Reviewerly/exHarmony huggingface dataset:
Public key encryption with equality test via hash proof system

Public key encryption with equality test (PKEET) allows a tester to know whether ciphertexts are the encryptions of a same message or not by using the trapdoors issued from their owners, which is a useful cryptographic primitive can be deployed in many applications, such as in the mechanism of searching over encrypted data. Based on Hash Proof System (HPS) introduced by Cramer and Shoup, this paper presents an oversimplified paradigm for constructing PKEET in the standard model. Compared with the previous works that use identity-based encryption, strongly unforgeable one-time signature or other strong cryptographic primitives, our paradigm requires only the universal2 property of HPS and provides an efficient way to obtain concrete PKEET schemes based on different assumptions in the standard model, since HPS has been shown can be easily realized from a board range of NP languages (e.g., DLIN-based, DCR-based, Lattice-based and so on). Moreover, to demonstrate the practicality of the proposed paradigm, we instantiate it based on two kinds of NP languages respectively, one is based on the decisional Diffie-Hellman (DDH) assumption, the other one is based on the decisional composite residuosity (DCR) assumption, which results in the first concrete PKEET schemes that in the standard model without using pairing operations, and the schemes' security are also based on the standard DDH assumption and the standard DCR assumption respectively.
Towards development of a cyber security capability maturity model

Business processes are increasingly becoming technology driven. Disruptive technologies like social, mobility, analytics and cloud (SMAC) and internet of things (IOTs) have made a paradigm shift in the information technology space. With this evolution, the cyber security threats to digital infrastructure also have been increasing manifold and are a concern for the sustenance of business growth scenario. There is a need for a continuous improvement program in the cyber security posture of the organisations to keep pace with the increasing threats. Capability maturity models help to achieve this purpose. This paper presents the formulation and validation of a new cyber security capability maturity model (CSCMM) by comparing the strengths and limitations of nine contemporary maturity models and performing an empirical analysis of inputs from 200 odd relevant cross industry sector professionals. CSCMM is expected to improve the cyber security posture of the organisations to combat to the new generation threats.
A Brief Survey of Relation Extraction Based on Distant Supervision

As a core task and important part of Information ExtractionEntity Relation Extraction can realize the identification of the semantic relation between entity pairs. And it plays an important role in semantic understanding of sentences and the construction of entity knowledge base. It has the potential of employing distant supervision method, end-to-end model and other deep learning model with the creation of large datasets. In this review, we compare the contributions and defect of the various models that have been used for the task, to help guide the path ahead.
An Improved Algorithm Based on Particle Filter for 3D UAV Target Tracking

The widespread application of unmanned aerial vehicles (UAVs) urgently requires an effective tracking algorithm as technical support. Particle filter has been widely applied in maneuvering target tracking, however, there has been no suitable solution to the trade-off between weight degeneracy and particle diversity during the process of resampling. In this paper, we propose an improved particle filter algorithm based on systematic resampling with additional random perturbation. This method ensures that particle filter maintains particle diversity and reduces weight degeneracy under environments with different noise types, simultaneously. The simulation results demonstrate that the proposed algorithm generates more accurate filtered trajectory than generic particle filter, especially under the environment with low noise.
Statistical indicator for the detection of anomalies in gas, electricity and water consumption: Application of smart monitoring for educational buildings

Building facility managers are increasingly equipping their buildings with extensive sets of sensors. This article aims to develop an analysis decision-making methodology based on the production of statistical indicators. The tracking of such indicators allows detecting any systems performance problems. The automatic pinpointing of malfunctions can serve to activate alerts. Our approach focuses on the processing of data stemming from secondary schools managed by departmental services in the Pas-de-Calais, where 117 secondary school buildings have been instrumented with various sensors and supplying data since 2015. This article starts with a close-up on data mining for water, gas and electricity consumption. Data mining and machine learning methods, including the Clustering approach (K-Means), have been used to extract information from the measurements conducted in 2015 and 2016. This information is used to classify the 2017 measurements according to supervised approaches (SVM). The specificity of this work is to delve deeper into the analysis by combining into the same algorithm a set of various sensors related to both energy use and building occupancy. The data classification results have allowed highlighting "atypical" operations during the daytime, through interpreting data classification results in an effort to define the status of every day in year 2017.
Empirical Studies on Software Product Maintainability Prediction: A Systematic Mapping and Review

Background: Software product maintainability prediction (SPMP) is an important task to control software maintenance activity, and many SPMP techniques for improving software maintainability have been proposed. In this study, we performed a systematic mapping and review on SPMP studies to analyze and summarize the empirical evidence on the prediction accuracy of SPMP techniques in current research.Objective: The objective of this study is twofold: (1) to classify SPMP studies reported in the literature using the following criteria: publication year, publication source, research type, empirical approach, software application type, datasets, independent variables used as predictors, dependent variables (e.g. how maintainability is expressed in terms of the variable to be predicted), tools used to gather the predictors, the successful predictors and SPMP techniques, (2) to analyze these studies from three perspectives: prediction accuracy, techniques reported to be superior in comparative studies and accuracy comparison of these techniques.Methodology: We performed a systematic mapping and review of the SPMP empirical studies published from 2000 up to 2018 based on an automated search of nine electronic databases.Results: We identified 82 primary studies and classified them according to the above criteria. The mapping study revealed that most studies were solution proposals using a history-based empirical evaluation approach, the datasets most used were historical using object-oriented software applications, maintainability in terms of the independent variable to be predicted was most frequently expressed in terms of the number of changes made to the source code, maintainability predictors most used were those provided by Chidamber and Kemerer (C&K), Li and Henry (L&H) and source code size measures, while the most used techniques were ML techniques, in particular artificial neural networks. Detailed analysis revealed that fuzzy & neuro fuzzy (FNF), artificial neural network (ANN) showed good prediction for the change topic, while multilayer perceptron (MLP), support vector machine (SVM), and group method of data handling (GMDH) techniques presented greater accuracy prediction in comparative studies. Based on our findings SPMP is still limited. Developing more accurate techniques may facilitate their use in industry and well-formed, generalizable results be obtained. We also provide guidelines for improving the maintainability of software.
<i>netflower:</i> Dynamic Network Visualization for Data Journalists

Abstract Journalists need visual interfaces that cater to the exploratory nature of their investigative activities. In this paper, we report on a four‐year design study with data journalists. The main result is netflower, a visual exploration tool that supports journalists in investigating quantitative flows in dynamic network data for story‐finding. The visual metaphor is based on Sankey diagrams and has been extended to make it capable of processing large amounts of input data as well as network change over time. We followed a structured, iterative design process including requirement analysis and multiple design and prototyping iterations in close cooperation with journalists. To validate our concept and prototype, a workshop series and two diary studies were conducted with journalists. Our findings indicate that the prototype can be picked up quickly by journalists and valuable insights can be achieved in a few hours. The prototype can be accessed at: http://netflower.fhstp.ac.at/
Enhancing Steganography via Stego Post-processing by Reducing Image Residual Difference

Most modern steganography methods focus on designing an effective cost function. To our best knowledge, there is no related works concerned about modifying stego to enhance steganography security. In this paper, therefore, we propose a novel post-processing for stego image in the spatial domain. To ensure the correct extraction of hidden message, our method restricts the modification amplitude of each pixel according to the characteristics of STCs (Syndrome-Trellis Codes). To enhance steganography security, our method traverses the stego image pixel by pixel, and modifies those pixels that can reduce the image residual difference between cover and stego under some criterion. Experimental results show that the proposed method can improve the security of current steganography especially for large payloads, e.g. larger than 0.3 bpp. In addition, the post-modification rate is rather low, for instance less than 8 \textpertenthousand \ pixels have been changed in the enhanced stego image for the five existing steganography methods for payload as large as 0.5 bpp.
Evolutionary predator-prey robot systems

We present a feasibility study on evolving controllers for a group of wheeled robot predators that need to capture a prey robot. Our solution method works by evolving controllers in simulation for 100 generations, followed by 10 generations on real robots. The best controllers are further evaluated by their sensitivity for the initial positions. The results demonstrate the practical feasibility of this approach and give an indication of the time required to develop good solutions for the predator-prey problem.
Learning of Gaussian Processes in Distributed and Communication Limited Systems

It is of fundamental importance to find algorithms obtaining optimal performance for learning of statistical models in distributed and communication limited systems. Aiming at characterizing the optimal strategies, we consider learning of Gaussian Processes (GP) in distributed systems as a pivotal example. We first address a very basic problem: how many bits are required to estimate the inner-products of some Gaussian vectors across distributed machines? Using information theoretic bounds, we obtain an optimal solution for the problem which is based on vector quantization. Two suboptimal and more practical schemes are also presented as substitutes for the vector quantization scheme. In particular, it is shown that the performance of one of the practical schemes which is called per-symbol quantization is very close to the optimal one. Schemes provided for the inner-product calculations are incorporated into our proposed distributed learning methods for GPs. Experimental results show that with spending few bits per symbol in our communication scheme, our proposed methods outperform previous zero rate distributed GP learning schemes such as Bayesian Committee Model (BCM) and Product of experts (PoE).
Uniform decoherence effect on localizable entanglement in random multiqubit pure states

We investigate the patterns in distributions of localizable entanglement over a pair of qubits for random multiqubit pure states. We observe that the mean of localizable entanglement increases gradually with increasing the number of qubits of random pure states while the standard deviation of the distribution decreases. The effects on the distributions, when the random pure multiqubit states are subjected to local as well as global noisy channels, are also investigated. Unlike the noiseless scenario, the average value of the localizable entanglement remains almost constant with the increase in the number of parties for a fixed value of noise parameter. We also find that the maximum strength of noise under which entanglement survives can be independent of the localizable entanglement content of the initial random pure states.
Continuous Long-Term Evolution of Genetic Programming

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. Programs with almost 400 000 000 instructions are created by crossover. T...
The Dynamic Practice and Static Theory of Gradual Typing

We can tease apart the research on types into two `lineages': a pragmatic, implementation-oriented dynamic-first lineage and a formal, type-theoretic, static-first lineage. The dynamic-first lineage's focus is on taming particular idioms - `pre-existing conditions' in untyped programming languages. The static-first lineage's focus is on interoperation and individual type system features, rather than the collection of features found in any particular language. Both appear in programming languages research under the name gradual typing, and they are in active conversation with each other.What are these two lineages? What challenges and opportunities await the static-first lineage? What progress has been made so far?
Differential logical relations, Part I: The simply-typed case

We introduce a new form of logical relation which, in the spirit of metric relations, allows us to assign each pair of programs a quantity measuring their distance, rather than a boolean value standing for their being equivalent. The novelty of differential logical relations consists in measuring the distance between terms not (necessarily) by a numerical value, but by a mathematical object which somehow reflects the interactive complexity, i.e. the type, of the compared terms. We exemplify this concept in the simply-typed lambda-calculus, and show a form of soundness theorem. We also see how ordinary logical relations and metric relations can be seen as instances of differential logical relations. Finally, we show that differential logical relations can be organised in a cartesian closed category, contrarily to metric relations, which are well-known not to have such a structure, but only that of a monoidal closed category.
Registrating Oblique SAR Images Based on Complementary Integrated Filtering and Multilevel Matching

This paper presents a novel registration method for oblique synthetic aperture radar (SAR) images based on complementary integrated filtering (CIF) and multilevel matching. Our algorithm is divided into three steps. First, we considered different type of noises and employed the CIF to increase the signal-to-noise ratio of SAR images. Second, complementary affine invariant features, namely maximally stable extremal regions and Harris-affine features, were extracted simultaneously from image pairs, and then the initial matches were obtained based on the scale invariant feature transform (SIFT) descriptor and Euclidean distance. Therefore, the fundamental and homography matrixes could be calculated between image pairs, and then more matches were obtained under quasi-affine invariant SIFT (QAISIFT) and the hybrid geometric constraints. We further implemented the least square matching (LSM) based on the second-polynomial geometric model (SPGM), and thus the matching error of each corresponding point can be compensated according to the optimal SPGM. Third, the precise registration was achieved based on the matches of the second step. Experiments on four groups of oblique SAR images demonstrated the effectiveness of the proposed method. The contribution of this paper includes three aspects. One is that the proposed CIF can remove SAR image noise as much as possible; another is that the proposed QAISIFT can achieve near affine invariance across viewpoint change images; the third is that the advanced LSM can notably improve the accuracy of feature matches.
Encryption Algorithm Based on Neural Network

Security is one of the most important needs in network communication. Cryptography is a science which involves two techniques encryption and decryption and it basically enables to send sensitive and confidential data over the unsecure network. The basic idea of cryptography is concealing of the data from unauthenticated users as they can misuse the data. In this paper we use auto associative neural network concept of soft computing in combination with encryption technique to send data securely on communication network.
Towards a Conceptual Model for Unifying Variability in Space and Time

Effectively managing variability in space and time is among the main challenges of developing and maintaining large-scale yet long-living software-intensive systems. Over the last decades, two large research fields, Software Configuration Management (SCM) and Software Product Line Engineering (SPLE), have focused on version management and the systematic handling of variability, respectively. However, neither research community has been successful in producing unified management techniques that are effective in practice, and both communities have developed largely independently of each other. As a step towards overcoming this unfortunate situation, in this paper, we report on ongoing work on conceiving a conceptual yet integrated model of SCM and SPLE concepts, originating from a recent Dagstuhl seminar on the unification of version and variant management. Our goal is to provide discussion grounds for a wider exploration of a unified methodology supporting software evolution in both space and time.
Catalog of earthquake parameters and description of seismograph and infrasound stations at Alaskan volcanoes—January 1, 2013, through December 31, 2017

First posted August 21, 2019 For additional information, contact: Director,Volcano Science CenterU.S. Geological Survey4210 University DriveAnchorage, AK 99508 Between January 1, 2013, and December 31, 2017, the Alaska Volcano Observatory (AVO) located a total of 28,172 earthquakes at volcanoes in Alaska. The annual totals are 3,840, 5,819, 5,297, 6,151, and 7,065 earthquakes for the years 2013 through 2017, respectively. This represents an average of 5,634 earthquakes per year, which is comparable to the yearly number of earthquakes AVO located in the previous decade when AVO monitored a similar number of volcanoes. During the reporting period, there was significant seismic activity at 20 of the 34 volcanoes monitored by a seismograph network (Akutan Peak, Aniakchak Crater, Augustine, Mount Cerberus, Mount Cleveland, Fourpeaked Mountain, Mount Gareloi, Great Sitkin, Ilimana, Kanaga, Korovin, Makushin, Mount Martin, Okmok Caldera, Pavlof, Shishaldin, Mount Spurr, Tanaga, Ugashik-Peulik, and Mount Veniaminof) and two volcanoes without a monitoring network (Mount Recheshnoi and Bogoslof Island). Instrumentation highlights for this period include the establishment of a new subnetwork on Mount Cleveland, an accelerated transition from analog to digital telemetry at most subnetworks, and an increased number of broadband and infrasound sensors throughout the AVO network. The operational highlight was the return of seismic monitoring at Korovin and Ugashik-Peulik Volcanoes following network repairs. This catalog includes hypocenters, magnitudes, and statistics of the earthquakes located in 2013–17, along with the associated station parameters, and velocity models.
Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input

Equilibrium Propagation (EP) is a biologically inspired learning algorithm for convergent recurrent neural networks, i.e. RNNs that are fed by a static input x and settle to a steady state. Training convergent RNNs consists in adjusting the weights until the steady state of output neurons coincides with a target y. Convergent RNNs can also be trained with the more conventional Backpropagation Through Time (BPTT) algorithm. In its original formulation EP was described in the case of real-time neuronal dynamics, which is computationally costly. In this work, we introduce a discrete-time version of EP with simplified equations and with reduced simulation time, bringing EP closer to practical machine learning tasks. We first prove theoretically, as well as numerically that the neural and weight updates of EP, computed by forward-time dynamics, are step-by-step equal to the ones obtained by BPTT, with gradients computed backward in time. The equality is strict when the transition function of the dynamics derives from a primitive function and the steady state is maintained long enough. We then show for more standard discrete-time neural network dynamics that the same property is approximately respected and we subsequently demonstrate training with EP with equivalent performance to BPTT. In particular, we define the first convolutional architecture trained with EP achieving ∼ 1% test error on MNIST, which is the lowest error reported with EP. These results can guide the development of deep neural networks trained with EP.
auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics

Machine learning models have spread to almost every area of life. They are successfully applied in biology, medicine, finance, physics, and other fields. With modern software it is easy to train even a~complex model that fits the training data and results in high accuracy on the test set. The problem arises when models fail confronted with real-world data. This paper describes methodology and tools for model-agnostic audit. Introduced techniques facilitate assessing and comparing the goodness of fit and performance of models. In~addition, they may be used for the analysis of the similarity of residuals and for identification of~outliers and influential observations. The examination is carried out by diagnostic scores and visual verification. Presented methods were implemented in the auditor package for R. Due to flexible and~consistent grammar, it is simple to validate models of any classes.
LINSPECTOR WEB: A Multilingual Probing Suite for Word Representations

We present LINSPECTOR WEB, an open source multilingual inspector to analyze word representations.Our system provides researchers working in low-resource settings with an easily accessible web based probing tool to gain quick insights into their word embeddings especially outside of the English language.To do this we employ 16 simple linguistic probing tasks such as gender, case marking, and tense for a diverse set of 28 languages.We support probing of static word embeddings along with pretrained Al-lenNLP models that are commonly used for NLP downstream tasks such as named entity recognition, natural language inference and dependency parsing.The results are visualized in a polar chart and also provided as a table.LINSPECTOR WEB is available as an offline tool or at https://linspector.ukp.