Reviewerly/exHarmony huggingface dataset:
Greedy additive approximation algorithms for minimum-entropy coupling problem

Given two probability distributions p = (p <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ,p <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ,…,p <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> ) and q = (q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ,q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ,…,q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</inf> ) of two discrete random variables X and Y respectively, the minimum-entropy coupling problem is to find the minimum-entropy joint distribution among all possible joint distributions of X and Y having p and q as marginals. This problem is known to be NP-hard and recently have been proposed greedy algorithms that provide different guarantees, i.e. solutions that are local minimum [Kocaoglu et al. AAAI’17] and 1-bit approximation [Cicalese et al. ISIT’17]. In this paper, we show that the algorithm proposed by Kocaoglu et al. provides, in addition, a 1-bit approximation guarantee in the case of 2 variables. Then, we provide a general criteria for guaranteeing an additive approximation factor of 1 that might be of independent interest in other contexts where couplings are used.
An Efficient Single Image Enhancement Approach Using Luminance Perception Transformation

Sufficient illumination enables the observer to identify the true colors of an object captured by an imaging sensor, whereas dim illumination restricts proper observation. This paper proposes a single image enhancement algorithm based on human visual system. First, the image is transformed by the proposed Luminance Perception procedure from the spatial domain into the proposed Brightness Domain, where the image can be assessed as to whether or not it has sufficient illumination. Further, if the image is determined to have insufficient illumination, the assessed range of insufficient illumination and sharpness is used to restore the image's true illumination and textures to best fulfill the requirements for human visual perception. Our experiments show that the enhanced images generated by the proposed algorithm exhibit more brightness and appreciably better contrast than those generated by other state-of-the-art image enhancement algorithms that use images captured under various illuminant levels in various kinds of natural scenes.
Deep Learning rooted Potential piloted RRT* for expeditious Path Planning

Randomised sampling-based algorithms such as RRT and RRT* have widespread use in path planning, but they tend to take a considerable amount of time and space to converge towards the destination. RRT* with artificial potential field (RRT*-APF) is a novel solution to pilot the RRT* sampling towards the destination and away from the obstacles, thus leading to faster convergence. But the ideal potential function varies from one configuration space to another and different sections within a single configuration space as well. Finding the potential function for each section for every configuration space is a grueling task. In this paper, we divide the 2 dimensional configuration space into multiple regions and propose a deep learning based approach in the form of a custom feedforward neural network to tune the sensitive parameters, upon which the potential function depends. These parameters act as a heuristic and pilots the tree towards the destination, which has a substantial effect on both the rate of convergence and path length. Our algorithm, DL-P-RRT* has shown the ability to learn and emulate the shortest path and converges much faster than the current random sampling algorithms as well as deterministic path planning algorithms. So, this algorithm can be used effectively in environments where the path planner is called multiple times, which is typical to events such as Robo-Soccer.
Satisfiability of Modal Inclusion Logic

We investigate the computational complexity of the satisfiability problem of modal inclusion logic. We distinguish two variants of the problem: one for the strict and another one for the lax semantics. Both problems turn out to be EXPTIME-complete on general structures. Finally, we show how for a specific class of structures NEXPTIME-completeness for these problems under strict semantics can be achieved.
Scaling up CCG-Based Plan Recognition via Monte-Carlo Tree Search

This paper focuses on the problem of scaling Combinatory Categorial Grammar (CCG)-based plan recognition to large CCG representations in the context of Real-Time Strategy (RTS) games. Specifically, we present a technique to scale plan recognition to large domain representations using Monte-Carlo Tree Search (MCTS). CCG-based planning and plan recognition (like other domain-configurable planning frameworks) require domain definitions to be either manually authored or learned from data. Prior work has demonstrated successful learning of these CCG domain definitions from data, but these representations can be very large for complex application domains. We propose a MCTS-based approach to search for explanations and predict the goal of a given sequence of observed actions. We evaluate our approach on the RTS game AI testbed microRTS. Our experimental results show our method scales better to these large, learned CCGs than previous CCG-based approaches.
Exploratory Search Pipes with Scoped Facets

This paper presents faceted search technology tailored to the peculiarities of exploratory search tasks. In contrast to traditional faceted search systems, facets in our system are arranged as a pipe, i.e., are applied sequentially one after the other. Moreover, the facets in a pipe are not applied throughout the whole sequence, but are limited to a user-definable scope, such that the search query can be broadened by adding a facet to the pipe. By this modification, the user interaction with our exploratory search engine resembles rather an open walk through the facet space spanned by the connections between documents and facets, than a progressive filtering of the document collection. We argue that this shift in the interaction paradigm is a much better fit to exploratory search scenarios. A user study with a prototype implementation attests an improved usability of our system compared to traditional faceted search systems for complex exploratory search tasks.
iCATS: Scheduling Big Data Workflows in the Cloud Using Cultural Algorithms

Workflow scheduling has remained a critical functionality of modern data-centric workflow management systems. Cloud computing, which provides practically unlimited computing and storage resources, has enabled a new generation of data-centric workflows, called big data workflows. New big data workflow scheduling algorithms should optimally utilize the characteristics of cloud computing such as heterogeneous virtual machines, the elastic resource provisioning model, and the pay-as-you-go pricing model, as well as the time and monetary cost to transfer large amounts of data. In this paper, we consider one case of the general big data workflow scheduling problem where a deadline, δ, is given for a workflow, W, and the goal is to minimize the monetary cost of running W in the cloud while satisfying the given deadline, δ. To this end, we leverage the power of Evolutionary Algorithms (EA) in order to search for the best solution within a reasonable planning time. More specifically, we introduce an innovative fitness function that combines the time and monetary cost of a workflow in one metric. Based on the EA and the fitness function, we design a deadline-constrained big data workflow scheduling algorithm, called iCATS (Improved Cultural Algorithms-based Task Scheduling). Extensive experiments demonstrate the statistical advantages of iCATS over existing representative EA workflow scheduling algorithms, including random (Rand), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Cultural Algorithms (CA).
Autonomous Assistance for Versatile Grasping with Rescue Robots

The deployment of mobile robots in urban search and rescue (USAR) scenarios often requires manipulation abilities, for example, for clearing debris or opening a door. Conventional teleoperated control of mobile manipulator arms with a high number of degrees of freedom in unknown and unstructured environments is highly challenging and error-prone. Thus, flexible semi-autonomous manipulation capabilities promise valuable support to the operator and possibly also prevent failures during missions. However, most existing approaches are not flexible enough as, e.g., they either assume a-priori known objects or object classes or require manual selection of grasp poses. In this paper, an approach is presented that combines a segmented 3D model of the scene with grasp pose detection. It enables grasping arbitrary rigid objects based on a geometric segmentation approach that divides the scene into objects. Antipodal grasp candidates sampled by the grasp pose detection are ranked to ensure a robust grasp. The human remotely operating the robot is able to control the grasping process using two short interactions in the user interface. Our real robot experiments demonstrate the capability to grasp various objects in cluttered environments.
Simulation of QoS-Based Task Scheduling Policy for Dependent and Independent Tasks in a Cloud Environment

Virtualization allows the cloud computing environment to execute several tasks with high efficiency. Using quality of service factors, namely makespan, time, cost, and CPU utilization and availability, many algorithms are used in the cloud to measure the service of the cloud service provider. In this paper, a new hybrid task scheduling algorithm is proposed to schedule dependent and independent tasks in a cloud environment. Min-Min task scheduling algorithm can be used to schedule dependent tasks whereas independent TS algorithm can be used to schedule the independent tasks. These two algorithms are compared with hybrid tasks scheduling algorithm by varying the number of tasks. New hybrid QoS-based task scheduling algorithm is giving better results when compared with Min-Min and TS algorithms. These algorithms are implemented and analyzed by using CloudSim simulator.
Local Geometry of Cross Entropy Loss in Learning One-Hidden-Layer Neural Networks

We study model recovery for data classification, where the training labels are generated from a one-hidden-layer neural network with sigmoid activations, and the goal is to recover the weights of the neural network. We consider two network models, the fully-connected network (FCN) and the non-overlapping convolutional neural network (CNN). We prove that with Gaussian inputs, the empirical risk based on cross entropy exhibits strong convexity and smoothness uniformly in a local neighborhood of the ground truth, as soon as the sample complexity is sufficiently large. Hence, if initialized in this neighborhood, it establishes the local convergence guarantee for empirical risk minimization using cross entropy via gradient descent for learning one-hidden-layer neural networks, at the near-optimal sample and computational complexity with respect to the network input dimension without unrealistic assumptions such as requiring a fresh set of samples at each iteration.
Steganalysis of AMR Based on Statistical Features of Pitch Delay

The calibrated matrix of the second-order difference of the pitch delay (C-MSDPD) feature has been proven to be effective in detecting steganography based on pitch delay. In this article, a new steganalysis scheme based on multiple statistical features of pitch delay is present. Analyzing the principle of the adaptive multi-rate (AMR) codec, the pitch delay values in the same frame is divided into groups, in each of which, a pitch delay has a closer correlation with the other ones. To depict the characteristic of the pitch delay, two new types of statistical features are adopted in this article. The new features and C-MSDPD feature are together employed to train a classifier based on support vector machine (SVM). The experimental result shows that, the proposed scheme outperforms the existing one at different embedding bit rates and with different speech lengths.
A Survey of Cyber Security Practices in Small Businesses

Small businesses are a unique class of organization with challenging cyber security problems that are frequently overlooked. These firms are being increasingly targeted for cyber-attack. These firms are particularly vulnerable to cyber-attack due to the often-valuable information they handle coupled with overworked and undertrained IT support. The University of Alabama in Huntsville is conducting an on-going survey of the cyber security practices of small businesses seeking to implement the NIST SP 800-171 cyber security standard. The data gathered indicates that small businesses in this field are likely to engage in poor security practices arising from common cyber security misconceptions.
Digital rights management (DRM) in libraries of public universities in Jordan

Purpose The purpose of this paper is to illustrate some methods to protect digital libraries using digital rights management (DRM) technologies and specifically the methods applied at the digital libraries of Jordan universities. Design/methodology/approach A descriptive research method has been used to examine the extent of DRM in digital libraries of universities in Jordan. Findings Majority of the universities in Jordan use different methods of protection including coding, identification, authenticity and digital watermark. However, methods including digital signature, digital fingerprint, copy detection systems and pay system have not been employed within Jordanian universities. The use of copyright management systems has been limited, which shows the weakness in the use of technological protection within the investigated libraries. Originality/value This study has shown contribution to the development of digital libraries that are based on the protection of new technology and are able to provide better services to users without concern about protecting publishers’ rights.
Arabic Phonemes Recognition Using Convolutional Neural Network

This paper focuses on a machine learning that learn the correct pronunciation Arabic phonemes. In this study, the researchers develop using convolutional neural network as feature extraction in order to enhance the performance of the model and Multi layer perceptron as the classifier to classify classes. Different parameters of CNN model are used in order to investigate the best parameter for the recognition purpose. The dataset have been recorded from experts using smartphone which consist of 880 recorded audios to train the model (210 for each class). The researchers have experimented the models to measure the accuracy and the cross entropy in the training process.
Mek: Mechanics Prototyping Tool for 2D Tile-Based Turn-Based Deterministic Games

There are few digital tools to help designers create game mechanics. A general language to express game mechanics is necessary for rapid game design iteration. The first iteration of a mechanics-focused language, together with its interfacing tool, are introduced in this paper. The language is restricted to two-dimensional, turn-based, tile-based, deterministic, complete-information games. The tool is compared to the existing alternatives for game mechanics prototyping and shown to be capable of succinctly implementing a range of well-known game mechanics.
Comparative Study of the Ensemble Learning Methods for Classification of Animals in the Zoo

Understanding that Machine Learning concepts can help generate a strong classification system and provide a good prediction on out-of-sample data which allows us to realize it’s importance in fields of Taxonomic Classification and makes it easier to handle huge datasets. In this paper, we are aiming to solve a classification problem using supervised machine learning in Python. The zoo dataset taken from UCI data repository contains data items that describe animals according to certain attributes that categorize them under seven different classes. Our central aim in this paper is to provide a detailed comparative study of few of the major ensemble learners with respect to the base learner. We are also looking into all the factors that are primarily responsible for reaching the conclusion and how they affect the decision and select the most important ones. Our results show bagged decision tree as the best performing ensemble classifier with an accuracy score of 96.94%.
Advancing informatics with electronic medical records bots (EMRBots)

Electronic medical records (EMRs) contain sensitive and detailed documentation on a variety of conditions at the individual level. Because EMRs are subject to confidentiality requirements, access to them is limited. In an attempt to address privacy limitations, knowledge-driven experimental artificially generated electronic medical records (EMRBots) have been introduced. EMRBot repositories have been used in a variety of scenarios to advance teaching, enhance student dissertations, facilitate hackathons, and produce R packages. In addition to describing its methodology, the manuscript reviews EMRBot use cases published by independent researchers.
Modelling Player Preferences in AR Mobile Games

In this paper, we use preference learning techniques to model players’ emotional preferences in an AR mobile game. This exploratory study uses player behaviour to make these preference predictions. The described techniques successfully predict players’ frustration and challenge levels with high accuracy while all other preferences tested (boredom, excitement and fun) perform better than random chance. This paper describes the AR treasure hunt game we developed, the user study conducted to collect player preference data, analysis performed, and preference learning techniques applied to model this data. This work is motivated to personalize players’ experiences by using these computational models to optimize content creation and game balancing systems in these environments. The generality of our technique, limitations, and usability as a tool for personalization of AR mobile games is discussed.
Automated analysis of single-tone spectroscopic data for cQED systems

Physical systems for quantum computation require calibration of the control parameters based on their physical characteristics by performing a chain of experiments that gather most precise information about the given device. It follows that there is a need for automated data acquisition and interpretation. In this work, we have developed a tool that allows for automatic analysis of single-tone spectroscopy (STS) results for a single cell consisting of qubit and resonator in the circuit quantum electrodynamics (cQED) architecture. Using analytic approaches and maximum likelihood estimation, our algorithm is capable of finding all relevant physical characteristics of the cell by using only the measured STS data. The described approach is fast and robust to noise, and its open-source Python implementation can readily be used to calibrate transmon qubits coupled to notch-port resonators.
Remarks on Rényi versions of conditional entropy and mutual information

We examine the analogue of Arimoto's definition of conditional Rényi entropy and Rényi mutual information for abstract alphabets. Despite being dependent on the reference measure, these notions have useful properties similar to those known in the discrete setting. Moreover, we explore relationships between the families of mutual informations defined by Sibson, Csiszár, and Lapidoth-Pfister. In particular, we show that various notions of Rényi capacity and center coincide, extending results of Csiszár and Nakiboğlu.
mWarp: Accelerating Intra-Host Live Container Migration via Memory Warping

Live container migration allows containers to roam from one server to another to achieve agility goals like load balancing, tackling machine failures, scaling in/out and reallocating resources. However, migrating a container is also costly mainly due to memory state migration - a large number of memory pages need to be copied from the source server to the destination server. In this paper, we propose a fast and live container migration approach, mWarp, in an intra-host scenario, where both the source and destination virtual machine (VM) servers reside on the same physical host. Instead of copying a container's memory, mWarp relocates the ownership of the container's physical memory pages from the source VM to the destination VM with a highly-efficient memory remapping mechanism. As relocation of memory ownership is light-weight, mWarp leads to fast and live container migration with less service disruption to applications running in containers being migrated. We implement mWarp upon a well-known live container migration tool (CRIU) with key kernel/hypervisor-level support. The evaluation with both micro benchmarks and real-world applications shows that mWarp greatly reduces the total container migration time and downtime (e.g., by an order of magnitude) with significantly improved application-level performance (e.g., by 20%).
The Swiss Post/Scytl Transparency Exercise and Its Possible Impact on Internet Voting Regulation

In Switzerland, internet voting has been in the experimental phase for over fifteen years. With a view to putting an end to trials and normalizing its use alongside the paper-based channels (polling station and postal voting), a thoroughly updated federal regulation entered into force in January 2014. Only systems that are formally certified and offer complete verifiability can be authorized to propose internet voting in an unrestricted manner, i.e. to all the electorate. Furthermore, since July 2018, the publication of the source code of fully verifiable systems is mandatory. A major transparency exercise took place in February – March 2019. The first system to introduce complete verifiability – the Swiss Post/Scytl system – was submitted to a public intrusion test (PIT), open to anyone interested. In a parallel development, the source code of the same system was published on the internet. Researchers found critical errors in the source code of both individual and universal verifiability. The PIT revealed other, less critical issues. This experience has fuelled the already heated debate over the future development of internet voting in Switzerland. It questions the procedures for controlling verifiability solutions and, ultimately, the consensus to develop such solutions. Lessons learned will most probably be reflected in the future update of the regulation.