Reviewerly/exHarmony huggingface dataset:
Agile Development at Scale: The Next Frontier

Agile methods have transformed the way software is developed, emphasizing active end-user involvement, tolerance to change, and evolutionary delivery of products. The first special issue on agile development described the methods as focusing on feedback and change.<sup>1</sup> These methods have led to major changes in how software is developed. Scrum is now the most common framework for development in most countries, and other methods such as extreme programming (XP), elements of lean software development, and Kanban are widely used. What started as a bottom-up movement among software practitioners and consultants has been taken up by major international consulting companies who prescribe agile development, particularly for contexts where learning and innovation are key. Agile development methods have attracted interest primarily in software engineering<sup>1, 2</sup> but also in a number of other disciplines including information systems<sup>3</sup> and project management.<sup>4</sup>
A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound

To evaluate the value of the computer-aided diagnosis (CAD) program applied to diagnostic breast ultrasonography (US) based on operator experience.US images of 100 breast masses from 91 women over 2 months (from May to June 2015) were collected and retrospectively analyzed. Three less experienced and 2 experienced breast imaging radiologists analyzed the US features of the breast masses without and with CAD according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and categories. We then compared the diagnostic performance between the experienced and less experienced radiologists and analyzed the interobserver agreement among the radiologists.Of the 100 breast masses, 41 (41%) were malignant and 59 (59%) were benign. Compared with the experienced radiologists, the less experienced radiologists had significantly improved negative predictive value (86.7%-94.7% vs 53.3%-76.2%, respectively) and area under receiver operating characteristics curve (0.823-0.839 vs 0.623-0.759, respectively) with CAD assistance (all P < .05). In contrast, experienced radiologists had significantly improved specificity (52.5% and 54.2% vs 66.1% and 66.1%) and positive predictive value (55.6% and 58.5% vs 64.9% and 64.9%, respectively) with CAD assistance (all P < .05). Interobserver variability of US features and final assessment by categories were significantly improved and moderate agreement was seen in the final assessment after CAD combination regardless of the radiologist's experience.CAD is a useful additional diagnostic tool for breast US in all radiologists, with benefits differing depending on the radiologist's level of experience. In this study, CAD improved the interobserver agreement and showed acceptable agreement in the characterization of breast masses.
Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters

In this paper, simple methodologies of deep learning application to conventional multiple-input multiple-output (MIMO) communication systems are presented. The deep learning technologies with deep neural network (DNN) structure, emerging technologies in various engineering areas, have been actively investigated in the field of communication engineering as well. In the physical layer of conventional communication systems, there are practical challenges of application of DNN: calculating complex number in DNN and designing proper DNN structure for a specific communication system model. This paper proposes and verifies simple solutions for the difficulty. First, we apply a basic DNN structure for signal detection of one-tap MIMO channel. Second, convolutional neural network (CNN) and recurrent neural network (RNN) structures are presented for MIMO system with multipath fading channel. Our DNN structure for one-tap MIMO channel can achieve the optimal maximum likelihood detection performance, and furthermore, our CNN and RNN structures for multipath fading channel can detect the transmitted signal properly.
A new hyperparameters optimization method for convolutional neural networks

The use of convolutional neural networks involves hyperparameters optimization. Gaussian process based Bayesian optimization (GPEI) has proven to be an effective algorithm to optimize several hyperparameters. Then deep networks for global optimization algorithm (DNGO) that used neural network as an alternative to Gaussian process was proposed to optimize more hyperparameters. This paper presents a new algorithm that combines multiscale and multilevel evolutionary optimization (MSMLEO) with GPEI to optimize dozens of hyperparameters. These hyperparameters are divided into two groups. The first group related with the sizes of layers and kernels are discrete integers. The second group related with learning rates and so on is continuous floating-point numbers. All combinations of the first group are corresponding to the combinations of grid points on multi-scale grids and MSMLEO launches GPEI to optimize the second group of hyperparameters while the first group keeps fixed. The output of convolutional networks configured with above two groups of optimized hyperparameters is used as the fitness of MSMLEO. MSMLEO alternates with GPEI to search the optimal hyperparameters from coarsest scale to finest scale. Experimental results show that our algorithm has better performance and adaptability on optimizing dozens of hyperparameters of neural networks with a variety of numerical types.
Unsupervised Blind Image Quality Evaluation via Statistical Measurements of Structure, Naturalness, and Perception

Most existing blind image quality assessment (BIQA) methods belong to supervised methods, which always need a large number of image samples and expensive subjective scores for training a quality prediction model. In this paper, we focus our attention on the unsupervised BIQA methods and put forward a novel unsupervised approach. The main idea of our method is to quantify the image quality degradation through measuring the structure, naturalness, and the perception quality variations of the distorted image from the pristine natural images. In specific, the structure variation is captured by the deviations of the image phase congruency and gradients distributions. The naturalness variation is characterized through the distributions variations of the locally mean subtracted and contrast normalized (MSCN) coefficients and the products of pairs of the adjacent MSCN coefficients. Compared with existing unsupervised methods, we initiatively introduce the perception quality measurement into the construction of unsupervised BIQA method, which is conducted by characterizing the prediction discrepancy between the image and its brain prediction based on the free-energy principle in the newly revealed brain theory. After feature extraction, we learn a pristine multivariate Gaussian (MVG) model with the extracted features from a set of pristine natural images. The quality of a new image is finally defined as the distance between its MVG model and the learned pristine MVG model. The extensive experiments conducted on LIVE, TID2013, CSIQ, Toyama, CID2013, and the Waterloo Exploration databases demonstrate that the proposed method achieves comparative prediction performance with the state-of-the-art BIQA methods.
Transformer-Based Neural Network for Answer Selection in Question Answering

Answer selection is a crucial subtask in the question answering (QA) system.Conventional avenues for this task mainly concentrate on developing linguistic tools that are limited in both performance and practicability.Answer selection approaches based on deep learning have been well investigated with the tremendous success of deep learning in natural language processing.However, the traditional neural networks employed in existing answer selection models, i.e., recursive neural network or convolutional neural network, typically suffer from obtaining the global text information due to their operating mechanisms.The recent Transformer neural network is considered to be good at extracting the global information by employing only self-attention mechanism.Thus, in this paper, we design a Transformer-based neural network for answer selection, where we deploy a bidirectional long short-term memory (BiLSTM) behind the Transformer to acquire both global information and sequential features in the question or answer sentence.Different from the original Transformer, our Transformer-based network focuses on sentence embedding rather than the seq2seq task.In addition, we employ a BiLSTM rather than utilizing the position encoding to incorporate sequential features as the universal Transformer does.Furthermore, we apply three aggregated strategies to generate sentence embeddings for question and answer, i.e., the weighted mean pooling, the max pooling, and the attentive pooling, leading to three corresponding Transformer-based models, i.e., QA-TF WP , QA-TF MP , and QA-TF AP , respectively.Finally, we evaluate our proposals on a popular QA dataset WikiQA.The experimental results demonstrate that our proposed Transformer-based answer selection models can produce a better performance compared with several competitive baselines.In detail, our best model outperforms the state-of-the-art baseline by up to 2.37%, 2.83%, and 3.79% in terms of MAP, MRR, and accuracy, respectively.
TSP Security in Intelligent and Connected Vehicles: Challenges and Solutions

The rapid development of IoT, cloud computing, Artificial Intelligence (AI), big data, and 5G technologies has promoted the transformation of traditional vehicles toward ICVs. Compared to traditional vehicles, ICVs face many security threats introduced by network technologies and intelligent devices, especially in the field of remote wireless communications using Telematics Service Provider (TSP). As the core communication system in ICVs, TSP integrates diverse communication systems, and thus inherits the original vulnerabilities of these systems inevitably. TSP provides various methods for the ICVs to access the Internet, which makes them vulnerable to remote attacks. However, existing auto manufacturers mostly focused on the user experiences of the ICVs, and paid little attention to these potential security risks raised by TSP. Toward this end, in this article we analyze and summarize the TSP security threats in ICVs, and present some attack methodologies. After that, we discuss a practical attack case against an ICV by leveraging the vulnerabilities of TSP, and some countermeasures are proposed to enhance ICV security against TSP attacks.
Efficient Video Classification Using Fewer Frames

Recently, there has been a lot of interest in building compact models for video classification which have a small memory footprint (<1 GB). While these models are compact, they typically operate by repeated application of a small weight matrix to all the frames in a video. For example, recurrent neural network based methods compute a hidden state for every frame of the video using a recurrent weight matrix. Similarly, cluster-and-aggregate based methods such as NetVLAD have a learnable clustering matrix which is used to assign soft-clusters to every frame in the video. Since these models look at every frame in the video, the number of floating point operations (FLOPs) is still large even though the memory footprint is small. In this work, we focus on building compute-efficient video classification models which process fewer frames and hence have less number of FLOPs. Similar to memory efficient models, we use the idea of distillation albeit in a different setting. Specifically, in our case, a compute-heavy teacher which looks at all the frames in the video is used to train a compute-efficient student which looks at only a small fraction of frames in the video. This is in contrast to a typical memory efficient Teacher-Student setting, wherein both the teacher and the student look at all the frames in the video but the student has fewer parameters. Our work thus complements the research on memory efficient video classification. We do an extensive evaluation with three types of models for video classification, viz., (i) recurrent models (ii) cluster-and-aggregate models and (iii) memory-efficient cluster-and-aggregate models and show that in each of these cases, a see-it-all teacher can be used to train a compute efficient see-very-little student. Overall, we show that the proposed student network can reduce the inference time by 30% and the number of FLOPs by approximately 90% with a negligent drop in the performance.
A Blockchain based Witness Model for Trustworthy Cloud Service Level Agreement Enforcement

Traditional cloud Service Level Agreement (SLA) suffers from lacking a trustworthy platform for automatic enforcement.The emerging blockchain technique brings in an immutable solution for tracking transactions among business partners.However, it is still very challenging to prove the credibility of possible violations in the SLA before recording them onto the blockchain.To tackle this challenge, we propose a witness model using game theory and the smart contract techniques.The proposed model extends the existing service model with a new role called "witness" for detecting and reporting service violations.Witnesses gain revenue as an incentive for performing these duties, and the payoff function is carefully designed in a way that trustworthiness is guaranteed: in order to get the maximum profit, the witness has to always tell the truth.This is analyzed and proved through game theory using the Nash equilibrium principle.In addition, an unbiased sortition algorithm is proposed to ensure the randomness of the independent witnesses selection from the decentralized witness pool, to avoid possible unfairness or collusion.An auditing mechanism is also introduced in the paper to detect potential irrational or malicious witnesses.We have prototyped the system leveraging the smart contracts of Ethereum blockchain.Experimental results demonstrate the feasibility of the proposed model and indicate good performance in accordance with the design expectations.
Spatial Task Scheduling for Cost Minimization in Distributed Green Cloud Data Centers

The infrastructure resources in distributed green cloud data centers (DGCDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGCDC provider in a market, where bandwidth prices of Internet service providers (ISPs), electricity prices, and the availability of renewable green energy all vary with geographical locations. Unlike existing studies, this paper proposes a spatial task scheduling and resource optimization (STSRO) method to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks' delay-bound constraints. STSRO well exploits spatial diversity in DGCDCs. In each time slot, the cost minimization problem for DGCDCs is formulated as a constrained optimization one and solved by the proposed simulated annealing-based bat algorithm (SBA). Trace-driven experiments demonstrate that STSRO achieves lower total cost and higher throughput than two typical scheduling methods.
CNN-FL: An Effective Approach for Localizing Faults using Convolutional Neural Networks

Fault localization aims at identifying suspicious statements potentially responsible for failures. The recent rapid progress on deep learning shows the promising potential of many neural network architectures in making sense of data, and more importantly, this potential offers a new prospective probably benefiting fault localization. Thus, this paper proposes CNN-FL: an approach for localizing faults based on convolutional neural networks to explore the promising potential of deep learning in fault localization. Specifically, CNN-FL constructs a convolutional neural network customized for fault localization, and then trains the network with test cases, and finally evaluates the suspiciousness of each statement by testing the trained model using a virtual test set. Our empirical results show that CNN-FL significantly improves fault localization effectiveness.
Spam Review Detection Techniques: A Systematic Literature Review

Online reviews about the purchase of products or services provided have become the main source of users’ opinions. In order to gain profit or fame, usually spam reviews are written to promote or demote a few target products or services. This practice is known as review spamming. In the past few years, a variety of methods have been suggested in order to solve the issue of spam reviews. In this study, the researchers carry out a comprehensive review of existing studies on spam review detection using the Systematic Literature Review (SLR) approach. Overall, 76 existing studies are reviewed and analyzed. The researchers evaluated the studies based on how features are extracted from review datasets and different methods and techniques that are employed to solve the review spam detection problem. Moreover, this study analyzes different metrics that are used for the evaluation of the review spam detection methods. This literature review identified two major feature extraction techniques and two different approaches to review spam detection. In addition, this study has identified different performance metrics that are commonly used to evaluate the accuracy of the review spam detection models. Lastly, this work presents an overall discussion about different feature extraction approaches from review datasets, the proposed taxonomy of spam review detection approaches, evaluation measures, and publicly available review datasets. Research gaps and future directions in the domain of spam review detection are also presented. This research identified that success factors of any review spam detection method have interdependencies. The feature’s extraction depends upon the review dataset, and the accuracy of review spam detection methods is dependent upon the selection of the feature engineering approach. Therefore, for the successful implementation of the spam review detection model and to achieve better accuracy, these factors are required to be considered in accordance with each other. To the best of the researchers’ knowledge, this is the first comprehensive review of existing studies in the domain of spam review detection using SLR process.
A path and velocity planning method for lane changing collision avoidance of intelligent vehicle based on cubic 3-D Bezier curve

A novel path and velocity planning method for lane changing collision avoidance of intelligent vehicle based on cubic 3-D Bezier curve is proposed in this paper to enhance the flexibility of path and guarantee the conformity to the realistic lane changing maneuver. Owing to the characteristic of 3-D Bezier curve, the velocity information is appended to the path to establish a real-time correspondence between them. In the XY plane, the integrated path is divided into a collision avoidance path and a lane changing path, which is combined with two asymmetrical Bezier curves. And the velocity planning of two paths is implemented by the same curve in the Z axis. The objective functions and dynamic constraints are formulated to solve the optimal problems of the path and velocity planning for satisfying different traffic situations. The simulations are performed in the CarSim-Simulink co-simulation platform and the results show the performance and effectiveness of the proposed approach.
Motion parallax for 360° RGBD video

We present a method for adding parallax and real-time playback of 360° videos in Virtual Reality headsets. In current video players, the playback does not respond to translational head movement, which reduces the feeling of immersion, and causes motion sickness for some viewers. Given a 360° video and its corresponding depth (provided by current stereo 360° stitching algorithms), a naive image-based rendering approach would use the depth to generate a 3D mesh around the viewer, then translate it appropriately as the viewer moves their head. However, this approach breaks at depth discontinuities, showing visible distortions, whereas cutting the mesh at such discontinuities leads to ragged silhouettes and holes at disocclusions. We address these issues by improving the given initial depth map to yield cleaner, more natural silhouettes. We rely on a three-layer scene representation, made up of a foreground layer and two static background layers, to handle disocclusions by propagating information from multiple frames for the first background layer, and then inpainting for the second one. Our system works with input from many of today's most popular 360° stereo capture devices (e.g., Yi Halo or GoPro Odyssey), and works well even if the original video does not provide depth information. Our user studies confirm that our method provides a more compelling viewing experience than without parallax, increasing immersion while reducing discomfort and nausea.
Mental models for conservation research and practice

Abstract Conservation practice requires an understanding of complex social‐ecological processes of a system and the different meanings and values that people attach to them. Mental models research offers a suite of methods that can be used to reveal these understandings and how they might affect conservation outcomes. Mental models are representations in people's minds of how parts of the world work. We seek to demonstrate their value to conservation and assist practitioners and researchers in navigating the choices of methods available to elicit them. We begin by explaining some of the dominant applications of mental models in conservation: revealing individual assumptions about a system, developing a stakeholder‐based model of the system, and creating a shared pathway to conservation. We then provide a framework to “walk through” the stepwise decisions in mental models research, with a focus on diagram‐based methods. Finally, we discuss some of the limitations of mental models research and application that are important to consider. This work extends the use of mental models research in improving our ability to understand social‐ecological systems, creating a powerful set of tools to inform and shape conservation initiatives.
Analyzing the use of Facebook among university libraries in Hong Kong

In recent years, many academic libraries have started adopting social media by, for example, establishing their own Facebook pages. Our research investigates the current Facebook usage among seven university libraries in Hong Kong. Using a mixed-method research approach, we examined the characteristics of academic libraries Facebook usage and its effectiveness to engage with their patrons, based on online observations and Web content analysis from the publicly available data of the Facebook pages. Our results indicated that most libraries in Hong Kong adopted Facebook as a marketing tool, yet the user engagement level in these pages is low. In addition, we also discovered that communication-related posts and video-type posts can attract the most attention of patrons. Based on these outcomes, we provided some recommendations on the application of Facebook for academic libraries.