Reviewerly/exHarmony huggingface dataset:
Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm

To provide an available diagnostic model for diagnosing coronary atherosclerotic heart disease to provide an auxiliary function for doctors, we proposed a new evolutionary classification model in this paper. The core of the prediction model is a kernel extreme learning machine (KELM) optimized by an improved salp swarm algorithm (SSA). To get a better subset of parameters and features, the space transformation mechanism is introduced in the optimization core to improve SSA for obtaining an optimal KELM model. The KELM model for the diagnosis of coronary atherosclerotic heart disease (STSSA-KELM) is developed based on the optimal parameters and a subset of features. In the experiment, STSSA-KELM is compared with some widely adopted machine learning methods (MLM) in coronary atherosclerotic heart disease prediction. The experimental results show that STSSA-KELM can realize excellent classification performance and more robust stability under four indications. We also compare the convergence of STSSA-KELM with other MLM; the STSSA-KELM model has demonstrated a higher classification performance. Therefore, the STSSA-KELM model can effectively help doctors to diagnose coronary heart disease.
Nowcasting solar irradiance for effective solar power plants operation and smart grid management

Abstract The equilibrium of the electricity grid is very often changing due to demand fluctuations and it is further threatened by the fluctuation in the output of solar power plants. With the aim of increasing the share of solar electricity in the energy mix, near real-time forecasting of the solar power production represents a key issue for smart-control of the grid. Since the accuracy of forecasting of the performance of a solar plant is strongly conditioned by the accuracy of forecasting of the solar resource, this chapter deals with solar irradiance forecasting. First, a brief introduction to solar radiation and its inherent variability is presented. Then, the performance of solar irradiance forecasting models at time horizons within an hour is analyzed in-depth. The present limitations and perspectives are discussed. A survey on the metrics used to quantify the forecasts accuracy is also presented. As a case study, the accuracies of three models, different in nature and complexity, are evaluated from different perspectives: (1) forecast time horizon, (2) forecast precision, and (3) influence of the solar irradiance variability on forecast accuracy. The study is conducted with high-quality radiometric data recorded at high frequency on the Solar Platform of the West University of Timisoara, Romania.
Blockchain-based Information Sharing: A New Opportunity for Construction Supply Chains

In the supply chain, information sharing is critical for facilitating decision making, accelerating the schedule, reducing costs, and enhancing the final quality of the project. However, information sharing is challenging in the construction supply chain when information asymmetry could help certain individuals avoid liability or help others maximize their profits by using substandard materials. In this environment, the stakeholders may not truly trust one another and may be reluctant to share complete information. Even though several studies have emphasized the importance of trust for information sharing, few studies have focused on promoting trust and verifying information using technology. The purpose of this paper is to examine the potential of Blockchain technology for establishing a trust-enhanced environment with distributed trust that will benefit the construction supply chain. This paper presents three logical scenarios for the application of Blockchain technology in information-sharing systems in order to demonstrate that the technology helps establish a trust-enhanced environment for reliable information sharing while eliminating counterfeiting and reducing bullwhip effect. These scenarios will demonstrate the value of Blockchain-based information sharing in the construction supply chain and possibly provide direction for future research with greater depth and specificity.
Possibility-Based Robust Control for Fuzzy Mechanical Systems

This article proposes a new robust control design framework for uncertain mechanical systems, which may be fully actuated or underactuated. The uncertainty is (possibly fast) time varying, which lies in prescribed fuzzy sets (hence fuzzy mechanical systems) and may be unbounded. The control goal is formulated as servo constraints (hence constraint-following control), which may be holonomic or nonholonomic. We introduce the possibility theory into the Lyapunov stability analysis (LSA), proposing possibility-based LSA (PBLSA), which allows a maximum failure possibility (generally small) prescribed by designers. It can be viewed as a generalization of the conventional LSA, and the resultant performance is interpreted in the context of possibility. By the PBLSA, a class of robust constraint-following controls that is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> IF–THEN heuristic rules based is proposed, which renders approximate constraint following for the system performance with a prescribed maximal failure possibility. Optimal design of a control parameter considering both system performance and control cost is investigated. The benefits of the proposed design framework are discussed and simulations on two applications are given for demonstrations.
TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams

Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.
Effectiveness of e-learning implementation as a distance learning strategy during coronavirus disease (covid-19) pandemic

Abstract . This study aims to determine the effectiveness of the implementation of elearning as a distance learning strategy during pandemic coronavirus disease (Covid-19). The effectiveness will be examined from the results of previous studies through metaanalysis methods Meta-analysis is the study of several research results in a similar study. The unit of analysis in the study of meta-analysis of these are documents written are taken from the journals the results of the research are purposive in accordance with the theme of research on the implementation of e-learning in the process of learning. The primary research instrument is there on the researchers themselves aided by the guidance documentation. Data analysis that was carried out was the analysis of quantitative and qualitative data. Quantitative analysis is used to analyze data in the form of numbers and the percentage of e-learning effectiveness. Qualitative data analysis is used for the study of data in the form of descriptive data about the implementation of e-learning. The results showed that the implementation of e-learning was effective as a distance learning strategy during coronavirus disease (Covid-19) pandemic. Keywords: E-learning, distance learning, Coronavirus Disease (Covid-19)
Cybercriminals and Coronavirus cybercrimes in Nigeria, the United States of America and the United Kingdom: cyber hygiene and preventive enforcement measures

There seems to be no lockdown for cybercriminals who are capitalizing on the global lockdown to perpetrate cyber coronavirus crimes. Qualitatively, this paper examines these crimes, their peculiarities, and how they can be curtailed. Although the United States of America (US) and the United Kingdom (UK) have put in place cyber hygiene and preventive enforcement measures to curtail the activities of cybercriminals in cyberspace, the same cannot be said of Nigeria. Arguably, cybercrime institutions in Nigeria lack adequate capacity building, professional competence, and inter-agency cooperation concerning cyber coronavirus crimes. Consequently, it calls for the adaptation of the US and UK measures to protect cybercitizens.
Background dominant colors extraction method based on color image quick fuzzy c-means clustering algorithm

A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design. This paper proposes a Color Image Quick Fuzzy C-Means (CIQFCM) clustering algorithm based on clustering spatial mapping. First, the clustering sample space was mapped from the image pixels to the quantized color space, and several methods were adopted to compress the amount of clustering samples. Then, an improved pedigree clustering algorithm was applied to obtain the initial class centers. Finally, CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image. After theoretical analysis of the effect and efficiency of the CIQFCM algorithm, several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm. The results indicated that the value of quantization intervals should be set to 4, and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect. In addition, as the image size increased from 128 × 128 to 1024 × 1024, the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times, which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.
QCB: Efficient Quantum-Secure Authenticated Encryption

It was long thought that symmetric cryptography was only mildly affected by quantum attacks, and that doubling the key length was sufficient to restore security. However, recent works have shown that Simon's quantum period finding algorithm breaks a large number of MAC and authenticated encryption algorithms when the adversary can query the MAC/encryption oracle with a quantum superposition of messages. In particular, the OCB authenticated encryption mode is broken in this setting, and no quantum-secure mode is known with the same efficiency (rate-one and parallelizable).
Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams

Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.
That Sounds Familiar: An Analysis of Phonetic Representations Transfer Across Languages

Only a handful of the world’s languages are abundant with the resources that enable practical applications of speech processing technologies. One of the methods to overcome this problem is to use the resources existing in other languages to train a multilingual automatic speech recognition (ASR) model, which, intuitively, should learn some universal phonetic representations. In this work, we focus on gaining a deeper understanding of how general these representations might be, and how individual phones are getting improved in a multilingual setting. To that end, we select a phonetically diverse set of languages, and perform a series of monolingual, multilingual and crosslingual (zero-shot) experiments. The ASR is trained to recognize the International Phonetic Alphabet (IPA) token sequences. We observe significant improvements across all languages in the multilingual setting, and stark degradation in the crosslingual setting, where the model, among other errors, considers Javanese as a tone language. Notably, as little as 10 hours of the target language training data tremendously reduces ASR error rates. Our analysis uncovered that even the phones that are unique to a single language can benefit greatly from adding training data from other languages — an encouraging result for the low-resource speech community.
Floating-Point Calculations on a Quantum Annealer: Division and Matrix Inversion

Systems of linear equations are employed almost universally across a wide range of disciplines, from physics and engineering to biology, chemistry and statistics. Traditional solution methods such as Gaussian elimination become very time consuming for large matrices, and more efficient computational methods are desired. In the twilight of Moore\rq{}s Law, quantum computing is perhaps the most direct path out of the darkness. There are two complementary paradigms for quantum computing, namely, gated systems and quantum annealers. In this paper, we express floating point operations such as division and matrix inversion in terms of a {\em quadratic unconstrained binary optimization} (QUBO) problem, a formulation that is ideal for a quantum annealer. We first address floating point division, and then move on to matrix inversion. We provide a general algorithm for any number of dimensions, but we provide results from the D-Wave quantum anneler for 2x2 and 3x3 general matrices. Our algorithm scales to very large numbers of linear equations. We should also mention that our algorithm provides the full solution the the matrix problem, while HHL provides only an expectation value.
Temporal Models for History-Aware Explainability

On one hand, there has been a growing interest towards the application of AI-based learning and evolutionary programming for self-adaptation under uncertainty. On the other hand, self-explanation is one of the self-* properties that has been neglected. This is paradoxical as self-explanation is inevitably needed when using such techniques. In this paper, we argue that a self-adaptive autonomous system (SAS) needs an infrastructure and capabilities to be able to look at its own history to explain and reason why the system has reached its current state. The infrastructure and capabilities need to be built based on the right conceptual models in such a way that the system's history can be stored, queried to be used in the context of the decision-making algorithms.
Socio-Educational Impact of Augmented Reality (AR) in Sustainable Learning Ecologies: A Semantic Modeling Approach

The current educational processes must be supported by sustainable learning ecologies, where the digitalization of training is enhanced. In this area, augmented reality (AR) plays an important role. It is a technology that for certain educational goals can facilitate the understanding of the course contents and increase the motivation and interest of the student. This research aims to measure the socio-educational impact that AR presents in the teaching processes of university students of social education. These are professionals in training dedicated, precisely, to social and educational actions. In order to reach the pursued objective, an exploratory study of qualitative and descriptive nature was approached from a methodological conception based on the action-research. The study was carried out during three academic courses and consisted of an experience of integration of AR in the classroom in order to determine which applications, and advantages or limitations of a socio-educational nature, were perceived by the participants in that process. The documents generated were analyzed mainly using semantic methods. The main results were that AR is positive overall for its use in learning processes and, specifically in its field, optimal for the development of professional skills within the framework of social education. As to benefits, it highlighted the strengthening of learning dynamism, motivation, and interaction among students; as to limitations, these included the fact that it is not an accessible technology, the need for previous training and that it can reduce sociability. It was also stressed that it can be a useful resource in many areas of social education (childhood and adolescence, gerontology, drug addiction, etc.). In general, therefore, it can be concluded that the use of AR in university training, in this area of knowledge, allows content to be more dynamic and real in a sustainable way, thus achieving a highly transferable and motivating path to develop content and competencies.
Fˆ2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax

Despite recent advances in neural text generation, encoding the rich diversity in human language remains elusive.We argue that the sub-optimal text generation is mainly attributable to the imbalanced token distribution, which particularly misdirects the learning model when trained with the maximumlikelihood objective.As a simple yet effective remedy, we propose two novel methods, F 2 -Softmax and MefMax, for a balanced training even with the skewed frequency distribution.MefMax assigns tokens uniquely to frequency classes, trying to group tokens with similar frequencies and equalize frequency mass between the classes.F 2 -Softmax then decomposes a probability distribution of the target token into a product of two conditional probabilities of (i) frequency class, and (ii) token from the target frequency class.Models learn more uniform probability distributions because they are confined to subsets of vocabularies.Significant performance gains on seven relevant metrics suggest the supremacy of our approach in improving not only the diversity but also the quality of generated texts.
Reversible video data hiding using zero QDCT coefficient-pairs

H.264/Advanced Video Coding (AVC) is one of the most commonly used video compression standard currently. In this paper, we propose a Reversible Data Hiding (RDH) method based on H.264/AVC videos. In the proposed method, the macroblocks with intra-frame 4 × 4 prediction modes in intra frames are first selected as embeddable blocks. Then, the last zero Quantized Discrete Cosine Transform (QDCT) coefficients in all 4 × 4 blocks of the embeddable macroblocks are paired. In the following, a modification mapping rule based on making full use of modification directions are given. Finally, each zero coefficient-pair is changed by combining the given mapping rule with the to-be-embedded information bits. Since most of last QDCT coefficients in all 4 × 4 blocks are zero and they are located in high frequency area. Therefore, the proposed method can obtain high embedding capacity and low distortion.
Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics

In drug development, protocols define how clinical trials are conducted, and are therefore of paramount importance.They contain key patient-, investigator-, medication-, and studyrelated information, often elaborated in different sections in the protocol texts.Granularlevel parsing on large quantity of existing protocols can accelerate clinical trial design and provide actionable insights into trial optimization.Here, we report our progresses in using deep learning NLP algorithms to enable automated protocol analytics.In particular, we combined a pre-trained BERT transformer model with joint-learning strategies to simultaneously identify clinically relevant entities (i.e.Named Entity Recognition) and extract the syntactic relations between these entities (i.e.Relation Extraction) from the eligibility criteria section in protocol texts.When comparing to standalone NER and RE models, our joint-learning strategy can effectively improve the performance of RE task while retaining similarly high NER performance, likely due to the synergy of optimizing toward both tasks' objectives via shared parameters.The derived NLP model provides an end-to-end solution to convert unstructured protocol texts into structured data source, which will be embedded into a comprehensive clinical analytics workflow for downstream trial design missions such like patient population extraction, patient enrollment rate estimation, and protocol amendment prediction.
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

We consider three important challenges in conference peer review: (i) reviewers maliciously attempting to get assigned to certain papers to provide positive reviews, possibly as part of quid-pro-quo arrangements with the authors; (ii) torpedo reviewing, where reviewers deliberately attempt to get assigned to certain papers that they dislike in order to reject them; (iii) reviewer de-anonymization on release of the similarities and the reviewer-assignment code. On the conceptual front, we identify connections between these three problems and present a framework that brings all these challenges under a common umbrella. We then present a (randomized) algorithm for reviewer assignment that can optimally solve the reviewer-assignment problem under any given constraints on the probability of assignment for any reviewer-paper pair. We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case. Finally, we experimentally evaluate our algorithms on datasets from past conferences, where we observe that they can limit the chance that any malicious reviewer gets assigned to their desired paper to 50% while producing assignments with over 90% of the total optimal similarity. Our algorithms still achieve this similarity while also preventing reviewers with close associations from being assigned to the same paper.
SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics

We present SacreROUGE, an open-source library for using and developing summarization evaluation metrics. 1 SacreROUGE removes many obstacles that researchers face when using or developing metrics: (1) The library provides Python wrappers around the official implementations of existing evaluation metrics so they share a common, easy-to-use interface;(2) it provides functionality to evaluate how well any metric implemented in the library correlates to human-annotated judgments, so no additional code needs to be written for a new evaluation metric; and (3) it includes scripts for loading datasets that contain human judgments so they can easily be used for evaluation.This work describes the design of the library, including the core Metric interface, the command-line API for evaluating summarization models and metrics, and the scripts to load and reformat publicly available datasets.The development of SacreROUGE is ongoing and open to contributions from the community.