Reviewerly/exHarmony huggingface dataset:
A Danish Nonsense Word Corpus for Phoneme Recognition Measurements

Signal processing algorithms intended to improve speech intelligibility are common, e.g. in hearing aids.Due to the spectral and temporal variations between speech segments, the effect of such processing is likely to vary across the signal.To facilitate the analysis of these varying effects, a Danish speech corpus for measuring the perception of individual phonemes was developed.More than 1150 nonsense words were created according to a common template and audio-visually recorded with two male and two female talkers.Carrier sentences were also recorded.The audio recordings of all words were presented to ten normal-hearing listeners to ensure that phoneme recognition scores under optimal conditions were close to 100%.Accepted words were compiled into test lists with different characteristics.These lists were evaluated with seven normal-hearing listeners to determine norm data and to investigate memory effects.The speech recognition thresholds (SRTs) of the test lists varied between -7 to -0.9 dB signal-to-noise ratio (SNR) depending on list type and talker.The recognition score sensitivity to the SNR was 5.2 to 8.9%/dB.Memory effects were small and not significant.The presented speech materials seem well suited for measuring phoneme recognition scores and thus for assessing signal processing effects that vary across speech segments.
Behavioral Biometrics and Machine Learning to Secure Website Logins

In a world dominated by e-commerce and electronic transactions, the business value of a secure website is immeasurable. With the ongoing wave of Artificial Intelligence and Big Data, hackers have far more sophisticated tools at their disposal to orchestrate identity fraud on login portals. Such attacks bypass static security rules and hence protection against them requires the use of machine learning based ‘intelligent’ security algorithms. This paper explores the use of client behavioral biometrics to secure website logins. A client’s mouse dynamics, keystrokes and click patterns during login are used to create a customized security model for each user that can differentiate the user of interest from any other impersonator. Such a model, combined with existing protocols, will provide enhanced security for the user’ profile, even if credentials are compromised. The module first employs a means of collecting relevant behavioral data from the client side when a new account is created. The collection module can easily be integrated with any web application without impacting website performance. After sufficient collection of login data, a biometric-based fraud detection algorithm is created that secures the account against future impersonators. Our choice of algorithms is the Multilayer Perceptron, Support Vector Machine and Adaptive Boosting, the outcomes of which are polled to give the prediction. We find that such a model shows good performance (accuracy, precision and recall) for different train: test splits. Moreover, the model is easily implementable for any web based authentication, is scalable and can be fully automated, if a dataset like ours can be created from client activity on the web application of interest.
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction

We study the interplay between surrogate methods for structured prediction and techniques from multitask learning designed to leverage relationships between surrogate outputs. We propose an efficient algorithm based on trace norm regularization which, differently from previous methods, does not require explicit knowledge of the coding/decoding functions of the surrogate framework. As a result, our algorithm can be applied to the broad class of problems in which the surrogate space is large or even infinite dimensional. We study excess risk bounds for trace norm regularized structured prediction, implying the consistency and learning rates for our estimator. We also identify relevant regimes in which our approach can enjoy better generalization performance than previous methods. Numerical experiments on ranking problems indicate that enforcing low-rank relations among surrogate outputs may indeed provide a significant advantage in practice.
Generative Models for Deep Learning with Very Scarce Data

The goal of this paper is to deal with a data scarcity scenario where deep learning techniques use to fail. We compare the use of two well established techniques, Restricted Boltzmann Machines and Variational Auto-encoders, as generative models in order to increase the training set in a classification framework. Essentially, we rely on Markov Chain Monte Carlo (MCMC) algorithms for generating new samples. We show that generalization can be improved comparing this methodology to other state-of-the-art techniques, e.g. semi-supervised learning with ladder networks. Furthermore, we show that RBM is better than VAE generating new samples for training a classifier with good generalization capabilities.
Bandwidth-Efficient Live Virtual Reality Streaming Scheme for Reducing View Adaptation Delay

This paper proposes a dynamic-tiling-based bandwidth-efficient (DTBE) virtual reality (VR) streaming scheme.We consider 360-degree VR contents with multiple view points such as the front, back, upper, and bottom sides.At a given time, the focus of a client is always bound to a certain view among multiple view points.By utilizing this perspective, under our proposed scheme, tiles with high encoding rates are selectively assigned to the focused view where multiple view points consist of multiple tiles with different encoding rates.The other tiles with low encoding rates are assigned to the remaining view points.Furthermore, for reducing view adaptation delay, we design a novel rapid view adaptation mechanism that selectively delivers an I-frame during view point updates by using frame indexing.We implement the proposed scheme on a commercial VR test bed where we adopt the MPEG media transport (MMT) standard with high-efficiency video coding (HEVC) tile modes.The measurement-based experiments show that the proposed scheme achieves an average data usage reduction of almost 65.2% as well as average view adaptation delay reduction of almost 57.7%.
Location Privacy Assured Internet of Things

Internet of Things (IoT) is in the booming age of its growth, therefore a vast amount of applications, projects, hardware/software solutions, and customized concepts are being developed. The proliferation of IoT will enable location-based services to be available everywhere for everyone, and this will raise a large number of privacy issues related to the collection, usage, retention, and disclosure of the user’s location information. In order to provide a solution to this unique problem of IoT, this paper proposes Location Privacy Assured Internet of Things (LPA-IoT) scheme, which uses the concepts of Mix-Zone, location-obfuscation along with context-awareness. To the authors’ best knowledge, the proposed LPA-IoT scheme is the first location-based privacy-preserving scheme for IoT that provides flexible privacy levels associated with the present context of the user.
Improved Semi-Supervised Learning with Multiple Graphs

We present a new approach for graph based semi-supervised learning based on a multi-component extension to the Gaussian MRF model. This approach models the observations on the vertices as jointly Gaussian with an inverse covariance matrix that is a weighted linear combination of multiple matrices. Building on randomized matrix trace estimation and fast Laplacian solvers, we develop fast and efficient algorithms for computing the best-fit (maximum likelihood) model and the predicted labels using gradient descent. Our model is considerably simpler, with just tens of parameters, and a single hyperparameter, in contrast with state-of-the-art approaches using deep learning techniques. Our experiments on benchmark citation networks show that the best-fit model estimated by our algorithm leads to significant improvements on all datasets compared to baseline models. Further, our performance compares favorably with several state-of-the-art methods on these datasets, and is comparable with the best performances.
Robust controllability of two-qubit Hamiltonian dynamics

Physically, quantum gates (unitary gates) for quantum computation are implemented by controlling the Hamiltonian dynamics of quantum systems. When full descriptions of the Hamiltonians are given, the set of implementable quantum gates is easily characterized by quantum control theory. In many real systems, however, the Hamiltonians may include unknown parameters due to the difficulty of performing precise measurements or instability of the system. In this paper, we consider the situation that some parameters of the Hamiltonian are unknown, but we still want to perform a robust control of the Hamiltonian dynamics to implement a quantum gate irrespectively to the unknown parameters. The existence of the robust control was previously shown for single-qubit systems, and a constructive method was developed for two-qubit systems if a full control of each qubit is available. We analytically investigate the robust controllability of two-qubit systems, and apply Lie-algebraic approaches to handle the cases where only one of the two qubits is controllable. We also numerically analyze the robust controllability of the two-qubit systems where the analytical approach is not necessarily applicable and investigate the relationship between the robust controllability of systems with a discrete and continuous unknown parameter.
Obstacles to quantum annealing in a planar embedding of XORSAT

We introduce a planar embedding of the k-regular k-XORSAT problem, in which solutions are encoded in the ground state of a classical statistical mechanics model of reversible logic gates arranged on a square grid and acting on bits that represent the Boolean variables of the problem. The special feature of this embedding is that the resulting model lacks a finite-temperature phase transition, thus bypassing the first-order thermodynamic transition known to occur in the random graph representation of XORSAT. In spite of this attractive feature, the thermal relaxation into the ground state displays remarkably slow glassy behavior. The question addressed in this paper is whether this planar embedding can afford an efficient path to solution of k-regular k-XORSAT via quantum adiabatic annealing. We first show that our model bypasses an avoided level crossing and consequent exponentially small gap in the limit of small transverse fields. We then present quantum Monte Carlo results for our embedding of the k-regular k-XORSAT that strongly support a picture in which second-order and first-order transitions develop at a finite transverse field for k = 2 and k = 3, respectively. This translates into power-law and exponential dependences in the scaling of energy gaps with system size, corresponding to times-to-solution which are, respectively, polynomial and exponential in the number of variables. We conclude that neither classical nor quantum annealing can efficiently solve our reformulation of XORSAT, even though the original problem can be solved in polynomial time by Gaussian elimination.
Using FDD for Small Project: An Empirical Case Study

Empirical analysis evaluates the proposed system via practical experience and reveals its pros and cons.Such type of evaluation is one of the widely used validation approach in software engineering.Conventional software process models performed well till mid of 1990s, but then gradually replaced by agile methodologies.This happened due to the various features, the agile family offered, which the conventional models failed to provide.However besides the advantages, agile models lacked at some areas as well.To get the extreme benefits from any agile model, it is necessary to eliminate the weaknesses of that model by customizing its development structure.Feature Driven Development (FDD) is one of the widely used agile models in software industry particularly for large scale projects.This model has been criticized by many researchers due to its weaknesses such as explicit dependency on experienced staff, little or no guidance for requirement gathering, rigid nature to accommodate requirement changes and heavy development structure.All these weaknesses make the FDD model suitable only for large scale projects where the requirements are less likely to change.This paper deals with the empirical evaluation of FDD during the development of a small scale web project so that the areas and practices of this model can be identified with empirical proof, which made this model suitable only for large projects.For effective evaluation, the results of FDD case study are compared with a published case study of Extreme Programing (XP), which is widely used for the development of small scale projects.
The Effect of Tactility and Socio-Relational Context on Social Presence and User Satisfaction

The objective of this study is to investigate the effect of socio-relational context and robot's tactility on the sense of social presence and user satisfaction. We executed a 2(socio-relational context: an intimate remote sender vs. a non-intimate remote sender) x 2(tactility: anthropomorphic tactility vs. non-anthropomorphic tactility) within-participants experiment $(\pmb{N}=24)$ . As a result, participants felt a stronger sense of a remote sender's presence when the remote sender was an intimate person than a non-intimate person, and felt a greater sense of a remote sender's presence when a robot had an anthropomorphic tactility than when a robot had a non-anthropomorphic tactility. In addition, participants preferred a telepresence robot with an anthropomorphic tactility in the context that they interacted with an intimate person, whereas a telepresence robot with a non-anthropomorphic tactility was preferred in the context that they interact with a non-intimate person in robot-mediated communication.
Efficient Image Deblurring Using Alpha Plane Blending on Images Recovered with Linearly Varied Point Spread Function (PSF)

The medical imaging has seen a significant technological shift from analog-film-based imaging to high-end digital computer imaging. One of the problems this domain has always faced is the blurred images due to the natural motion in the body and mechanical motion of the capturing devices. This work utilizes the image blending technique to improve the image quality while deblurring through motion adaptive point spread functions. The idea is to first get a sample set of images restored through Lucy RC deconvolution techniques using varied PSFs and then applying the image blending over the image set in alpha plane to get deblurred sharp images.
Asymptotic Optimality of a Time Optimal Path Parametrization Algorithm

The time optimal path parametrization problem addresses minimizing the traversal time of a specified path by an actuation constrained agent. Recently, an efficient numerical algorithm for solving this problem has been proposed. This letter theoretically establishes convergence of the former algorithm to the optimum for the whole class of problems solved optimally by computationally more demanding approaches based on convex programming. Additionally, we provide a characterization of the optimum, which may be of independent interest.
Designing and Implementing Fuzzy Expert System for Diagnosis of Psoriasis

The differential diagnosis of Erythemato-Squamous disease is a very important issue in dermatology. These diseases include dermatitis seborrhea, lichen planus, Pityriasis rosea, Chronic Dermatiticand Pityriasis rubra pilaris. The differential diagnosis of psoriasis need to spend a lot of time and costs and incorrect or delayed diagnosis will lead to risky consequences for the patient. In this regard, a fuzzy expert system was created to diagnose psoriasis at any time and in any place.Methods: In this study, an expert system is created to diagnose psoriasis based on fuzzy logic and rule-based database using MATLAB software. Also to test system the data from 190 patients with psoriasis was used among which 126 people with psoriasis were selected.Results: The accuracy, sensitivity and accuracy of the system, was obtained 96%, 97% and 95%, respectively. The area under the ROC curve was obtained 0.97 and that of Kappa test was obtained 0.92 (P &lt; 0.001).Conclusion: Given the importance of early diagnosis of psoriasis, the use of expert systems can play an important role in preventing disease progression, reducing severe pain and costs.
Toward action comprehension for searching: Mining actionable intents in query entities

Understanding search engine users' intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users' future actions. In this article, we present a novel research method for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, that is, the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment, based on the Action Mining (AM) query entity data set from the Actionable Knowledge Graph (AKG) task at NTCIR‐13, suggests that the proposed approach is effective in generating an informative and early‐satisfying ranking of potential actions for search users.
Dynamic Provisioning of Cloud Resources Based on Workload Prediction

Most of the businesses nowadays have started using cloud platforms to host their software applications. A cloud platform is a shared resource that provides various services like software as a service (SAAS), infrastructure as a service (IAAS) or anything as a service (XAAS) that is required to develop and deploy any business application. These cloud services are provided as virtual machines (VM) that can handle the end-user’s requirements. The cloud providers have to ensure efficient resource handling mechanisms for different time intervals to avoid wastage of resources. Auto-scaling mechanisms would take care of using these resources appropriately along with providing an excellent quality of service. The researchers have used various approaches to perform auto-scaling. In this paper, a framework based on dynamic provisioning of cloud resources using workload prediction is discussed.
Networks, start-up capital and womens entrepreneurial performance in Africa: evidence from Eswatini

This chapter analyses the role of networks in the access of women entrepreneurs to start-up capital and firm performance in Eswatini, a country with one of the highest female unemployment rates in Africa. The chapter first shows that higher initial capital is associated with better sales performance for both men and women entrepreneurs. Women entrepreneurs start their firms with smaller start-up capital than men and are more likely to fund it from their own sources, which reduces the size of their firm and sales level. However, women with higher education start their firms with more capital than their less educated counterparts. Moreover, women who receive support from professional networks have higher initial capital, while those trained in financial literacy more often access external funding sources, including through their networks.
Formal Verification of Open Multi-Agent Systems

We study open multi-agent systems in which countably many agents may leave and join the system at run-time. We introduce a semantics, based on interpreted systems, to capture the openness of the system and show how an indexed variant of temporal-epistemic logic can be used to express specifications on them. We define the verification problem and show it is undecidable. We isolate one decidable class of open multi-agent systems and give a partial decision procedure for another one. We introduce MCMAS-OP, an open-source toolkit implementing the verification procedures. We present the results obtained using our tool on two examples.
Discovering Student Interest and Talent in Graduate Cybersecurity Education

There has been a large and increasing demand for professionals and leaders in the cybersecurity field. Graduate schools in America are facing the challenge and opportunity to meet the demand by discovering talent and producing qualified workforce for the cybersecurity industry. This research paper proposes a model of discovering student interest and talent in graduate cybersecurity education through career guidance and curriculum development. This study is to illustrate the model with sample graduate student admissions and graduate cybersecurity program data at a private university in the northeast region of the United States.
Self-Discriminative Learning for Unsupervised Document Embedding

Unsupervised document representation learning is an important task providing pre-trained features for NLP applications. Unlike most previous work which learn the embedding based on self-prediction of the surface of text, we explicitly exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. Extensive experiments on both small and large public datasets show the competitiveness of the proposed method. In evaluations on standard document classification, our model has errors that are 5 to 13% lower than state-of-the-art unsupervised embedding models. The reduction in error is even more pronounced in scarce label setting.
Quality Assessment of Objects Detection and Localization in а Video Stream

The paper presents a software and hardware system for quality assessment of detection and localization of objects of interest (in most cases, the functional systems of this type coincides with the functionality of television machines), depending on the content of the input video. The development of a generator for recording the input video sequence along the trajectories of objects, the trajectory of the camera and image distortion in the frame is performed. The description of the main modules of the complex is presented. The description of the simulation process in a distributed testing system is given. Client applications can be run on the same computer as the server, and on remote computers