Reviewerly/exHarmony huggingface dataset:
Preliminary data-based matrix factorization approach for recommendation

Existing collaborative filtering algorithms suffer from the problem of data sparsity. Imputation-based methods are promising algorithms, which alleviate data sparsity without using side information, to solve this problem. However, existing imputation recommendation methods based on matrix factorization only separately factorize the rating matrix and the imputed data matrix, which limits the power of imputed data. In this paper, we propose an efficient method, which can make full use of the imputed data, to alleviate data sparsity. Firstly, our method, called Preliminary Data-based Matrix Factorization (PDMF), generates preliminary prediction data based on neighborhood-based methods. Secondly, PDMF consists of two models of learning the user and item preferences. One firstly makes the original preferences get close to preliminary preferences, and then creates the concatenated preferences. The other one creates the concatenated preferences firstly, and then makes the original, preliminary and concatenated preferences get close to each other. To the best of our knowledge, our method is the first to constrain the learning procedure in matrix factorization by using imputed data. We test our method on the MovieLens100k, MovieLens1M, Netflix, Filmtrust and Jester datasets. Experiment results show that the PDMF outperforms the state-of-the-art methods in recommendation accuracy.
Toward a Deep Smart Waste Management System based on Pattern Recognition and Transfer learning

Smart cities essentially combine the use of Information and communications technology (ICT) to provide services for better living conditions inside. The recent developments in the Internet of Things (IoT) opened vast opportunities for researchers and developers to implement various systems and applications in the field of Smart Cities and Intelligent Transportation Systems (ITS). It is a diverse topic of discussion with several application areas such as smart traffic management, smart street lights or gas and water leak detection. Among these applications, Efficient Waste Collection (WC) is considered a fundamental service for helping maintain a healthier environment for the citizens while reducing operating costs. This paper proposes a new architecture for a smart wastes management system based on artificial intelligence techniques and focuses on the combined use of the Internet of Things (IoT) and surveillance systems as an assistive technology for high Quality of Service (QoS) in waste collection. By integrating deep learning techniques in this sector, important cost reductions can be made while maintaining optimal performance.
Text Encryption: Hybrid cryptographic method using Vigenere and Hill Ciphers

Cryptography is a method of controlling and protecting communications, which has been used exclusively in areas that require confidentiality. Today it is undergoing a considerable evolution, and computer networks require a phase of cryptography as a fundamental mechanism to ensure the privacy of digital information. It was beginning with the first Cesar encryption algorithm or those that appeared just afterwards, such as mono-alphabetic substitution encryption, which has a weakness against statistical attacks. In this paper, we will present a hybridization of Vigenere and Hill encryption that belongs to this family. By exploiting the enhancements, they have already implemented in Hill encryption, to hide the weak point of the Vigenere algorithm, represented in the ease to detect the size of the key to start a statistical attack, as well as the weakness of the algorithm to encrypt two same letters located in the same place in different blocks. This cohesion between these two methods will provide us with a reliable hybrid algorithm, resistant to varying attacks, including statistical attacks.
Virtual Collaborative Design Environment: Supporting Seamless Integration of Multitouch Table and Immersive VR

Throughout the design of a new building it is essential that all involved stakeholders understand, communicate, participate, and collaborate with each other to acquire a high-quality result. To support these shared and creative design processes, this paper presents the design and evaluation of virtual collaborative design environment (ViCoDE)—a new collaborative design system. ViCoDE features seamless integration of several immersive VR systems and a multitouch table that facilitates collaborative and interactive design work with immediate feedback. The evaluation of the system has been done during two real-life collaborative design workshops in a context of designing new healthcare environments. The results show that the VR system and multitouch table complement each other very well by facilitating both collaborative, and individual design spaces—and that it fosters better understanding, participation, communication, knowledge sharing, and collaboration among the different stakeholders. The contribution is two-fold: presentation of a new collaborative design system and the evaluation of its use in a real-life design context, which demonstrate how these new technology-based workshops may facilitate design management.
Fi-Fo Detector: Figure and Formula Detection Using Deformable Networks

We propose a novel hybrid approach that fuses traditional computer vision techniques with deep learning models to detect figures and formulas from document images. The proposed approach first fuses the different computer vision based image representations, i.e., color transform, connected component analysis, and distance transform, termed as Fi-Fo image representation. The Fi-Fo image representation is then fed to deep models for further refined representation-learning for detecting figures and formulas from document images. The proposed approach is evaluated on a publicly available ICDAR-2017 Page Object Detection (POD) dataset and its corrected version. It produces the state-of-the-art results for formula and figure detection in document images with an f1-score of 0.954 and 0.922, respectively. Ablation study results reveal that the Fi-Fo image representation helps in achieving superior performance in comparison to raw image representation. Results also establish that the hybrid approach helps deep models to learn more discriminating and refined features.
5G Network Slice Brokering: A Distributed Blockchain-based Market

In this paper, we study the business ecosystem around 5G network slice brokering, where a dynamic mechanism conducts multiple trades among resource providers and the network operators, to dynamically provision slices (e.g., in the order of minutes). Then we address a significant and realistic scenario, where there is a lack of trust between market players. This typically occurs when the central slice broker has conflicting interests (e.g., being simultaneously infrastructure provider and virtual operator). We propose a distributed market design, lever-aging smart contracts technology, where the brokering mechanism is operated and validated by all of the parties involved. In addition, we use blockchain technology to enable a manipulation-proof record-keeping system, where a record is accepted into the ledger only when all the parties have reached a consensus to do so. We deploy a realistic blockchain application/network hosted on the cloud-based Hyperledger Fabric framework. Finally, we investigate the performance of the blockchain-based slice brokering market in terms of transaction latency, throughput and computing intensity. Our results show that the proposed market could support 100 auction transactions per second with a latency below one second, which would add no considerable delay even to a highly dynamic slicing market mechanism.
Lightweight Ship Detection Methods Based on YOLOv3 and DenseNet

Ship detection is one of the most important research contents of ship intelligent navigation and monitoring. As a supplement to classical navigational equipment such as radar and the Automatic Identification System (AIS), target detection based on computer vision and deep learning has become a new important method. A target detector called YOLOv3 has the advantages of detection speed and accuracy and meets the real-time requirements for ship detection. However, YOLOv3 has a large number of backbone network parameters and requires high hardware performance, which is not conducive to the popularization of applications. On the basis of YOLOv3, this paper proposes a lightweight ship detection model (LSDM) in which the backbone network is improved by using dense connection inspired from DenseNet, and the feature pyramid networks are improved by using spatial separation convolution to replace normal convolution. The two improvements reduce parameters and optimize the network structure greatly. The experimental results show that, with only one-third of parameters of YOLOv3, the LSDM has higher accuracy and speed for ship detection. In addition, the LSDM is simplified further by reducing the number of densely connected units to form a model called LSDM-tiny. The experimental results show that, LSDM-tiny has similar detection speed with YOLOv3-tiny, but has a lot higher accuracy.
INFRASTRUCTURE AND ENTREPRENEURSHIP: EVIDENCE FROM AFRICA

Closing the infrastructural gaps and fostering the entrepreneurial processes are considered the key to reduce African unemployment and boost productivity to achieve inclusive development. Therefore, investment in infrastructure is crucial for creating a conducive entrepreneurial environment. In this paper, we provide a contribution for this purpose, by evaluating the impact of infrastructure on entrepreneurship in a panel of twenty African countries for a period of 2006–2018. Consistent with previous studies, we find that infrastructures play a significant role in improving entrepreneurial development. In specific, we show that transport, electricity, water and sanitation facilities, ICT and broadband infrastructures have a positive and significant effect on entrepreneurial startups in Africa. Our reports show clearly there is a positive association between infrastructures and entrepreneurial startups at a one percent significance level. These findings are robust to alternative estimation. It points out that physical infrastructure is more relevant in the case of less developed countries in promoting entrepreneurial development.
SMOOTH-GAN: Towards Sharp and Smooth Synthetic EHR Data Generation

Generative adversarial networks (GANs) have been highly successful for generating realistic synthetic data. In healthcare, synthetic data generation can be helpful for producing annotated data and improving data-driven research without worries on data privacy. However, electronic health records (EHRs) are noisy, incomplete and complex, and existing work on EHR data is mainly devoted to generating discrete elements such as diagnosis codes and medications or frequent laboratory values. In this work, we propose SMOOTH-GAN, a novel approach for generating reliable EHR data such as laboratory values and medications given diagnosis codes. SMOOTH-GAN takes advantage of a conditional GAN architecture with WGAN-GP loss, and is able to learn transitions between disease stages with high flexibility over data customization. Our experiments demonstrate the model's effectiveness in terms of both statistical similarity and accuracy on machine learning based prediction. To further demonstrate the usage of our model, we apply counterfactual reasoning and generate data with occurrence of multiple diseases, which can provide unique datasets for artificial intelligence driven healthcare research.
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures

Recent advances in generalized image understanding have seen a surge in the use of deep convolutional neural networks (CNN) across a broad range of image-based detection, classification and prediction tasks.Whilst the reported performance of these approaches is impressive, this study investigates the hitherto unapproached question of the impact of commonplace image and video compression techniques on the performance of such deep learning architectures.Focusing on the JPEG and H.264 (MPEG-4 AVC) as a representative proxy for contemporary lossy image/video compression techniques that are in common use within network-connected image/video devices and infrastructure, we examine the impact on performance across five discrete tasks: human pose estimation, semantic segmentation, object detection, action recognition, and monocular depth estimation.As such, within this study we include a variety of network architectures and domains spanning end-toend convolution, encoder-decoder, region-based CNN (R-CNN), dual-stream, and generative adversarial networks (GAN).Our results show a non-linear and non-uniform relationship between network performance and the level of lossy compression applied.Notably, performance decreases significantly below a JPEG quality (quantization) level of 15% and a H.264 Constant Rate Factor (CRF) of 40.However, retraining said architectures on precompressed imagery conversely recovers network performance by up to 78.4% in some cases.Furthermore, there is a correlation between architectures employing an encoder-decoder pipeline and those that demonstrate resilience to lossy image compression.The characteristics of the relationship between input compression to output task performance can be used to inform design decisions within future image/video devices and infrastructure.
Detecting bad smells with machine learning algorithms

Bad smells are symptoms of bad design choices implemented on the source code. They are one of the key indicators of technical debts, specifically, design debt. To manage this kind of debt, it is important to be aware of bad smells and refactor them whenever possible. Therefore, several bad smell detection tools and techniques have been proposed over the years. These tools and techniques present different strategies to perform detections. More recently, machine learning algorithms have also been proposed to support bad smell detection. However, we lack empirical evidence on the accuracy and efficiency of these machine learning based techniques. In this paper, we present an evaluation of seven different machine learning algorithms on the task of detecting four types of bad smells. We also provide an analysis of the impact of software metrics for bad smell detection using a unified approach for interpreting the models' decisions. We found that with the right optimization, machine learning algorithms can achieve good performance (F1 score) for two bad smells: God Class (0.86) and Refused Parent Bequest (0.67). We also uncovered which metrics play fundamental roles for detecting each bad smell.
A Blockchain based Secured Healthcare Framework

The world's healthcare systems are experiencing major transition in access, data management, control and disease treatment. With the advancements in data capturing and connected technology the amount of healthcare data is projected approximately 2314 exabyte for year 2020[10]. The cyber criminals are putting lots of efforts, time and resources to exploit and monetize healthcare data. With this threat, the growth of cybersecurity market in healthcare industry is expected to reach USD 27.10 billion by 2026[11]. The blockchain can help in creating a single database to collect data during clinical trials and allow patients' data security. In this paper we propose a secure framework which is based on blockchain to ensure the security of healthcare data. The proposed framework considers wearable sensors, Internet of things (IoT) devices, and computational power. The framework improves the security, privacy and efficiency of the data in the conventional healthcare system.
Hyperparameter optimization for recommender systems through Bayesian optimization

Abstract Recommender systems represent one of the most successful applications of machine learning in B2C online services, to help the users in their choices in many web services. Recommender system aims to predict the user preferences from a huge amount of data, basically the past behaviour of the user, using an efficient prediction algorithm. One of the most used is the matrix-factorization algorithm. Like many machine learning algorithms, its effectiveness goes through the tuning of its hyper-parameters, and the associated optimization problem also called hyper-parameter optimization. This represents a noisy time-consuming black-box optimization problem. The related objective function maps any possible hyper-parameter configuration to a numeric score quantifying the algorithm performance. In this work, we show how Bayesian optimization can help the tuning of three hyper-parameters: the number of latent factors, the regularization parameter, and the learning rate. Numerical results are obtained on a benchmark problem and show that Bayesian optimization obtains a better result than the default setting of the hyper-parameters and the random search.
3D tumor detection in automated breast ultrasound using deep convolutional neural network

Automated breast ultrasound (ABUS) has drawn attention in breast disease detection and diagnosis applications. Reviewing hundreds of slices produced by ABUS is time-consuming. In this paper, a tumor detection method for ABUS image based on convolutional neural network is proposed.First, integrating multitask learning with YOLOv3, an improved YOLOv3 detection network is designed to detect tumor candidate in two-dimensional (2D) slices. Two-dimensional detection separately treats each slice, leading to larger differences of position and score for tumor candidate in adjacent slices. Due to the influence of artifact, noise, and mammary tissues, 2D detection may include many false positive regions. To alleviate these problems, a rescoring processing algorithm is first designed. Then three-dimensional volume forming and FP reduction scheme are built.This method was tested on 340 volumes (124 patients, 181 tumors) with fivefold cross validation. It achieved sensitivities of 90%, 85%, 80%, 75%, and 70% at 7.42, 3.31, 1.62, 1.23, and 0.88 false positives per volume.Compared with existing ABUS tumor detection methods, our method gets a promising result.
Automatic Image Thresholding Based on Shannon Entropy Difference and Dynamic Synergic Entropy

An automatic thresholding method based on Shannon entropy difference and dynamic synergic entropy is proposed to select a reasonable threshold from the gray level image with a unimodal, bimodal, multimodal, or peakless gray level histogram.Firstly, a new concept called Shannon entropy difference is proposed, and the stopping condition of a multi-scale multiplication transformation is automatically controlled by maximizing Shannon entropy difference to produce edge images.Secondly, the gray level image is thresholded by the gray levels in order from smallest to largest to generate a series of binary images, followed by extracting contour images from the binary images.Then, a series of gray level histograms that can dynamically reflect gray level distributions and pixel positions are constructed using the edge images and the contour images synergically.Finally, dynamic synergic Shannon entropy is calculated from this series of gray level histograms, and the threshold corresponding to maximum dynamic synergic entropy is taken as the final segmentation threshold.The experimental results on 40 synthetic images and 50 realworld images show that, although the proposed method is not superior to 8 automatic segmentation methods in computational efficiency, it has more flexible adaptivity of selecting threshold and better segmentation accuracy.
Lightweight image classifier using dilated and depthwise separable convolutions

Abstract The image classification based on cloud computing suffers from difficult deployment as the network depth and data volume increase. Due to the depth of the model and the convolution process of each layer will produce a great amount of calculation, the GPU and storage performance of the device are extremely demanding, and the GPU and storage devices equipped on the embedded and mobile terminals cannot support large models. So it is necessary to compress the model so that the model can be deployed on these devices. Meanwhile, traditional compression based methods often miss many global features during the compression process, resulting in low classification accuracy. To solve the problem, this paper proposes a lightweight neural network model based on dilated convolution and depthwise separable convolution with twenty-nine layers for image classification. The proposed model employs the dilated convolution to expand the receptive field during the convolution process while maintaining the number of convolution parameters, which can extract more high-level global semantic features to improve the classification accuracy. Also, the depthwise separable convolution is applied to reduce the network parameters and computational complexity in convolution operations, which reduces the size of the network. The proposed model introduces three hyperparameters: width multiplier, image resolution, and dilated rate, to compress the network on the premise of ensuring accuracy. The experimental results show that compared with GoogleNet, the network proposed in this paper improves the classification accuracy by nearly 1%, and the number of parameters is reduced by 3.7 million.
Pruning Depthwise Separable Convolutions for MobileNet Compression

Deep convolutional neural networks are good at accuracy while bad at efficiency. To improve the inference speed, two directions have been explored in the past, lightweight model designing and network weight pruning. Lightweight models have been proposed to improve the speed with good enough accuracy. It is, however, not trivial if we can further speed up these "compact" models by weight pruning. In this paper, we present a technique to gradually prune the depthwise separable convolution networks, such as MobileNet, for improving the speed of this kind of "dense" network. When pruning depthwise separable convolutions, we need to consider more structural constraints to ensure the speedup of inference. Instead of pruning the model with the desired ratio in one stage, the proposed multi-stage gradual pruning approach can stably prune the filters with a finer pruning ratio. Our method achieves satisfiable speedup with little accuracy drop for MobileNets. Code is available at https://github.com/ivclab/Multistage_Pruning.
Using Others' Tests to Identify Breaking Updates

The reuse of third-party packages has become a common practice in contemporary software development. Software dependencies are constantly evolving with newly added features and patches that fix bugs in older versions. However, updating dependencies could introduce new bugs or break backward compatibility. In this work, we propose a technique to detect breakage-inducing versions of third-party dependencies. The key insight behind our approach is to leverage the automated test suites of other projects that depend upon the same dependency to test newly released versions. We conjecture that this crowd-based approach will help to detect breakage-inducing versions because it broadens the set of realistic usage scenarios to which a package version has been exposed. To evaluate our conjecture, we perform an empirical study of 391,553 npm packages. We use the dependency network from these packages to identify candidate tests of third-party packages. Moreover, to evaluate our proposed technique, we mine the history of this dependency network to identify ten breakage-inducing versions. We find that our proposed technique can detect six of the ten studied breakage-inducing versions. Our findings can help developers to make more informed decisions when they update their dependencies.