Reviewerly/exHarmony huggingface dataset:
Data-Driven Simulation for Augmented Surgery

To build an augmented view of an organ during surgery, it is essential to have a biomechanical model with appropriate material parameters and boundary conditions, able to match patient specific properties. Adaptation to the patient’s anatomy is obtained by exploiting the image-rich context specific to our application domain. While information about the organ shape, for instance, can be obtained preoperatively, other patient-specific parameters can only be determined intraoperatively. To this end, we are developing data-driven simulations, which exploit information extracted from a stream of medical images. Such simulations need to run in realtime. To this end we have developed dedicated numerical methods, which allow for real-time computation of finite element simulations. The general principle consists in combining finite element approaches with Bayesian methods or deep learning techniques, that allow to keep control over the underlying computational model while allowing for inputs from the real world. Based on a priori knowledge of the mechanical behavior of the considered organ, we select a constitutive law to model its deformations. The predictive power of such constitutive law highly depends on the knowledge of the material parameters and the boundary conditions. In our first approach, material properties are modeled as stochastic parameters whose probability distributions will be modified in real-time using Kalman filters, given observations extracted from intraoperative data. The second option we propose is to directly learn material parameters and boundary conditions from patient-specific data using deep neural networks. This has been applied to the modeling of liver biomechanics, its real-time simulation, and parametrization to achieve patient-specific augmented reality during
A Stacked Ensemble Approach to Bengali Sentiment Analysis

Sentiment analysis is a crucial step in the social media data analysis. The majority of research works on sentiment analysis focus on sentiment polarity detection which identifies whether an input text is positive, negative or neutral. In this paper, we have implemented a stacked ensemble approach to sentiment polarity detection in Bengali tweets. The basic concept of stacked generalization is to fuse the outputs of the first level base classifiers using a second-level Meta classifier in an ensemble. In our ensemble method, we have used two types of base classifiers- multinomial Naïve Bayes classifiers and SVM that make use of a diverse set of features. Our proposed approach shows an improvement over some existing Bengali sentiment analysis approaches reported in the literature.
The information capacity of entanglement-assisted continuous variable quantum measurement

The present paper is devoted to investigation of the entropy reduction and entanglement-assisted classical capacity (information gain) of continuous variable quantum measurements. These quantities are computed explicitly for multimode Gaussian measurement channels. For this we establish a fundamental property of the entropy reduction of a measurement: under a restriction on the second moments of the input state it is maximized by a Gaussian state (providing an analytical expression for the maximum). In the case of one mode, the gain of entanglement assistance is investigated in detail.
A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations

With growing consumer adoption of online grocery shopping through platforms such as Amazon Fresh, Instacart, and Walmart Grocery, there is a pressing business need to provide relevant recommendations throughout the customer journey. In this paper, we introduce a production within-basket grocery recommendation system, RTT2Vec, which generates real-time personalized product recommendations to supplement the user’s current grocery basket. We conduct extensive offline evaluation of our system and demonstrate a 9.4% uplift in prediction metrics over baseline stateof-the-art within-basket recommendation models. We also propose an approximate inference technique 11.6x times faster than exact inference approaches. In production, our system has resulted in an increase in average basket size, improved product discovery, and enabled faster user check-out.
CPWC: Contextual Point Wise Convolution for Object Recognition

Convolutional layers are a major driving force behind the successes of deep learning.Pointwise convolution (PWC) is a 1 × 1 convolutional filter that is primarily used for parameter reduction.However, the PWC ignores the spatial information around the points it is processing.This design is by choice, in order to reduce the overall parameters and computations.However, we hypothesize that this shortcoming of PWC has a significant impact on the network performance.We propose an alternative design for pointwise convolution, which uses spatial information from the input efficiently.Our design significantly improves the performance of the networks without substantially increasing the number of parameters and computations.We experimentally show that our design results in significant improvement in the performance of the network for classification as well as detection.
Collaborating with Users in Proximity for Decentralized Mobile Recommender Systems

Typically, recommender systems from any domain, be it movies, music, restaurants, etc., are organized in a centralized fashion. The service provider holds all the data, biases in the recommender algorithms are not transparent to the user, and the service providers often create lock-in effects making it inconvenient for the user to switch providers. In this paper, we argue that the user's smartphone already holds a lot of the data that feeds into typical recommender systems for movies, music, or POIs. With the ubiquity of the smartphone and other users in proximity in public places or public transportation, data can be exchanged directly between users in a device-to-device manner. This way, each smartphone can build its own database and calculate its own recommendations. One of the benefits of such a system is that it is not restricted to recommendations for just one user - ad-hoc group recommendations are also possible. While the infrastructure for such a platform already exists - the smartphones already in the palms of the users - there are challenges both with respect to the mobile recommender system platform as well as to its recommender algorithms. In this paper, we present a mobile architecture for the described system - consisting of data collection, data exchange, and recommender system - and highlight its challenges and opportunities.
A New Deep Learning Architecture for Person Detection

Person detection is a branch of object detection. It refers to positioning people in the image, finding the location and range of the person, and has a wide range of applications in fields such as video surveillance and target tracking. Yolo3 is currently one of the best deep learning structure for object detection. In this paper we further improve the Yolo3 network by combining the excellent characteristics of the end-to-end network for person detection. We propose a new person detection network model called PDnet. Among the main contributions, we further optimize the Yolo3 feature extraction network structure, change the three output ports of Yolo3 to one, and improve the anchor boxe clustering algorithm, so that our network model can extract the person features better, speed up the convergence of the category loss in the original loss function. The experimental results show that compared to vannila Yolo3, our proposed PDnet has better robustness and higher accuracy in person detection.
The nexus between financial inclusion, trade and economic growth in Africa?

Numerous studies have investigated the nexus between economic growth and financial inclusion, or between economic growth and trade openness. This paper advanced on earlier work and uses granger causality tests and cointegration techniques so as to establish whether there exists a long-run equilibrium relationship between all three variables in Africa using a panel of 30 African countries for the period 2004–2017. Study findings unveil a unidirectional causality relationship from economic growth to financial inclusion; from economic growth to trade in Africa. In particular, enhancing financial inclusion in the financial system promotes the trade-growth nexus. We recommended policy makers to concentrate on pro-growth policies so as to enhance financial inclusion which drives trade.
A fully spatial personalized differentially private mechanism to provide non-uniform privacy guarantees for spatial databases

Spatial databases are essential to applications in a wide variety of domains. One of the main privacy concerns when answering statistical queries, such as range counting queries, over a spatial database is that an adversary observing changes in query answers may be able to determine whether or not a particular geometric object is present in the database. Differential privacy addresses this concern by guaranteeing that the presence or absence of a geometric object has little effect on query answers. Most of the current differentially private mechanisms for spatial databases ignore the fact that privacy is personal and, thus, provide the same privacy protection for all geometric objects. However, some particular geometric objects may be more sensitive to privacy issues than others, requiring stronger differential privacy guarantees. In this paper, we introduce the concept of spatial personalized differential privacy for spatial databases where different geometric objects have different privacy protection requirements. Also, we present SPDP-PCE, a novel spatial personalized differentially private mechanism to answer range counting queries over spatial databases that fully considers the privacy protection requirements of geometric objects in the underlying geometric space in both steps of noise addition and consistency enforcement. Our experimental results on real datasets demonstrate the effectiveness of SPDP-PCE under various total privacy budgets, query shapes, and privacy level distributions.
Gradient Delay Analysis in Asynchronous Distributed Optimization

Gradient-based algorithms play an important role in solving a wide range of stochastic optimization problems. In recent years, implementing such schemes in parallel has become the new paradigm. In this work, we focus on the asynchronous implementation of gradient-based algorithms. In asynchronous distributed optimization, the gradient delay problem arises since optimization parameters may be updated using stale gradients. We consider a hub-and-spoke system and derive the expected gradient staleness in terms of other system parameters such as the number of nodes, communication delay, and the expected compute time. Our derivations provide a means to compare different algorithms based on the expected gradient staleness they suffer from.
Cross-View Attention Network for Breast Cancer Screening from Multi-View Mammograms

In this paper, we address the problem of breast caner detection from multi-view mammograms. We present a novel cross-view attention module (CvAM) which implicitly learns to focus on the cancer-related local abnormal regions and highlighting salient features by exploring cross-view information among four views of a screening mammography exam, e.g. asymmetries between left and right breasts and lesion correspondence between two views of the same breast. More specifically, the proposed CvAM calculates spatial attention maps based on the same view of different breasts to enhance bilateral asymmetric regions, and channel attention maps based on two different views of the same breast to enhance the feature channels corresponding to the same lesion in a single breast. CvAMs can be easily integrated into standard convolutional neural networks (CNN) architectures such as ResNet to form a multi-view classification model. Experiments are conducted on DDSM dataset, and results show that CvAMs can not only provide better classification accuracy over non-attention and single-view attention models, but also demonstrate better abnormality localization power using CNN visualization tools.
Challenges and Opportunities in Turning Large U.S. Archives of Analog Seismograms into a Modern Usable Resource

Abstract Quantitative seismology is based firmly on the analysis of actual ground motions, and the transition to digital recording in the 1980s enabled sophisticated new capabilities to extract useful results from waveforms. With some effort, these tools can also be applied to analog records. Focusing on assets available within U.S. institutions, we review the necessary steps and the challenges in enabling “data rescue”—that is, preserving the scientific information latent in large analog seismogram archives and making it usable. They include: determining what assets are available (the analog seismogram archives held by various institutions, with associated metadata on instrument responses, station locations, and timing information); developing a consensus on the top level of a triage process (which analog records most definitely should be rescued?); deciding the level of quality needed in copying original seismograms to media suitable for digitizing; assessing the relative merits of scanning and digitizing; and, the need for a community service in distributing scans and digital records, as they accumulate. The necessary level of effort can benefit from practical experience. For example, specific studies have used digitized versions of analog recordings to model earthquake sources and assess seismic hazard. Other studies have used them to gain experience with nuclear explosion signals recorded at regional distances, noting that regional signals enable explosions to be monitored down to levels much lower than those attainable teleseismically. The opportunities presented by large archives of analog seismograms include the insights they present to current and future seismologists studying earthquakes and explosions, into the practical areas of assessing seismic hazard, monitoring for test ban compliance down to low explosion yields—and prompt characterization of actual explosions should they occur, as well the traditional academic pursuit of a better understanding of earthquake physics.
An Ego-Vision System for Discovering Human Joint Attention

Joint attention often happens during social interactions, in which individuals share focus on the same object. This article proposes an egocentric vision-based system (ego-vision system) that aims to discover the objects looked at jointly by a group of persons engaged in interactive activities. The proposed system relies on a collection of wearable eye-tracking cameras that provide an egocentric view of the interaction scenes as well as points-of-gaze measurement of each participant. Technically in our system, we develop a hierarchical conditional random field (CRF) based graphical model that can temporally localize joint attention periods and spatially segment objects of joint attention. By solving these two coupled tasks together in an iterative optimization procedure, we show that human joint attention can be reliably discovered from videos even with cluttered background and noisy gaze measurement. A new dataset of joint attention is collected and annotated for evaluating the two tasks of joint attention where two to four persons are involved. Experimental results demonstrate that our approach achieves state-of-the-art performance on both tasks of spatial segmentation and temporal localization of joint attention.
Institutional change of cloud ERP implementation in the public sector

Purpose This study aims to explain the transformation process from using regular enterprise resource planning (ERP) system into implementing Cloud ERP system in the UAE public sector. Design/methodology/approach The study uses a qualitative case study and analyzes the primary evidences from in-depth interviews with the case organization’s personnel. It conducts a thematic analysis of the interviews’ findings. Furthermore, the study uses secondary and tertiary resources from published sources comprising the case organization’s website and previous studies. Findings The findings demonstrated that the transformation process to Cloud ERP could result in different practical benefits in an organization’s controlling system, cost reduction and profitability. Conversely, results revealed that the effectiveness of implementing cloud ERP is dependable on the provider’s professionalism; hence resulting in issues related to minimized organizational independence. In addition, the paper’s findings provide evidenced clarifications about the controversial misconceptions of Cloud ERP’s privacy issues. Research limitations/implications The paper is exposed to the regular case studies limitations, as the study is a qualitative research studying one case company. Thus, the study findings should be strengthened by future quantitative researches on more companies with implying more statistical analysis. Practical implications The paper has important implications for practitioners and decision-makers, as it presents significant and reality-based information about Cloud ERP implementation’s benefits and drawbacks. It thus enhances decision-makers’ ability to make an appropriate and suitable decision about adopting the Cloud ERP in the public sector. Originality/value As Cloud ERP is still emerging, this study is one of the very few case studies that discuss and present some experienced benefits and issues related to Cloud ERP implementation in UAE Government. This study also uses transformation theory to analyze the data.
A new ensemble method for outlier identification

A vast number of factors influence the applicability of machine learning methods and the use of statistical models for a given task. The existence of outliers in a data set is a common issue that needs to be tackled. The identification of such values is a difficult, yet very useful project. In many cases errors or dissimilar values to the majority of the data are useless. Nevertheless, valuable information can be hidden in outliers' set. During the last years, although several models have been developed for outlier detection, there is always space for new, intelligent, more efficient and less time consuming techniques for this issue. In the present work we provide a new ensemble method for outlier detection. In order to test the proposed methodology, comparisons are made with widely used techniques for outlier detection. The results obtained indicate that our model is robust and quite competitive to the other methods.
Combining Deep Embeddings of Acoustic and Articulatory Features for Speaker Identification

In this study, deep embedding of acoustic and articulatory features are combined for speaker identification. First, a convolutional neural network (CNN)-based universal background model (UBM) is constructed to generate acoustic feature (AC) embedding. In addition, as the articulatory features (AFs) represent some important phonological properties during speech production, a multilayer perceptron (MLP)-based AF embedding extraction model is also constructed for AF embedding extraction. The extracted AC and AF embeddings are concatenated as a combined feature vector for speaker identification using a fully-connected neural network. This proposed system was evaluated by three corpora consisting of King-ASR, LibriSpeech and SITW, and the experiments were conducted according to the properties of the datasets. We adopted all three corpora to evaluate the effect of AF embedding, and the results showed that combining AF embedding into the input feature vector improved the performance of speaker identification. The LibriSpeech corpus was used to evaluate the effect of the number of enrolled speakers. The proposed system achieved an EER of 7.80% outperforming the method based on x-vector with PLDA (8.25%). And we further evaluated the effect of signal mismatch using the SITW corpus. The proposed system achieved an EER of 25.19%, which outperformed the other baseline methods.
Development of a Modular Case-Based Reasoning System for Data Analysis

This paper is devoted to the issues of developing a modular case-based reasoning system, capable of performing all stages of the learning cycle and its implementation as a web application. The main modules of the system and an example of its usage for solving the task of data classification is presented. k-NN algorithm is used for classification. The system also used to optimize case library by reducing the number of cases in case library using various classification (based on the k-NN algorithm) and cluster algorithms (based on the k-means algorithm).
Histogram of maximal point-edge orientation for multi-source image matching

Features determined from keypoints or edges are widely used in multi-source image matching. However, for cases that exhibit non-linear intensity changes and significant noise, identifying sufficient identical features for multi-source image pairs can be complicated. Motivated by problems encountered in existing multi-source image matching algorithms, a robust and effective algorithm for multi-source image matching was proposed in this paper. First, keypoints uniformly and sufficiently distributed on the significant edge were extracted by a location-based boosting detector. Second, in order to effectively describe the corresponding region and reduce the influence of noise and non-linear intensity changes, a novel descriptor, denoted as the histogram of point-edge orientation (HPEO) was proposed for multi-source image matching. A bilateral matching process was then used to remove the incorrect matches. Experiments were performed with standard infrared-visible datasets and the results demonstrate that the proposed algorithm achieves a more accurate matching performance.
Dashcam forensics: A preliminary analysis of 7 dashcam devices

Dashboard cameras (“dashcams”) are becoming an important in-car accessory used to record audio and visual footage of car journeys. The audio/video footage produced by dashcams have become important items of evidence. This paper explores the problems related to the management and processing of dashcam evidence, and in particular, highlights challenges to the admissibility of evidence submitted online. The key contribution of this paper is to outline the results of an experiment which aimed to reveal the prevalence and provenance of artefacts created by the use of dashcams on the SD storage system of seven dashcam systems. The research describes the provenance of evidential artefacts relating to: the dashcam recording mode, GPS data, vehicular speed data, licence plate data, and temporal data which was found in at least six locations - namely: NMEA files, configuration/diagnostic files, EXIF metadata, directory structures, filename structures and imagery watermarks.
Leveraging User Comments for Recommendation in E-Commerce

Collaborative filtering recommender systems traditionally recommend products to users solely based on the given user-item rating matrix. Two main issues, data sparsity and scalability, have long been concerns. In our previous work, an approach was proposed to address the scalability issue by clustering the products using the content of the user-item rating matrix. However, it still suffers from these concerns. In this paper, we improve the approach by employing user comments to address the issues of data sparsity and scalability. Word2Vec is applied to produce item vectors, one item vector for each product, from the comments made by users on their previously bought goods. Through the user-item rating matrix, the user vectors of all the customers are produced. By clustering, products and users are partitioned into item groups and user groups, respectively. Based on these groups, recommendations to a user can be made. Experimental results show that both the inaccuracy caused by a sparse user-item rating matrix and the inefficiency due to an enormous amount of data can be much alleviated.