Reviewerly/exHarmony huggingface dataset:
Application of Artificial Intelligence in Disaster Response

Centre for Research on the Epidemiology of Disasters (CRED) defines a disaster as an unforeseen and often sudden situation or event that causes great damage, destruction, and human suffering, overwhelming local capacity and thus requiring external assistance. Earthquakes, floods, wildfires, landslides, and volcanic activities are some of the natural occurrences that have caused disasters in the past, and likely will continue to cause disasters on a large scale in the future, unless properly managed. Disaster management is a prerequisite to manage and prevent such disasters. Disaster management comprises four phases, namely mitigation, preparedness, response, and recovery. Among all the four phases, artificial intelligence (AI) is extensively used in the response phase to effectively help in better and more rapid response to disasters. Recent advancements in cloud computing and availability of high-computational resources both on-site and in the cloud have given a boost to AI applications in understanding natural disasters, and it is becoming increasingly possible to apply AI techniques with greater ease on big datasets collected from variegated sources including in situ sensors, unmanned aerial vehicle (UAV) images, optical and radar satellite images, remote sensing–derived products, weather reports, social media, historical datasets such as palaeo-datasets on rainfall, and even simulated data about disasters. AI has the ability to intake such big and varied datasets to extract meaningful signals of information, with the advantage that different AI techniques can extract different kinds of useful information. Such information can be used by different stakeholders to take necessary actions for successfully responding to disasters. In the present chapter, the focus is on understanding the application of AI techniques to effectively respond to natural disasters caused by different natural hazards including earthquakes, floods, landslides, volcanoes, and wildfires. In particular, we explore the application of different AI techniques to understand aforementioned natural disasters and how they are assisting in disaster response. The chapter highlights some of the salient points in the application of AI in disaster response, such as properties of input datasets; architecture of AI models that have been applied for disaster response; outputs from the AI models used for event mapping, assessment of damages, and allocation of resources; and recommendations and limitations of AI models. Collectively, this chapter will help us understand why and how the application of AI is bringing a real change in disaster response.
Causal Attention for Unbiased Visual Recognition

Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different backgrounds. This is because the confounders trick the attention to capture spurious correlations that benefit the prediction when the training and testing data are IID (identical & independent distribution); while harm the prediction when the data are OOD (out-of-distribution). The sole fundamental solution to learn causal attention is by causal intervention, which requires additional annotations of the confounders, e.g., a dog model is learned within grass+dog and road+dog respectively, so the grass and road contexts will no longer confound the dog recognition. However, such annotation is not only prohibitively expensive, but also inherently problematic, as the confounders are elusive in nature. In this paper, we propose a causal attention module (CaaM) that self-annotates the confounders in unsupervised fashion. In particular, multiple CaaMs can be stacked and integrated in conventional attention CNN and self-attention Vision Transformer. In OOD settings, deep models with CaaM outperform those without it significantly; even in IID settings, the attention localization is also improved by CaaM, showing a great potential in applications that require robust visual saliency. Codes are available at \url{this https URL}.
Technology roadmap for transforming enterprises into cloud native

Nowadays, digital transformation has become a broad consensus among many companies in traditional industries, and many companies have taken digital transformation as the strategic core of their future development. Although these companies may have realized the importance of digital transformation, they are not clear enough about the specific technical path of digital transformation, and they are not familiar with the information technology involved. This paper provides a gradual roadmap for the digital transformation of enterprises to cloud native from a technical perspective and provides three main technical goals for how enterprises migrate their business from traditional stand-alone business to cloud native: virtualization, containerization, and clustering. It also discussed in detail the key issues and benefits in the process of achieving each goal.
HuMoR: 3D Human Motion Model for Robust Pose Estimation

We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose and shape. Though substantial progress has been made in estimating 3D human motion and shape from dynamic observations, recovering plausible pose sequences in the presence of noise and occlusions remains a challenge. For this purpose, we propose an expressive generative model in the form of a conditional variational autoencoder, which learns a distribution of the change in pose at each step of a motion sequence. Furthermore, we introduce a flexible optimization-based approach that leverages HuMoR as a motion prior to robustly estimate plausible pose and shape from ambiguous observations. Through extensive evaluations, we demonstrate that our model generalizes to diverse motions and body shapes after training on a large motion capture dataset, and enables motion reconstruction from multiple input modalities including 3D keypoints and RGB(-D) videos.
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition

Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking architectures. Building a high-quality accuracy predictor usually costs enormous computation. To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures. The Zen-Score represents the network expressivity and positively correlates with the model accuracy. The calculation of Zen-Score only takes a few forward inferences through a randomly initialized network, without training network parameters. Built upon the Zen-Score, we further propose a new NAS algorithm, termed as Zen-NAS, by maximizing the Zen-Score of the target network under given inference budgets. Within less than half GPU day, Zen-NAS is able to directly search high performance architectures in a data-free style. Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet. Our source code and pre-trained models are released on this https URL.
Learners' and teachers' acceptance of mobile learning: an exploratory study in a developing country

The current study used the technology acceptance model (TAM) to examine learners' and teachers' acceptance of mobile learning of science, technology, engineering, and mathematics (STEM) in a develo...
DYNAMIC NONLINEAR EXPRESSION RECOGNITION TECHNOLOGY USING NEURAL NETWORK AND ATTENTION MECHANISM

The purpose is to apply deep learning to facial expression recognition and improve the efficiency of facial expression recognition in real scenes: first, convolutional neural network (CNN) theoretical model is proposed. On this basis, a new CNN model is proposed, which has six learning layers, including three convolutional layers and three fully connected layers. Then, based on the shortcomings of the model, weight decay and dropout optimization methods are proposed. Moreover, the attention module is designed based on the theory of attention mechanism. After each convolutional layer, a hybrid attention module (channel domain attention and spatial domain attention) is added. Finally, the frame error rate (FER) 2013 dataset and Cohn–Kanade (CK)+ dataset are used to test and compare the performance of the model. The results show that after 50Epoch, the accuracy rate of the original model fluctuates greatly in the process of convergence; however, after attention mechanism is added, the fluctuation of the accuracy of the model is obviously reduced. In the CK+ dataset, the accuracy of each model is maintained at about 95%, while the accuracy of FER2013 dataset is about 71%. After attention mechanism is added, the recognition rate of dynamic nonlinear expression is higher than that of basic model. The recognition rate of the original model and the optimized model with attention mechanism for the dynamic nonlinear expression with occlusion decreases in varying degrees, but the reduction of the recognition rate of the latter is alleviated.
Documenting multiple temporalities

Purpose This article explores the varied ways that individuals create and use calendars, planners and other cognitive artifacts to document the multiple temporalities that make up their everyday lives. It reveals the hidden documentary time work required to synchronize, coordinate or entrain their activities to those of others. Design/methodology/approach We interviewed 47 Canadian participants in their homes, workplaces or other locations and photographed their documents. We analyzed qualitatively; first thematically to identify mentions of times, and then relationally to reveal how documentary time work was situated within participants' broader contexts. Findings Participants' documents revealed a wide variety of temporalities, some embedded in the templates they used, and others added by document creators and users. Participants' documentary time work involved creating and using a variety of tools and strategies to reconcile and manage multiple temporalities and indexical time concepts that held multiple meanings. Their work employed both standard “off the shelf” and individualized “do-it-yourself” approaches. Originality/value This article combines several concepts of invisible work (document work, time work, articulation work) to show both how individuals engage in documentary time work and how that work is situated within broader social and temporal contexts and standards.
Factors Affecting COVID-19 Transmission and Modelling of Close Contact Tracing Strategies

Close contact tracing is an essential measure that countries are applying to combat the epidemic of COVID-19. The purpose of contact tracing is to rapidly identify potentially infected individuals and prevent further spread of the disease. In this study, based on the factors affecting the COVID-19 transmission, a scoring protocol is provided for close contact tracing.First, the factors affecting the COVID-19 transmission in close contacts were identified by a rapid review of the literature. Data were gathered by searching the Embase, PubMed, Google Scholar, and Scopus databases. Then, by formulating and scoring the identified factors with two sessions of the expert panel, close contact transmission risk score determined, and a protocol for contacts tracing was designed.Close contact transmission risk depends on the contact environment characteristics, the infectivity (virus shedding) of the sentinel case, and contact characteristics. Based on these factors, the close contact transmission risk score and contact tracing protocol were prepared.The close contact transmission risk scores will provide the ability to contact classifications and developing specific tracing strategies for them. Given that there are not any specific treatments for COVID-19 and lack of universal vaccination, applying nonpharmaceutical measures such as contact tracing along with physical distancing is very crucial. Therefore, we recommended this model to the evaluation of exposure risk and contact tracing.
DMC: Decentralized Mixer with Channel for Transaction Privacy Protection on Ethereum

Ethereum is a public blockchain platform with smart contract. However, it has transaction privacy issues due to the openness of the underlying ledger. Decentralized mixing schemes are presented to hide transaction relationship and transferred amount, but suffer from high transaction cost and long transaction latency. To overcome the two challenges, we propose the idea of batch accounting, adopting batch processing at the time of accounting. For further realization, we introduce payment channel technology into decentralized mixer. Since intermediate transactions between two parties do not need network consensus, our scheme can reduce both transaction cost and transaction latency. Moreover, we provide informal definitions and proofs of our scheme's security. Finally, our scheme is implemented based on zk-SNARKs and Ganache, and experimental results show that the higher number of transactions in batch, the better our scheme performs.
Experimental Analysis of Attacks on RSA &amp; Rabin Cryptosystems using Quantum Shor’s Algorithm

In this world of massive communication networks, data security and confidentiality are of crucial importance for maintaining secured private communication and protecting information against eavesdropping attacks. Existing cryptosystems provide data security and confidentiality by the use of encryption and signature algorithms for secured communication. Classical computers use cryptographic algorithms that use the product of two large prime numbers for generating public and private keys. These classical algorithms are based on the fact that integer factorization is a non-deterministic polynomial-time (NP) problem and requires super-polynomial time making it impossible for large enough integers. Shor’s algorithm is a well-known algorithm for factoring large integers in polynomial time and takes only O(b3) time and O(b) space on b-bit number inputs. Shor’s algorithm poses a potential threat to the current security system with the ongoing advancements of Quantum computers. This paper discusses how Shor’s algorithm will be able to break integer factorization-based cryptographic algorithms, for example, Rivest–Shamir–Adleman (RSA) and Rabin Algorithms. As a proof of concept, experimental analysis of Quantum Shor’s algorithm on existing public-key cryptosystems using IBM Quantum Experience is performed for factorizing integers of moderate length (seven bits) due to limitations of thirty-two qubits in present IBM quantum computers. In a nutshell, this work will demonstrate how Shor’s algorithm poses threat to confidentiality and authentication services.
Epsilon Greedy Strategy for Hyper Parameters Tuning of A Neural Network Equalizer

In wireless communications, equalization can be used to remove channel impairments from transmissions. Neural networks (NNs) have proven to be an effective technique against conventional equalizers (i.e. decision-feedback, zero-forcing, etc.). High Frequency (HF) channels require high-performance equalizers to overcome Doppler shifts and large delay spreads. When using a NN equalizer, tuning its structure (i.e. activation function, optimizer, etc …) can be time-consuming. This work proposes using an annealing epsilon greedy algorithm, a reinforcement learning technique, to tune the attributes of a neural network equalizer. Reinforcement learning has been used to tune NNs in different applications, but to the best of our knowledge, it has not been done for NN equalization. The objective of this work is to analyze if using reinforcement learning can improve the performance of a NN equalizer.
Trust yourself! Or maybe not: factors related to overconfidence and uncertainty assessments of software effort estimates

Software effort estimates are uncertain, given that they are probabilistic assessments of the future. Evaluating their uncertainty involves assigning them an appropriate confidence level and is paramount for satisfying commitments in software projects. However, estimators tend to be overconfident about their estimates, hampering the accuracy of their uncertainty assessments. Our research goal is to identify the factors related to overconfidence and uncertainty assessments in software estimation. To do so, we carried out a Systematic Literature Mapping (SLM), based on automated and snowballing searches. Our findings include eight factors related to overconfidence and uncertainty assessment. Some of them resulted in unexpected implications for practice. We also identified valuable and easy-to-use metrics that software practitioners can apply smoothly in their daily practice. Additionally, very few field and respondent studies exist about the topic. The software engineering area can significantly benefit from investigating how much practitioners know about the overconfidence effect, as well as of a better comprehension of the perceived importance, practices, and accuracy of uncertainty assessments in the software industry.
Use of Shapley Additive Explanations in Interpreting Agent-Based Simulations of Military Operational Scenarios

Military defense modernization initiatives often involve complex systems that must be understood to inform design, planning, implementation and acquisition decisions. To gain a basic understanding of the system and identify key initial parameters, simulation experiments can be used to generate – or farm – data efficiently and effectively over a large parametric space. While machine learning models can be used for post-simulation analysis to identify key parameters, interpretability and their black-box nature can present challenges when the intent is to provide support to decision makers. In this paper, we apply a model-agnostic method for interpreting machine learning predictions, known as SHapley Additive exPlanations (SHAP), to data farmed from an agent-based simulation that models a military operational scenario. The scenario is motivated by a Canadian Army initiative to modernize its intelligence, surveillance, and reconnaissance assets and abstracted to minimize the complexity of the modeled system and validate the findings of SHAP.
Containerization and Virtualization

So far, you have learned to design and develop cloud-native services from the functional and nonfunctional perspectives. In this chapter, you will learn more about deploying and running your developed binaries.
Cloud Native Services

In the previous chapter, we discussed the cloud-native architecture and its importance in the IT industry. In this chapter, we will explore that topic further, but in the context of the supporting cloud services.
An Application of Model Predictive Control to Reactive Motion Planning of Robot Manipulators

In recent years, a number of trajectory optimization algorithms have been proposed and established for motion planning of robot manipulators in complex, but static, predefined environments. To enable reactive motion planning under uncertain conditions caused, for example, by moving obstacles, this paper proposes a formulation of the trajectory optimization problem that is tailored for model predictive control. The proposed algorithmic solution leverages off-the-shelf computational tools for nonlinear model predictive control, optimization, and collision checking. In addition, a motion planning paradigm is introduced to allow for online collision-free motion when following a joystick command. The approach is validated in the context of an industrial pick-and-place application using MATLAB® and a Kinova® robot manipulator, both in simulation and with actual hardware.
Virtual Image Representation and Adaptive Weighted Score Level Fusion for Genetic Face Recognition

Face recognition is the most interesting and wide area of research over the past few decades. This research work proposes the effective virtual image representation and adaptive weighted score level fusion face recognition-based algorithm to classify the genetic faces and non-genetic faces. The algorithm integrates the virtual image and original image, which is a nonlinear transformation of original image. This virtual representation improves moderate intensities pixels and changes the high or low intensities pixels. In face images, various pixels have distinctive significance. Subsequently, it is sensible to fix various weights to individual pixels. Then score level fusion is performed for recognizing the faces, which overcomes illumination due to lighting in the image. Genetic face recognition accuracy gets impacted by the variations in face due to age. The proposed score level fusion scheme gives the best optimal solution for genetic face recognition with respect to age variation. Using statistical measure, distance is calculated between the virtual image and original image for both test and training set of images. The performance results reveal that the proposed score level fusion is robust to different test conditions and performs well compared to existing algorithms.
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

The growing energy and performance costs of deep learning have driven the community to reduce the size of neural networks by selectively pruning components. Similarly to their biological counterparts, sparse networks generalize just as well, if not better than, the original dense networks. Sparsity can reduce the memory footprint of regular networks to fit mobile devices, as well as shorten training time for ever growing networks. In this paper, we survey prior work on sparsity in deep learning and provide an extensive tutorial of sparsification for both inference and training. We describe approaches to remove and add elements of neural networks, different training strategies to achieve model sparsity, and mechanisms to exploit sparsity in practice. Our work distills ideas from more than 300 research papers and provides guidance to practitioners who wish to utilize sparsity today, as well as to researchers whose goal is to push the frontier forward. We include the necessary background on mathematical methods in sparsification, describe phenomena such as early structure adaptation, the intricate relations between sparsity and the training process, and show techniques for achieving acceleration on real hardware. We also define a metric of pruned parameter efficiency that could serve as a baseline for comparison of different sparse networks. We close by speculating on how sparsity can improve future workloads and outline major open problems in the field.
Anomaly Detection in Drones with Machine Learning Algorithms

Drones are expected to be widely used in near future in the areas of agriculture, surveillance and logistics. The behavior of the autonomous systems, especially the drones, needs to be robust, irrespective of the status of the sensors, environment and the collective conditions of the system. The dynamics of the autonomous system can go into unpredictable behavior because of the faultiness of the sensors, power failure or unexpected environment condition. This work detects whether the state of the drone is in order or not, using multiple streams of sensory data. An ensemble of machine learning models (supervised, unsupervised) is implemented to detect the anomaly in the drone. They are k-nearest neighbor (k-NN), k-means square, decision tree, support vector machine (SVM) and logistic regression. The models were developed and trained with individual sensor data as well as selected combinations of sensor data. Our experimental results demonstrate that detection of anomaly in drones with high accuracies close to 100%.