Reviewerly/exHarmony huggingface dataset:
Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory

Alarm messages for grid monitoring are an important way to supervise the operation of power grids. Since the use of alarm messages is increasing exponentially due to the continuous expansion of the scale of power grids, a processing method for alarm messages based on statistics is proposed in this study. Entropy theory in information theory is introduced into the calculation of information value in power-grid alarming. By means of multiple entropy definitions, an evaluation index system for information value is constructed. Based on the analytic hierarchy process (AHP), various alarm-message entropies are used as indices to comprehensively assess the information value and level of each alarm message. Finally, an example is given to illustrate the effectiveness and practicality of the proposed method. This study provides a new idea for the intelligent classification of alarm messages.
Exploring the User Engagement Factors in Computer Mediated Communication

Abstract User engagement can be defined as the perception of the user to qualify the experience towards certain application, which focus on the positive aspects of the interaction through Internet in the context of the desire to use it continuously and for longer time. It is fundamental concept in the design of online applications regardless of the platform, driven by the observation that successful applications are not only used but those that work. However, user engagement in the technology advancement is a paradox phenomenon, as they recognize the potentiality but reluctant to adopt or they realize its use to solve problem but prefer the other solution for longer of time. The usual ways to evaluate them can be through self-report measures, observational methods, speech analysis or web analytics. These methods represent different compensations in term of configuration, the size of object and the scale of data to be collected. For example, some study might find detail and deep analysis but they are limited in term of generalizability, while the other might found out resourceful but denies the user reasoning and the context. During this millennial, the diffusion of innovation became the acceptable theory that majority academician and practical expert use to explain the phenomenon of the reason and factor to adopt certain product. Therefore, due to the assumption of several factors such as technology advancement and paradigm shift, this study want to explore current situation in the user engagement factors, which focused to computer mediated communication.
Fast and Accurate Approximation of the Full Conditional for Gamma Shape Parameters

The gamma distribution arises frequently in Bayesian models, but there is not an easy-to-use conjugate prior for the shape parameter of a gamma. This inconvenience is usually dealt with by using either Metropolis–Hastings moves, rejection sampling methods, or numerical integration. However, in models with a large number of shape parameters, these existing methods are slower or more complicated than one would like, making them burdensome in practice. It turns out that the full conditional distribution of the gamma shape parameter is well approximated by a gamma distribution, even for small sample sizes, when the prior on the shape parameter is also a gamma distribution. This article introduces a quick and easy algorithm for finding a gamma distribution that approximates the full conditional distribution of the shape parameter. We empirically demonstrate the speed and accuracy of the approximation across a wide range of conditions. If exactness is required, the approximation can be used as a proposal distribution for Metropolis–Hastings. Supplementary material for this article is available online.
StressedNets: Efficient feature representations via stress-induced evolutionary synthesis of deep neural networks

The computational complexity of leveraging deep neural networks for extracting deep feature representations is a significant barrier to its widespread adoption. This is particularly a bottleneck for use in embedded devices and application such as self-driving cars. One promising strategy to addressing the complexity issue is the notion of evolutionary synthesis of deep neural networks. It was demonstrated that it successfully produces highly efficient deep neural networks while retaining modeling performance. Here, we further extend upon the evolutionary synthesis strategy for achieving efficient feature extraction. A stress-induced evolutionary synthesis framework is proposed where the stress signals are imposed upon the synapses of a deep neural network during training step. This process induces stress and steers the synthesis process towards the production of more efficient deep neural networks over successive generations. As a result, it improves model fidelity at a greater efficiency. Applying stress during the training phase helps a network to adopt itself for the changes which would happen at the evolution step. The proposed stress-induced evolutionary synthesis approach is evaluated on a variety of different deep neural network architectures (LeNet5, AlexNet, and YOLOv2), different tasks (object classification and object detection) to synthesize efficient StressedNets over multiple generations. Experimental results demonstrate the efficacy of the proposed framework to synthesize StressedNets with significant improvement in network architecture efficiency (e.g., 40 × for AlexNet and 33 × for YOLOv2). It is also shown the speed improvements by the synthesized networks (e.g., 5.5 × inference speed-up for YOLOv2 on an Nvidia Tegra X1 mobile processor).
Optimal graph Laplacian

This paper provides a construction method of the nearest graph Laplacian to a matrix identified from measurement data of graph Laplacian dynamics that include biochemical, synchronization, and multi-agent systems. We consider the case in which the network structure, i.e., the relationship between edges of a given graph, is known. A problem of finding the nearest graph Laplacian is formulated as a convex optimization problem. Thus, our problem can be solved using interior point methods. Moreover, we can also implement the alternating direction method of multipliers (ADMM) for our problem. However, the complexities of each iteration due to the use of interior point methods and ADMM are larger than O(n6) and O(n3), respectively, where n is the number of nodes of the network. That is, if n is large, interior point methods and ADMM cannot solve our problem within a practical time. To resolve this issue, we propose a simple and efficient algorithm with calculation complexity O(n2). The simulation experiments demonstrate that our method is useful to perform data-driven modeling of graph Laplacian dynamics.
Virtual private ledgers

Distributed ledgers allow us to replicate databases of records across mutually untrusted parties. The best known example of distributed ledger is perhaps the Bitcoin blockchain, which maintains a consistent history of financial transactions organized as a hashed chain of blocks. Distributed ledgers can be public, i.e., accessible by everyone, or private, i.e., accessible only by a given consortium of parties. In this paper, we explore the technological possibilities of applying Identity-Based Encryption and Attribute-Based Encryption to distributed ledgers. We introduce the novel concept of Virtual Private Ledger. A Virtual Private Ledger is a private distributed ledger embedded in a public cryptocurrency ledger by means of cryptography. A Virtual Private Ledger provides for the same confidentiality and integrity of a private distributed ledger, but without its high operational costs. In particular, nodes that maintain the ledger do not have to be always online to trust the order and the integrity of the records. We analytically show that Virtual Private Ledgers can be implemented over many existing cryptocurrency ledgers like Ethereum, EOS.IO, IOTA, XRP. Different cryptocurrencies lead to different trade-offs between the Virtual Private Ledger max record size, cost, validation time, and max consortium members.
On certain maximal hyperelliptic curves related to Chebyshev polynomials

We study hyperelliptic curves arising from Chebyshev polynomials. The aim of this paper is to characterize the pairs (q,d) such that the hyperelliptic curve C over a finite field Fq2 given by y2=φd(x) is maximal over the finite field Fq2 of cardinality q2. Here φd(x) denotes the Chebyshev polynomial of degree d. The same question is studied for the curves given by y2=(x±2)φd(x), and also for y2=(x2−4)φd(x). Our results generalize some of the statements in [12].
A Bi-directional Multiple Timescales LSTM Model for Grounding of Actions and Verbs

In this paper we present a neural architecture to learn a bi-directional mapping between actions and language. We implement a Multiple Timescale Long Short-Term Memory (MT-LSTM) network comprised of 7 layers with different timescale factors, to connect actions to language without explicitly learning an intermediate representation. Instead, the model self-organizes such representations at the level of a slow-varying latent layer, linking action branch and language branch at the center. We train the model in a bi-directional way, learning how to produce a sentence from a certain action sequence input and, simultaneously, how to generate an action sequence given a sentence as input. Furthermore we show this model preserves some of the generalization behaviour of Multiple Timescale Recurrent Neural Networks (MTRNN) in generating sentences and actions that were not explicitly trained. We compare this model with a number of different baseline models, confirming the importance of both the bi-directional training and the multiple timescales architecture. Finally, the network was evaluated on motor actions performed by an iCub robot and their corresponding letter-based description. The results of these experiments are presented at the end of the paper.
Effective quality metric for contrast-distorted images based on SVD

Proper contrast usually makes an object distinguishable, while contrast degradation will affect our understanding of the object. Many experiments found that the human visual system is more sensitive to contrast than to luminance in distinguishing an object. However, few studies have been conducted to assess the quality of contrast change effectively. In our study, we first reveal some characteristics of contrast-distorted images by analyzing image data. Based on these characteristics, we then present two novel and fast full-reference image quality metrics: FC-SVD for a full-reference model and RC-SVD for a reduced-reference image quality assessment model, both using singular value decomposition (SVD). The proposed methods are evaluated through four public contrast-related databases (TID2013, TID2008, CCID2014, and CSIQ). The results show that they have excellent performance in prediction accuracy and much lower computation complexity than other state-of-the-art image quality assessment methods.
A survey of 25 years of evaluation

Abstract Evaluation was not a thing when the first author was a graduate student in the late 1970s. There was an Artificial Intelligence (AI) boom then, but that boom was quickly followed by a bust and a long AI Winter. Charles Wayne restarted funding in the mid-1980s by emphasizing evaluation. No other sort of program could have been funded at the time, at least in America. His program was so successful that these days, shared tasks and leaderboards have become common place in speech and language (and Vision and Machine Learning). It is hard to remember that evaluation was a tough sell 25 years ago. That said, we may be a bit too satisfied with current state of the art. This paper will survey considerations from other fields such as reliability and validity from psychology and generalization from systems. There has been a trend for publications to report better and better numbers, but what do these numbers mean? Sometimes the numbers are too good to be true, and sometimes the truth is better than the numbers. It is one thing for an evaluation to fail to find a difference between man and machine, and quite another thing to pass the Turing Test. As Feynman said, “the first principle is that you must not fool yourself–and you are the easiest person to fool.”
On the existence of primitive completely normal bases of finite fields

Let Fq be the finite field of characteristic p with q elements and Fqn its extension of degree n. We prove that there exists a primitive element of Fqn that produces a completely normal basis of Fqn over Fq, provided that n=pℓm with (m,p)=1 and q>m.
Relative Depth Order Estimation Using Multi-Scale Densely Connected Convolutional Networks

We study the problem of estimating the relative depth order of point pairs in a monocular image. Recent advances mainly focus on using deep convolutional neural networks to learn and infer the ordinal information from multiple contextual information of the point pairs, such as global scene context, local contextual information, and the locations. However, it remains unclear how much each context contributes to the task. To address this, we first examine the contribution of each context cue to the performance in the context of depth order estimation. We find out that the local context surrounding the point pairs contributes the most, and the global scene context helps little. Based on the findings, we propose a simple method, using a multi-scale densely-connected network to tackle the task. Instead of learning the global structure, we dedicate to explore the local structure by learning to regress from the regions of multiple sizes around the point pairs. Moreover, we use the recent densely connected network to encourage the substantial feature reuse as well as deepen our network to boost the performance. We show in experiments that the results of our approach are on par with or better than the state-of-the-art methods with the benefit of using only a small number of training data.
Unifying Probabilistic Models for Time-frequency Analysis

In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts. They adapt to the incoming signal, quantify uncertainty, and measure correlation between the signal’s amplitude and phase information, making time domain resynthesis straightforward. However, these models are still not widely used since they come at a high computational cost, and because they are formulated in such a way that it can be difficult to interpret all the modelling assumptions. By showing their equivalence to Spectral Mixture Gaussian processes, we illuminate the underlying model assumptions and provide a general framework for constructing more complex models that better approximate real-world signals. Our interpretation makes it intuitive to inspect, compare, and alter the models since all prior knowledge is encoded in the Gaussian process kernel functions. We utilise a state space representation to perform efficient inference via Kalman smoothing, and we demonstrate how our interpretation allows for efficient parameter learning in the frequency domain.
Trajectory prediction of cylinders freely dropped into water in two dimensions based on state space model

Experiments show that the behavior of dropped cylindrical objects may be stochastic if small disturbances exist at the initial moment. An envelope of various trajectories can be obtained from experimental tests. In addition, numerical results also indicate that small changes of initial states, e.g., drop angle, rolling frequency, and the selection of drag coefficient, affect the trajectory greatly. If the initial state follows the Gaussian distribution,it can be considered as a nonlinear prediction problem. Then the Unscented Transformation (UT) method, as a deterministic sampling method, can be used to predict the trajectory. In this paper, we firstly present a systematic formulation of the cylinder's three-degree-of-freedom (3DOF) of motions as a state space model. Secondly, the UT method and the interval estimation method are employed to solve this prediction problem. Finally, the predicted trajectories will be compared with the experimental data. Meanwhile, two performance parameters are first proposed to evaluate the accuracy of predicted trajectory. Both parameters can be directly applied to the salvage and recovery of dropped objects in the offshore operations. In addition, the numerical results also show that the UT method is an efficient way to predict the trajectory of cylinders freely dropped into water.
Prediction with High Dimensional Regression Via Hierarchically Structured Gaussian Mixtures and Latent Variables

Summary We propose a hierarchical Gaussian locally linear mapping structured mixture model, named HGLLiM, to predict low dimensional responses based on high dimensional covariates when the associations between the responses and the covariates are non-linear. For tractability, HGLLiM adopts inverse regression to handle the high dimension and locally linear mappings to capture potentially non-linear relations. Data with similar associations are grouped together to form a cluster. A mixture is composed of several clusters following a hierarchical structure. This structure enables shared covariance matrices and latent factors across smaller clusters to limit the number of parameters to estimate. Moreover, HGLLiM adopts a robust estimation procedure for model stability. We use three real data sets to demonstrate different features of HGLLiM. With the face data set, HGLLiM shows ability to model non-linear relationships through mixtures. With the orange juice data set, we show that the prediction performance of HGLLiM is robust to the presence of outliers. Moreover, we demonstrate that HGLLiM is capable of handling large-scale complex data by using the data acquired from a magnetic resonance vascular fingerprinting study. These examples illustrate the wide applicability of HGLLiM to handle different aspects of a complex data structure in prediction.
A General Hybrid Clustering Technique

Here, we propose a clustering technique for general clustering problems including those that have nonconvex clusters. For a given desired number of clusters K, we use three stages to find clusters. The first stage uses a hybrid clustering technique to produce a series of clusterings of various sizes (randomly selected). The key step in this stage is to find a K-means clustering using Kℓ clusters where Kℓ≫K and then join these small clusters by using single linkage clustering. The second stage stabilizes the result of stage one by reclustering via the “membership matrix” under Hamming distance to generate a dendrogram. The third stage is to cut the dendrogram to get K* clusters where K*≥K and then prune back to K to give a final clustering. A variant on our technique also gives a reasonable estimate for KT, the true number of clusters. We provide arguments to justify the steps in the stages of our methods and we provide examples involving simulated and published data to compare our technique with other techniques. An R library, GHC, implementing our method is available from Github.
Toward Developing a Cloud Computing Model Curriculum

Cloud Computing is a rapidly evolving field that is triggering a wave of innovations in various domains such as machine learning and artificial intelligence. Cloud skills are becoming essential for any technology-related profession. Furthermore, the accelerated adoption of cloud technologies by industry is increasing the demand for cloud-trained professionals. Thus, higher education institutions are offering training opportunities and programs in cloud computing, however, the lack of well-rounded and high-quality curricular materials continues to be a challenge for educators. A 2018 Working Group (WG) created a report that --among other artifacts-- described fourteen Knowledge Areas (KAs), with numerous Learning Objectives (LOs) for each KA, to teach cloud concepts. Expanding on that work, this WG will focus on providing a collection of resources that would eventually constitute a model cloud curriculum. By relying on two particular surveys: one that looks at the existing curricular offerings, and another one that maps knowledge areas to job titles in cloud computing, we plan to provide a dynamic and configurable curricular exemplars repository for the cloud community-at-large, following the popularity of hands-on, project-based learning methodologies as our primary focus.
Extracting, Mining and Predicting Users' Interests from Social Networks

The abundance of user generated content on social networks provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users' interests and preferences. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) we introduce the information sources that are used for extracting user interests, (2) various types of user interest profiles that have been proposed in the literature, (3) techniques that have been adopted or proposed for mining user interests, (4) the scalability and resource requirements of the state of the art methods, and finally (5) the evaluation methodologies that are adopted in the literature for validating the appropriateness of the mined user interest profiles. We also introduce existing challenges, open research question and exciting opportunities for further work.
Coverage path planning optimization based on Q-learning algorithm

Mobile robot applications are increasing its usability in industries and services (examples: vacuum cleaning, painting and farming robots, among others). Some of the applications require that the robot moves in an environment between two positions while others require that the robot scans all the positions (Coverage Path Planning). Optimizing the traveled distance or the time to scan the path, should be done in order to reduce the costs. This paper addresses an optimization approach of the coverage path planning using Q-Learning algorithm. Comparison with other methods allows to validate the methodology.
Energy-Efficient Mining on a Quantum-Enabled Blockchain Using Light

We outline a quantum-enabled blockchain architecture based on a consortium of quantum servers. The network is hybridised, utilising digital systems for sharing and processing classical information combined with a fibre-optic infrastructure and quantum devices for transmitting and processing quantum information. We deliver an energy efficient interactive mining protocol enacted between clients and servers which uses quantum information encoded in light and removes the need for trust in network infrastructure. Instead, clients on the network need only trust the transparent network code, and that their devices adhere to the rules of quantum physics. To demonstrate the energy efficiency of the mining protocol, we elaborate upon the results of two previous experiments (one performed over 1km of optical fibre) as applied to this work. Finally, we address some key vulnerabilities, explore open questions, and observe forward-compatibility with the quantum internet and quantum computing technologies.