Reviewerly/exHarmony huggingface dataset:
Learning to Learn with Variational Information Bottleneck for Domain Generalization

Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We introduce a probabilistic meta-learning model for domain generalization, in which classifier parameters shared across domains are modeled as distributions. This enables better handling of prediction uncertainty on unseen domains. To deal with domain shift, we learn domain-invariant representations by the proposed principle of meta variational information bottleneck, we call MetaVIB. MetaVIB is derived from novel variational bounds of mutual information, by leveraging the meta-learning setting of domain generalization. Through episodic training, MetaVIB learns to gradually narrow domain gaps to establish domain-invariant representations, while simultaneously maximizing prediction accuracy. We conduct experiments on three benchmarks for cross-domain visual recognition. Comprehensive ablation studies validate the benefits of MetaVIB for domain generalization. The comparison results demonstrate our method outperforms previous approaches consistently.
Automatic Door Lock System by Face Recognition

Abstract In this paper we have proposed face recognition door lock system using raspberry pi for security purpose. Implementation of the system is for monitoring whether any unknown person is entering in to the door. We have established communication with electronic devices through face detection with the help of Pi camera Raspberry Pi platform. For software coding Python and Open CV libraries are used. In order to get accurate and clear picture of an intruder we have proposed Haar classifier method for face detection. As soon as the person enters near the door, pi camera captures the image and face detection process is done then if it matches with database images then the door is unlocked otherwise a message with the picture of a person will be sent to the registered mobile through GSM and LAN network.
Sparsely Connected CNN for Efficient Automatic Modulation Recognition

This paper proposes a convolutional neural network (CNN), called SCGNet, for low-complexity and robust modulation recognition in intelligent communication receivers. Principally, the network combines two types of sparse convolutional layers-depthwise and regular grouped in an architecture to achieve high recognition accuracy while keeping the network more lightweight. The network architecture leverages sparsely connected convolutional layers in three principal modules: speed-accuracy tradeoff (SAT), deep feature extraction and processing (DFEP), and generic feature extraction (GFE) data pre-processing module. For a good tradeoff between complexity and accuracy, SAT deploys depthwise convolutional layers to enrich the relevant features outputted by the former GFE module. In addition to SAT, DFEP employs a cascade of regular grouped convolutional layers for mining more discriminative features from SAT via a multilayer transformation module. This cascade structure aims to prevent a loss of essential details of the signal as the network becomes deeper. Additionally, skip connections are deployed between sub-blocks within SAT and DFEP to allow inter-module feature sharing and to handle inter-block features loss. Experimental results on the RadioML2018.01A dataset indicate that SCGNet achieves an overall recognition accuracy of around 94.39% at a signal-to-noise ratio of +20 dB.
Who does or does not use the ‘Corona-Warn-App’ and why?

Abstract To slow the spread of SARS-CoV-2, the German government released the ‘Corona-Warn-App’, a smartphone application that warns users if they have come into contact with other users tested positive for SARS-CoV-2. Since using the ‘Corona-Warn-App’ is health-relevant behavior, it is essential to understand who is (and who is not) using it and why. In N = 1972 German adults, we found that non-users were on average older, female, healthier, in training and had low general trust in others. The most frequently named reasons by non-users were privacy concerns, doubts about the effectiveness of the app and lack of technical equipment.
ARtention: A design space for gaze-adaptive user interfaces in augmented reality

Augmented Reality (AR) headsets extended with eye-tracking, a promising input technology for its natural and implicit nature, open a wide range of new interaction capabilities for everyday use. In this paper we present ARtention, a design space for gaze interaction specifically tailored for in-situ AR information interfaces. It highlights three important dimensions to consider in the UI design of such gaze-enabled applications: transitions from reality to the virtual interface, from single- to multi-layer content, and from information consumption to selection tasks. Such transitional aspects bring previously isolated gaze interaction concepts together to form a unified AR space, enabling more advanced application control seamlessly mediated by gaze. We describe these factors in detail. To illustrate how the design space can be used, we present three prototype applications and report informal user feedback obtained from different scenarios: a conversational UI, viewing a 3D visualization, and browsing items for shopping. We conclude with design considerations derived from our development and evaluation of the prototypes. We expect these to be valuable for researchers and designers investigating the use of gaze input in AR systems and applications.
Image captioning with transformer and knowledge graph

The Transformer model has achieved very good results in machine translation tasks. In this paper, we adopt the Transformer model for the image captioning task. To promote the performance of image captioning, we improve the Transformer model from two aspects. First, we augment the maximum likelihood estimation (MLE) with an extra Kullback-Leibler (KL) divergence term to distinguish the difference between incorrect predictions. Second, we introduce a method to help the Transformer model generate captions by leveraging the knowledge graph. Experiments on benchmark datasets demonstrate the effectiveness of our method.
Agile trends in Chinese global software development industry: Fuzzy AHP based conceptual mapping

Global Software Development (GSD) has gained great attention during the past decade or so, and it is also being adopted by most of the software development organizations. The GSD approach is practiced by the software development organizations in China and a wide range of firms have established its operations in various Chinese cities. However, GSD practices becomes more challenging for the projects based on the agile concepts. The aim of this study is to develop a taxonomy of the factors that could positively impact the scaling process of agile methods in the Chinese GSD industry. The factors are identified by exploring the available literature and conducting industrial study with the Chinese agile and GSD practitioners. The finally reported factors are categorized, prioritized and developed their taxonomy using the multi-criterion decision making (MCDM) Fuzzy AHP approach. The given taxonomy is significant and progressive for GSD industry to assess and improve the scaling process of agile methods.
Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programming

There has been an increasing interest in using neural networks in closed-loop control systems to improve performance and reduce computational costs for on-line implementation. However, providing safety and stability guarantees for these systems is challenging due to the nonlinear and compositional structure of neural networks. In this paper, we propose a novel forward reachability analysis method for the safety verification of linear time-varying systems with neural networks in feedback interconnection. Our technical approach relies on abstracting the nonlinear activation functions by quadratic constraints, which leads to an outer-approximation of forward reachable sets of the closed-loop system. We show that we can compute these approximate reachable sets using semidefinite programming. We illustrate our method in a quadrotor example, in which we first approximate a nonlinear model predictive controller via a deep neural network and then apply our analysis tool to certify finite-time reachability and constraint satisfaction of the closed-loop system.
Understanding the greater diffusion of mobile money innovations in Africa

The present research extends Lashitew et al. (2019, RP) in order to understand the greater diffusion of mobile money innovations in Africa. To make this assessment, a comparative analysis is engaged between sampled African countries and the corresponding sampled developing countries. Three main types of predictor groups are used for the study, namely: demand, supply and macro-level factors. The empirical evidence is based on Tobit regressions. The tested hypothesis is confirmed because from a comparative analysis between African-specific estimates and those of the sampled countries, not all factors driving mobile money innovations in Africa are apparent in the findings of Lashitew et al. (2019). An extended analysis is also performed to take on board the concern of multicollinearity from which, the best estimators from the study are derived. Comparative findings from correlation analysis show that an African specificity is largely traceable to the 'unique mobile subscription rate' variable. An in-depth empirical analysis further confirms an African specificity in the outcome variables (especially in the mobile used to send/receive money) which, may be traceable to informal sector variables not documented in Lashitew et al. (2019). Scholarly and policy implications are discussed.
Problems and opportunities in training deep learning software systems

Deep learning (DL) training algorithms utilize nondeterminism to improve models' accuracy and training efficiency. Hence, multiple identical training runs (e.g., identical training data, algorithm, and network) produce different models with different accuracies and training times. In addition to these algorithmic factors, DL libraries (e.g., TensorFlow and cuDNN) introduce additional variance (referred to as implementation-level variance) due to parallelism, optimization, and floating-point computation.
Robust Nonlinear Model Predictive Control Based Visual Servoing of Quadrotor UAVs

In this article, a robust nonlinear model predictive control (NMPC) scheme is proposed for the visual servoing of quadrotors subject to external disturbances. By using the virtual camera approach, the image moments are defined in the virtual camera plane and adopted as visual features to derive the decoupled image kinematics. As a result, the image-based visual servoing (IBVS) system model is established by integrating the image kinematics and quadrotor dynamics. To handle the visibility constraint, a robust NMPC scheme is developed for the IBVS of the quadrotor such that the visual target can stay within the field of view of the camera. In addition, based on the Lipschitz condition, the tightened state constraints are constructed to tackle external disturbances. The sufficient conditions on guaranteeing recursive feasibility of the proposed NMPC algorithm are derived. Furthermore, we theoretically show that the tracking error will converge to a small set around the origin in finite time under some derived conditions. Finally, simulation studies and experimental tests are conducted to verify the efficacy of the proposed method.
Predicting Public Uptake of Digital Contact Tracing During the COVID-19 Pandemic: Results From a Nationwide Survey in Singapore

Background During the COVID-19 pandemic, new digital solutions have been developed for infection control. In particular, contact tracing mobile apps provide a means for governments to manage both health and economic concerns. However, public reception of these apps is paramount to their success, and global uptake rates have been low. Objective In this study, we sought to identify the characteristics of individuals or factors potentially associated with voluntary downloads of a contact tracing mobile app in Singapore. Methods A cohort of 505 adults from the general community completed an online survey. As the primary outcome measure, participants were asked to indicate whether they had downloaded the contact tracing app TraceTogether introduced at the national level. The following were assessed as predictor variables: (1) participant demographics, (2) behavioral modifications on account of the pandemic, and (3) pandemic severity (the number of cases and lockdown status). Results Within our data set, the strongest predictor of the uptake of TraceTogether was the extent to which individuals had already adjusted their lifestyles because of the pandemic (z=13.56; P&lt;.001). Network analyses revealed that uptake was most related to the following: using hand sanitizers, avoiding public transport, and preferring outdoor over indoor venues during the pandemic. However, demographic and situational characteristics were not significantly associated with app downloads. Conclusions Efforts to introduce contact tracing apps could capitalize on pandemic-related behavioral adjustments among individuals. Given that a large number of individuals is required to download contact tracing apps for contact tracing to be effective, further studies are required to understand how citizens respond to contact tracing apps. Trial Registration ClinicalTrials.gov NCT04468581, https://clinicaltrials.gov/ct2/show/NCT04468581
Multi-channel Transformers for Multi-articulatory Sign Language Translation

Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore. In this paper we tackle the multi-articulatory sign language translation task and propose a novel multi-channel transformer architecture. The proposed architecture allows both the inter and intra contextual relationships between different sign articulators to be modelled within the transformer network itself, while also maintaining channel specific information. We evaluate our approach on the RWTH-PHOENIX-Weather-2014T dataset and report competitive translation performance. Importantly, we overcome the reliance on gloss annotations which underpin other state-of-the-art approaches, thereby removing the need for expensive curated datasets.
What and How: Generalized Lifelong Spectral Clustering via Dual Memory

Spectral clustering (SC) has become one of the most widely-adopted clustering algorithms, and been successfully applied into various applications. We in this work explore the problem of spectral clustering in a lifelong learning framework termed as <u>G</u> eneralized <u>L</u> ife <u>l</u> ong <u>S</u> pectral <u>C</u> lustering (GL <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> SC). Different from most current studies, which concentrate on a fixed spectral clustering task set and cannot efficiently incorporate a new clustering task, the goal of our work is to establish a generalized model for new spectral clustering tasks by “What” and “How” to lifelong learn from past tasks. In respect of “what to lifelong learn”, our GL <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> SC framework contains a dual memory mechanism with a deep orthogonal factorization manner: an orthogonal basis memory stores hidden and hierarchical clustering centers among learned tasks, and a feature embedding memory captures deep manifold representation common across multiple related tasks. When learning a new clustering task, the intuition here for “how to lifelong learn” is that GL <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> SC can transfer intrinsic knowledge from dual memory mechanism to obtain task-specific encoding matrix. Then the encoding matrix can redefine the dual memory over time to provide maximal benefits when learning future tasks, and reversely maximize performance for past tasks. To achieve this, we propose an alternative optimization formulation with convergence guarantee for solving our GL <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> SC model. To the end, empirical comparisons on several benchmark datasets show the effectiveness of our GL <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> SC, in comparison with several state-of-the-art clustering models.
The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches

The detection of software vulnerability requires critical attention during the development phase to make it secure and less vulnerable. Vulnerable software always invites hackers to perform malicious activities and disrupt the operation of the software, which leads to millions in financial losses to software companies. In order to reduce the losses, there are many reliable and effective vulnerability detection systems introduced by security communities aiming to detect the software vulnerabilities as early as in the development or testing phases. To summarise the software vulnerability detection system, existing surveys discussed the conventional and data mining approaches. These approaches are widely used and mostly consist of traditional detection techniques. However, they lack discussion on the newly trending machine learning approaches, such as supervised learning and deep learning techniques. Furthermore, existing studies fail to discuss the growing research interest in the software vulnerability detection community throughout the years. With more discussion on this, we can predict and focus on what are the research problems in software vulnerability detection that need to be urgently addressed. Aiming to reduce these gaps, this paper presents the research interests’ taxonomy in software vulnerability detection, such as methods, detection, features, code and dataset. The research interest categories exhibit current trends in software vulnerability detection. The analysis shows that there is considerable interest in addressing methods and detection problems, while only a few are interested in code and dataset problems. This indicates that there is still much work to be done in terms of code and dataset problems in the future. Furthermore, this paper extends the machine learning approaches taxonomy, which is used to detect the software vulnerabilities, like supervised learning, semi-supervised learning, ensemble learning and deep learning. Based on the analysis, supervised learning and deep learning approaches are trending in the software vulnerability detection community as these techniques are able to detect vulnerabilities such as buffer overflow, SQL injection and cross-site scripting effectively with a significant detection performance, up to 95% of F1 score. Finally, this paper concludes with several discussions on potential future work in software vulnerability detection in terms of datasets, multi-vulnerabilities detection, transfer learning and real-world applications.
Robust Unsupervised Video Anomaly Detection by Multipath Frame Prediction

Video anomaly detection is commonly used in many applications, such as security surveillance, and is very challenging. A majority of recent video anomaly detection approaches utilize deep reconstruction models, but their performance is often suboptimal because of insufficient reconstruction error differences between normal and abnormal video frames in practice. Meanwhile, frame prediction-based anomaly detection methods have shown promising performance. In this article, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with a proper design which is more in line with the characteristics of surveillance videos. The proposed method is equipped with a multipath ConvGRU-based frame prediction network that can better handle semantically informative objects and areas of different scales and capture spatial-temporal dependencies in normal videos. A noise tolerance loss is introduced during training to mitigate the interference caused by background noise. Extensive experiments have been conducted on the CUHK Avenue, ShanghaiTech Campus, and UCSD Pedestrian datasets, and the results show that our proposed method outperforms existing state-of-the-art approaches. Remarkably, our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset

Robots navigating autonomously need to perceive and track the motion of objects and other agents in its surroundings. This information enables planning and executing robust and safe trajectories. To facilitate these processes, the motion should be perceived in 3D Cartesian space. However, most recent multi-object tracking (MOT) research has focused on tracking people and moving objects in 2D RGB video sequences. In this work we present JRMOT, a novel 3D MOT system that integrates information from RGB images and 3D point clouds to achieve real-time, state-of-the-art tracking performance. Our system is built with recent neural networks for re-identification, 2D and 3D detection and track description, combined into a joint probabilistic data-association framework within a multi-modal recursive Kalman architecture. As part of our work, we release the JRDB dataset, a novel large scale 2D+3D dataset and benchmark, annotated with over 2 million boxes and 3500 time consistent 2D+3D trajectories across 54 indoor and outdoor scenes. JRDB contains over 60 minutes of data including 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sup> cylindrical RGB video and 3D pointclouds in social settings that we use to develop, train and evaluate JRMOT. The presented 3D MOT system demonstrates state-of-the-art performance against competing methods on the popular 2D tracking KITTI benchmark and serves as first 3D tracking solution for our benchmark. Real-robot tests on our social robot JackRabbot indicate that the system is capable of tracking multiple pedestrians fast and reliably. We provide the ROS code of our tracker at https://sites.google.com/view/jrmot.
The value proposition of blockchain technologies and its impact on Digital Platforms

Since the last few years there has been a massive spurt of Research on Blockchain Technologies and several attempts at incorporating them for a myriad of Business Applications. Blockchain Technologies promised to bring about decentralized trust having the potential to disrupt digital ecosystems by providing alternatives to centralized storage and management of data. This has the potential to radically transform industries and services through new models of data storage, transparency, tracking, payment systems amongst other advantages. However, Blockchain Technology is designed to provide other Value Propositions beyond decentralized storage, such as innovative crypto economic and investment models and radical new forms of decentralized participative governance models that could lead to the evolution of new generation of Digital Platforms and multi stakeholder business interactions. In this paper, through a systematic literature review, we present a set of key Blockchain Value Propositions and a discussion on how it can complement to the evolution of Digital Platforms and the Collaborative Economy. We also argue that while some of the value propositions are easier to integrate with existing Digital Platforms, more disruptive value propositions such as Business Automation, Economic and Governance Models present greater challenges but could lead to not just innovation at the technological level, but also metamorphosis of the existing social, economic and governance models.