Reviewerly/exHarmony huggingface dataset:
COVID-19 Contact Tracing and Privacy: A Longitudinal Study of Public Opinion

There is growing use of technology-enabled contact tracing, the process of identifying potentially infected COVID-19 patients by notifying all recent contacts of an infected person. Governments, technology companies, and research groups alike have been working towards releasing smartphone apps, using IoT devices, and distributing wearable technology to automatically track “close contacts” and identify prior contacts in the event an individual tests positive. However, there has been significant public discussion about the tensions between effective technology-based contact tracing and the privacy of individuals. To inform this discussion, we present the results of seven months of online surveys focused on contact tracing and privacy, each with 100 participants. Our first surveys were on April 1 and 3, 2020, before the first peak of the virus in the US, and we continued to conduct the surveys weekly for 10 weeks (through June), and then fortnightly through November, adding topical questions to reflect current discussions about contact tracing and COVID-19. Our results present the diversity of public opinion and can inform policy makers, technologists, researchers, and public health experts on whether and how to leverage technology to reduce the spread of COVID-19, while considering potential privacy concerns.
Machine Learning Empowered Software Defect Prediction System

Production of high-quality software at lower cost has always been the main concern of developers.However, due to exponential increases in size and complexity, the development of qualitative software with lower costs is almost impossible.This issue can be resolved by identifying defects at the early stages of the development lifecycle.As a significant amount of resources are consumed in testing activities, if only those software modules are shortlisted for testing that is identified as defective, then the overall cost of development can be reduced with the assurance of high quality.An artificial neural network is considered as one of the extensively used machine-learning techniques for predicting defect-prone software modules.In this paper, a cloud-based framework for real-time softwaredefect prediction is presented.In the proposed framework, empirical analysis is performed to compare the performance of four training algorithms of the backpropagation technique on software-defect prediction: Bayesian regularization (BR), Scaled Conjugate Gradient, Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton, and Levenberg-Marquardt algorithms.The proposed framework also includes a fuzzy layer to identify the best training function based on performance.Publicly available cleaned versions of NASA datasets are used in this study.Various measures are used for performance evaluation including specificity, precision, recall, F-measure, an area under the receiver operating characteristic curve, accuracy, R 2 , and mean-square error.Two graphical user interface tools are developed in MatLab software to implement the proposed framework.The first tool is developed for comparing training functions as well as for extracting the results; the second tool is developed for the selection of the best training function using fuzzy logic.A BR training algorithm is selected by the fuzzy layer as it
FedPA: An adaptively partial model aggregation strategy in Federated Learning

Federated Learning has sparked increasing interest as a promising approach to utilize large amounts of data stored on network edge devices. Federated Averaging is the most widely accepted Federated Learning framework. In Federated Averaging, the server keeps waiting for client models to compute the global model in each round unless all client models are received or a pre-configured timer expires, therefore it suffers seriously from participant devices with weak computation and/or communication capability, which is a kind of straggler problem. In this paper we design FedPA, a framework based on partial model aggregation strategy, in which the server waits for only an appropriate number of device models (referred to as aggregation number) in each round. Our experiment shows that the accuracy loss of the aggregated global model in a single round is not significant if the aggregation number is decided carefully. We propose a waiting strategy to determine the aggregation number for each round dynamically and the aggregation number is adaptive to achieve a tradeoff between single-round training time and the expected number of rounds to reach the target accuracy. Stale models are also included during aggregation when they arrive, and their positive value and negative effect are carefully evaluated and reflected in the aggregation strategy. Experiments show that FedPA outperforms the baseline strategy FedAvg and other three algorithms named FedAsync, FLANP and AD-SG. It can work well in all scenarios with different distributions of data samples (characterized by non-IID ratio) and computation/communication capability (characterized by level of heterogeneity) among devices. Experiments also show that FedPA is robust when a certain amount of noise is added into the input from clients for privacy concerns.
A metric-based meta-learning approach combined attention mechanism and ensemble learning for few-shot learning

Meta-learning is one of the latest research directions in machine learning, which is considered to be one of the most probably ways to realize strong artificial intelligence. Meta-learning focuses on seeking solutions for machines to learn like human beings do - to recognize things through only few sample data and quickly adapt to new tasks. Challenges occur in how to train an efficient machine model with limited labeled data, since the model is easily over-fitted. In this paper, we address this obvious but important problem and propose a metric-based meta-learning model, which combines attention mechanisms and ensemble learning method. In our model, we first design a dual path attention module which considers both channel attention and spatial attention module, and the attention modules have been stacked to conduct a meta-learner for few shot meta-learning. Then, we apply an ensemble method called snap-shot ensemble to the attention-based meta-learner in order to generate more models in a single episode. Features abstracted from the models are put into the metric-based architecture to compute a prototype for each class. Our proposed method intensifies the feature extracting ability of backbone network in meta-learner and reduces over-fitting through ensemble learning and metric learning method. Experimental results toward several meta-learning datasets show that our approach is effective.
Security and Privacy in Shared HitLCPS Using a GA-Based Multiple-Threshold Sanitization Model

In Cyber-Physical Systems (CPS), especially in human-in-the-loop situations (also known as HitLCPS), the security and privacy for keeping sensitive information private is considered an emerging topic in recent decades. Many techniques in privacy-preserving data mining (PPDM) can be applied directly to HitLCPS. However, most of them to date have focused on handling singular threshold problems for data sanitization. If a sensitive itemset includes more items, it has a higher probability of being identified due to its specificity. In this work, we propose a new concept of multiple support thresholds to assist in resolving this issue. The proposed method assigns a stricter threshold for an itemset. Furthermore, a genetic-algorithm (GA)-based model is involved in the designed algorithm to minimize side effects. In our experimental results, the GA-based PPDM approach is compared with traditional Greedy PPDM approaches. The strong experimental results clearly show that our proposed method can give similar performance to conventional algorithms while still maintaining higher-levels of security and privacy protection than previous methods.
Multi-lingual character handwriting framework based on an integrated deep learning based sequence-to-sequence attention model

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate.
Road PV production estimation at city scale: A predictive model towards feasible assessing regional energy generation from solar roads

Solar roads are roads embedded with solar panels which can converting solar energy radiated on the road into storable electricity. Over the recent years, pioneering solar road prototypes were tested in different regions around the world. Driven by demand, road photovoltaic production calculation, based on street view images (SVI) has been proposed (Liu et al., 2019). However, in addition to the high runtime overhead, this method cannot be applied to cities in which recent SVI are unavailable. At the meantime, researches on the estimation of road photovoltaic production of cities are rare, especially ones with spatially explicit inferences. This study proposes an innovative predictive model that can estimate road photovoltaic capacity of cities with urban features obtained from remote sensing images and other multi-source GIS data. As a scaffolding step, accurate estimation of potential road PV in 27 cities were calculated using SVI. Compared with the SVI approach, our predictive model is fast, robust and yet accurate as well. As a result, the spatial distribution of the potential energy production of solar roads for the 27 cities are mapped, which provides insights into which area should be prioritized for building solar roads. By analysing and comparing the estimated results and current vehicle energy demand, we propose different suggestions for the construction of photovoltaic roads for different types of cities. These suggestions may provide support for urban solar road planning in the course of adapting to cleaner energy sources. Additionally, data required by this predictive model is easy to access, which contributes to the universal applicability of this method.
CTI-SOC2M2 – The quest for mature, intelligence-driven security operations and incident response capabilities

Threats, cyber attacks, and security incidents pertain to organizations of all types. Everyday information security is essentially defined by the maturity of security operations and incident response capabilities. However, focusing on internal information only has proven insufficient in an ever-changing threat landscape. Cyber threat intelligence (CTI) and its sharing are deemed necessary to cope with advanced threats and strongly influence security capabilities. Therefore, in this work, we develop CTI-SOC2M2, a capability maturity model that uses the degree of CTI integration as a proxy for SOC service maturity. In the course, we examine existing maturity models in the domains of Security Operations Centers (SOCs), incident response, and CTI. In search of adequate maturity assessment, we show threat intelligence dependencies through applicable data formats. As the systematic development of maturity models demands, our mixed methodology approach contributes a new in-depth analysis of intelligence-driven security operations. The resulting CTI-SOC2M2 model contains CTI formats, SOC services and is complemented with an evaluation through expert interviews. A prototypical, tool-based implementation is aimed to document steps towards the model’s practical application.
Queasy Rider: How Head Movements Influence Motion Sickness in Passenger Use of Head-Mounted Displays

In autonomous cars, drivers will spend more time on non-driving-related activities. Getting their hands off the wheel and eyes off the road, the driver, similar to a rear-seat passenger today, can use multiple built-in displays for such activities or even mobile head-mounted displays (HMDs) in virtual reality (VR). A wider motion range is known to increase engagement, but might also amplify the risk of motion sickness while switching between displays. In a rear-seat VR field study (N=21) on a city highway, we found a head movement range of ± 50° with a speed of 1.95m/s to provide the best trade-off between motion sickness and engagement. Compared to the pitch (Y) axis, movement around the yaw (X) axis induced less discomfort and more engagement with less motion sickness. Our work provides a concrete starting point for future research on self-driving carsickness, starting from today's rear-seat passengers.
Single image dehazing with an independent Detail-Recovery Network

Single image dehazing is a prerequisite that affects the performance of many visually related tasks and has attracted increasing attention in recent years. However, most existing dehazing methods place more emphasis on haze removal but less on the detail recovery of the dehazed images. In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrating them into a coarse dehazed image. The overall network consists of two independent networks, named DRN and the dehazing network. Specifically, the DRN aims to recover the dehazed image details through the joint efforts of the local branch and the global branch. The local branch can obtain local detail information through the convolution layer, and the global branch can capture multi-scale global information by Smooth Dilated Convolution (SDC). In addition, we apply multi-faceted loss to improve the stability of the dehazing model. Extensive experiments on public image dehazing datasets illustrate the effectiveness of the modules in the proposed method and reveal that our method outperforms state-of-the-art dehazing methods. The code is released in https://github.com/YanLi-LY/Dehazing-DRN.
A Deep Learning Model for Small-size Defective Components Detection in Power Transmission Tower

Unmanned Aerial Vehicle (UAV) inspection has gradually replaced manual inspection of transmission tower, which produces many images. While it is laborious and time-consuming to manually analyze these images, there are also challenges in automatically detecting small-size defective components such as bolts in transmission tower images, due to problems including complex background, small size, and many similar objects of bolts. In this paper, by virtue of multi-scale features and context information, we propose a deep neural network named Camp-Net (Context Information and Multi-Scale Pyramid Network) to identify bolts defect in transmission tower images. First, multi-scale feature fusion combines deep features and shallow features in convolutional networks to detect small-size bolts. Second, context information fusion puts the information around bolts into the detection network to remove the disturbance of complex background and similar objects. An image dataset containing defective bolts and normal bolts is constructed for model training and testing. Experimental results show that bolts with loose pins and bolts without pins among fittings in transmission tower can be accurately identified with the proposed model. The Average Precision (AP) of defective bolts detection of this model can be 11.4% higher than that of the commonly used high performance model, Faster R-CNN.
Project developer options to enhance the value of solar electricity as solar and storage penetrations increase

Photovoltaic (PV) project developers have a range of configuration options.• Analyze more than ten strategies to preserve the marginal grid value of PV.• Limited impact of shifting the timing of PV generation at the expense of total generation.• Shifting the timing of generation becomes redundant when storage is added.• At high penetrations, leading options add storage to configurations that maximize PV generation.
Convergence between the real and the augmented: Experiences and perceptions in location-based games

Location-based augmented reality (AR) games have recently become massively popular, generating billions of dollars in revenue during the past five years. These games augment geographical areas with playful content, which makes the playing experience dependent on both the game and the playing location. Existing games have employed various methods for increasing the connection between the game and the physical world, such as (1) an AR mode that lets players see virtual objects in the physical world through the lens of their mobile device; and (2) connecting the game's virtual points of interest (PoIs) to physical world objects. To explore how these influence the playing experience, we surveyed players (N = 74) across four popular location-based AR games. We analyzed the data using a thematic clustering approach, which resulted in five central themes. Among our findings was that only 7% of the surveyed players regularly used or appreciated AR features that made use of the mobile device's camera. The most often given reason for this was that they slow down progression. By contrast, players appreciated location-based features and the augmentation of digital content on a map interface. Connecting the virtual content to physical world objects was seen to have the benefits of (1) supporting social meetings; (2) enabling learning about the real world; and (3) increasing the meaningfulness of the virtual content.
A Language-independent Network to Analyze the Impact of COVID-19 on the World via Sentiment Analysis

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread worldwide, resulting in a deadly pandemic that infected millions of people around the globe. The public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the lives of the people. In this paper, we study the sentiments of people from the top five worst affected countries by the virus, namely the USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net) , which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanisms to extract the positive, negative, and neutral sentiments. The network captures the subtle cues in a document by focusing on the local characteristics of text along with the past and future context information for the sentiment classification. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter and applying topic modeling to extract the hidden thematic structure of the document. The classification results demonstrate that the proposed model achieves an accuracy of 85%, which is higher than other well-known algorithms for sentiment classification. The findings show that the topics which evoked positive sentiments were related to frontline workers, entertainment, motivation, and spending quality time with family. The negative sentiments were related to socio-economic factors like racial injustice, unemployment rates, fake news, and deaths. Finally, this study provides feedback to the government and health professionals to handle future outbreaks and highlight future research directions for scientists and researchers.
Compositional Learning and Verification of Neural Network Controllers

Recent advances in deep learning have enabled data-driven controller design for autonomous systems. However, verifying safety of such controllers, which are often hard-to-analyze neural networks, remains a challenge. Inspired by compositional strategies for program verification, we propose a framework for compositional learning and verification of neural network controllers. Our approach is to decompose the task (e.g., car navigation) into a sequence of subtasks (e.g., segments of the track), each corresponding to a different mode of the system (e.g., go straight or turn). Then, we learn a separate controller for each mode, and verify correctness by proving that (i) each controller is correct within its mode, and (ii) transitions between modes are correct. This compositional strategy not only improves scalability of both learning and verification, but also enables our approach to verify correctness for arbitrary compositions of the subtasks. To handle partial observability (e.g., LiDAR), we additionally learn and verify a mode predictor that predicts which controller to use. Finally, our framework also incorporates an algorithm that, given a set of controllers, automatically synthesizes the pre- and postconditions required by our verification procedure. We validate our approach in a case study on a simulation model of the F1/10 autonomous car, a system that poses challenges for existing verification tools due to both its reliance on LiDAR observations, as well as the need to prove safety for complex track geometries. We leverage our framework to learn and verify a controller that safely completes any track consisting of an arbitrary sequence of five kinds of track segments.
Examining embedded apparatuses of AI in Facebook and TikTok

In popular discussions, the nuances of AI are often abridged as “the algorithm”, as the specific arrangements of machine learning (ML), deep learning (DL) and automated decision-making on social media platforms are typically shrouded in proprietary secrecy punctuated by press releases and transparency initiatives. What is clear, however, is that AI embedded on social media functions to recommend content, personalize ads, aggregate news stories, and moderate problematic material. It is also increasingly apparent that individuals are concerned with the uses, implications, and fairness of algorithmic systems. Perhaps in response to concerns about “the algorithm” by individuals and governments, social media platforms utilize transparency initiatives and official statements, in part, to deflect official regulation. In the following paper, I draw from transparency initiatives and statements from representatives of Facebook and TikTok as case studies of how AI is embedded in these platforms, with attention to the promotion of AI content moderation as a solution to the circulation of problematic material and misinformation. This examination considers the complexity of embedded AI as a material-discursive apparatus, predicated on discursive techniques—what is seeable, sayable, knowable in a given time period—as well as the material arrangements—algorithms, datasets, users, platforms, infrastructures, moderators, etc. As such, the use of AI as part of the immensely popular platforms Facebook and TikTok demonstrates that AI does not exist in isolation, instead functioning as human–machine ensemble reliant on strategies of acceptance via discursive techniques and the changing material arrangements of everyday embeddedness.