Reviewerly/exHarmony huggingface dataset:
Voice biometrics security: Extrapolating false alarm rate via hierarchical Bayesian modeling of speaker verification scores

How secure automatic speaker verification (ASV) technology is? More concretely, given a specific target speaker, how likely is it to find another person who gets falsely accepted as that target? This question may be addressed empirically by studying naturally confusable pairs of speakers within a large enough corpus. To this end, one might expect to find at least some speaker pairs that are indistinguishable from each other in terms of ASV. To a certain extent, such aim is mirrored in the standardized ASV evaluation benchmarks, for instance, the series of speaker recognition evaluation (SRE) organized by the National Institute of Standards and Technology (NIST). Nonetheless, arguably the number of speakers in such evaluation benchmarks represents only a small fraction of all possible human voices, making it challenging to extrapolate performance beyond a given corpus. Furthermore, the impostors used in performance evaluation are usually selected randomly. A potentially more meaningful definition of an impostor — at least in the context of security-driven ASV applications — would be closest (most confusable) other speaker to a given target. We put forward a novel performance assessment framework to address both the inadequacy of the random-impostor evaluation model and the size limitation of evaluation corpora by addressing ASV security against closest impostors on arbitrarily large datasets. The framework allows one to make a prediction of the safety of given ASV technology, in its current state, for arbitrarily large speaker database size consisting of virtual (sampled) speakers. As a proof-of-concept, we analyze the performance of two state-of-the-art ASV systems, based on i-vector and x-vector speaker embeddings (as implemented in the popular Kaldi toolkit), on the recent VoxCeleb 1, and 2 corpora, containing a total of 7365 speakers. We fix the number of target speakers to 1000, and generate up to N=100,000 virtual impostors sampled from the generative model. The model-based false alarm rates are in a reasonable agreement with empirical false alarm rates and, as predicted, increase substantially (values up to 98%) with N=100,000 impostors. Neither the i-vector or x-vector system is immune to increased false alarm rate at increased impostor database size, as predicted by the model.
Depth Estimation From a Single Image Using Guided Deep Network

This paper addresses the problem of monocular depth estimation, which plays a key role to understand a given scene. Owing to the success of the generative model using deep neural networks, the performance of depth estimation from a single image has been significantly improved. However, most previous approaches still fail to accurately estimate the depth boundary and thus lead to the result of the blurry restoration. In this paper, a novel and simple method is proposed by exploiting the latent space of the depth-to-depth network, which contains useful encoded features for guiding the process of depth generation. This network, so-called guided network, simply consists of convolution layers and their corresponding deconvolution ones, and is also easily trained by only using single depth images. For efficiently learning the relationship between a color value and its related depth value in a given image, we propose to train the color-to-depth network via loss defined along with features from the latent space of our guided network (i.e., depth-to-depth network). One important advantage of the proposed method is to greatly enhance local details even under complicated background regions. Moreover, the proposed method works very fast (at 125 fps with GPU). Experimental results on various benchmark datasets show the efficiency and robustness of the proposed approach compared to state-of-the-art methods.
Finding optimal qubit permutations for IBM's quantum computer architectures

IBM offers quantum processors for Clifford+T circuits. The only restriction is that not all CNOT gates are implemented and must be substituted with alternate sequences of gates. Each CNOT has its own mapping with a respective cost. However, by permuting the qubits, the number of CNOT that need mappings can be reduced. The problem is to find a good permutation without an exhaustive search. In this paper we propose a solution for this problem. The permutation problem is formulated as an Integer Linear Programming (ILP) problem. Solving the ILP problem, the lowest cost permutation for the CNOT mappings is guaranteed. To test and validated the proposed formulation, quantum architectures with 5 and 16 qubits were used. The ILP formulation along with mapping techniques found circuits with up to 64% fewer gates than other approaches.
Conjugate utilization of Landsat-8 OLI, ground gravity and magnetic data for targeting mafic cumulates within anorthositic-layered complex of Sittampundi, India

In this article, we have processed Landsat-8 spectral bands, ground gravity, and magnetic data for delineating the mafic cumulates within the anorthositic-layered complex in Sittampundi, Tamilnadu, India. In this regard, surface anomaly map of mafic cumulates was derived by processing spectral bands of Operational Land Imager (OLI) sensor using matched filtering method. Residual gravity, magnetic anomaly maps are also derived. Spatial position of geophysical anomalies corroborate well with the spectral anomalies. A promising zone of chromitite occurrences is identified at the eastern fringe of the study area based on the concurrence of spectral and geophysical anomalies. Spatial profiles of geophysical and spectral anomalies around this promising zone are similar to the respective spatial profiles observed around the known chromite bearing mafic cumulates. Depth continuity of the mafic cumulates is confirmed by deriving of gravity and magnetic anomaly map at different depth using upward continuation filters to confirm concealed deposit.
Methodical IoT-Based Information System in Healthcare

Elderly monitoring has become essential to provide comfort and flexible life for the aged. Internet of things is a field that aids in providing accurate information through communication. Using these devices, emergency alerts can be raised. The chapter provides an assistance model that aids the caretakers and doctors to provide appropriate action during emergencies. Different conditions of the patient are considered to analyze the working of the system. The work shows considerable amount of flexibility from the previous system.
The effect of probe-Ohmic environment coupling type and probe information flow on quantum probing of the cutoff frequency

This work aims to compare results of dissipating and pure dephasing single-qubit probes in characterizing the cutoff frequency of a harmonic reservoir Ohmic spectral density. In particular, we proved that a dissipating single-qubit improves the estimation precision of the cutoff frequency estimator. The information backflow and outflow of the dissipating and the dephasing single-qubit are compared. The relation between the quantum Fisher information and the information outflow and backflow of the probe is obtained. The results show that difference between the values of information outflow and backflow of a dissipating single-qubit is larger than a dephasing single-qubit. So, a single-qubit probe extracts more information from an Ohmic reservoir by increasing the difference between the values of information outflow and backflow of the probe.
Hand-Over-Face Input Sensing for Interaction with Smartphones through the Built-in Camera

This paper proposes using face as a touch surface and employing hand-over-face (HOF) gestures as a novel input modality for interaction with smartphones, especially when touch input is limited. We contribute InterFace, a general system framework that enables the HOF input modality using advanced computer vision techniques. As an examplar of the usage of this framework, we demonstrate the feasibility and usefulness of HOF with an Android application for improving single-user and group selfie-taking experience through providing appearance customization in real-time. In a within-subjects study comparing HOF against touch input for single-user interaction, we found that HOF input led to significant improvements in accuracy and perceived workload, and was preferred by the participants. Qualitative results of an observational study also demonstrated the potential of HOF input modality to improve the user experience in multi-user interactions. Based on the lessons learned from our studies, we propose a set of potential applications of HOF to support smartphone interaction. We envision that the affordances provided by the this modality can expand the mobile interaction vocabulary and facilitate scenarios where touch input is limited or even not possible.
IoT in Healthcare

One of the best-known features of IoT is automation. Because of this, IoT is a much-needed field for many applications, namely emergency and healthcare domains. IoT has made many revolutionary changes in the healthcare industry. IoT paves the way to numerous advancements for healthcare. The possibilities of IoT have reached their peak in the commercial industry and health sector. In recent years, serious concerns have been raised over the control and access of one's individual information. Privacy and security of the IoT devices can be compromised by intruders. Apart from the numerous benefits of IoTs, there are several security and privacy concerns to consider. A brief overview of different kinds of security attacks, solution for the attacks, privacy and security issues are discussed in this chapter.
Modeling and simulation of movement of dispersed group of mobile robots using Simscape multibody software

The paper presents the step by step process of modelling the movement of dispersed group of mobile robots using the MATLAB Simulink and Simscape Multibody software. As a modelled object, a group of laboratory robots, TurtleBot2, was adopted. First, the construction of a robot CAD model was described. Next, the control system developed in Simulink was described. At the end, the robot components were defined in Simscape Multibody and a group movements analysis was carried out.
Exploiting Augmented Reality to Enhance Piping and Instrumentation Diagrams for Information Retrieval Tasks in Industry 4.0 Maintenance

In this work, we present an Augmented Reality (AR) application for handheld devices that support operators in information retrieval tasks in maintenance procedures in the context of Industry 4.0. Indeed, using AR allows the integration of knowledge-based information, traditionally used by operators and mainly provided in the form of technical drawings, and data available from sensors on the equipment. This approach is suggested by companies, especially Small and Medium-sized Enterprises, that want a gradual introduction of Industry 4.0 technologies within their established practices. We implemented a prototype of the application for the case study of a milling plant. The application augments on a Piping and Instrumentation Diagram (P&ID) of the plant some virtual interactive graphics (hotspots) referenced to specific components drawn. Component data are retrieved, through a user interface, directly from the factory database and displayed on the screen. We evaluated the application through a user study aimed at comparing the AR application with the current practice, based on paper documentation, for an information retrieval task within a maintenance procedure. Results of the study revealed that AR is effective for this task in terms of task time reduction and usability. The AR application was tested both with a tablet and a smartphone, but results revealed that using tablet does not improve user performance in terms of task time, error rate, and usability.
Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

In general, the development of economic infrastructure systems requires a behavioural comprehensive analysis of different financial variables or rates to establish its long-term success with regards to the Equity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizations have developed economic scenarios supported by computational techniques and models to identify the evolution of these financial rates. However, these models and techniques have shown a series of limitations with regard to the financial management process and its impact on EIRR over time. To address these limitations in an inclusive way, researchers have developed different approaches and methodologies focused on the development of financial models using stochastic simulation methods and computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where the input consists in financial rates that are characterized as linguistic rates through a series of adaptive logistic functions. The stochastic process that explains the behaviour of the financial rates over time and their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financing scenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it was possible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM was validated using three different models: (1) a financial management tool; (2) a general FCM without pre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where the causalities between the concepts or rates were obtained as a result of an independent effects analysis applying a cross modelling between variables and by using a statistical multi-linear model (statistical significance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporation of an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. This allowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financing period. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in different knowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by the traditional computational models used to design these financial scenarios.
Importance and Aptitude of Source Code Density for Commit Classification into Maintenance Activities

Commit classification, the automatic classification of the purpose of changes to software, can support the understanding and quality improvement of software and its development process. We introduce code density of a commit, a measure of the net size of a commit, as a novel feature and study how well it is suited to determine the purpose of a change. We also compare the accuracy of code-density-based classifications with existing size-based classifications. By applying standard classification models, we demonstrate the significance of code density for the accuracy of commit classification. We achieve up to 89% accuracy and a Kappa of 0.82 for the cross-project commit classification where the model is trained on one project and applied to other projects. Such highly accurate classification of the purpose of software changes helps to improve the confidence in software (process) quality analyses exploiting this classification information.
Assessment of Spear Phishing User Experience and Awareness: An Evaluation Framework Model of Spear Phishing Exposure Level (SPEL) in the Namibian Financial Industry

Social engineering has become a major threat to organisations' IT systems. Users are normally the weakest links in security chains and they put their organisations at risk of internet attacks through various social engineering tricks implemented by online criminals. One of the thriving electronic social engineering attacks that is increasingly targeting electronic banking systems and users is spear phishing attack. Consequently, this research assessed user experience and awareness by evaluating users' abilities to detect if a specific email is spear phishing. Moreover, the research proposes an evaluation framework for an effective assessment of the organisations' exposure level to spear phishing threat. In the SPEL evaluation framework, two information security frameworks (ISO27001:2013) and (NIST SP 800 -53) were applied to identify threat vital signs within the organisation, whereas the Protection Motivation Theory (PMT) theory was used in the identification of the user vulnerability signs.
Adversarial Domain Adaptation via Category Transfer

Adversarial domain adaptation has achieved some success in learning transferable feature representations and reducing distribution discrepancy between source and target domains. However, existing approaches mainly focus on alignment of global source and target distributions without considering complex structures in categories underlying different distributions, resulting in domain confusion and the mix of distinguishable structures. In this paper, we propose an adversarial domain adaptation via category transfer (ADACT) approach for unsupervised domain adaptation (UDA). ADACT first captures multi-category information through training source and target feature generators as well as a label predictor. Secondly, it uses multi-category domain critic networks to category-wisely estimate Wasserstein distances across domains. Then it learns category-invariant feature representations by finely-grained matching different data distributions with the estimated Wasserstein distances. The adaptation can be achieved by the standard back-propagation training approach with this two-step iteration. The effectiveness of ADACT is demonstrated since it outperforms several state-of-the-art UDA methods on common domain adaptation datasets.
Label Distribution Feature Selection Based on Mutual Information in Fuzzy Rough Set Theory

As we all know, the multi-label learning is faced with the "dimension disaster", and the label distribution learning is actually faced with the same problem. Therefore, it is necessary to perform some appropriate pre-processing on the data before the label distribution learning, such as feature selection. Nowadays, researchers have proposed many feature selection algorithms for multi-label learning, while feature selection algorithms for label distribution learning are few. The difference between label distribution learning and multi-label learning is that in the existing multi-label classification methods, the importance of each label is considered to be the same, but in actual cases, different labels has different importance when they are describing the same instance, that is, there is an imbalance between the labels. Therefore, this paper proposed a feature selection algorithm based on information measures in fuzzy rough set theory for label distribution learning. The mutual information in fuzzy rough set theory is used to calculate the correlation between features and labels, and then a feature subset is selected by correlation maximization and redundancy minimization strategy. Finally, the experimental results demonstrate that the proposed feature selection algorithm is able to select valid subsets of features.
Randomness and permutations in coordinate descent methods

We consider coordinate descent (CD) methods with exact line search on convex quadratic problems. Our main focus is to study the performance of the CD method that use random permutations in each epoch and compare it to the performance of the CD methods that use deterministic orders and random sampling with replacement. We focus on a class of convex quadratic problems with a diagonally dominant Hessian matrix, for which we show that using random permutations instead of random with-replacement sampling improves the performance of the CD method in the worst-case. Furthermore, we prove that as the Hessian matrix becomes more diagonally dominant, the performance improvement attained by using random permutations increases. We also show that for this problem class, using any fixed deterministic order yields a superior performance than using random permutations. We present detailed theoretical analyses with respect to three different convergence criteria that are used in the literature and support our theoretical results with numerical experiments.
Chaotic Neural Network Reservoir

Simple structure and robust property of a reservoir neural network are preferable for hardware implementation of a high performance learning system, especially for time-series data processing. One of salient feature of the reservoir network is reproducibility or consistency of its responses to the same or similar inputs. This is usually guaranteed through the echo state property of the network by properly choosing synaptic weights among reservoir neurons. Another important feature is a variety of dynamics in the reservoir, which makes the reservoir to process complex time-varying input signals. One way to increase the variety of dynamics is introducing chaotic behavior by destabilize the reservoir network by changing weight values. However, this will violate the echo state property, therefore, chaotic dynamics are usually avoided in the reservoir computing.In this paper, we propose a method to introduce high-dimensional chaotic dynamics into the reservoir network, but keeping its consistency. To achieve this, we use a chaotic neural network model in the reservoir network, while keeping the weight matrix in the reservoir network to satisfy the echo state property criteria. In order to show the consistency of the chaotic neural network reservoir, preliminary results for chaotic time-series predictions through the chaotic neural network reservoir are illustrated. In addition, we discuss the application of the chaotic neural network reservoir to a self-aware hardware system.
Short Text Topic Modeling with Flexible Word Patterns

Since effective semantic representations are utilized in many practical applications, inferring discriminative and coherent latent topics from short texts is a critical and basic task. Traditional topic models like Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) behave not well on short texts due to data sparsity problem. One novel model called Biterm Topic Model (BTM) which models unordered word-pairs (i.e., biterms) from whole corpus was proposed to solve this problem. However, both the performance and efficiency of BTM are reduced because of many irrelevant and useless biterms. In this paper, we propose a Multiterm Topic Model (MTM) for short text topic modeling. MTM extracts variable-length and more correlative word patterns (i.e., multiterms) from the whole corpus. By directly modeling the generative process of multiterms, MTM can infer the word distributions of each topic and the topic distribution of each short text to alleviate the sparsity problem in short text modeling. With the the proper amount of flexible multiterms, learning process of MTM is enhanced. Through extensive experiments on two real-world short text collections, we show that MTM is more efficient and outperforms the baseline models in terms of topic coherence and text classification.