Reviewerly/exHarmony huggingface dataset:
Authorship Attribution via Coupon-Collector-Type Indices

Authorship attribution is the process of determining the author of a text in question by capturing an author’s writing style based on selected stylistic features. In this paper, we propose a new methodology for authorship attribution based on a profile of indices related to the generalized coupon collector problem, called coupon-collector-type indices. The coupon collector problem and its generalizations are of traditional and recurrent interests. Coupons are drawn one at a time from a population containing n distinct type of coupons. The process continues until a complete set of n distinct coupons is obtained and the total number of draws, X, is recorded. We base our methodology on function words. We establish a testing procedure by constructing a confidence band of the coupon-collector-type indices using an empirical bootstrap technique. We validate our proposed methodology using several writing samples whose authorship is known. We then apply this methodology to explore the question of who wrote the fifteenth Oz book, whose authorship is disputed between Lyman Frank Baum (1856–1919) and his successor) on the Oz series, Ruth Plumly Thompson (1891–1976).
Structure-Preserving Certificateless Encryption and Its Application

Certificateless encryption (CLE) combines the advantages of public-key encryption (PKE) and identity-based encryption (IBE) by removing the certificate management of PKE and the key escrow problem of IBE. In this paper, we propose structure-preserving CLE schemes. Structure preservation enables efficient non-interactive proof of certain ciphertext properties, thus supporting efficient modular constructions of advanced cryptographic protocols with a simple design. As an illustration, we propose a structure-preserving group signature scheme with certified limited (CL) opening from structure-preserving CLE. CL opening allows a master certifier to certify openers. The opener who is the designated one for a group signature can open it (i.e., revoke its anonymity). Neither the certifier nor any non-designated openers can perform the opening. The structure-preserving property of our scheme can also hide who is the designated opener among a list of possibilities.
Experimental detection of qubit-ququart pseudo-bound entanglement using three nuclear spins

In this work, we experimentally created and characterized a class of qubit-ququart PPT (positive under partial transpose) entangled states using three nuclear spins on an nuclear magnetic resonance (NMR) quantum information processor. Entanglement detection and characterization for systems with a Hilbert space dimension $\ge 2 \otimes 3$ is nontrivial since there are states in such systems which are both PPT as well as entangled. The experimental detection scheme that we devised for the detection of qubit-ququart PPT entanglement was based on the measurement of three Pauli operators with high precision, and is a key ingredient of the protocol in detecting entanglement. The family of PPT-entangled states considered in the current study are incoherent mixtures of five pure states. All the five states were prepared with high fidelities and the resulting PPT entangled states were prepared with mean fidelity $\ge 0.95$. The entanglement thus detected was validated by carrying out full quantum state tomography (QST).
Tablet computers in Philippine public schools: school-level factors that influence technology management and use

Many developing countries are investing in large-scale initiatives to deploy information and communications technology in schools. However, merely equipping schools with hardware and software does not guarantee that the technology is used effectively to improve learning outcomes. This study aims to identify factors that influence the use of technology in schools in a developing-country context. It investigates this through a multiple-case study of the Australian AID tablet computer programme in the Philippines, in which 1000 tablets were given to nine public schools for student use. Focusing specifically on the impact at a whole-school level, the study revealed factors affecting outcomes that were specific to a developing-country context, as well as confirming the relevance of more general factors identified in the literature. The study also brings to light tablet-specific benefits and issues in this context. These results have implications for the effective school-level implementation of technology programmes in developing countries. Recommendations that proceed from these are presented for policy-makers and school administrators looking to use these devices in schools.
Images of Image Machines. Visual Interpretability in Computer Vision for Art

Despite the emergence of interpretable machine learning as a distinct area of research, the role and possible uses of interpretability in digital art history are still unclear. Focusing on feature visualization as the most common technical manifestation of visual interpretability, we argue that in computer vision for art visual interpretability is desirable, if not indispensable. We propose that feature visualization images can be a useful tool if they are used in a non-traditional way that embraces their peculiar representational status. Moreover, we suggest that exactly because of this peculiar representational status, feature visualization images themselves deserve more attention from the computer vision and digital art history communities.
Design and simulation of sensor fusion using symbolic engines

Sensor fusion is the art of estimating accurate information from noisy multi-sensor data. Due to the complexity of stochastic sensor errors, design and testing of sensor fusion algorithms have been always challenging. Existing design approaches are mainly mission specific with fixed system models that do not verify if the filter can estimate hidden errors. To address these challenges, this paper presents a flexible design and simulation environment for sensor fusion. The environment utilizes symbolic engine as a flexible representation of system models to enable flexible and accurate generation of linearized error models. Inverse kinematic is used to generate pseudo-error-free inertial data to test the ability of the filte to estimate sensor errors. The developed environment is demonstrated on an Attitude and Heading Reference System using Extended Kalman Filter. The demonstration includes both simulation and experimental tests. The designed filter supports both loosely and tightly coupled filtering approaches.
Real time FPGA-ANN architecture for outdoor obstacle detection focused in road safety

Object detection is a technologically challenging issue, which is useful for safety in outdoor environments, where this object, frequently, represents an obstacle that must be avoided. Although several object detection methods have been developed in recent years, they usually tend to produce poor results in outdoor environments, being mainly affected by sunlight, light intensity, shadows, and limited computational resources. This open problem is the main motivation for exploring the challenge of developing low-cost object detection solutions, with the characteristic of being easily adaptable and having low power requirements, such as the ones needed in on-board obstacle detection systems in automobiles. In this work, we present a trade-off analysis of several architectures using an FPGA-based design that implements ANNs (FPGA-ANN) for outdoor obstacle detection, focused in road safety. The analyzed FPGA-ANN architectures merge outdoor data gathered by a Kinect sensor, images and infrared data, to construct an outdoor environment model for object detection, which allows to detect if there is an obstacle in the near surroundings of a vehicle.
Mediated Musical Interactions in Virtual Environments

Musical interactions performed within virtual spaces are mediated by the rules and realities inherent to each environment itself. Control interactions in physical space driving actor motion and gesture in virtual spaces pass through a layer of influence dictated by the environment—as well as by any additional actors or processes existing within that environment—before being mapped to parameters of sonic and musical creation and control. Such mediation layers are themselves intrinsic attributes of musical works built within game and virtual environments and play fundamental roles in the sonic and musical experiences realized in such spaces. These meditated musical interactions and the interfaces that support them should be considered and approached as a distinct form of musical interaction with their own performance practices, approaches and taxonomies. This paper explores the notion of musical mediation as a composed and designed attribute of real-time virtual musical performance environments and describes three virtual musical performance environments designed to act as mediation layers upon musical performance gesture and intention carried out within.
Cache timing attacks on NoC-based MPSoCs

Rising demands for increased performance, lower energy consumption, connectivity and programming flexibility are nowadays driving the platforms, so-called Multi-Processor Systems-on-Chips (MPSoCs). These platforms are composed of several processing elements, custom IP cores, and memories, all of which are in general interconnected by a Network-on-Chip (NoC). As attractive as these characteristics are, they introduce several security concerns, specially when applications with different trust and protection levels share resources. When a malicious software acquires access to an IP, it opens a door to external surveillance of the cache-memory, processing units and the NoC communication structure. The cache memory was already exploited by several authors in ASICs and Systems-on-Chips through Side-Channel Attacks (SCAs). In this work, we expand this concept, exploring the timing attacks on caches in the MPSoC scenario. We implement two well established attacks in the literature on a real hardware platform, the MPSoC Glass. Furthermore, we present the NoC as a novel vulnerability to increase attack efficiency, resulting in the Earthquake Attack. Results show that the attacks from literature can succeed inside the MPSoC, and obtain better results. Additionally, Earthquake improves the base attack by using the NoC timing attack, reducing the remaining attack complexity from 236.9 to 232 with 216.6 encryptions instead of 227.97.
Effects of Aging on Small Target Selection with Touch Input

Age-related declines in physical and cognitive function can result in target selection difficulties that hinder device operation. Previous studies have detailed the different types of target selection errors encountered, as well as how they vary with age and with input device for mouse and pen interaction. We extend this work to describe the types of age-related selection errors encountered with small touchscreen devices. Consistent with prior results, we found that older adults had longer target selection times, generated higher error rates, and encountered a broader range of selection difficulties (e.g., miss errors and slip errors) relative to a younger comparison group. However, in contrast to the patterns previously found with pen interaction, we found that miss error (i.e., both landing and lifting outside the target bounds) was a more common source of errors for older adults than slip error (i.e., landing on the target but slipping outside the target bounds before lifting). Moreover, aging influenced both miss and slip errors in our study of touch interaction, whereas for pen interaction, age has been found to influence only slip errors. These differences highlight the need to consider pen and touch interaction separately despite both being forms of direct input. Based on our findings, we discuss possible approaches for improving the accessibility of touch interaction for older adults.
Heterogeneous Distributed Big Data Clustering on Sparse Grids

Clustering is an important task in data mining that has become more challenging due to the ever-increasing size of available datasets. To cope with these big data scenarios, a high-performance clustering approach is required. Sparse grid clustering is a density-based clustering method that uses a sparse grid density estimation as its central building block. The underlying density estimation approach enables the detection of clusters with non-convex shapes and without a predetermined number of clusters. In this work, we introduce a new distributed and performance-portable variant of the sparse grid clustering algorithm that is suited for big data settings. Our computed kernels were implemented in OpenCL to enable portability across a wide range of architectures. For distributed environments, we added a manager–worker scheme that was implemented using MPI. In experiments on two supercomputers, Piz Daint and Hazel Hen, with up to 100 million data points in a ten-dimensional dataset, we show the performance and scalability of our approach. The dataset with 100 million data points was clustered in 1198 s using 128 nodes of Piz Daint. This translates to an overall performance of 352 TFLOPS . On the node-level, we provide results for two GPUs, Nvidia’s Tesla P100 and the AMD FirePro W8100, and one processor-based platform that uses Intel Xeon E5-2680v3 processors. In these experiments, we achieved between 43% and 66% of the peak performance across all computed kernels and devices, demonstrating the performance portability of our approach.
Deep Learning and Hierarchical Reinforcement Learning for modeling a Conversational Recommender System

In this paper, we propose a framework based on Hierarchical Reinforcement Learning for dialogue management in a Conversational Recommender System scenario. The framework splits the dialogue into more manageable tasks whose achievement corresponds to goals of the dialogue with the user. The framewor k consists of a meta-controller, which receives the user utterance and understands which goal should pursue, and a controller, which exploits a goal-specific representation to generate an answer composed by a sequence of tokens. The modules are trained using a two-stage strategy based on a preliminary Supervised Learning stage and a successive Reinforcement Learning stage.
Development of Sindhi text corpus

Sindhi language is a rich language with plenty of literary and general texts. There are number of books, newspapers, magazines and internet material available to develop Sindhi text corpus but yet proper and useful text corpus could not be developed and presented online for research, language features analysis, linguistics analysis and information retrieval systems. The lack of resources for research on computational linguistics and NLP applications for Sindhi language are challenging tasks at this stage. However, we have developed Sindhi text corpora in order to provide text resources to computational linguists, Natural Languages process (NLP) experts and researchers. Online books, newspapers, magazines, blogs and social websites are utilized to build Sindhi text corpus. Sindhi sentiment based text corpus is developed and analyzed with Document Term Matrix and TF-IDF models using 2-gram technique of n-gram model. The corpus may be useful for research on language variation analysis, sentiment analysis, aspect based sentiment analysis, semantic analysis, machine translation, information retrieval, Word2Vec, topic modeling and cluster analysis.
A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things

Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this article starts by reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this article identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts.
Chapter 4. World-building as cognitive feedback loop

Text World Theory (Gavins 2007; Werth 1999) has traditionally assumed a unidirectional model of knowledge transmission from discourse-world to text-world. In this chapter I follow Troscianko (2017) to suggest that world-building in discourse occurs within a cognitive feedback loop in which existing knowledge is applied toward the construction of a text-world network, and new information feeds from this network back into the minds of readers. In what follows, I demonstrate the utility of a feedback-loop approach in accounting for knowledge accrual in discourse through a case-study analysis of Canadian author Sheldon Currie’s (1995) novella The Glace Bay Miners’ Museum. I argue that Currie’s rhetorical positioning of the reader as the recipient of a highly politicised subtext at two levels of the discourse results in the incorporation of new or modified knowledge into a reader’s knowledge base.
A robot motion design scheme for watching the elderly based on human gesture sensing

Watching the elderly is one of promising IoT applications in the field of nursing care. Humanoid robots are considered to be useful agents for not only relaxing the elderly but also watching and alerting them in a daily life. Detecting and preventing the risk of indoor heat stroke is an important issue especially for the elderly who live alone. A method for reliably conveying the possible danger of the indoor heat stroke to the elderly is a crucial factor to implement a practical system. In this paper, we describe the system for detecting possible risk of suffering from the indoor heat stroke and informing the detected state to the elderly by using a communication robot. While the robot normally gives users healing, the proposed system implements a method for certainly notifying the abnormal conditions to the elderly by using robot motions. Because designing natural robot motions is a difficult task even for skilled engineers, we also devise a method to easily creating new robot motions by imitating human body gestures. We design a set of normal and special motions for a desktop humanoid robot and evaluated whether the robot motions can effectively make users aware of abnormal situations.
Topological Qubits as Carriers of Quantum Information in Optics

Winding number is a topologically significant quantity that has found valuable applications in various areas of mathematical physics. Here, topological qubits are shown capable of formation from winding number superpositions and so of being used in the communication of quantum information in linear optical systems, the most common realm for quantum communication. In particular, it is shown that winding number qubits appear in several aspects of such systems, including quantum electromagnetic states of spin, momentum, orbital angular momentum, polarization of beams of particles propagating in free-space, optical fiber, beam splitters, and optical multiports.
Implementation of narrow-width automatic digital tuner using popular swarm intelligence technique

Via signals all types of valuable information can be transmitted irrespective of distance. Generally, most of the starting points of communication/broadcast receiving systems require tuner circuits for the effective separation of the desired signal from its broadband spectrum. This work proposes the automatic realization of narrow-width digital signal tuning over its complete range employing most popular artificial swarm intelligence (SI) in order to fulfill different modern signal processing and communication network applications. Modern SI acts as the successful controlling brain behind arbitrary tuning procedure requiring minimal hardware components. The design strategy is devised in order to nullify signal loss and co-channel interference while applied for tuning in smart signal processing networks. Most popular cat swarm optimization (CSO) technique and its improved version, named as improved cat swarm optimization (ICSO) technique are proposed for the effective searching of tuning parameters generally, for narrow-width and arbitrary tuning position. The robustness of the proposed ICSO is validated through eight CEC 2005 benchmark functions. The efficacy of the proposed ICSO method of automation is also compared with a few popular methods in order to narrate its complete implementation procedures.
Self-supervised Segmentation by Grouping Optical-Flow

We propose to self-supervise a convolutional neural network operating on images using temporal information from videos. The task is to learn a representation of single images and the supervision for this is obtained by learning to group image pixels in such a way that their collective motion is "coherent". This learning by grouping approach is used as a pre-training as well as segmentation strategy. Preliminary results suggest that the segments obtained are reasonable and the representation learned transfers well for classification.
Reducing the carbon emission by early prediction of peak time load in a data center

Predicting the peak time load among data center and distributing the load will minimize the usage of the power consumption and also will minimize the carbon emission from data center. Reducing the carbon emission by lessening the energy consumption in a data center will impact on environment which will lead to a reduced carbon footprint. The proposed Water Shower Model (WSM) with Circular Peak Time Services (CPTS) has reduced the execution time to 10 ms comparing with Round Robin Algorithm. The load is shared among the data centers by predicting the type of request by the user as Read Only Request (ROR) or Read Write Request (RWR). The ROR will assign the load to an optimized Container and the RWR will assign the load to a Virtual Machine. CPTS is a proposed model used to measure the carbon emission right from the idle state of the server in a datacenter and till it reaches the peak time of the load and vice versa. The advantage of existing Dynamic Voltage Frequency Scaling (DVFS) techniques is used in the proposed model to optimize the resource allotment and adjust the power and speed in computing devices which allocates only the required minimal amount of power for performing a task.