Reviewerly/exHarmony huggingface dataset:
Matchable Image Retrieval by Learning from Surface Reconstruction

Convolutional Neural Networks (CNNs) have achieved superior performance on object image retrieval, while Bag-of-Words (BoW) models with handcrafted local features still dominate the retrieval of overlapping images in 3D reconstruction. In this paper, we narrow down this gap by presenting an efficient CNN-based method to retrieve images with overlaps, which we refer to as the matchable image retrieval problem. Different from previous methods that generates training data based on sparse reconstruction, we create a large-scale image database with rich 3D geometrics and exploit information from surface reconstruction to obtain fine-grained training data. We propose a batched triplet-based loss function combined with mesh re-projection to effectively learn the CNN representation. The proposed method significantly accelerates the image retrieval process in 3D reconstruction and outperforms the state-of-the-art CNN-based and BoW methods for matchable image retrieval. The code and data are available at https://github.com/hlzz/mirror .
Untangling the knot: The information practices of enthusiast car restorers

A study of enthusiast car restorers is used to illustrate how an information practice approach can provide information science researchers with a richer, more nuanced understanding of the complex interrelationship between people, technology, and information. An ethnographic approach incorporating both semistructured interviews and in the garage ethnographic observation was employed. Analysis was undertaken using an inductive, thematic approach. The findings demonstrate that participants' information environments are rich and complex. Participants' accounts emphasized the corporeal and embodied nature of the restoration process, and this may account for why they privileged the social networks they had developed, often over many decades, over online resources and communities. The findings indicate that participants are engaged in much more than applied problem solving. What is also evident is that engagement in the social world of car restoration, and the networks of social knowledge sharing it affords, is significant for the emotional support it provides for older men who often lose these networks later in life. In a sense, the participants are not only rebuilding their cars but also their own sense of self.
Autoencoders for QoS Prediction at the Edge

In service-oriented architectures, collaborative filtering is a key technique for service recommendation based on QoS prediction. Matrix factorisation has emerged as one of the main approaches for collaborative filtering as it can handle sparse matrices and produces good prediction accuracy. However, this process is resource-intensive and training must take place in the cloud, which can lead to a number of issues for user privacy and being able to update the model with new QoS information. Due to the time-varying nature of QoS it is essential to update the QoS prediction model to ensure that it is using the most recent values to maintain prediction accuracy. The request time, which is the time for a middleware to submit a user's information and receive QoS metrics for a candidate services is also important due to the limited time during dynamic service adaptations to choose suitable replacement services. In this paper we propose a stacked autoencoder with dropout on a deep edge architecture and show how this can be used to reduce training and request time compared to traditional matrix factorisation algorithms, while maintaining predictive accuracy. To evaluate the accuracy of the algorithms we compare the actual and predicted QoS values using standard error metrics such as MAE and RMSE. In addition, we propose an alternative evaluation technique using the predictions as part of a service composition and measuring the impact that the predictions have on the response time and throughput of the final composition. This more clearly shows the direct impact that these algorithms will have in practice.
Constructions of Involutions Over Finite Fields

An involution over finite fields is a permutation polynomial whose inverse is itself. Owing to this property, involutions over finite fields have been widely used in applications, such as cryptography and coding theory. Following the idea by Wang to characterize the involutory behavior of the generalized cyclotomic mappings, this paper gives a more concise criterion for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x^{r}h(x^{s})\in {\mathbb F} _{q}[x]$ </tex-math></inline-formula> being involutions over the finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{q}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r\geq 1$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s\,|\, (q-1)$ </tex-math></inline-formula> . By using this criterion, we propose a general method to construct involutions of the form <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x^{r}h(x^{s})$ </tex-math></inline-formula> over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{q}$ </tex-math></inline-formula> from given involutions over some subgroups of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{q}^{*}$ </tex-math></inline-formula> by solving congruent and linear equations over finite fields. Then, many classes of explicit involutions of the form <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x^{r}h(x^{s})$ </tex-math></inline-formula> over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb F}_{q}$ </tex-math></inline-formula> are obtained.
Automatic generation of sentimental texts via mixture adversarial networks

Automatic generation of texts with different sentiment labels has wide use in artificial intelligence applications such as conversational agents. It is an important problem to be addressed for achieving emotional intelligence. In this paper, we propose two novel models, SentiGAN and C-SentiGAN, which have multiple generators and one multi-class discriminator, to address this problem. In our models, multiple generators are trained simultaneously, aiming at generating texts of different sentiment labels without supervision. We propose a penalty-based objective in generators to force each of them to generate diversified examples of a specific sentiment label. Moreover, the use of multiple generators and one multi-class discriminator can make each generator focus on generating its own texts of a specific sentiment label accurately. Experimental results on a variety of datasets demonstrate that our SentiGAN model consistently outperforms several state-of-the-art text generation models in the sentiment accuracy and quality of generated texts. In addition, experiments on conditional text generation tasks show that our C-SentiGAN model has good prospects for specific text generation tasks.
A Recurrent Neural Network Architecture for Failure Prediction in Deep Drawing Sensory Time Series Data

In the course of the fourth industrial revolution and the coherent phenomenon of industrial big data, production lines and, more specifically, single production processes are complemented by sensor systems to facilitate the acquisition of sensory data on the shop floor level, providing a number of possibilities for data driven optimization applications. One possible application is to support manual quality control at the end of a production line by an automated quality control system based on data driven predictive analysis of the acquired shop floor data. In this paper, we demonstrate the application of such a predictive quality system for a deep drawing manufacturing process of car body parts. As a first step, we formalized the experience and expertise of the domain experts to enable automated a-posteriori quality assessment of the deep drawing process data. As a second step, we trained a combination of two long short-term memory neural networks with the auto-labeled data to predict the occurrence of process failures, i.e. cracks within the manufactured car body part, enabling automated a-priori quality assessment of the deep drawing process. We show that our model correctly predicts the occurrence of more than 94% of the process failures, facilitating the possibility to react to failures pre-emptively before they actually occur. We argue that our approach can be transferred to many other production processes whose quality control can be supported by the acquisition of time series data from the involved production machines.
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations

With the development of graph convolutional networks (GCN), deep learning methods have started to be used on graph data. In additional to convolutional layers, pooling layers are another important components of deep learning. However, no effective pooling methods have been developed for graphs currently. In this work, we propose the graph pooling (gPool) layer, which employs a trainable projection vector to measure the importance of nodes in graphs. By selecting the k-most important nodes to form the new graph, gPool achieves the same objective as regular max pooling layers operation on images and texts. Another limitation of GCN when used on graph-based text representation tasks is that, GCNs do not consider the order information of nodes in graph. To address this limitation, we propose the hybrid convolutional (hConv) layer that combines GCN and regular convolutional operations. The hConv layer is capable of increasing receptive fields quickly and computing features automatically. Based on the proposed gPool and hConv layers, we develop new deep networks for text categorization tasks. Our experimental results show that the networks based on gPool and hConv layers achieves new state-of-the-art performance as compared to baseline methods.
Natural language processing applications in library and information science

Purpose With the recent developments in information technologies, natural language processing (NLP) practices have made tasks in many areas easier and more practical. Nowadays, especially when big data are used in most research, NLP provides fast and easy methods for processing these data. The purpose of this paper is to identify subfields of library and information science (LIS) where NLP can be used and to provide a guide based on bibliometrics and social network analyses for researchers who intend to study this subject. Design/methodology/approach Within the scope of this study, 6,607 publications, including NLP methods published in the field of LIS, are examined and visualized by social network analysis methods. Findings After evaluating the obtained results, the subject categories of publications, frequently used keywords in these publications and the relationships between these words are revealed. Finally, the core journals and articles are classified thematically for researchers working in the field of LIS and planning to apply NLP in their research. Originality/value The results of this paper draw a general framework for LIS field and guides researchers on new techniques that may be useful in the field.
Robust and sparse k-means clustering for high-dimensional data

In real-world application scenarios, the identification of groups poses a significant challenge due to possibly occurring outliers and existing noise variables. Therefore, there is a need for a clustering method which is capable of revealing the group structure in data containing both outliers and noise variables without any pre-knowledge. In this paper, we propose a k-means-based algorithm incorporating a weighting function which leads to an automatic weight assignment for each observation. In order to cope with noise variables, a lasso-type penalty is used in an objective function adjusted by observation weights. We finally introduce a framework for selecting both the number of clusters and variables based on a modified gap statistic. The conducted experiments on simulated and real-world data demonstrate the advantage of the method to identify groups, outliers, and informative variables simultaneously.
Construction and enumeration for self-dual cyclic codes of even length over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub><mml:mo linebreak="goodbreak" linebreakstyle="after">+</mml:mo><mml:mi>u</mml:mi><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</…

Let F2m be a finite field of cardinality 2m, R=F2m+uF2m (u2=0) and s,n be positive integers such that n is odd. In this paper, we give an explicit representation for every self-dual cyclic code over the finite chain ring R of length 2sn and provide a calculation method to obtain all distinct codes. Moreover, we obtain a clear formula to count the number of all these self-dual cyclic codes. As an application, self-dual and 2-quasi-cyclic codes over F2m of length 2s+1n can be obtained from self-dual cyclic code over R of length 2sn and by a Gray map preserving orthogonality and distances from R onto F2m2.
Artificial Intelligence in Financial Services – Need to Blend Automation with Human Touch

Artificial Intelligence is the latest in the series of disruptions manifested by computer science. AI has been rapidly transforming the dynamics of banking and financial services industry also. The established and emerging capabilities of AI are being combined, reconstituted and re-formulated in unexpected ways and are throwing up new opportunities and new challenges but at the same time posing new threats also. Apart from the ethico-neutral character of technology and its attendant threats like cyber-crimes and macro-financial risks, a major question to be inquired into is its sustainability as it tends to replace humans and the related personal touch which most often is the essence of financial services industry thriving on the art of customization and customer delight. The instant paper attempts to examine relatively under-explored perspective of AI replacing humans in the space of banking and financial services and unmindfully heralding the flight of personal touch and service customization which are the cornerstone of customer satisfaction and delight in industries like banking and financial services known for their fiduciary and responsible character.
Cellular System Identification Using Deep Learning: GSM, UMTS and LTE

Deep learning (DL) is an effective tool in artificial intelligence (AI), especially in image based and human behavior recognition applications. However, there are many applications that are not very well explored using the DL tools. The telecommunications and networking applications are among those applications that can be explored more extensively using DL. In this paper, the neural network is utilized to identify different cellular communications signals including GSM, UMTS, and LTE. Our study results show that the cellular system identification method achieves very good identification performance without any necessity to select signal features manually.
Phase-shifting VU factorization for interferometry

The problem of phase recovery in phase-shifting interferometry (PSI), when the introduced phase-shifts are unknown, has been analyzed by existing methods through the modification or combination of the least-squares and the principal component analysis (PCA) approaches. In this paper, we show that the PSI sequence of images can be written in a matrix form as the product of two matrices V and U, where the matrix V contains the components of the modulating phase and the matrix U contains the components of the phase-shifts. The VU matrix describes the PSI images without restrictions on the background illumination or the phase shifts, thus, the phase-shifts can be given arbitrarily and they do not need to cover the interval [0, 2π]. In particular, we introduce a numerical method that can separate these matrices V and U from a given set of PSI images, which enables us to recover both the modulating phase and the phase-shifts, albeit, we are only interested in the modulating phase. We refer to this process as phase-shifting VU factorization. This factorization method is fast, stable, and converges well to the desired solution. An important feature of the method is that the convergence of the VU factorization is robust to the initial guess and the only parameter it requires is the convergence accuracy. The method can reach a convergence accuracy of the order of 10−3 in a few iterations. Results from our method using both simulated and experimental interferograms are shown and compared with recovered modulating phases obtained by three other state-of-the-art approaches: the PCA, least squares and a combination of both.
Adaptive windows multiple deep residual networks for speech recognition

The hybrid convolutional neural network and hidden Markov model (CNN-HMM) has recently achieved considerable performance in speech recognition because deep neural networks, model complex correlations between features. Automatic speech recognition (ASR) as an input to many intelligent and expert systems has impacts in various fields such as evolving search engines (inclusion of speech recognition in search engines), healthcare industry (medical reporting by medical personnel, and disease diagnosis expert systems), service delivery, communication in service providers (to establish the callers demands and then direct them to the appropriate operator for assistance), etc. This paper introduces a method, which further reduces the recognition error rate. In this paper, we first propose adaptive windows convolutional neural network (AWCNN) to analyze joint temporal-spectral features variation. AWCNN makes the model more robust against both intra- and inter-speaker variations. We further propose a new residual learning, which leads to better utilization of information in deep layers and provides a better control on transferring input information. The proposed speech recognition system can be used as the vocal input for many artificial and expert systems. We evaluated the proposed method on TIMIT, FARSDAT, Switchboard, and CallHome datasets and one image database i.e. MNIST. The experimental results show that the proposed method reduces the absolute error rate by 7% compared with the state-of-the-art methods in some speech recognition tasks.
Probability Fusion Decision Framework of Multiple Deep Neural Networks for Fine-Grained Visual Classification

Fine-grained visual classification tasks often suffer from that the subordinate categories within a basic-level category have low inter-class discrepancy and high intra-class variances, which is still challenging research for traditional deep neural networks (DNNs). However, different models extract local parts' features in isolation and neglect the inherent correlations and distribution in high-dimensional space, which limit the single model to achieve better accuracy. In this paper, we propose a novel probability fusion decision framework (named as PFDM-Net) for fine-grained visual classification. More specifically, it first employs data-augmented tricks to enlarge the dataset and pretrain the basic VGG19 and ResNet networks on high-quality images datasets to learn common and domain knowledge simultaneously while fine-tuning with professional skill. Next, refined multiple DNNs with transfer learning are applied to design a multi-stream feature extractor, which utilizes the mixture-granularity information to exploit high-dimensionality features for distinguishing interclass discrepancy and tolerating intra-class variances. Finally, a probability fusion module equipped with gating network and probability fusion layer is developed to fuse different components model with Gaussian distribution as a unified probability representation for the ultimate fine-grained recognition. The input of this module is the various features of multi-models and the output is the fused classification probability. The end-to-end implementation of our framework contain an inner loop about the EM algorithm within an outer loop with the gradient back-propagation optimization of the whole network. Experimental results demonstrate the outperforming performance of PFDM-Net with higher classification accuracy on different fine-grained datasets compared with the state-of-the-arts methods. More discussions are provided to indicate the potential applications in combination with other work.
The Impact of Information Security Breach Incidents on CIO Turnover

ABSTRACT We investigate the relationship between security breaches and chief information officer (CIO) turnover. Because CIOs are directly responsible for IT performance, we argue that their turnover likelihood is higher when they fail to meet IT performance expectations, as reflected by information security breaches. Specifically, we find that breaches caused by system deficiency increase CIO turnover likelihood by 72 percent. However, we find no such association for breaches caused by criminal fraud or human error. We extend our analyses to other executives and document that CEOs are more likely to turn over following breaches caused by both system deficiency and human error, consistent with their broader role within the firm. By contrast, we find no evidence suggesting that CFOs are more likely to turn over following breaches. The findings indicate negative labor market consequences for executives who fail to meet performance expectations within the scope of their duties.
Absolute Pose Estimation of Central Cameras Using Planar Regions

A novel method is proposed for the absolute pose estimation of a central 2D camera with respect to 3D depth data without the use of any dedicated calibration pattern or explicit point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the 3D sensing device and a central camera is used to capture the 2D images. Both the perspective and omnidirectional central cameras are handled within a single generic camera model. Pose estimation is formulated as a 2D-3D nonlinear shape registration task which is solved without point correspondences or complex similarity metrics. It relies on a set of corresponding planar regions, and the pose parameters are obtained by solving an overdetermined system of nonlinear equations. The efficiency and robustness of the proposed method were confirmed on both large scale synthetic data and on real data acquired from various types of sensors.