Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence in Financial Services

The use of artificial intelligence tools has escalated recently in all sectors of the economy owing, among other factors, to the growing volume of digital data and higher computational capacity. Major benefits may be reaped from applying these tools to the provision of financial services, not only for financial institutions but also for society as a whole. This article describes some of those benefits, and also some of the main uses being made of these tools, both by financial institutions and central banks. It also indicates the main limitations of the technology and its possible implications for the correct functioning of the financial system.
Software Reuse in the Era of Opportunistic Design

Opportunistic design, an approach in which people develop new software systems by routinely reusing and combining components that were not designed to be used together, has become very popular. This emergent pattern places focus on largescale reuse and developer convenience with the developers "trawling" for most suitable open source components and modules online. The availability of open source assets for almost all imaginable domains has led to software systems in which the visible application code, as written by the application developers themselves, forms only the "tip of the iceberg," compared to the reused bulk that remains mostly unknown to the developers. The actual reuse takes place in an ad hoc, mix-and-match fashion. In this article, we take a look at this increasingly popular approach in light of our industry experiences. We argue that challenges associated with such a development model are quite different from traditional software development and reuse.
Multi-speaker Emotional Acoustic Modeling for CNN-based Speech Synthesis

In this paper, we investigate multi-speaker emotional acoustic modeling methods for convolutional neural network (CNN) based speech synthesis system. For emotion modeling, we extend to the speech synthesis system that learns a latent embedding space of emotion, derived from a desired emotional identity, and we use emotion code and mel-frequency spectrogram as an emotion identity. In order to model speaker variation in a text-to-speech (TTS) system, we use speaker representations such as trainable speaker embedding and speaker code. We have implemented speech synthesis systems combining speaker representation and emotion representation and compared them by experiments. Experimental results have demonstrated that the multi-speaker emotional speech synthesis approach using trainable speaker embedding and emotion representation from mel spectrogram achieves higher performance when compared with other approaches in terms of naturalness, speaker similarity, and emotion similarity.
Trajectory and vibration control of a flexible joint manipulator using interval type-2 fuzzy logic

This study presents the development of a novel interval type-2 fuzzy logic controller for real time trajectory and vibration control of a flexible joint manipulator. The controller is designed on the Mamdani based interval type-2 fuzzy logic toolbox, which is developed by the authors, using interval triangular membership functions and Karnik-Mendel type reduction algorithm. The closed-loop stability of the system is proved based on Lyapunov stability theorem. In order to observe the effectiveness and robustness of the proposed controller to variations of system parameters (change in link length and payload), the experimental results of interval type-2 and conventional type-1 fuzzy logic controllers are compared. The results show that proposed controller clearly improves the link vibration and trajectory tracking behavior of the system.
Face recognition based on dictionary learning and subspace learning

Dictionary learning plays an important role in sparse representation based face recognition. Many dictionary learning algorithms have been successfully applied to face recognition. However, for corrupted data because of noise or face variations (e.g. occlusion and large pose variation), their performances decline due to the disparity between domains. In this paper, we propose a face recognition algorithm based on dictionary learning and subspace learning (DLSL). In DLSL, a new subspace learning algorithm (SL) is proposed by using sparse constraint, low-rank technology and our label relaxation model to reduce the disparity between domains. Meanwhile, we propose a high-performance dictionary learning algorithm (HPDL) by constructing the embedding term, non-local self-similarity term, and time complexity drop term. In the obtained subspace, we use HPDL to classify these mapped test samples. DLSL is compared with other 28 algorithms on FRGC, LFW, CVL, Yale B and AR face databases. Experimental results show that DLSL achieves better performance than those 28 algorithms, including many state-of-the-art algorithms, such as recurrent regression neural network (RRNN), multimodal deep face recognition (MDFR) and projective low-rank representation (PLR).
How to evoke consumer approach intention toward VR stores? Sequential mediation through telepresence and experiential value

Abstract This study explores the roles of vividness and interactivity—two technological dimensions of virtual reality mediums—for consumers’ approach intention toward immersive virtual reality stores. In addition to telepresence, a well-established variable in explaining the effectiveness of virtual mediums, this study proposes experiential value as the second mediator that can lead to successful consumer experiences in commercial virtual environments. We recruited 101 volunteers who were willing to experience a virtual reality store with head-mounted displays. The results show that participants who perceive higher vividness and interactivity of an immersive virtual reality store tend to show stronger approach intentions, and that such positive influences are serially mediated by perceived telepresence and experiential shopping value. In multiple-step mediation analysis, the perceived experiential shopping value is found to be the key determinant in evoking consumers’ approach intention. The study concludes that virtual reality stores should be built in a way that provides experiential value by utilizing strong telepresence to achieve positive consumers’ approach intention. Suggestions for further research and practical applications are also discussed.
Review and critique of the information systems development project failure literature: An argument for exploring information systems development project distress

High failure rates of information systems development (ISD) projects continue to trouble organizations and information systems practices. Such a state of affairs has been of great concern for the information systems discipline for decades, motivating information systems researchers to focus on and extensively study ISD project failure. However, the increasing complexity and uncertainty of ISD projects and contemporary system development processes are challenging ISD project failure scholarship. In this article, we ask the questions: What are the contributions and weaknesses of the extant ISD project failure/success literature? What are potential avenues to move the ISD literature forward? To answer these questions, first, we present a literature review that assesses research contributions within the major perspectives on ISD failure (i.e. rationalist, process and narrative). While the extant research within all perspectives make significant contributions to knowledge, we find that researchers remain preoccupied with ‘project failure’ as an end state of an ISD project. They pay little attention to problematic situations arising during ISD projects before they become failed projects. Based on the review and critique of the literature, we then argue that there is a significant benefit in extending research focus from ISD project failure to ‘ISD project distress’, which we define as a harmful project condition involving dynamic and fluid constellation of critical problems that are difficult to identify, understand and resolve. While ISD project distress is an increasingly perilous and consequential phenomenon, little is known about its nature and potential responses. Drawing from the sensemaking literature, we propose a multilevel theoretical framework for understanding the nature and sources of ISD project distress that provides a foundation for exploring early detection and timely response. We demonstrate the theoretical and practical relevance of the concept of ISD project distress and propose a corresponding research agenda.
Practical security of continuous-variable quantum key distribution with reduced optical attenuation

In a practical continuous-variable quantum key distribution (CVQKD) system, the optical attenuator can adjust the Gaussian-modulated coherent states and the local oscillator signal to an optimal value for guaranteeing the security of the system and optimizing the performance of the system. However, the performance of the optical attenuator may deteriorate due to the intentional and unintentional damage of the device. In this paper, we investigate the practical security of a CVQKD system with reduced optical attenuation. We find that the secret key rate of the system may be overestimated based on the investigation of parameter estimation under the effects of reduced optical attenuation. This opens a security loophole for Eve to successfully perform an intercept-resend attack in a practical CVQKD system. To close this loophole, we add an optical fuse at Alice's output port and design a scheme to monitor the level of optical attenuation in real time, which can make the secret key rate of the system evaluated precisely. The analysis shows that these countermeasures can effectively resist this potential attack.
Particle Filtering: the First 25 Years and beyond

This paper presents a survey of the ideas behind the particle filtering, or sequential Monte Carlo, method, from at least 1930 up to the present day. The particle filter, which is now 25 years old, has been an immensely successful and widely used suite of methods for filtering and smoothing in state space models, and it is still under research today. The key ideas that led to the development in 1993 of the original particle filter, the bootstrap filter, were Monte Carlo integration, Importance Sampling, Bayesian updating, Probabilistic State Space models, and Sampling-Importance-Resampling. We survey these methods within their historical context and then provide a general framework for description of most current variants on the particle filtering methodology, based upon updating the joint smoothing distribution of the states. This framework aids in the understanding of the various elements of a particle filter, including resampling. prediction and weighting. We further summarise recent developments and look to the future of the methodology.
BLHUC: Bayesian Learning of Hidden Unit Contributions for Deep Neural Network Speaker Adaptation

Speaker adaptation techniques play a key role in reducing the mismatch between speech recognition systems and target users. In order to robustly learn speaker-dependent adaptation parameters, model based DNN adaptation techniques often require a significant amount of data. For example, in the commonly used learning hidden unit contributions (LHUC) based DNN adaptation, speaker-dependent high-dimensional hidden layer output scaling vectors are used. When limited adaptation data are available, the standard L-HUC is prone to over-fitting and poor generalization. To address the issue, Bayesian learning of hidden unit contributions (BLHUC) is proposed in this paper. A posterior distribution over the LHUC scaling vectors is used to explicitly model the uncertainty associated with the adaptation parameters. An efficient variational inference based approach is adopted to estimate the LHUC parameter posterior distribution. Experiments conducted on a 300-hour Switchboard setup showed that the proposed BLHUC method outperformed the baseline speaker-independent DNN systems and LHUC adapted DNN systems by up to 1.4% and 1.1% absolute reductions of word error rate respectively, when only using 1 utterance of adaptation data from each speaker. Consistent performance improvements were also obtained over the baseline, LHUC adapted and LHUC SAT systems when increasing the amount of adaptation data.
The effect of numerical aperture on quantitative use-wear studies and its implication on reproducibility

Many archeologists are skeptical about the capabilities of use-wear analysis to infer on the function of archeological tools, mainly because the method is seen as subjective, not standardized and not reproducible. Quantitative methods in particular have been developed and applied to address these issues. However, the importance of equipment, acquisition and analysis settings remains underestimated. One of those settings, the numerical aperture of the objective, has the potential to be one of the major factors leading to reproducibility issues. Here, experimental flint and quartzite tools were imaged using laser-scanning confocal microscopy with two objectives having the same magnification but different numerical apertures. The results demonstrate that 3D surface texture ISO 25178 parameters differ significantly when the same surface is measured with objectives having different numerical apertures. It is, however, unknown whether this property would blur or mask information related to use of the tools. Other acquisition and analyses settings are also discussed. We argue that to move use-wear analysis toward standardization, repeatability and reproducibility, the first step is to report all acquisition and analysis settings. This will allow the reproduction of use-wear studies, as well as tracing the differences between studies to given settings.
Learning based no-reference metric for assessing quality of experience of stereoscopic images

Human’s perception plays a very important role on image assessment, especially for stereoscopic images. In general, viewing stereoscopic 3D images will cause visual fatigue, eyestrain, dizziness or headache. Therefore, how to evaluate human’s perception of visual quality on 3D images becomes an emerging topic. In this paper, we propose a no-reference assessment metric for stereoscopic image quality of experience (QoE). First, the stereoscopic image pairs are used to calculate the disparity maps by optical flow estimation. Then the depth information are extracted from the disparity map, called as disparity-depth map. Next, we extract four types of features based on pixel value and distribution of disparity-depth map. Two regression models are used to predict visual discomfort scores. Also, we test the proposed method on EPFL 3D image database and IEEE-SA stereoscopic image database, respectively. The experiment results show that our proposed QoE assessment metric achieves excellent performance compared with state-of-the-art methods.
Voice Recognition based intelligent Wheelchair and GPS Tracking System

Development of a voice recognition based intelligent wheelchair system for physically handicapped people who are unable to drive the wheelchair by hand is represented in this paper where the patient can operate the wheelchair using voice commands and the location of patient can be traced using GPS module in the wheelchair that tracks and sends the information to smartphone application (app) via Firebase. Voice module V3 is used to record patient's voice and recognize that voice to follow the instructions of the patient. This kit converts the voice commands to hexadecimal numbers and then the data is fed to the Wi-Fi module to control the wheelchair. Wi-Fi module directs the motor driver IC to move the wheels in desired direction. Motor speed can also be controlled in three stages-low, medium, high. This system also offers obstacle detection automatically using IR sensor and a smartphone app has been developed for the family members of patient to know about the location of the patient. Use of firebase makes the system fast and android app offers low cost and user friendly environment than the conventional GSM based navigation systems. As this system simultaneously offers voice operated wheelchair, motor speed control, obstacle detection and GPS tracking of patient using android app, hopefully it will be a fruitful system for the handicapped people worldwide.
Attention-based Graph Convolutional Network for Recommendation System

Matrix completion with rating data and auxiliary information for users and items is a challenging task in recommendation systems. In this paper, we propose an end-to-end architecture named Attention-based Graph Convolutional Network (AGCN) to embed both rating data and auxiliary information in a unified space, and subsequently learn low-rank dense representations via graph convolutional networks and attention layers. Compared to previous work, AGCN reduces computational complexity with Chebyshev polynomial graph filters. The introduced attention layer, which encourages weighing the neighbor information to learn more expressive structural graph representations, can improve the prediction accuracy, and lead to faster and more stable convergence. Experimental results show that our model can perform better and converge faster than current state-of-the-art methods on the real-world MovieLens and Flixster datasets.
A study on Human Activity Recognition Using Gyroscope, Accelerometer, Temperature and Humidity data

An adequate variety of sensors employed in smartphones, being accessible, a great prospect concerning the research sphere towards data mining and machine learning has been discerned. Regarding the discerned arena of data mining and machine learning, a total of ten human activities encompassing sitting, walking, jogging, lying, walking upstairs and downstairs, cycling, standing, squatting in a toilet and fallen down have been subjected to be recognized in this paper. Employing a total of four sensors comprising Accelerometer sensor, Gyroscope sensor, Humidity sensor and Temperature sensor, we collected labeled data in connection with daily activities of three subjects and epitomized in 1Hz frequency. Afterwards, a model respecting the forecasting of activity recognition was brought out incorporating our training dataset. We consider the recognition of pristine activities as well as higher accuracy as the quirky attribute of our research. Through this research, heterogeneous applications regarding analysis of disease associated with physical action, overseeing of physical action and elderly care can be accomplished.
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series

Gaussian processes (GP) for machine learning have been studied systematically over the past two decades and they are by now widely used in a number of diverse applications. However, GP kernel design and the associated hyper-parameter optimization are still hard and to a large extend open problems. In this paper, we consider the task of GP regression for time series modeling and analysis. The underlying stationary kernel can be approximated arbitrarily close by a new proposed grid spectral mixture (GSM) kernel, which turns out to be a linear combination of low-rank sub-kernels. In the case where a large number of the sub-kernels are used, either the Nystr\"{o}m or the random Fourier feature approximations can be adopted to deal efficiently with the computational demands. The unknown GP hyper-parameters consist of the non-negative weights of all sub-kernels as well as the noise variance; their estimation is performed via the maximum-likelihood (ML) estimation framework. Two efficient numerical optimization methods for solving the unknown hyper-parameters are derived, including a sequential majorization-minimization (MM) method and a non-linearly constrained alternating direction of multiplier method (ADMM). The MM matches perfectly with the proven low-rank property of the proposed GSM sub-kernels and turns out to be a part of efficiency, stable, and efficient solver, while the ADMM has the potential to generate better local minimum in terms of the test MSE. Experimental results, based on various classic time series data sets, corroborate that the proposed GSM kernel-based GP regression model outperforms several salient competitors of similar kind in terms of prediction mean-squared-error and numerical stability.
Deep Learning based Pedestrian Detection at all Light Conditions

Multispectral pedestrian detection is becoming increasingly important in the field of computer vision due to its applications in driver assistance, surveillance, and monitoring. In this paper, we propose a brightness aware model for pedestrian detection using deep learning. A novel brightness aware mechanism depicts various illumination conditions, so as to enable prediction of day/ night scenario. Based on the detection of the brightness aware mechanism, a color or thermal model is used to detect pedestrians under day or night conditions respectively. The proposed method trained on FLIR-ADAS Thermal dataset and PASCAL VOC Color dataset, has achieved a mAP of `81.27%', which outperforms the current state of the art.
Vocal Shortcuts for Creative Experts

Vocal shortcuts, short spoken phrases to control interfaces, have the potential to reduce cognitive and physical costs of interactions. They may benefit expert users of creative applications (e.g., designers, illustrators) by helping them maintain creative focus. To aid the design of vocal shortcuts and gather use cases and design guidelines for speech interaction, we interviewed ten creative experts. Based on our findings, we built VoiceCuts, a prototype implementation of vocal shortcuts in the context of an existing creative application. In contrast to other speech interfaces, VoiceCuts targets experts' unique needs by handling short and partial commands and leverages document model and application context to disambiguate user utterances. We report on the viability and limitations of our approach based on feedback from creative experts.
Extractive summarization of documents with images based on multi-modal RNN

Rapid growth of multi-modal documents containing images on the Internet expresses strong demand on multi-modal summarization. The challenge is to create a computing method that can uniformly process text and image. Deep learning provides basic models for meeting this challenge. This paper treats extractive multi-modal summarization as a classification problem and proposes a sentence–image classification method based on the multi-modal RNN model. Our method encodes words and sentences with the hierarchical RNN models and encodes the ordered image set with the CNN model and the RNN model, and then calculates the selection probability of sentences and the sentence–image alignment probability through a logistic classifier taking text coverage, text redundancy, image set coverage, and image set redundancy as features. Two methods are proposed to compute the image set redundancy feature by combining the important scores of sentences and the hidden sentence–image alignment. Experiments on the extended DailyMail corpora constructed by collecting images and captions from the Web show that our method outperforms 11 baseline text summarization methods and that adopting the two image-related features in the classification method can improve text summarization. Our method is able to mine the hidden sentence–image alignments and to create informative well-aligned multi-modal summaries.