Reviewerly/exHarmony huggingface dataset:
Google’s Use of Artificial Intelligence in a Two-Sided Advertising Market Causing Trust Loss, and Data Monopolization

Google, an American multinational technology company, offers internet products, e.g., digital advertising, search engine, cloud computing, software, and hardware. This paper describes Google search engine and Google Ads, two of its flagship products in relationship to the role of AI employed that may produce a network effect in a two-sided market. This is discussed through the structure of the search engine market and Google’s position in this market. The digital use of AI robots is ubiquitous. This study brings about a new thinking, beyond the analogy between platform operations and city operations, notably along the dimension of private vs. public spaces since people are familiar with thinking of issues in that paradigm as the pre-internet days.
Local control editing paradigms for part‐based 3D face morphable models

Abstract We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attribute editing workflow. Current face modeling methods using 3DMM suffer from a lack of local control. We thus create a 3DMM by combining local part‐based 3DMM for the eyes, nose, mouth, ears, and facial mask regions. Our local principal component analysis (PCA)‐based approach uses a novel method to select the best eigenvectors from the local 3DMM to ensure that the combined 3DMM is expressive, while allowing accurate reconstruction. We provide different editing paradigms, all designed from the analysis of the data set. Some use anthropometric measurements from the literature and others allow the user to control the dominant modes of variation extracted from the data set. Our part‐based 3DMM is compact, yet accurate, and compared to other 3DMM methods, it provides a new trade‐off between local and global control. We tested our approach on a data set of 135 scans used to derive the 3DMM, plus 19 scans that served for validation. The results show that our part‐based 3DMM approach has excellent generative properties and allows the user intuitive local control.
Masa: Responsive Multi-DNN Inference on the Edge

Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices, especially for real time image-based analysis. Increasingly, multi-faced knowledge is extracted via executing multiple DNNs inference models, e.g., identifying objects, faces, and genders from images. The response times of multi-DNN highly affect users' quality of experience and safety as well. Different DNNs exhibit diversified resource requirements and execution patterns across layers and networks, which may easily exceed the available device memory and riskily degrade the responsiveness. In this paper, we design and implement Masa, a responsive memory-aware multi-DNN execution framework, an on-device middleware featuring on modeling inter- and intra-network dependency and leveraging complimentary memory usage of each layer. Masa can consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. We extensively evaluate Masa on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different generation patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 90% on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.
A State of the Art Regressor Model’s comparison for Effort Estimation of Agile software

Advances and innovations in the field of software engineering are increasing rapidly. This sensitizes researchers to explore the various cross-cutting concerns incorporated to handle the complexities of various domains of interest. One such thrust area is effort estimation in Agile-inspired software. Estimation has always been challenging in an Agile environment because of its requirement volatility. This paper introduces a critical review of state-of-the-art regression techniques to estimate the efforts of Agile projects. It can be concluded from the obtained results that ensemble estimation techniques outperformed single techniques of estimation. The data have been taken from various companies implementing Agile practices. Different regressors have been trained, tested, cross-validated, and optimized to fill the actual and estimated effort gap. We have used six regression techniques in this paper, Extreme Gradient Boosting (XGB), Decision Tree (DT), Linear Regressor (LR), Random Forest (RF), Adaptive Boosting (AdaBoost) and, Categorical boosting (CatBoost) regressors. Cat Boost regressor wins with the lowest Root Mean Square Error (RMSE) in comparison to other regressors.
Visual Analysis of Spatio‐temporal Phenomena with 1D Projections

Abstract It is crucial to visually extrapolate the characteristics of their evolution to understand critical spatio‐temporal events such as earthquakes, fires, or the spreading of a disease. Animations embedded in the spatial context can be helpful for understanding details, but have proven to be less effective for overview and comparison tasks. We present an interactive approach for the exploration of spatio‐temporal data, based on a set of neighborhood‐preserving 1D projections which help identify patterns and support the comparison of numerous time steps and multivariate data. An important objective of the proposed approach is the visual description of local neighborhoods in the 1D projection to reveal patterns of similarity and propagation. As this locality cannot generally be guaranteed, we provide a selection of different projection techniques, as well as a hierarchical approach, to support the analysis of different data characteristics. In addition, we offer an interactive exploration technique to reorganize and improve the mapping locally to users' foci of interest. We demonstrate the usefulness of our approach with different real‐world application scenarios and discuss the feedback we received from domain and visualization experts.
Automatic Detection and Location of Microseismic Events from Sparse Network and Its Application to Post-mining Monitoring

In post-mining regions with seismic hazard, timely decision making for risk management faces the challenge of quick and reliable detection and location of seismic events. As a response to the increasing density of monitoring stations, generating large volumes of seismic data, automatic, full waveform-based methods have been developed in recent years in global seismology. Such methods often cannot be directly applied to post-mining monitoring with a limited station coverage, as it is the case when temporarily networks are installed as an emergency response. In this paper we propose a new methodology that bridges this gap and enables the application of a full waveform, backprojection based method (BackTrackBB) to data of sparse network. The methodology was successfully tested on an abandoned and flooded underground coalmine in South-eastern France. Steps preceding BackTrackBB application were implemented in order to remove the coherent noise that otherwise results in numerous false detections. First results indicate that seismic activity in the study area is controlled by water level variation within former room-and-pillar mine works and fault segments (re)activation below them.
Water Surface Target Detection Based on Improved YOLOv3 in UAV Images

In order to better manage and protect rivers and lakes, the most important requirement is to find the objects on the surface of rivers and lakes in time. Generally, image segmentation and target detection are used to detect water surface targets. The former is sensitive to the selection of target features, with poor generalization ability and slow detection speed. The latter has not yet been applied to surface target detection in UAV images. In view of this situation, this paper proposes a target detection model based on YOLOv3, which is used to detect surface targets in UAV images. In order to verify the performance of the model, the images collected in this paper include five types of surface targets. These images are then enhanced by rotation transform, brightness transform and mirror transform, and the enhanced images are used to generate data sets. In the YOLOv3 model, we use the inception module for multi-scale depth features to process the deep features of the network. The module can activate the multi-scale sensing field of the deep features, so as to fully utilize the deep features and improve the detection accuracy of small and medium targets in the UAV image. In addition, we optimize the loss function to train the network better. The experimental results show that the mAP of the proposed Yolov3-inception is 81%, the detection speed is 23 frames per second, and the overall performance is better than YOLOv3, Faster RCNN and SSD. Therefore, this method is suitable for surface target detection in UAV images.
Treatment initiation prediction by EHR mapped PPD tensor based convolutional neural networks boosting algorithm

• Patient’s Procedures, Prescriptions, and Diagnoses (PPD) feature data correlation from EHR can be detected. • Map EHR records to image tensors based on raw PPD feature values and detected interactions. • Construct Convolutional Neural Network models based on PPD tensor image. • Improve treatment initiation prediction precision for Chronic Lymphocytic Leukemia (CLL) patients by proposed modeling pipeline. Electronic health records contain patient’s information that can be used for health analytics tasks such as disease detection, disease progression prediction, patient profiling, etc. Traditional machine learning or deep learning methods treat EHR entities as individual features, and no relationships between them are taken into consideration. We propose to evaluate the relationships between EHR features and map them into Procedures, Prescriptions, and Diagnoses (PPD) tensor data, which can be formatted as images. The mapped images are then fed into deep convolutional networks for local pattern and feature learning. We add this relationship-learning part as a boosting module on a commonly used classical machine learning model. Experiments were performed on a Chronic Lymphocytic Leukemia dataset for treatment initiation prediction. Experimental results show that the proposed approach has better real world modeling performance than the baseline models in terms of prediction precision.
Healthcare Training Application: 3D First Aid Virtual Reality

Medical simulations in virtual reality (VR) offer a personalized learning environment that can be structured and adapted to various modes of learning in ways that conventional teaching can not offer. There is an increasing interest in using VR for training and learning. The use of VR in medical training, without requiring a real human being, will promote an effective medical learning process. The software helps learners to adapt to various types of learning in ways that are not suitable in conventional teaching. This paper presents VR and its important usages in education and healthcare and the development of our prototype 3D First Aid VR. Two teaching modules are presented in this prototype: a tutorial which explains the cause and symptoms of a seizure, and training which is used to train first aid in a 3D environment. The two modules were presented in the form of a 3D model kitchen, with a character having a seizure in the immersive environment, and viewed with Oculus Quest. This paper will be useful for researchers and developers in the field of VR.
Broken External Links on Stack Overflow

Stack Overflow hosts valuable programming-related knowledge with 11,926,354 links that reference to the third-party websites. The links that reference to the resources hosted outside the Stack Overflow websites extend the Stack Overflow knowledge base substantially. However, with the rapid development of programming-related knowledge, many resources hosted on the Internet are not available anymore. Based on our analysis of the Stack Overflow data that was released on Jun. 2, 2019, 14.2 percent of the links on Stack Overflow are broken links. The broken links on Stack Overflow can obstruct viewers from obtaining desired programming-related knowledge, and potentially damage the reputation of the Stack Overflow as viewers might regard the posts with broken links as obsolete. In this paper, we characterize the broken links on Stack Overflow. 65 percent of the broken links in our sampled questions are used to show examples, e.g., code examples. 70 percent of the broken links in our sampled answers are used to provide supporting information, e.g., explaining a certain concept and describing a step to solve a problem. Only 1.67 percent of the posts with broken links are highlighted as such by viewers in the posts’ comments. Only 5.8 percent of the posts with broken links removed the broken links. Viewers cannot fully rely on the vote scores to detect broken links, as broken links are common across posts with different vote scores. The websites that host resources that can be maintained by their users are referenced by broken links the most on Stack Overflow – a prominent example of such websites is GitHub. The posts and comments related to the web technologies, i.e., JavaScript, HTML, CSS, and jQuery, are associated with more broken links. Based on our findings, we shed lights for future directions and provide recommendations for practitioners and researchers.
PNAS: A privacy preserving framework for neural architecture search services

The success of deep neural networks has contributed to many fields, such as finance, medic and speech recognition. Machine learning models adopted in these fields are always trained with a massive amount of distributed and highly personalized data harvested directly from users. Concerns for data privacy and the demand for better data exploitation have prompted the design of several secure schemes that allow an untrusted server to train ML models for one or multiple parties. However, these existing schemes only focus on network parameter, and hardly extend their optimization range to model architecture scope. Sine the performance of a neural network is closely related to both parameter and its architecture, service providers are difficult to deliver customized and flexible neural networks to each client. To this end, in this paper we propose PNAS, a novel MLaaS framework that enables a server to jointly optimize network parameter and architecture while ensuring the privacy of training sets. A double-encryption scheme is derived to prevent privacy leakage from sample itself, as well as intermediate feature maps during training. Specifically, we adopt functional encryption and feature transformation to secure forward and back propagation. Extensive experiments have demonstrated the superiority of our proposal.
A Multi-Factor Combinations Enhanced Reversible Privacy Protection System for Facial Images

With the abuse of deepfake and other deep learning technologies, anonymization and deanonymization for face images have become one of the essential tasks for privacy protection. Thus, we propose a novel reversible privacy protection framework for facial images based on conditional encoder and de-coder framework. For the purpose to increase the diversity and controllability over the anonymized faces, we also introduce facial attributes and a style vector from a reference back-ground face dataset and pretrained face recognition model and thus name the proposed framework as the Multi-factor Modifier (MfM) to achieve multi-factor facial de/re-identification. Specifically, with the correct password, our method produces near-original reconstructed images. Otherwise, it can generate photo-realistic and diverse anonymized images. With extensive experiments, it shows that the proposed approach can successfully anonymize face images in high fidelity according to the given conditions as compared with other methods and deanonymize without altering the facial data distributions.
Group Delegated ID-Based Proxy Reencryption for the Enterprise IoT-Cloud Storage Environment

In general, ID-based proxy reencryption (IBPRE) includes data transfer in a 1 : 1 manner between a sender and receiver. Therefore, only the data owner has the authority to decrypt or reencrypt the data that is encrypted with his/her public key. However, in an environment with data self-sovereignty, such as an enterprise IoT-cloud environment, the data are directly managed by cloud once data is uploaded from user-controlled IoT devices. In such a situation, there is no way of sharing data if the data owner has no access over the data due to being outside the workplace and other issues. In this study, to solve this problem, data can be shared even when the data cannot be accessed by delegating the authority of the data owner to generate the reencryption key to other users. In addition, by solving the security threats that may appear in this process, data sharing can be performed securely and efficiently in the corporate environment.
A Novel Coding Scheme for Large-Scale Point Cloud Sequences Based on Clustering and Registration

Due to the huge volume of point cloud data, storing and transmitting it is currently difficult and expensive in autonomous driving. Learning from the high-efficiency video coding (HEVC) framework, we propose a novel compression scheme for large-scale point cloud sequences, in which several techniques have been developed to remove the spatial and temporal redundancy. The proposed strategy consists mainly of three parts: intracoding, intercoding, and residual data coding. For intracoding, inspired by the depth modeling modes (DMMs), in 3-D HEVC (3-D-HEVC), a cluster-based prediction method is proposed to remove the spatial redundancy. For intercoding, a point cloud registration algorithm is utilized to transform two adjacent point clouds into the same coordinate system. By calculating the residual map of their corresponding depth image, the temporal redundancy can be removed. Finally, the residual data are compressed either by lossless or lossy methods. Our approach can deal with multiple types of point cloud data, from simple to more complex. The lossless method can compress the point cloud data to 3.63% of its original size by intracoding and 2.99% by intercoding without distance distortion. Experiments on the KITTI dataset also demonstrate that our method yields better performance compared with recent well-known methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article deals with the problem of efficient compression of point cloud sequences that come from light detection and ranging (LiDARs) mounted on autonomous mobile robots. The vast amount of point cloud data could be an important bottleneck for transmission and storage. Inspired by the HEVC algorithm, we develop a novel coding architecture for the point cloud sequence. The scans are divided into intraframe and interframe, which are encoded separately using different techniques. Our method can be used for the compression of LiDAR point cloud sequences or dense LiDAR point cloud map and will significantly reduce the transmission bandwidth and storage spaces. We have to admit that although our method is less effective for real-time solutions, it can be highly efficient for off-line applications. Future studies will concentrate on further optimizing the coding algorithm to reduce the computational complexity and trying to find a balance between them.
HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology

Model compression through quantization is commonly applied to convolutional neural networks (CNNs) deployed on compute and memory-constrained embedded platforms. Different layers of the CNN can have varying degrees of numerical precision for both weights and activations, resulting in a large search space. Together with the hardware (HW) design space, the challenge of finding the globally optimal HW-CNN combination for a given application becomes daunting. To this end, we propose HW-FlowQ, a systematic approach that enables the co-design of the target hardware platform and the compressed CNN model through quantization. The search space is viewed at three levels of abstraction, allowing for an iterative approach for narrowing down the solution space before reaching a high-fidelity CNN hardware modeling tool, capable of capturing the effects of mixed-precision quantization strategies on different hardware architectures (processing unit counts, memory levels, cost models, dataflows) and two types of computation engines (bit-parallel vectorized, bit-serial). To combine both worlds, a multi-objective non-dominated sorting genetic algorithm (NSGA-II) is leveraged to establish a Pareto-optimal set of quantization strategies for the target HW-metrics at each abstraction level. HW-FlowQ detects optima in a discrete search space and maximizes the task-related accuracy of the underlying CNN while minimizing hardware-related costs. The Pareto-front approach keeps the design space open to a range of non-dominated solutions before refining the design to a more detailed level of abstraction. With equivalent prediction accuracy, we improve the energy and latency by 20% and 45% respectively for ResNet56 compared to existing mixed-precision search methods.
LSOS: Line-search second-order stochastic optimization methods for nonconvex finite sums

We develop a line-search second-order algorithmic framework for minimizing finite sums. We do not make any convexity assumptions, but require the terms of the sum to be continuously differentiable and have Lipschitz-continuous gradients. The methods fitting into this framework combine line searches and suitably decaying step lengths. A key issue is a two-step sampling at each iteration, which allows us to control the error present in the line-search procedure. Stationarity of limit points is proved in the almost-sure sense, while almost-sure convergence of the sequence of approximations to the solution holds with the additional hypothesis that the functions are strongly convex. Numerical experiments, including comparisons with state-of-the art stochastic optimization methods, show the efficiency of our approach.
A Study About the Knowledge and Use of Requirements Engineering Standards in Industry

Context: The use of standards is considered a vital part of any engineering discipline. So one could expect that standards play an important role in Requirements Engineering (RE) as well. However, little is known about the actual knowledge and use of RE-related standards in industry. Objective: In this article, we investigate to which extent standards and related artifacts such as templates or guidelines are known and used by RE practitioners. Method: To this end, we have conducted a questionnaire-based online survey. We could analyze the replies from 90 RE practitioners using a combination of closed and open-text questions. Results: Our results indicate that the knowledge and use of standards and related artifacts in RE is less widespread than one might expect from an engineering perspective. For example, about 47% of the respondents working as requirements engineers or business analysts do not know the core standard in RE, ISO/IEC/IEEE 29148. Participants in our study mostly use standards by personal decision rather than being imposed by their respective company, customer, or regulator. Beyond insufficient knowledge, we also found cultural and organizational factors impeding the widespread adoption of standards in RE. Conclusions: Overall, our results provide empirically informed insights into the actual use of standards and related artifacts in RE practice and - indirectly - about the value that the current standards create for RE practitioners.