Reviewerly/exHarmony huggingface dataset:
Zeno++: Robust Fully Asynchronous SGD

We propose Zeno++, a new robust asynchronous Stochastic Gradient Descent~(SGD) procedure which tolerates Byzantine failures of the workers. In contrast to previous work, Zeno++ removes some unrealistic restrictions on worker-server communications, allowing for fully asynchronous updates from anonymous workers, arbitrarily stale worker updates, and the possibility of an unbounded number of Byzantine workers. The key idea is to estimate the descent of the loss value after the candidate gradient is applied, where large descent values indicate that the update results in optimization progress. We prove the convergence of Zeno++ for non-convex problems under Byzantine failures. Experimental results show that Zeno++ outperforms existing approaches.
Infographics and Their Application in the Educational Process

The purpose of this study is to improve the effectiveness of the educational process by using infographic content that allows to create interactive infographics with the application of the origi-nal markup method. The importance of complex graphic information in the information processes of modern society, the need to know the tools for its creation, rules and techniques of develop-ment, as well as the skills of posting infographic information as an integral part of specialists’ competence were shown. The paper describes in detail the features of using infographics when creating presentations, mnemonic diagrams, instructions, as well as in the process of searching for information on the Internet. The use of infographics in educational process will reduce lan-guage barriers when teaching in an educational institution for both Russian-speaking and foreign students, since infographics improve the perception of educational content regardless of the lan-guage environment of students.
Developing Concept Enriched Models for Big Data Processing Within the Medical Domain

Within the past few years, the medical domain has endeavored to incorporate artificial intelligence, including cognitive computing tools, to develop enriched models for processing and synthesizing knowledge from Big Data. Due to the rapid growth in published medical research, the ability of medical practitioners to keep up with research developments has become a persistent challenge. Despite this challenge, using data-driven artificial intelligence to process large amounts of data can overcome this difficulty. This research summarizes cognitive computing methodologies and applications utilized in the medical domain. Likewise, this research describes the development process for a novel, concept-enriched model using the IBM Watson service and a publicly available diabetes dataset and knowledge-base. Finally, reflection is offered on the strengths and limitations of the model and enhancements for future experiments. This work thus provides an initial framework for those interested in effectively developing, maintaining, and using cognitive models to enhance the quality of healthcare.
Exploring the Privacy-Preserving Properties of Word Embeddings: Algorithmic Validation Study

Background Word embeddings are dense numeric vectors used to represent language in neural networks. Until recently, there had been no publicly released embeddings trained on clinical data. Our work is the first to study the privacy implications of releasing these models. Objective This paper aims to demonstrate that traditional word embeddings created on clinical corpora that have been deidentified by removing personal health information (PHI) can nonetheless be exploited to reveal sensitive patient information. Methods We used embeddings created from 400,000 doctor-written consultation notes and experimented with 3 common word embedding methods to explore the privacy-preserving properties of each. Results We found that if publicly released embeddings are trained from a corpus anonymized by PHI removal, it is possible to reconstruct up to 68.5% (n=411/600) of the full names that remain in the deidentified corpus and associated sensitive information to specific patients in the corpus from which the embeddings were created. We also found that the distance between the word vector representation of a patient’s name and a diagnostic billing code is informative and differs significantly from the distance between the name and a code not billed for that patient. Conclusions Special care must be taken when sharing word embeddings created from clinical texts, as current approaches may compromise patient privacy. If PHI removal is used for anonymization before traditional word embeddings are trained, it is possible to attribute sensitive information to patients who have not been fully deidentified by the (necessarily imperfect) removal algorithms. A promising alternative (ie, anonymization by PHI replacement) may avoid these flaws. Our results are timely and critical, as an increasing number of researchers are pushing for publicly available health data.
Cryptanalysis and Improvement of a Proxy Signcryption Scheme in the Standard Computational Model

Proxy signcryption is essential security primitive for emerging secure communication such as e-business, mobile agents, online voting, contract signing, and online auction. It combines the functionality of a proxy signature and encryption to achieve basic security features maintaining a low computational and communicational cost. Ming proposed Proxy Signcryption (PSC) scheme in the standard computational model, claimed it to be secured against: (1) Indistinguishable Chosen Ciphertext Attack (IND-CCA) under the Decisional Bi-linear Diffie-Hellman (DBDH) assumption (2) Existentially Unforgeable Chosen Message Attack (EUF-CMA) under the Computational Diffie Hellman (CDH) assumption. This paper first provides a security analysis to check the correctness and validity of the said PSC scheme. Furthermore, it proves PSC is vulnerable to the launched cryptanalysis attacks. It is established that the PSC is neither semantically secured against IND-CCA nor existentially secured against EUF-CMA in its defined security model. Secondly, we propose an improved new proxy signcryption scheme (N-PSC) based on Elliptic Curve Cryptosystem (ECC) without bi-linear pairing secure against IND-CCA and EUF-CMA for Type-1 adversary A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> in the standard computational model. It is also proved that the new proposed N-PSC scheme achieves an extra security property of judge verification in case of signature dispute between the proxy correspondents, as well as it outperforms the existing states of the art schemes including the Ming scheme in terms of cost efficiency which makes the new proposed scheme suitable for scarce resources constraint proxy enabled communication applications.
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks

We propose novel approaches for simultaneously identifying important weights of a convolutional neural network (ConvNet) and providing more attention to the important weights during training. More formally, we identify two characteristics of a weight, its magnitude and its location, which can be linked with the importance of the weight. By targeting these characteristics of a weight during training, we develop two separate weight excitation (WE) mechanisms via weight reparameterization-based backpropagation modifications. We demonstrate significant improvements over popular baseline ConvNets on multiple computer vision applications using WE (e.g. 1.3% accuracy improvement over ResNet50 baseline on ImageNet image classification, etc.). These improvements come at no extra computational cost or ConvNet structural change during inference. Additionally, including WE methods in a convolution block is straightforward, requiring few lines of extra code. Lastly, WE mechanisms can provide complementary benefits when used with external attention mechanisms such as the popular Squeeze-and-Excitation attention block.
An Intelligent AIEgen with Nonmonotonic Multiresponses to Multistimuli

[This corrects the article DOI: 10.1002/advs.202001845.].
On the Feasibility of TERO-Based True Random Number Generator on Xilinx FPGAs

A True Random Number Generator (TRNG) is an essential component for security applications of FPGAs. Its requirements include small logic area, high throughput, sufficient randomness backed with a mathematical model, and feasibility — ease of implementation. This paper focuses on TRNGs based on a Transition Effect Ring Oscillator (TERO) and presents a three-path configurable TERO (TC-TERO), an improved implementation of TERO that achieves high feasibility with a minimal amount of hardware. According to the evaluation with a Xilinx Artix-7 FPGA, a TC-TERO with a 20-bit configurable parameter only required 40 LUTs. By selecting one of the promising parameters, the proposed TRNG passed AIS-31 Procedure A without post-processing and NIST SP 800-22 with a simple debiasing.
Application of Knowledge-Driven Methods for Mineral Prospectivity Mapping of Polymetallic Sulfide Deposits in the Southwest Indian Ridge between 46° and 52°E

As a product of hydrothermal activity, seafloor polymetallic sulfide deposit has become the focus of marine mineral exploration due to its great prospects for mineralization potential. The mineral prospectivity mapping is a multiple process that involves weighting and integrating evidential layers to further explore the potential target areas, which can be categorized into data-driven and knowledge-driven methods. This paper describes the application of fuzzy logic and fuzzy analytic hierarchy process (AHP) models to process the data of the Southwest Indian Ocean Mid-Ridge seafloor sulfide deposit and delineate prospect areas. Nine spatial evidential layers representing the controlling factors for the formation and occurrence of polymetallic sulfide deposit were extracted to establish a prospecting prediction model. Fuzzy logic and fuzzy AHP models combine expert experience and fuzzy sets to assign weights to each layer and integrate the evidence layers to generate prospectivity map. Based on prediction-area (P-A) model, the optimal gamma operator (γ) values were determined to be 0.95 and 0.90 for fuzzy logic and fuzzy AHP to synthesize the evidence layers. The concentration-area (C-A) fractal method was used to classify different levels of metallogenic probability by determining corresponding thresholds. Finally, Receiver Operating Characteristic (ROC) curves were applied to measure the performance of the two prospectivity models. The results show that the areas under the ROC curve of the fuzzy logic and the fuzzy AHP model are 0.813 and 0.887, respectively, indicating that prediction based on knowledge-driven methods can effectively predict the metallogenic favorable area in the study area, opening the door for future exploration of seafloor polymetallic sulfide deposits.
Semantically Inspired AMR Alignment for the Portuguese Language

Meaning Representation (AMR) is a graph-based semantic formalism where the nodes are concepts and edges are relations among them.Most of AMR parsing methods require alignment between the nodes of the graph and the words of the sentence.However, this alignment is not provided by manual annotations and available automatic aligners focus only on the English language, not performing well for other languages.Aiming to fulfill this gap, we developed an alignment method for the Portuguese language based on a more semantically matched word-concept pair.We performed both intrinsic and extrinsic evaluations and showed that our alignment approach outperforms the alignment strategies developed for English, improving AMR parsers, and achieving competitive results with a parser designed for the Portuguese language.
Integration of Blockchain and IoT: An Enhanced Security Perspective

Blockchain (BC), a by-product of Bitcoin cryptocurrency, has gained immense and wide scale popularity for its applicability in various diverse domains - especially in multifaceted non-monetary systems. By adopting cryptographic techniques such as hashing and asymmetric encryption - along with distributed consensus approach, a Blockchain based distributed ledger not only becomes highly secure but also immutable and thus eliminates the need for any third-party intermediators. On the contrary, innumerable IoT (Internet of Things) devices are increasingly being added to the network. This phenomenon poses higher risk in terms of security and privacy. It is thus extremely important to address the security aspects of the growing IoT ecosystem. This paper explores the applicability of BC for ensuring enhanced security and privacy in the IoT ecosystem. Recent research articles and projects or applications were surveyed to assess the implementation of BC for IoT Security and identify associated challenges and propose solutions for BC enabled enhanced security for the IoT ecosystem.
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks

Recently, Geometric Deep Learning (GDL) has been introduced as a novel and versatile framework for computer-aided disease classification. GDL uses patient meta-information such as age and gender to model patient cohort relations in a graph structure. Concepts from graph signal processing are leveraged to learn the optimal mapping of multi-modal features, e.g. from images to disease classes. Related studies so far have considered image features that are extracted in a pre-processing step. We hypothesize that such an approach prevents the network from optimizing feature representations towards achieving the best performance in the graph network. We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly. We validate this architecture against state-of-the-art inductive graph networks and demonstrate significantly improved classification scores on a modified MNIST toy dataset, as well as comparable classification results with higher stability on a chest X-ray image dataset. Additionally, we explain how the structural information of the graph affects both the image filters and the feature learning.
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding

Linguistic steganography studies how to hide secret messages in natural language cover texts.Traditional methods aim to transform a secret message into an innocent text via lexical substitution or syntactical modification.Recently, advances in neural language models (LMs) enable us to directly generate cover text conditioned on the secret message.In this study, we present a new linguistic steganography method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model.We formally analyze the statistical imperceptibility of this method and empirically show it outperforms the previous state-of-the-art methods on four datasets by 15.3% and 38.9% in terms of bits/word and KL metrics, respectively.Finally, human evaluations show that 51% of generated cover texts can indeed fool eavesdroppers. 1
Performance Evaluation of different Hyperledger Sawtooth transaction processors for Blockchain log storage with varying workloads

Blockchain technology enables the trustless sharing of distributed ledgers among peers. Despite having valuable properties like decentralisation, and immutability of transactions, it incurs a high performance overhead as compared with traditional databases thus discouraging its further adoption. Even the usage of different transaction processors within the same Blockchain platform, namely Hyperledger Sawtooth, may result in different performance, for the same use case and the same transaction type. This paper proposes a methodology for evaluating the performance of two different transaction processors deployed in the Hyperledger Sawtooth platform. We evaluated experimentally the methodology and present the results of the experimental evaluation which may be useful to blockchain practitioners for future solution designs.
Aligning text mining and machine learning algorithms with best practices for study selection in systematic literature reviews

Despite existing research on text mining and machine learning for title and abstract screening, the role of machine learning within systematic literature reviews (SLRs) for health technology assessment (HTA) remains unclear given lack of extensive testing and of guidance from HTA agencies. We sought to address two knowledge gaps: to extend ML algorithms to provide a reason for exclusion-to align with current practices-and to determine optimal parameter settings for feature-set generation and ML algorithms.We used abstract and full-text selection data from five large SLRs (n = 3089 to 12,769 abstracts) across a variety of disease areas. Each SLR was split into training and test sets. We developed a multi-step algorithm to categorize each citation into the following categories: included; excluded for each PICOS criterion; or unclassified. We used a bag-of-words approach for feature-set generation and compared machine learning algorithms using support vector machines (SVMs), naïve Bayes (NB), and bagged classification and regression trees (CART) for classification. We also compared alternative training set strategies: using full data versus downsampling (i.e., reducing excludes to balance includes/excludes because machine learning algorithms perform better with balanced data), and using inclusion/exclusion decisions from abstract versus full-text screening. Performance comparisons were in terms of specificity, sensitivity, accuracy, and matching the reason for exclusion.The best-fitting model (optimized sensitivity and specificity) was based on the SVM algorithm using training data based on full-text decisions, downsampling, and excluding words occurring fewer than five times. The sensitivity and specificity of this model ranged from 94 to 100%, and 54 to 89%, respectively, across the five SLRs. On average, 75% of excluded citations were excluded with a reason and 83% of these citations matched the reviewers' original reason for exclusion. Sensitivity significantly improved when both downsampling and abstract decisions were used.ML algorithms can improve the efficiency of the SLR process and the proposed algorithms could reduce the workload of a second reviewer by identifying exclusions with a relevant PICOS reason, thus aligning with HTA guidance. Downsampling can be used to improve study selection, and improvements using full-text exclusions have implications for a learn-as-you-go approach.
Listen, Understand and Translate: Triple Supervision Decouples End-to-end Speech-to-text Translation

An end-to-end speech-to-text translation (ST) takes audio in a source language and outputs the text in a target language. Existing methods are limited by the amount of parallel corpus. Can we build a system to fully utilize signals in a parallel ST corpus? We are inspired by human understanding system which is composed of auditory perception and cognitive processing. In this paper, we propose Listen-Understand-Translate, (LUT), a unified framework with triple supervision signals to decouple the end-to-end speech-to-text translation task. LUT is able to guide the acoustic encoder to extract as much information from the auditory input. In addition, LUT utilizes a pre-trained BERT model to enforce the upper encoder to produce as much semantic information as possible, without extra data. We perform experiments on a diverse set of speech translation benchmarks, including Librispeech English-French, IWSLT English-German and TED English-Chinese. Our results demonstrate LUT achieves the state-of-the-art performance, outperforming previous methods. The code is available at https://github.com/dqqcasia/st.
Stronger Security and Constructions of Multi-designated Verifier Signatures

Off-the-Record (OTR) messaging is a two-party message authentication protocol that also provides plausible deniability: there is no record that can later convince a third party what messages were actually sent. The challenge in group OTR, is to enable the sender to sign his messages so that group members can verify who sent a message (signatures should be unforgeable, even by group members). Also, we want the off-the-record property: even if some verifiers are corrupt and collude, they should not be able to prove the authenticity of a message to any outsider. Finally, we need consistency, meaning that if any group member accepts a signature, then all of them do. To achieve these properties it is natural to consider Multi-Designated Verifier Signatures (MDVS). However, existing literature defines and builds only limited notions of MDVS, where (a) the off-the-record property (source hiding) only holds when all verifiers could conceivably collude, and (b) the consistency property is not considered. The contributions of this paper are two-fold: stronger definitions for MDVS, and new constructions meeting those definitions. We strengthen source-hiding to support any subset of corrupt verifiers, and give the first formal definition of consistency. We build three new MDVS: one from generic standard primitives (PRF, key agreement, NIZK), one with concrete efficiency and one from functional encryption.
A hybrid model for photovoltaic power prediction of both convolutional and long short-term memory neural networks optimized by genetic algorithm

Photovoltaic power generation is affected by weather and geographical environment, showing fluctuations and random multi-interference, and its output power is easy to change with changes in external factors. Therefore, the prediction of output power is crucial to optimize the grid-connected operation of photovoltaic power generation and reduce the impact of uncertainty. This paper proposes a hybrid model of both convolutional neural network (CNN) and long short-term memory neural network (LSTM) based on genetic algorithm (GA) optimization (GA-CNN-LSTM). First, the CNN module is used to extract the spatial features of the data, and then the LSTM module is used to extract the temporal features and nearby hidden states. Optimizing the hyperparameter weights and bias values of the LSTM training network through GA. At the initial stage, the historical data is normalized, and all features were analyzed by grey relational degree. Important features are extracted to reduce the computational complexity of the data. Then, the GA-optimized CNN-LSTM hybrid neural network model (GA-CNN-LSTM) is applied for photovoltaic power prediction experiment. The GA-CNN-LSTM model was compared with the single neural network models such as CNN and LSTM, and the CNN-LSTM hybrid neural network model without GA optimization. Under the Mean Absolute Percentage Error index, the GA-CNN-LSTM algorithm proposed in this paper reduces the error by 1.537% compared with the ordinary single neural network model, and 0.873% compared with the unoptimized CNN-LSTM hybrid neural network algorithm model. From the perspective of training and test running time, the GA-CNN-LSTM model takes a little longer than the single neural network model, but the disadvantage is not obvious. To sum up, the performance of GA-CNN-LSTM model for photovoltaic power predicting is better.
Dynamic Multiparty Quantum Secret Sharing With a Trusted Party Based on Generalized GHZ State

This paper proposes a dynamic multiparty to multiparty quantum secret sharing scheme, where two distant groups of participants can share the common secrets.In our present scheme, a trusted third party is required to reliably prepare all quantum resources.Each agent only needs to perform the local unitary operations or single-particle measurement completing the process of secret sharing.Secret can be recovered only if all agents cooperate each other.Furthermore, a new agent can be added or deleted from the current scheme with the help of the trusted third party.Finally, security analysis shows that our scheme can resist various common attacks.Compared to former existing researches, our scheme can be more efficient in real application.