Reviewerly/exHarmony huggingface dataset:
Blockchain-based solutions for clinical trial data management: a systematic review

Blockchain technology can reduce the need for intermediaries in various types of transactions by providing a decentralized and secure ledger that can be accessed and updated by all parties involved in the transaction. Clinical trials are essential for bringing new drugs and therapies to market, but the current clinical research process is often marred by inefficiencies, data inaccuracies, and a lack of transparency. The implementation of blockchain technology in clinical trials has the potential to address these challenges by providing a secure and transparent platform for data management. By leveraging the power of blockchain, healthcare providers can improve the integrity and accuracy of clinical trial data, enhance trust in the clinical research process, and ultimately improve patient outcomes. In this article, we propose the use of blockchain technology in clinical trials and explore its potential benefits for the healthcare. The implementation of a blockchain-based data management system for clinical trials holds significant potential to address several challenges associated with the current clinical research process. By improving the integrity and security of medical data, enhancing trust, and easing regulatory burden, such a system can promote the efficient and effective conduct of clinical trials. The adoption of a blockchain-based solution for clinical trial data management has the potential to optimize costs, contributing to the sustainability of healthcare services. It also provides a model for future research and development of blockchain-based solutions in the field of clinical research.
Learning from Demonstration for Goal-Driven Autonomy

Goal-driven autonomy (GDA) is a conceptual model for creating an autonomous agent that monitors a set of expectations during plan execution, detects when discrepancies occur, builds explanations for the cause of failures, and formulates new goals to pursue when planning failures arise. While this framework enables the development of agents that can operate in complex and dynamic environments, implementing the logic for each of the subtasks in the model requires substantial domain engineering. We present a method using case-based reasoning and intent recognition in order to build GDA agents that learn from demonstrations. Our approach reduces the amount of domain engineering necessary to implement GDA agents and learns expectations, explanations, and goals from expert demonstrations. We have applied this approach to build an agent for the real-time strategy game StarCraft. Our results show that integrating the GDA conceptual model into the agent greatly improves its win rate.
An Evolutionary Paradigm

What could explain the evolution of the architecture of self-reflection, as outlined so far? According to Section 4.1, any such explanation faces a variety of formidable puzzles, such as the human uniqueness of self-reflection, the absence of a specialized DNA basis, the absence of a dedicated brain location, the inward turn of the self-reflective mind, and the apparent recency and speed of its evolution. To handle these puzzles and explain why and how young human minds respond in unprecedented ways to the selection pressures they face in mid-childhood and later, the remaining sections of the chapter assemble an evolutionary paradigm that finds revealing and fruitful explanatory connections among recent and independently elaborated approaches in three distinct research areas – genetics (Section 4.2), brain organization (Section 4.3) and, most importantly, developmental evolution (Section 4.4).
A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study

Background: The data missing from patient profiles in intensive care units (ICUs) are substantial and unavoidable. However, this incompleteness is not always random or because of imperfections in the data collection process.
Semantic Segmentation with Transfer Learning for Off-Road Autonomous Driving

Since the state-of-the-art deep learning algorithms demand a large training dataset, which is often unavailable in some domains, the transfer of knowledge from one domain to another has been a trending technique in the computer vision field. However, this method may not be a straight-forward task considering several issues such as original network size or large differences between the source and target domain. In this paper, we perform transfer learning for semantic segmentation of off-road driving environments using a pre-trained segmentation network called DeconvNet. We explore and verify two important aspects regarding transfer learning. First, since the original network size was very large and did not perform well for our application, we proposed a smaller network, which we call the light-weight network. This light-weight network is half the size to the original DeconvNet architecture. We transferred the knowledge from the pre-trained DeconvNet to our light-weight network and fine-tuned it. Second, we used synthetic datasets as the intermediate domain before training with the real-world off-road driving data. Fine-tuning the model trained with the synthetic dataset that simulates the off-road driving environment provides more accurate results for the segmentation of real-world off-road driving environments than transfer learning without using a synthetic dataset does, as long as the synthetic dataset is generated considering real-world variations. We also explore the issue whereby the use of a too simple and/or too random synthetic dataset results in negative transfer. We consider the Freiburg Forest dataset as a real-world off-road driving dataset.
An Updated Evaluation of Google Translate Accuracy

In 2011, a comprehensive evaluation of accuracy using 51 languages with Google Translate showed that many European languages had good results, but several Asian languages performed poorly. The online service has improved its accuracy over the intervening eight years, and a reevaluation using the same text as the original study shows a 34% improvement based upon BLEU scores. This new study shows that translations between English and German, Afrikaans, Portuguese, Spanish, Danish, Greek, Polish, Hungarian, Finnish, and Chinese tend to be the most accurate.
Hyperbolic Graph Neural Networks

Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated by recent advances in geometric representation learning, we propose a novel GNN architecture for learning representations on Riemannian manifolds with differentiable exponential and logarithmic maps. We develop a scalable algorithm for modeling the structural properties of graphs, comparing Euclidean and hyperbolic geometry. In our experiments, we show that hyperbolic GNNs can lead to substantial improvements on various benchmark datasets.
OSIAN: Open Source International Arabic News Corpus - Preparation and Integration into the CLARIN-infrastructure

The World Wide Web has become a fundamental resource for building large text corpora.Broadcasting platforms such as news websites are rich sources of data regarding diverse topics and form a valuable foundation for research.The Arabic language is extensively utilized on the Web.Still, Arabic is relatively an under-resourced language in terms of availability of freely annotated corpora.This paper presents the first version of the Open Source International Arabic News (OSIAN) corpus.The corpus data was collected from international Arabic news websites, all being freely available on the Web.The corpus consists of about 3.5 million articles comprising more than 37 million sentences and roughly 1 billion tokens.It is encoded in XML; each article is annotated with metadata information.Moreover, each word is annotated with lemma and part-ofspeech.The described corpus is processed, archived and published into the CLARIN infrastructure.
An intelligent agriculture network security system based on private blockchains

Countries around the world are nowadays actively promoting development in intelligent agriculture. Each of them must develop a specific plan tailored to environmental farming indices of each individual farm, and such information would be both important and sensitive. This is why information in intelligent agriculture requires protection from network security to ensure data privacy and integrity. This study proposes applying dark web technology to ensure the privacy of blockchains and servers. The study will monitor packet transmission frequency in intelligent agriculture to prevent distributed denial-of-service (DDOS) attacks. The main features of system include: (1) An identity authentication mechanism, (2) secure transmission of information, (3) establishment of private blockchains, (4) a faster, improved authentication system for blockchain information, and (5) resistance against DDOS attacks. The proposed scheme can safeguard network security for the IoT as well as the servers by way of applying dark web technology, which can avoid exposure of blockchains and server ID addresses and thus in turn lower the risks of DDOS attack damages. Experiment results indicate that the application of lightweight encryption of proposed scheme does indeed improve the authentication speed while also satisfying requirements of network security.
From Research to Production and Back: Ludicrously Fast Neural Machine Translation

This paper describes the submissions of the "Marian" team to the WNGT 2019 efficiency shared task.Taking our dominating submissions to the previous edition of the shared task as a starting point, we develop improved teacher-student training via multi-agent duallearning and noisy backward-forward translation for Transformer-based student models.For efficient CPU-based decoding, we propose pre-packed 8-bit matrix products, improved batched decoding, cache-friendly student architectures with parameter sharing and light-weight RNN-based decoder architectures.GPU-based decoding benefits from the same architecture changes, from pervasive 16bit inference and concurrent streams.These modifications together with profiler-based C++ code optimization allow us to push the Pareto frontier established during the 2018 edition towards 24x (CPU) and 14x (GPU) faster models at comparable or higher BLEU values.Our fastest CPU model is more than 4x faster than last year's fastest submission at more than 3 points higher BLEU.Our fastest GPU model at 1.5 seconds translation time is slightly faster than last year's fastest RNN-based submissions, but outperforms them by more than 4 BLEU and 10 BLEU points respectively.
Silhouette width using generalized mean—A flexible method for assessing clustering efficiency

Abstract Cluster analysis plays vital role in pattern recognition in several fields of science. Silhouette width is a widely used index for assessing the fit of individual objects in the classification, as well as the quality of clusters and the entire classification. Silhouette combines two clustering criteria, compactness and separation, which imply that spherical cluster shapes are preferred over others—a property that can be seen as a disadvantage in the presence of complex, nonspherical clusters, which is common in real situations. We suggest a generalization of the silhouette width using the generalized mean. By changing the p parameter of the generalized mean between −∞ and +∞, several specific summary statistics, including the minimum, maximum, the arithmetic, harmonic, and geometric means, can be reproduced. Implementing the generalized mean in the calculation of silhouette width allows for changing the sensitivity of the index to compactness versus connectedness. With higher sensitivity to connectedness, the preference of silhouette width toward spherical clusters should reduce. We test the performance of the generalized silhouette width on artificial data sets and on the Iris data set. We examine how classifications with different numbers of clusters prepared by different algorithms are evaluated, if p is set to different values. When p was negative, well‐separated clusters achieved high silhouette widths despite their elongated or circular shapes. Positive values of p increased the importance of compactness; hence, the preference toward spherical clusters became even more detectable. With low p , single linkage clustering was deemed the most efficient clustering method, while with higher parameter values the performance of group average, complete linkage, and beta flexible with beta = −0.25 seemed better. The generalized silhouette allows for adjusting the contribution of compactness and connectedness criteria, thus avoiding underestimation of clustering efficiency in the presence of clusters with high internal heterogeneity.
Studying Software Engineering Patterns for Designing Machine Learning Systems

Machine-learning (ML) techniques are becoming more prevalent. ML techniques rely on mathematics and software engineering. Researchers and practitioners studying best practices strive to design ML systems and software that address software complexity and quality issues. Such design practices are often formalized as architecture and design patterns by encapsulating reusable solutions to common problems within given contexts. However, a systematic study to collect, classify, and discuss these software-engineering (SE) design patterns for ML techniques have yet to be reported. Our research collects good/bad SE design patterns for ML techniques to provide developers with a comprehensive classification of such patterns. Herein we report the preliminary results of a systematic-literature review (SLR) of good/bad design patterns for ML.
Depth vision guided hand gesture recognition using electromyographic signals

Hand gesture recognition has been applied to many research fields and has shown its prominent advantages in increasing the practicality of Human-Robot Interaction (HRI). The development of advanced techniques in data science, such as big data and machine learning, facilitate the accurate classification of the hand gestures using electromyography (EMG) signals. However, the processing of the collection and label of the large data set imposes a high work burden and results in time-consuming implementations. Therefore, a novel method is proposed to combine the benefits of depth vision learning and EMG based hand gesture recognition. It is capable of labeling the class of the collected EMG data under the guidance of depth vision automatically, without consideration of the hand motion sequence. Finally, we demonstrated the proposed method for recognizing the ten hand gestures using a Myo armband. The experiment is set in a supervised learning way to evaluate the performance of the designed Hk-means algorithm. It shows that the proposed method can succeed in hand gesture recognition without labeling the data in advance.
Dense Light Field Coding: A Survey

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates

Decentralized stochastic optimization methods have gained a lot of attention recently, mainly because of their cheap per iteration cost, data locality, and their communication-efficiency. In this paper we introduce a unified convergence analysis that covers a large variety of decentralized SGD methods which so far have required different intuitions, have different applications, and which have been developed separately in various communities. Our algorithmic framework covers local SGD updates and synchronous and pairwise gossip updates on adaptive network topology. We derive universal convergence rates for smooth (convex and non-convex) problems and the rates interpolate between the heterogeneous (non-identically distributed data) and iid-data settings, recovering linear convergence rates in many special cases, for instance for over-parametrized models. Our proofs rely on weak assumptions (typically improving over prior work in several aspects) and recover (and improve) the best known complexity results for a host of important scenarios, such as for instance coorperative SGD and federated averaging (local SGD).
Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health. The appearance of this new data source, which was previously unimaginable, has opened up a new way in which to improve public health systems, resulting in better communication policies and better detection systems. However, the unstructured nature of the Internet, along with the complexity of the infectious disease domain, prevents the information extracted from being easily understood. Moreover, when dealing with languages other than English, for which some of the most common Natural Language Processing resources are not available, the correct exploitation of this data becomes even more difficult. We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public’s opinions as regards infectious diseases when expressed in Spanish by employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America. Our proposal is based on two technologies. We first use ontologies in order to model the infectious disease domain with concepts such as risks, symptoms, transmission methods or drugs, among other concepts. We then measure the relationship between these concepts in order to determine the degree to which one concept influences other concepts. This new information is subsequently applied in order to build an aspect-based sentiment analysis model based on statistical and linguistic features. This is done by applying deep-learning models. Our proposal is available on a web platform, where users can see the sentiment for each concept at a glance and analyse how each concept influences the sentiment of the others.
Learning Cross-Modal Context Graph for Visual Grounding

Visual grounding is a ubiquitous building block in many vision-language tasks and yet remains challenging due to large variations in visual and linguistic features of grounding entities, strong context effect and the resulting semantic ambiguities. Prior works typically focus on learning representations of individual phrases with limited context information. To address their limitations, this paper proposes a language-guided graph representation to capture the global context of grounding entities and their relations, and develop a cross-modal graph matching strategy for the multiple-phrase visual grounding task. In particular, we introduce a modular graph neural network to compute context-aware representations of phrases and object proposals respectively via message propagation, followed by a graph-based matching module to generate globally consistent localization of grounding phrases. We train the entire graph neural network jointly in a two-stage strategy and evaluate it on the Flickr30K Entities benchmark. Extensive experiments show that our method outperforms the prior state of the arts by a sizable margin, evidencing the efficacy of our grounding framework. Code is available at https://github.com/youngfly11/LCMCG-PyTorch.
Ultrafast charging in a two-photon Dicke quantum battery

We consider a collection of two level systems, such as qubits, embedded into a microwave cavity as a promising candidate for the realization of high power quantum batteries. In this perspective, the possibility to design devices where the conventional single-photon coupling is suppressed and the dominant interaction is mediated by two-photon processes is investigated, opening the way to an even further enhancement of the charging performance. By solving a Dicke model with both single- and two-photon coupling we determine the range of parameters where the latter unconventional interaction dominates the dynamics of the system leading to better performances both in the charging times and average charging power of the QB compared to the single-photon case. In addition, the scaling of the maximum stored energy, fluctuations and charging power with the finite number of qubits $N$ is inspected. While the energy and fluctuations scale linearly with $N$, the quadratic growth of the average power leads to a relevant improvement of the charging performance of quantum batteries based on this scheme with respect to the purely single-photon coupling case. Moreover, it is shown that the charging process is progressively faster by increasing the coupling from the weak to the ultra-strong regime.
The Multilingual Amazon Reviews Corpus

We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification.The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019.Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID, and the coarse-grained product category (e.g., 'books', 'appliances', etc.)The corpus is balanced across the 5 possible star ratings, so each rating constitutes 20% of the reviews in each language.For each language, there are 200,000, 5,000, and 5,000 reviews in the training, development, and test sets, respectively.We report baseline results for supervised text classification and zero-shot crosslingual transfer learning by fine-tuning a multilingual BERT model on reviews data.We propose the use of mean absolute error (MAE) instead of classification accuracy for this task, since MAE accounts for the ordinal nature of the ratings.