Reviewerly/exHarmony huggingface dataset:
Faster Single Model Vigilance Detection Based on Deep Learning

Various reports have shown that the rate of road traffic accidents has increased due to reduced driver vigilance. Therefore, an accurate estimation of the driver's alertness status plays an important part. To estimate vigilance, we adopt a novel strategy that is a deep autoencoder with subnetwork nodes (DAE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SN</sub> ). The proposed network model is designed not only for sparse representation but also for dimension reduction. Some hidden layers are not calculated by randomly acquired, but by replacement technologies. Unlike the traditional electrooculogram (EOG) signals, the forehead EOG (EOG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> ) signals are collected through forehead electrodes that do not have to surround the eyes, which has a convenient and effective practical application. The root-mean-square error (RMSE) and correlation coefficient (COR) while separately using three EOG <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</sub> features improved to 0.11/0.79, 0.10/0.83, and 0.11/0.80, respectively. Implemented in an experimental environment, percentage of eye closure over time is calculated in real time through SMI eye-tracking-glasses, up to 120 frames/s. In addition, the time to extract features from the raw signal and display the prediction is only 34 ms, that is the level of the driver's fatigue can be detected quickly. The experimental study shows that the proposed model for vigilance analysis has better robustness and learning capability.
Sonum: Software-Defined Synergetic Sampling Approach and Optimal Network Utilization Mechanism for Long Flow in a Data Center Network

Long flow detection and load balancing are crucial techniques for data center running and management. However, both of them have been independently studied in previous studies. In this paper, we propose a complete solution called Sonum, which can complete long flow detection and scheduling at the same time. Sonum consists of a software-defined synergetic sampling approach and an optimal network utilization mechanism. Sonum detects long flows through consolidating and processing sampling information from multiple switches. Compared with the existing prime solution, the missed detection rate of Sonum is reduced by 2.3%–5.1%. After obtaining the long flow information, Sonum minimizes the potential packet loss rate as the optimization target and then translates load balancing into an optimization problem of arranging a minimum packet loss path for long flows. This paper also introduces a heuristic algorithm for solving this optimization problem. The experimental results show that Sonum outperforms ECMP and Hedera in terms of network throughput and flow completion time.
Explicitation in children’s literature translated from English to Chinese: a corpus-based study of personal pronouns

There is substantial empirical evidence that translated texts demonstrate increased explicitness of lexicogrammatical encoding (or explicitation) compared to both source texts and non-translated texts in the target language. This increased explicitness has been ascribed to a number of causes, including source-language transfer or cross-linguistic priming, cognitive complexity or effort, and conservatism or risk aversion. This study investigates the occurrence of and the proposed reasons for the increased explicitness of translation, focusing on translated Chinese children's literature as a test case. Quantitative corpus-linguistic methods are used to analyse the frequency of personal pronouns (as an operationalisation of lexicogrammatical explicitness) in a custom-built comparable corpus of translated and non-translated Chinese children's literature. Qualitative analysis is used to explore the potential reasons for the differences in explicitness between these two subcorpora. The findings show that personal pronouns are more frequently used in Chinese children's literature translated from English, compared to non-translated Chinese children's books. However, this tendency does not play out across all the individual personal pronouns, suggesting that cross-linguistic influence or the 'shining through' of the source language is at the root of this increased explicitness.
Force-Directed Graph Layouts by Edge Sampling

Recent work shows that sampling algorithms can be an effective tool for graph visualization. This paper extends prior work by applying edge sampling algorithms to speed up the spring force calculation in force-directed graph layout algorithms. An experiment on 72 graphs finds that some sampling algorithms achieve comparable quality as no sampling. This result is confirmed with visualizations of the graph layout results. However, runtime improvements are small, especially for graphs with 10,000 vertices or fewer, indicating that the runtime savings might not be worth the risk to layout quality. Therefore, this paper suggests that accurate spring forces may be more important to force-directed graph layout algorithms than accurate electric forces. A copy of this paper plus the code and data to reproduce the results are available at https://osf.io/4ja29/.
Design Technology for Scalable and Robust Photonic Integrated Circuits: Invited Paper

Photonic integrated circuit (PIC), as a promising alternative to traditional CMOS circuit, has demonstrated the potential to accomplish on-chip optical signal transmission and computations in ultra-high speed and/or low power consumption. One of the critical challenges of PIC, however, is that its scalability and robustness are limited by cascaded optical power loss and noise error. In this paper, we analyze the scalability and noise robustness challenges facing photonic integrated circuits, for two representative PIC applications: logic computing and neural networks. Automated design algorithms and learning methodologies are proposed to resolve these issues.
Topology-Based Spectral Sparsification

Graph sampling is often used to reduce a large graph, with challenges to ensure the sample is representative of the original graph. Spectral sparsification is a related concept that creates a sparsified version of graphs that preserves the spectrum of the original graph. We present TSS, a sampling method combining spectral sparsification with topology-based decomposition of graphs. TSS aims to improve the runtime efficiency of spectral sparsification-based sampling through a Divide-and-Conquer approach using topology-based decomposition and combine it with the superior sampling quality spectral sparsification-based sampling offers over stochastic sampling. We also present DTSS, the distributed version of TSS, aimed for further runtime gains over sequential TSS. Experiments verify that TSS produces samples of the same quality as spectral sparsification-based sampling while attaining significant runtime improvements of up to 60% on real world datasets. DTSS on 5 servers runs up to another 80% faster compared to TSS.
An Intelligent Approach for a Two-robot Team to Perform a Cooperative Task

Complex future space missions will increasingly rely on human-robot or robot-robot collaboration, which requires the robots to have intelligent capabilities. This paper introduces a decentralized approach of two independent robots to cooperatively accomplish a common task. The task of the robot team is to transport a payload to outside a room through a narrow door. There is no pre-programmed motion trajectory for the two robots to follow and no communication between the robots which means each robot does not know how the other robot will react at any instant. The only information available to each robot is its own position and the pose of the payload. Genetic Fuzzy System (GFS) is employed to train the controllers of individual robots. The kinematics and dynamics models are considered and simulated in MATLAB environment, which have shown that the proposed intelligent control method can be trained to control the two individual robots to successfully achieve their common goal. To demonstrate the effectiveness of this approach, the robot team is trained with a set of scenarios and then verified with an extensive test set.
Fast Approximation of Frequent<i>k</i>-Mers and Applications to Metagenomics

Estimating the abundances of all k-mers in a set of biological sequences is a fundamental and challenging problem with many applications in biological analysis. Although several methods have been designed for the exact or approximate solution of this problem, they all require to process the entire data set, which can be extremely expensive for high-throughput sequencing data sets. Although in some applications it is crucial to estimate all k-mers and their abundances, in other situations it may be sufficient to report only frequent k-mers, which appear with relatively high frequency in a data set. This is the case, for example, in the computation of k-mers' abundance-based distances among data sets of reads, commonly used in metagenomic analyses. In this study, we develop, analyze, and test a sampling-based approach, called Sampling Algorithm for K-mErs approxIMAtion (SAKEIMA), to approximate the frequent k-mers and their frequencies in a high-throughput sequencing data set while providing rigorous guarantees on the quality of the approximation. SAKEIMA employs an advanced sampling scheme and we show how the characterization of the Vapnik–Chervonenkis dimension, a core concept from statistical learning theory, of a properly defined set of functions leads to practical bounds on the sample size required for a rigorous approximation. Our experimental evaluation shows that SAKEIMA allows to rigorously approximate frequent k-mers by processing only a fraction of a data set and that the frequencies estimated by SAKEIMA lead to accurate estimates of k-mer-based distances between high-throughput sequencing data sets. Overall, SAKEIMA is an efficient and rigorous tool to estimate k-mers' abundances providing significant speedups in the analysis of large sequencing data sets.
CCE: An ensemble architecture based on coupled ANN for solving multiclass problems

The resolution of multiclass classification problems has been usually addressed by using a "divide and conquer" strategy that splits the original problem into several binary subproblems. This approach is mandatory when the learning algorithm has been designed to solve binary problems and a multiclass version cannot be devised. Artificial Neural Networks, ANN, are binary learning models whose extension to multiclass problems is rather straightforward by using the standard 1-out-of N codification of the classes. However, the use of a single ANN can be inefficient in terms of accuracy and computational complexity when the data set is large, or the number of classes is high. In this work, we exhaustively describe CCE, a new classifier ensemble based on ANN. Each member of this new ensemble is a couple of multiclass ANN's. Each ANN is trained using different subsets of the dataset ensuring these subsets to be disjoint. This new approach allows to combine the benefits of the divide and conquer methodology, with the use of multiclass ANNs and with the combination of individual classification modules that give a complete answer to the addressed problem. The combination of these elements results in a classifier ensemble in which the diversity of the base classifiers provides high accuracy values. Moreover, the use of couples of ANN proves to be tolerant to labeling noise and computationally efficient. The performance of CCE has been tested on various datasets and the results show the higher performance of this approach with respect to other used classification systems.
Theory and applications of linearized multivariate skew polynomials

In this work, linearized multivariate skew polynomials over division rings are introduced. Such polynomials are right linear over the corresponding centralizer and generalize linearized polynomial rings over finite fields, group rings or differential polynomial rings. Their natural evaluation is connected to the remainder-based evaluation of free multivariate skew polynomials. It is shown that P-independent sets are those given by right linearly independent sets when partitioned into conjugacy classes. Hence finitely generated P-closed sets correspond to lists of finite-dimensional right vector spaces, extending Lam and Leroy's results on univariate skew polynomials. It is also shown that products of free multivariate skew polynomials translate into coordinate-wise compositions of linearized multivariate skew polynomials, which in turn translate into matrix products over the corresponding centralizers. Later, linearized multivariate Vandermonde matrices are introduced, which generalize multivariate Vandermonde, Moore and Wronskian matrices. The previous results explicitly give their ranks in general. P-Galois extensions of division rings are then introduced, which generalize classical (finite) Galois extensions. Three Galois-theoretic results are generalized to such extensions: Artin's theorem on extension degrees, the Galois correspondence and Hilbert's Theorem 90.
A semantic approach for timeseries data fusion

The data deluge following the rise of Internet of Things contributes towards the creation of non-reusable data silos. Especially in the environmental sciences domain, syntactic and semantic heterogeneity hinders data re-usability as most times manual labour and domain expertise is required. Both the different syntaxes under which environmental timeseries are formatted and the implicit semantics which are used to describe them contribute to this end. Usually, the real meaning of data is obscured in a combination of short data labels, titles and various value codes, that require domain or institutional knowledge to decipher. The FAIR data principles for scientific data sharing are stewardship offer a framework based on community-adopted metadata. In this work, we present the Environmental Data Acquisition Module (EDAM) which focuses on data interoperability and reuse, and deals with syntactic and semantic heterogeneity using a template approach. Data curators draft templates to describe in an abstract fashion the syntax of the timeseries datasets they want to acquire or disseminate. They complement each template with a metadata file, which is used to annotate observables and their properties (including physical quantities and units of measurement) with terms from an ontology. EDAM employs a reasoner to infer compatibility among syntactically and semantically heterogeneous datasets, and enables timeseries, format and units of measurement transformation on-the-fly. Our approach utilizes a local ontology to store metadata about datasets, which enables EDAM to acquire and transform datasets which were originally stored with different semantics and syntaxes. We demonstrate EDAM in a case study where we transform meteorological input files of four agricultural models. Our approach, allows to cut across environmental data silos and facilitate timeseries reusability, as it enables users to (a) discover datasets in other formats, (b) transform them and (c) reuse them in their scientific workflows. This directly contributes to the toolshed for FAIR data management in environmental sciences. EDAM implementation has been released under an open-source license.
Ostrom Amongst the Machines: Blockchain as a Knowledge Commons

Blockchains improve on previous ledgers on several margins, including transparency, immutability, and openness. Though cryptocurrency tokens such as Bitcoin and nonfungible tokens (NFTs) are now part of the popular lexicon, institutional analysis of blockchains is just beginning to blossom. We add to the burgeoning institutional analysis of blockchains and their governance by considering novel insights from Hayekian and Ostromian perspectives on blockchain governance. We contend that concept of taxis is especially useful to describe the origins of any individual blockchain and that the concept of spontaneous order is uniquely suited to analyze the competitive features of blockchains. A novel insight is that blockchains may arise without government design, but they are centralized entities. Ostromian analysis is especially useful to characterize blockchains as knowledge commons and to provide an empirical method to analyze how rules internal to and external to blockchains combine to explain the performance of any individual blockchain network.
The expressivity and training of deep neural networks: Toward the edge of chaos?

Expressivity is one of the most significant issues in assessing neural networks. In this paper, we provide a quantitative analysis of the expressivity for the deep neural network (DNN) from its dynamic model, where the Hilbert space is employed to analyze the convergence and criticality. We study the feature mapping of several widely used activation functions obtained by Hermite polynomials, and find sharp declines or even saddle points in the feature space, which stagnate the information transfer in DNNs. We then present a new activation function design based on the Hermite polynomials for better utilization of spatial representation. Moreover, we analyze the information transfer of DNNs, emphasizing the convergence problem caused by the mismatch between input and topological structure. We also study the effects of input perturbations and regularization operators on critical expressivity. Our theoretical analysis reveals that DNNs use spatial domains for information representation and evolve to the edge of chaos as depth increases. In actual training, whether a particular network can ultimately arrive the edge of chaos depends on its ability to overcome convergence and pass information to the required network depth. Finally, we demonstrate the empirical performance of the proposed hypothesis via multivariate time series prediction and image classification examples.
Improving Quality of the Multiclass SVM Classification Based on the Feature Engineering

The SVM classifier is effective in solving binary classification problems. However, in practical problems of classification, there are often cases of the presence of more than two classes of objects in the original data set. This work is devoted to the study of approaches to improving the quality of the SVM classification based on the engineering of new features of objects in the original data set using tools of such well-known multiclass classification algorithms as the Decision Tree, Random Forest and AdaBoost.
Directionality and Momentum of Water in Weather: A Morphosemantic Study of Conceptualisation Based on Hantology

We present in this paper a study of the conceptualisation of meteorological events involving water in Chinese based on Hantology, a SUMO-based ontology of Chinese orthography. Our comprehensive investigation of the morphosemantic behaviours of these weather words in both Mandarin and Sinitic languages reveals that they are predicted by the directionality and momentum of their formation and movement. We studied events involving water in both liquid and solid forms: such as rain, snow, hail, fog, dew and frost. They share the radical 雨, which can be linked to two SUMO nodes according to Hantology. This ontological bifurcation can be shown to bring about not only the diversity of direction expressions referring to these words for water, but also the differences of semantic features and PoS between them in Archaic Chinese. Moreover, the momentum of different water forms is proposed to be the physical basis for the differences of PoS, semantic features and node linking.
Identity-based encryption with leakage-amplified chosen-ciphertext attacks security

Alwen et al. (EUROCRYPT 2010) proposed the formal definition of identity-based hash proof system (IB-HPS). As an independent interest, they showed that a leakage-resilient identity-based encryption (LR-IBE) scheme can be created based on an IB-HPS. That is, a generic construction of LR-IBE scheme with the chosen-plaintext attacks (CPA) security be designed from the IB-HPS. However, in order to further improve the practicability of identity-based encryption (IBE) system, an IBE scheme must have the chosen-ciphertext attacks (CCA) security. Hence, in this paper, a generic construction of LR-IBE scheme with the CCA security is proposed from the IB-HPS. In additional, to design an LR-IBE schemes with an arbitrarily length leakage parameter, we design a novel generic construction of leakage amplified IBE scheme with CCA security, where we only increase the size of private key proportionally, while do not change all other parameters, and the upper bound of permitted leakage can be flexibly controlled by changing the leakage-size parameter. In other words, the length of allowed leakage of our generic construction is determined by the leakage requirements of actual applications.
A Heuristic-Based Private Bitcoin Payment Network Formation Using Off-Chain Links

While Bitcoin dominates the market for cryptocurrencies, its use in micropayments is still a challenge due to its long transaction validation times and high fees. Recently, the concept of off-chain payments is introduced that led to the idea of establishing a payment network called Lightning Network (LN). Off-chain links provide the ability to do transactions without writing to Blockchain. However, LN's design still favors fees and is creating hub nodes that defeat the purpose of Blockchain. In addition, it is still not reliable as not all the transactions are guaranteed to be transmitted to their destinations. If current retailers would like to use it, these problems might hinder its adoption. To address this issue, in this paper, we advocate creating a private payment network among a given set of retailers that will serve their business needs, just like the idea of private Blockchains. The goal is to build a pure peer-to-peer topology that will eliminate the need for relays and increase the robustness of payments. Using off-chain links as edges and retailers as nodes, the problem is formulated as a multi-flow commodity problem where transactions represent the commodities from various sources to destinations. As the multi-flow commodity problem is NP-Complete, we propose a heuristic approach that utilizes Dijkstra's shortest path algorithm in a dynamic way by updating the edge weights when new payment paths are to be found. The order of transactions is randomized to provide fairness among the retailers. The evaluations indicate that the proposed heuristic comes close to an optimal solution while providing scalability and user privacy.
Debiasing Word Embeddings from Sentiment Associations in Names

Word embeddings, trained through models like skip-gram, have shown to be prone to capturing the biases from the training corpus, e.g. gender bias. Such biases are unwanted as they spill in downstream tasks, thus, leading to discriminatory behavior.
New Filtering Scheme Based on Term Weighting to Improve Object Based Opinion Mining on Tourism Product Reviews

Reviews are an essential thing in tourism industry. Opinion mining used for processing a massive amount of review data, so it can be more useful for the industry. The utilization of filtering can improve the feature extraction result from object based on opinion mining and can improve opinion classification result generally. However, there is no proven method yet to develop filter data automatically. This work applies several term weighting methods such as TF-IDF mTFIDF and BM25 to develop filter data automatically. The result from this research is the best term weighting method for developing filter data, that can improve the feature extraction and opinion mining relatively. TFIDF become the best term weighting method applied for filter data combined with the most frequent objects, The accuracy is 37.98%, the precision is 50.69%, the recall is 44,28%, and F-measure 47.27% for hotel data. Meanwhile, for restaurant data, the accuracy is 37.98%, precision is 50.69%, recall is 44.28%, and F-measure 47.27%.