Reviewerly/exHarmony huggingface dataset:
Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction

Modeling user engagement dynamics on social media has compelling applications in market trend analysis, user-persona detection, and political discourse mining. Most existing approaches depend heavily on knowledge of the underlying user network. However, a large number of discussions happen on platforms that either lack any reliable social network (news portal, blogs, Buzzfeed) or reveal only partially the inter-user ties (Reddit, Stackoverflow). Many approaches require observing a discussion for some considerable period before they can make useful predictions. In real-time streaming scenarios, observations incur costs. Lastly, most models do not capture complex interactions between exogenous events (such as news articles published externally) and in-network effects (such as follow-up discussions on Reddit) to determine engagement levels. To address the three limitations noted above, we propose a novel framework, ChatterNet, which, to our knowledge, is the first that can model and predict user engagement without considering the underlying user network. Given streams of timestamped news articles and discussions, the task is to observe the streams for a short period leading up to a time horizon, then predict chatter: the volume of discussions through a specified period after the horizon. ChatterNet processes text from news and discussions using a novel time-evolving recurrent network architecture that captures both temporal properties within news and discussions, as well as influence of news on discussions. We report on extensive experiments using a two-month-long discussion corpus of Reddit, and a contemporaneous corpus of online news articles from the Common Crawl. ChatterNet shows considerable improvements beyond recent state-of-the-art models of engagement prediction. Detailed studies controlling observation and prediction windows, over 43 different subreddits, yield further useful insights.
Rethinking Pruning for Accelerating Deep Inference At the Edge

There is a growing trend to deploy deep neural networks at the edge for high-accuracy, real-time data mining and user interaction. Applications such as speech recognition and language understanding often apply a deep neural network to encode an input sequence and then use a decoder to generate the output sequence. A promising technique to accelerate these applications on resource-constrained devices is network pruning, which compresses the size of the deep neural network without severe drop in inference accuracy. However, we observe that although existing network pruning algorithms prove effective to speed up the prior deep neural network, they lead to dramatic slowdown of the subsequent decoding and may not always reduce the overall latency of the entire application. To rectify such drawbacks, we propose entropy-based pruning, a new regularizer that can be seamlessly integrated into existing network pruning algorithms. Our key theoretical insight is that reducing the information entropy of the deep neural network outputs decreases the upper bound of the subsequent decoding search space. We validate our solution with two state-of-the-art network pruning algorithms on two model architectures. Experimental results show that compared with existing network pruning algorithms, our entropy-based pruning method notably suppresses and even eliminates the increase of decoding time, and achieves shorter overall latency with only negligible extra accuracy loss in the applications.
RED-Nets: Redistribution Networks for Multi-View Classification

Multi-View Learning (MVL) focuses on effectively utilizing information from different views to promote analysis performance. To explore the correlation among multiple views, most existing multi-view algorithms usually maximize the correlation between different views for consistency. However, there are two main limitations for this strategy. First, the information from different views cannot be fully integrated only using some specific constraints on pair of views. Second, beyond consistency, the diversity can basically explore the underlying complementary information among multiple views. Therefore, we propose a novel multi-view classification model termed Redistribution Networks for Multi-View Classification (RED-Nets), which is able to jointly explore consistency and diversity in a flexible manner. Specifically, in our model, different (original) views are integrated first and then redistributed into multiple pseudo views to simultaneously capture the consistency and complementarity. The redistributed way is endowed with the ability of searching for the optimal combination for consistent classification and high complementarity from the original views, breaking the barriers among these views. Experimental results on multiple benchmark datasets validate that the proposed RED-Nets is more effective than the state-of-the-art methods.
Discrete space optical signal processing

As digital circuits are approaching the limits of Moore’s law, a great deal of effort has been directed to alternative computing approaches. Among them, the old concept of optical signal processing (OSP) has attracted attention, revisited in the light of metamaterials and nano-photonics. This approach has been successful in realizing basic mathematical operations, such as derivatives and integrals, but it is difficult to be applied to more complex ones. Here, inspired by digital filters, we propose a radically new OSP approach, able to realize arbitrary mathematical operations over a nano-photonic platform. Our concept consists in first sampling an optical signal in space through an array of optical antennas and then realizing the desired mathematical operation in discrete space through a network with a discrete number of input and output ports. The design of such network boils down to the design of a structure with a given scattering matrix, which for arbitrarily complex operations can be accomplished through inverse design algorithms. We demonstrate this concept for the case of spatial differentiation through a heuristic design based on a waveguide with periodic arrays of input/output channels at its opposite walls. Our approach combines the robustness and generality of traditional Fourier-based OSP with the compactness of nano-photonics and has the potential of transforming the design of OSP systems with applications in image processing and analog computing.
In-Time UAV Flight-Trajectory Estimation and Tracking Using Bayesian Filters

The expected increase of UAV operation in the next decade in areas of on-demand delivery, medical transportation services, law enforcement, traffic surveillance and several others pose potential risks to the low-altitude airspace above densely populated areas. Safety assessment of airspace demands the need for novel UAV traffic management frameworks for regulation and tracking of vehicles. Particularly for low-altitude UAV operations, quality of GPS measurements used for guidance, navigation, and control, is often compromised by loss of communication link caused by presence of trees or tall buildings in proximity to the UAV flight path. Inaccurate GPS measurements may yield unreliable monitoring and inaccurate prognosis of vehicle components such as remaining battery life and other safety metrics that rely on future expected trajectory of the UAV. This work therefore proposes a generalized trajectory for in-time monitoring and prediction of autonomous UAVs using GPS measurements. Firstly, a smooth 4D trajectory generation technique is presented using a series of waypoint locations with associated expected times-of-arrival based on B-spline curves. Initial uncertainty in the vehicle's expected cruise velocity is propagated through the trajectory to compute confidence intervals along the entire flight trajectory using error interval propagation approach. Further, the generated planned trajectory is considered as the prior knowledge that is updated during the UAV flight with incoming GPS measurements in order to refine its current location estimates and corresponding kinematic profiles. The estimation of the vehicle position is defined in a state-space representation such that the position at a future time step is derived from the position and velocity at the current time step and expected velocity at the future time step. A linear Bayesian filtering algorithm is employed to efficiently refine position estimation from noisy GPS measurements and update the confidence intervals. Further, a dynamic re-planning strategy is implemented to incorporate unexpected detour or delay scenarios. Finally, critical challenges related to uncertainty quantification in trajectory prognosis for autonomous vehicles are identified, and potential solutions are discussed at the end of the paper. The entire monitoring framework is demonstrated on real UAV flight experiments conducted at the NASA Langley Research Center.
Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms

Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by local training procedures may impact power-hungry clients. To tackle these issues, federated edge learning (FEEL) applies the network edge technologies of mobile edge computing. In this paper, we propose a novel control algorithm for high-performance and stabilized queue in FEEL system. We consider that the FEEL environment includes the clients transmit data to associated federated edges; these edges then locally update the global model, which is downloaded from the central server via a backhaul. Obtaining greater quantities of local data from the clients facilitates more accurate global model construction; however, this may be harmful in terms of queue stability in the edge, owing to substantial data arrivals from the clients. Therefore, the proposed algorithm varies the number of clients selected for transmission, with the aim of maximizing the time-averaged federated learning accuracy subject to queue stability. Based on this number of clients, the federated edge selects the clients to transmit on the basis of resource status.
When believing in technology leads to poor cyber security: Development of a trust in technical controls scale

While technical controls can reduce vulnerabilities to cyber threats, no technology provides absolute protection and we hypothesised that people may act less securely if they place unwarranted trust in these automated systems. This paper describes the development of a Trust in Technical Controls Scale (TTCS) that measures people's faith in four of these technical controls. In an online study (N = 607), Australian employees demonstrated a greater degree of trust in firewalls and anti-virus software than they did in spam filters and social media privacy settings. Lower scores on the four item TTCS were related to better information security awareness (ISA) and higher scores on tests of cognitive abilities such as non-verbal IQ and cognitive reflection. The TTCS predicted an individual's ability to detect a phishing email to a similar degree as other factors such as ISA, non-verbal IQ and cognitive impulsivity. However, unlike ISA, the scale did not predict the strength of passwords people constructed. Results suggest that the TTCS is a useful complement to ISA in understanding and predicting certain cyber security behaviours.
FDTA: Fully Convolutional Scene Text Detection With Text Attention

Text detection is the premise and guarantee of text recognition.Multi-oriented text detection is the current research hotspot.Due to the variability in size, spatial layout, color and the arrangement direction of natural scene text, natural scene text detection is still very challenging.Therefore, this paper proposes a simple and fast multi-oriented text detection method.Our method first optimizes the regression branch by designing a diagonal adjustment factor to make the position regression more accurate, which increases F-score by 0.8.Secondly, we add an attention module to the model, which improves the accuracy of detecting small text regions and increases F-score by 1.2.Then, we introduce DR Loss to solve the problem of positive and negative sample imbalance, which increases F-score by 0.5.Finally, we conduct experimental verification and analysis on the ICDAR2015, MSRA-TD500 and ICDAR2013 datasets.The experimental results demonstrate that this method can significantly improve the precision and recall of scene text detection, and it has achieved competitive results compared with existing advanced methods.On the ICDAR 2015 dataset, the proposed method achieves an F-score of 0.849 at 9.9fps at 720p resolution.On the MSRA-TD500 dataset, the proposed method achieves an F-score of 0.772 at 720p resolution.On the ICDAR 2013 dataset, the proposed method achieves an F-score of 0.887 at 720p resolution.
D3D: Dual 3-D Convolutional Network for Real-Time Action Recognition

Three-dimensional convolutional neural networks (3D CNNs) have been explored to learn spatio-temporal information for video-based human action recognition. Expensive computational cost and memory demand resulted from standard 3D CNNs, however, hinder their application in practical scenarios. In this article, we address the aforementioned limitations by proposing a novel dual 3-D convolutional network (D3DNet) with two complementary lightweight branches. A coarse branch maintains large temporal receptive field by a fast temporal downsampling strategy and simulates the expensive 3-D convolutions using a combination of more efficient spatial convolutions and temporal convolutions. Meanwhile, a fine branch progressively downsamples the video in the temporal domain and adopts 3-D convolutional units with reduced channel capacities to capture multiresolution spatio-temporal information. Instead of learning these two branches independently, a shallow spatiotemporal downsampling module is shared for these two branches for efficient low-level feature learning. Besides, lateral connections are learned to effectively fuse the information from the two branches at multiple stages. The proposed network makes good balance between inference speed and action recognition performance. Based on RGB information only, it achieves competing performance on five popular video-based action recognition datasets, with inference speed of 3200 FPS on a single NVIDIA GTX 2080Ti card.
Interclass-Relativity-Adaptive Metric Learning for Cross-Modal Matching and Beyond

Training under supervision of triplet ranking loss is a dominant methodology for cross-modal matching models, while good-performing losses in this domain are immensely under-explored since the majority of advanced metric losses are inapplicable due to the particularity of cross-modal setting. Current prominent approaches of metric learning have developed various weighting schemes that assign weights to separate positive or negative samples. It is the interclass relative order in a triplet, however, that matters. In this work, we propose a new Interclass-Relativity-Adaptive (IRA) loss that assigns weights to the relative similarities between positive and negative pairs instead of separate pairs, which allows us to regard a whole triplet as a weighable entity and achieve maximum utilization of sole positive under cross-modal setting. Our method outperforms the baselines by a large margin and obtains competitive results on two video-text matching benchmarks and two image-text matching benchmarks. We also further extend our method to two unimodal image retrieval benchmarks to test its generality and achieve new state-of-the-art results.
How Does Personification Impact Ad Performance and Empathy? An Experiment with Online Advertising

This research explores the value of personas for supporting professional advertisers to design adverts for social media. We test if a personified user group (PUG), when provided to online ad designers, results in better ad performance than when using a non-personified user group (NUG) that had no face picture or name. Our experiment has 30 participants that created Facebook ads using both PUG and NUG. We found that using PUG did increase advertising click performance of ads created by people who are more experienced with ads and personas. Moreover, an analysis of the ad texts showed that the use of PUG increased the empathy of the created ads, supporting the foundational empathy benefit cited in HCI literature. However, the use of PUG did not significantly increase purchase intent. The results imply that using PUG for online ad design evokes more empathy and improves click-through performance. More empathetic ads can have a positive impact on social media users, given that they appear to increase relevance.
A Social–Aware Recommender System Based on User’s Personal Smart Devices

Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE uses user clustering algorithm that includes contextual information for identifying similar users based on their profiles. The dynamically updated contextual information for the user profiles helps with user similarity clustering and provides more personalized recommendations. The proposed RS is evaluated using movie recommendations as a case study. The results show that the proposed RS can improve the accuracy and personalization level of recommendations as compared to two other widely applied collaborative filtering RSs. In addition, the performance of the USDE is evaluated in different scenarios. The conducted experimental results on USDE show that the proposed USDE outperforms widely applied similarity measures in cold start and data sparsity situations.
Systematic error control for deflectometry with iterative reconstruction

The slope calculation of phase measuring deflectometry is essentially a problem finding correspondence between camera, screen and reflecting point, while the reflecting point suffers from an ambiguity problem. This problem can be solved using an iterative reconstruction strategy, however, the existence of systematic error in the iteration circle significantly harm the accuracy performance. A linear relation is established to iteratively find the reflecting point according to the reconstructed height, and a systematic error control is integrated into the iteration circle to promote the iterative reconstruction accuracy. A spherical surface measurement is demonstrated. The best fit radius of curvature of the reconstructed height is 0.1 mm apart from the result of a spherometer. A 245.1 nm root mean square (RMS) error is achieved, and with the systematic error control integrated into the iteration circle, the low order systematic error is significantly reduced and the figure result can be further promoted to 37.2 nm RMS error.
A novel unified deep neural networks methodology for use by date recognition in retail food package image

Abstract There exist various types of information on retail food packages, including use by date, food product name and so on. The correct coding of use by dates on food packages is vitally important for avoiding potential health risks to customers caused by erroneous mislabelling of use by dates. It is extremely tedious and laborious to check the use by dates coding manually by a human operator, which is prone to generate errors thus an automatic system for validating the correctness of the coding of use by dates is needed. In order to construct such a system, firstly it needs to correctly automatic recognize use by dates on food packages. In this work, we propose a novel dual deep neural networks-based methodology for automatic recognition of use by dates in food package photographs recorded by a camera, which is a combination of two networks: a fully convolutional network for use by date ROI detection and a convolutional recurrent neuron network for date character recognition. The proposed methodology is the first attempt to apply deep learning for automatic use by date recognition. From comprehensive experimental evaluations, it is shown that the proposed method can achieve high accuracies in use by date recognition (more than 95% on our testing dataset), given food package images with varying lighting conditions, poor printing quality and varied textual/pictorial contents collected from multiple real retailer sites.
Adopting security maturity model to the organizations’ capability model

Each organization faces threats and risks in daily operations. One of the main risks is how to assess the security level to protect from the increasing risks associated with technology evolution. So, organizations can specify the required approaches and skills. In this paper, we propose a security maturity model that classifies the organizations into five levels. Each level determines the technologies and process capability used by the organizations. There is a set of factors that can help in determining the security maturity level, such as technology, people, and infrastructure. This paper adopts an Information Security Management model to assess organization's security level. The authors make a correspondence between maturity levels and security levels in an organization. Also, the proposed process capability controls influence both levels. The proposed model helps the organizations bridging the cybersecurity gaps. These gaps relate to talent, technology, organizational units, financial, management and operations gaps. Thus, the model helps the cybersecurity auditors to create a comprehensive plan for measuring the security level of the organization. This plan can manage and develop the organization's automated countermeasures. Also, it can help in applying the suitable standard and framework based on the organization's daily operation. Cybersecurity auditors use cybersecurity techniques and tools to assess the organization's postures. Finally, the authors applied the security maturity controls in two case studies: retirement organization and public telecommunication corporation in the Republic of Yemen.
What Crowdsourcing Can Offer to Cross-Cultural Psychological Science

Although the benefits of crowdsourcing research models have been outlined elsewhere, very little attention has been paid to the application of these models to cross-cultural behavioral research. In this manuscript, we delineate two types of crowdsourcing initiatives—researcher crowdsourced and participant crowdsourced. Researcher crowdsourced refers to initiatives where researchers are gathered to work toward a shared goal. Participant crowdsourced refers to those which allow a researcher to gather a large number of participants within a short time frame. We explore the utility of each type of initiative while providing readers with a framework that can be used when deciding whether researcher or participant crowdsourcing initiatives would be most fruitful for their work. Perceived strengths of a researcher crowdsourced initiative with a cross-cultural focus is based on contributor data from Psi Chi’s Network for International Collaborative Exchange (NICE) and is integrated into this framework. Claims are made for the utility of both researcher and participant crowdsourcing as a way to increase generalizability and reliability, decrease time burdens, democratize research, educate individuals on open science, and provide mentorship. These claims are supported with data from NICE contributors.