to the abstract space structure, an error related to the transition model estimation variance, an error related to the transition model estimation bias, and an error related to the transition model class bias. Finally, we evaluate the Bottleneck Simulator on two natural language processing tasks: a text adventure game and a real-world, complex dialogue response selection task. On both tasks, the Bottleneck Simulator yields excellent performance beating competing approaches.
Privacy Preserving on Updated Parameters in Federated Learning

Federated learning provides a framework in which many participants join together to train a deep learning model. Although data is not directly transmitted in federated learning in order to protect privacy, recent researches show that transmitted parameters also lead to information leakage, which violates participants' privacy. In this paper, we combine cryptographic tools (additively homomorphic encryption, AES and RSA) with federated learning to design privacy-preserving protocols, which protect every participant's parameters' information. Results of experiments show that the proposed cryptographic methods can protect single participant's uploaded parameters with acceptable computation overhead increasing.
COD3S: Diverse Generation with Discrete Semantic Signatures

We present COD3S, a novel method for generating semantically diverse sentences using neural sequence-to-sequence (seq2seq) models.Conditioned on an input, seq2seq models typically produce semantically and syntactically homogeneous sets of sentences and thus perform poorly on one-to-many sequence generation tasks.Our two-stage approach improves output diversity by conditioning generation on locality-sensitive hash (LSH)-based semantic sentence codes whose Hamming distances highly correlate with human judgments of semantic textual similarity.Though it is generally applicable, we apply COD3S to causal generation, the task of predicting a proposition's plausible causes or effects.We demonstrate through automatic and human evaluation that responses produced using our method exhibit improved diversity without degrading task performance.
A Lightweight Identity-Based Remote Data Auditing Scheme for Cloud Storage

Cloud storage enables data owners to use any device to store and access data anytime, anywhere. In a data auditing scheme, the data owner can entrust a third party auditor (TPA) to verify that the outsourced data remains unchanged. A secure data auditing scheme not only detects whether cloud service providers (CSP) maintain data integrity, but also prevents TPA from stealing data. In this paper, a new identity-based data auditing (IBDA) scheme for cloud storage systems is proposed. In the scheme, the data owner generates the tags using its private key and data blocks, and then uploads the data blocks along with the tags to CSP. In the challenge-proof phase, before returning the proof information, CSP performs the addition operation between the hash function value and the data block to hide the data, thereby preventing TPA from stealing the data. This scheme is proved to be secure in the random oracle model. Analysis of efficiency shows that it is more efficient than other schemes.
MP3: Medical Software for Processing Multi-Parametric Images Pipelines

This article presents an open source software able to convert, display, and process medical images. It differentiates itself from the existing software by its ability to design complex processing pipelines and to wisely execute them on a large databases. An MP3 pipeline can contain unlimited homemade or ready-made processes and can be carried out with a parallel execution system. As a viewer, MP3 allows display of up to four images together and to draw Regions Of Interest (ROI). Two applications showing the strengths of the software are presented as examples: a preclinical study involving Magnetic Resonance Imaging (MRI) data and a clinical one involving Computed Tomography (CT) images. MP3 is downloadable at https://github.com/nifm-gin/MP3.
Modeling Adolescents’ Online Writing Practices: the Sociolectometry of Non-Standard Writing on Social Media

The paper discusses four generalized linear mixed models fitted to capture distinct patterns of non-standard writing practices in Flemish adolescents’ social media messages. Apart from a general model that predicts the count of all “deviations” from the Dutch formal writing standard, additional models were fitted for specific types of non-standard features. These types relate to the so-called chatspeak “maxims” of orality, brevity and expressive compensation. While the general non-standardness model reveals interesting correlations between the teenagers’ online writing style and their socio-demographic profile, the more specific models allow for a better and more nuanced sociolinguistic understanding: for different types of non-standard writing practices, they reveal distinct dynamics between the social predictors gender, age and educational track. Strikingly different gender patterns are found for the oral features, representing traditional non-standard writing, compared to the expressive features, representing new kinds of non-standard writing, bound to digital media. Furthermore, gender does not appear to be a predicting factor for the brevity-related features, except for the most theory-oriented educational track. Consequently, we argue that non-standard writing on social media platforms should not be operationalized as one comprehensive cluster of deviations from the formal writing standard, but rather as different subsets of non-standard features that, by serving different purposes, appeal to a different extent to different groups of youngsters and consequently display distinct sociolinguistic patterns. In other words, although Flemish adolescents may have access to the same pool of non-standard markers, they do not share one and the same social “digilect”.
Defining Explanation in an AI Context

With the increase in the use of AI systems, a need for explanation systems arises.Building an explanation system requires a definition of explanation.However, the natural language term explanation is difficult to define formally as it includes multiple perspectives from different domains such as psychology, philosophy, and cognitive sciences.We study multiple perspectives and aspects of explainability of recommendations or predictions made by AI systems, and provide a generic definition of explanation.The proposed definition is ambitious and challenging to apply.With the intention to bridge the gap between theory and application, we also propose a possible architecture of an automated explanation system based on our definition of explanation.
Verification of Cause-and-Effect Relationships in Cognitive Models Using Visualization Metaphors of Fuzzy Cognitive Maps

The paper presents the continuation of the authors' research in the field of visualization of cognitive models based on fuzzy cognitive maps.Application of visualization metaphors of fuzzy cognitive maps for verification of cause-and-effect relationships in cognitive models is considered.It is shown that increasing the effectiveness of cognitive model verification is possible by activating analyst's cognitive potential.The most natural way of such activation is to increase cognitive clarity of the model through the use of visualization capabilities.For this purpose, a number of metaphors for visualizing fuzzy cognitive maps have been proposed, aimed at increasing their cognitive clarity during verification.Each of the metaphors is focused on the visualization of a certain type of fragments of a fuzzy cognitive map potentially containing errors, redundancy or incompleteness and therefore of interest from the point of view of verification.Examples are given of applying the proposed visualization metaphors.
Eigenvalues of Two-State Quantum Walks Induced by the Hadamard Walk

Existence of the eigenvalues of the discrete-time quantum walks is deeply related to localization of the walks. We revealed, for the first time, the distributions of the eigenvalues given by the splitted generating function method (the SGF method) of the space-inhomogeneous quantum walks in one dimension we had treated in our previous studies. Especially, we clarified the characteristic parameter dependence for the distributions of the eigenvalues with the aid of numerical simulation.
Word Frequency Does Not Predict Grammatical Knowledge in Language Models

Neural language models learn, to varying degrees of accuracy, the grammatical properties of natural languages.In this work, we investigate whether there are systematic sources of variation in the language models' accuracy.Focusing on subject-verb agreement and reflexive anaphora, we find that certain nouns are systematically understood better than others, an effect which is robust across grammatical tasks and different language models.Surprisingly, we find that across four orders of magnitude, corpus frequency is unrelated to a noun's performance on grammatical tasks.Finally, we find that a novel noun's grammatical properties can be few-shot learned from various types of training data.The results present a paradox: there should be less variation in grammatical performance than is actually observed.
Environmentally-Friendly Metrics for Evaluating the Performance of Deep Learning Models and Systems

Climate change is considered to be one of the most important issues we are facing right now as a specie and existent metrics and benchmarks used to evaluate the performance of different Deep Learning (DL) models and systems are currently focused mainly on their accuracy and speed, without also considering their energy consumption and cost. In this paper, we introduce four novel DL metrics, two regarding inference called Accuracy Per Consumption (APC) and Accuracy Per Energy Cost (APEC) and two regarding training called Time To Closest APC (TTCAPC) and Time To Closest APEC (TTCAPEC), which take into account not only a DL model's accuracy but also its energy consumption, energy cost and the time it takes to train it up to that point. Experimental results prove that all four DL metrics are promising, encouraging future DL researchers to make use of models and platforms that require low power consumption as well as of green energy when powering their DL-based systems.
AT-Text: Assembling Text Components for Efficient Dense Scene Text Detection

Text detection is a prerequisite for text recognition in scene images. Previous segmentation-based methods for detecting scene text have already achieved a promising performance. However, these kinds of approaches may produce spurious text instances, as they usually confuse the boundary of dense text instances, and then infer word/text line instances relying heavily on meticulous heuristic rules. We propose a novel Assembling Text Components (AT-text) that accurately detects dense text in scene images. The AT-text localizes word/text line instances in a bottom-up mechanism by assembling a parsimonious component set. We employ a segmentation model that encodes multi-scale text features, considerably improving the classification accuracy of text/non-text pixels. The text candidate components are finely classified and selected via discriminate segmentation results. This allows the AT-text to efficiently filter out false-positive candidate components, and then to assemble the remaining text components into different text instances. The AT-text works well on multi-oriented and multi-language text without complex post-processing and character-level annotation. Compared with the existing works, it achieves satisfactory results and a considerable balance between precision and recall without a large margin in ICDAR2013 and MSRA-TD 500 public benchmark datasets.
Evaluation of Machine Translation Methods applied to Medical Terminologies

Medical terminologies resources and standards play vital roles in clinical data exchanges, enabling significantly the services’ interoperability within healthcare national information networks. Health and medical science are constantly evolving causing requirements to advance the terminologies editions. In this paper, we present our evaluation work of the latest machine translation techniques addressing medical terminologies. Experiments have been conducted leveraging selected statistical and neural machine translation methods. The devised procedure is tested on a validated sample of ICD-11 and ICF terminologies from English to French with promising results.
Area and Energy Efficient 2D Max-Pooling For Convolutional Neural Network Hardware Accelerator

In the era of digital world, Convolutional Neural-network is widely adopted. To keep the network in a reasonable size, down-sampling is required. Max-pooling is a common solution for down-sampling. High throughput and area-efficient -hardware design of 2D max-pooling is essential for CNN accelerator. In this paper, we propose a highly efficient hardware design of max-pooling for 2D data input. The proposed design can be integrated into the neural-network accelerator as it can process the 2D input feature map data in parallel. The proposed solution, which is supporting up to 31 parallel data input, has been synthesized using at 40nm process node with area of 90×100 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and the estimated power is 2.9 mW @200Mhz @1.1V power supply.
Narrative Text Generation with a Latent Discrete Plan

Past work on story generation has demonstrated the usefulness of conditioning on a generation plan to generate coherent stories.However, these approaches have used heuristics or off-the-shelf models to first tag training stories with the desired type of plan, and then train generation models in a supervised fashion.In this paper, we propose a deep latent variable model that first samples a sequence of anchor words, one per sentence in the story, as part of its generative process.During training, our model treats the sequence of anchor words as a latent variable and attempts to induce anchoring sequences that help guide generation in an unsupervised fashion.We conduct experiments with several types of sentence decoder distributions -left-to-right and non-monotonic, with different degrees of restriction.Further, since we use amortized variational inference to train our model, we introduce two corresponding types of inference network for predicting the posterior on anchor words.We conduct human evaluations which demonstrate that the stories produced by our model are rated better in comparison with baselines which do not consider story plans, and are similar or better in quality relative to baselines which use external supervision for plans.Additionally, the proposed model gets favorable scores when evaluated on perplexity, diversity, and control of story via discrete plan.