Reviewerly/exHarmony huggingface dataset:
Partial Differential Equations is All You Need for Generating Neural  Architectures -- A Theory for Physical Artificial Intelligence Systems

In this work, we generalize the reaction-diffusion equation in statistical physics, Schr\"odinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research. We take finite difference method to discretize NPDE for finding numerical solution, and the basic building blocks of deep neural network architecture, including multi-layer perceptron, convolutional neural network and recurrent neural networks, are generated. The learning strategies, such as Adaptive moment estimation, L-BFGS, pseudoinverse learning algorithms and partial differential equation constrained optimization, are also presented. We believe it is of significance that presented clear physical image of interpretable deep neural networks, which makes it be possible for applying to analog computing device design, and pave the road to physical artificial intelligence.
Quality of Control in the Tavis-Cummings-Hubbard Model

The quality of controlling a system of optical cavities in the Tavis-Cummings-Hubbard (TCH) model is estimated with the examples of quantum gates, quantum walks on graphs, and of the detection of singlet states. This type of control of complex systems is important for quantum computing, for the optical interpretation of mechanical movements, and for quantum cryptography, where singlet states of photons and charges play an essential role. It has been found that the main reason for the decrease of the control quality in the THC model is due to the finite width of the atomic spectral lines, which is itself related to the time energy uncertainty relation. This paper evaluates the quality of a CSign-type quantum gate based on asynchronous atomic excitations and on the optical interpretation of the motion of a free particle.
Requirement Analyses and Evaluations of Blockchain Platforms per  Possible Use Cases

It is said that blockchain will contribute to the digital transformation of society in a wide range of ways, from the management of public and private documents to the traceability in various industries, as well as digital currencies. A number of so-called blockchain platforms have been developed, and experiments and applications have been carried out on them. But are these platforms really conducive to practical use of the blockchain concept? To answer the question, we need to better understand what the technology called blockchain really is. We need to sort out the confusion we see in understanding what blockchain was invented for and what it means. We also need to clarify the structure of its applications. This document provides a generic model of understanding blockchain and its applications. We introduce design patterns to classify the platforms. We categorize possible use cases by identifying the structure among applications, and organize the functional, performance, operational and legal requirements for each such case. Based on the categorization and criteria, we evaluated and compared the following platforms: Hyperledger Fabric, Hyperledger Iroha, Hyperledger Indy, Ethereum, Quorum/Hyperledger Besu, Ethereum 2.0, Polkadot, Corda and BBc-1. We have tried to be fair in our evaluations and comparisons, but we also expect to provoke discussion. The intended readers for this document is anyone involved in development of application systems who wants to understand blockchain and their platforms, including non-engineers and non-technologists. The assessments in this document will allow readers to understand the technological requirements for the blockchain platforms, to question existing technologies, and to choose the appropriate platforms for the applications they envision. The comparisons hopefully will also be useful as a guide for designing new technologies.
Chatbot Web Application using DIET

Abstract: A chatbot is a computer program that is capable of communicating with the user in natural language in order to understand and parse the technical query. Such a program is frequently employed by organizations in order to automate the task of customer care and services. We have focused on designing a chatbot for our college, the Bangalore Institute of Technology. The current chatbot that is present in the official website for BIT is only an interface connecting the user to a live agent and is therefore unavailable for use most of the time. This inspired us to design a much better alternative for the existing chatbot using the DIET algorithm, that is accessible all the time and provides accurate responses in the blink of an eye
Citizen Centric Panchayat System with Digital Management and Automation

Abstract: Individuals in provincial regions presently need to visit the panchayat office in their space to find out about the panchayat's benefits. It demands a ton of investment and may bring about work delays. The information in the workplace must be kept up with physically. There is no security for the information and deficiencies can be experienced during entering the information for the most part which require higher estimations. Individuals likewise deal with such countless issues in their area. They grumble to their separate ward individuals however they could conceivably answer rapidly. There are numerous different issues in the present-day panchayat raj framework. Thus, proposed framework gives answers for every one of the issues in the current framework. It offers online types of assistance to individuals living around there. Every one of the administrations which are done physically are made web-based in the venture with mechanization. Individuals can be familiar with their panchayat exercises, warnings, and any remaining data connected with their towns. Every one of the applications and declarations are applied and confirmed on the web. The clients in the town can whine about their concerns and criticism on the web. Ideas are additionally acknowledged by individuals for the advancement of their town.
Predicting Factors of the Life Insurance Policyholder’s Self-Disclosure on Medical History Information

In recent times, increased market awareness in Malaysia has demonstrated the value of life insurance safety.The fact that upward demands on life insurance applications have been observed since 2018, the medical and hospitalisation claims disputes have been reported to increase from 39% in 2018 to 41% in 2019.Among others, the reason underlying the disputes is due to non-disclosure or misrepresentation of material facts in the application.Thus, this study aims to examine the factors predicting the potential policyholder in self-disclosing their medical history information i.e, the Customer Perceived Benefit, Customer Perceived Risk, and Customer Perceived Confidence by means to avoid fraud allegations.This study employs a quantitative method using a questionnaire distributed to the policyholders from five insurance companies that are registered under the Life Insurance Association of Malaysia.Data were obtained from 139 respondents and further statistically analysed.Findings revealed the majority of respondents are willing to disclose their medical history information during the inception period of policy purchase as they are in a good state of health.They are also aware that failure to disclose medical history may result in claim rejection and policy cancelled, indicating potential fraud may occur.This study also provides further insights on the need to educate and provide appropriate advice to the potential policyholders by the insurers prior to policy activation.Future research may provide conclusive findings on selfdisclosure when it is conducted through interviews or observations in a controlled setting.This is to ensure the important information is fully and sufficiently disclosed for policyholders' protection from any serious allegation of fraud.
Critical Success Factor for Integration of Cyber Security in Context of Managed Services

The increasing frequency of cyber security attacks and their impact on value-added business processes requires companies to appropriately address cyber security risks in the Risk Management Process (RMP).In the context of cybersecurity risks, companies can identify, assess and manage their business aims by applying 4C (Consultation, Communication, Coordination, Cooperation).With this paper an increase in the efficiency of the RMP is proposed through an adaptation of the management activities by means of categorization of the information and group consolidation as well as the prioritization of measures.Trough adaptation it possible to establish a Cyber Security RMP (CS-RPM).The result is a predicted balancing of the benefits of the technology with the potential consequences of a CS risk threat event that drives proactive CS risk management.A system of guidelines for an information security-, cyber security-and data privacy-specific document structure of the managed services (MS) is also considered and be looked.The use of risk registers to map CS risks and the application of risk metrics is considered in more detail.Risk values to be quantified and their determination from risk measurements are explained in parts.
Secure Estimation and Attack Isolation for Connected and Automated  Driving in the Presence of Malicious Vehicles

Connected and Automated Vehicles (CAVs) rely on the correctness of position and other vehicle kinematics information to fulfill various driving tasks such as vehicle following, lane change, and collision avoidance. However, a malicious vehicle may send false sensor information to the other vehicles intentionally or unintentionally, which may cause traffic inconvenience or loss of human lives. Here, we take the advantage of cloud-computing and increase the resilience of CAVs to malicious vehicles by assuming each vehicle shares its local sensor information with other vehicles to create information redundancy on the cloud side. We exploit this redundancy and propose a sensor fusion algorithm for the cloud, capable of providing a robust state estimation of all vehicles in the cloud under the condition that the number of malicious information is sufficiently small. Using the proposed estimator, we provide an algorithm for isolating malicious vehicles. We use numerical examples to illustrate the effectiveness of our methods.
A Projection Algorithm for the Unitary Weights

Unitary neural networks are promising alternatives for solving the exploding and vanishing activation/gradient problem without the need for explicit normalization that reduces the inference speed. However, they often require longer training time due to the additional unitary constraints on their weight matrices. Here we show a novel algorithm using a backpropagation technique with Lie algebra for computing approximated unitary weights from their pre-trained, non-unitary counterparts. The unitary networks initialized with these approximations can reach the desired accuracies much faster, mitigating their training time penalties while maintaining inference speedups. Our approach will be instrumental in the adaptation of unitary networks, especially for those neural architectures where pre-trained weights are freely available.
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy  Evaluation Approach

Reliable automatic evaluation of dialogue systems under an interactive environment has long been overdue. An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments. Though researchers have attempted to use metrics (e.g., perplexity, BLEU) in language generation tasks or some model-based reinforcement learning methods (e.g., self-play evaluation) for automatic evaluation, these methods only show a very weak correlation with the actual human evaluation in practice. To bridge such a gap, we propose a new framework named ENIGMA for estimating human evaluation scores based on recent advances of off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation, making automatic evaluations feasible. More importantly, ENIGMA is model-free and agnostic to the behavior policies for collecting the experience data (see details in Section 2), which significantly alleviates the technical difficulties of modeling complex dialogue environments and human behaviors. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
Group Equivariant Conditional Neural Processes

We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural processes (CNPs), and it also has transformation equivariance in data space. Incorporating group equivariance, such as rotation and scaling equivariance, provides a way to consider the symmetry of real-world data. We give a decomposition theorem for permutation-invariant and group-equivariant maps, which leads us to construct EquivCNPs with an infinite-dimensional latent space to handle group symmetries. In this paper, we build architecture using Lie group convolutional layers for practical implementation. We show that EquivCNP with translation equivariance achieves comparable performance to conventional CNPs in a 1D regression task. Moreover, we demonstrate that incorporating an appropriate Lie group equivariance, EquivCNP is capable of zero-shot generalization for an image-completion task by selecting an appropriate Lie group equivariance.
Faking photon number on a transition-edge sensor

We study potential security vulnerabilities of a single-photon detector based on superconducting transition-edge sensor. In a simple experiment, we show that an adversary could fake a photon number result at a certain wavelength by sending a larger number of photons at a longer wavelength. In another experiment, we show that the detector can be blinded by bright continuous-wave light and then, a controlled response simulating single-photon detection can be produced by applying a bright light pulse. We model an intercept-and-resend attack on a quantum key distribution system that exploits the latter vulnerability and, under certain assumptions, succeeds to steal the key.
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex  Optimization

This paper considers decentralized stochastic optimization over a network of $n$ nodes, where each node possesses a smooth non-convex local cost function and the goal of the networked nodes is to find an $\epsilon$-accurate first-order stationary point of the sum of the local costs. We focus on an online setting, where each node accesses its local cost only by means of a stochastic first-order oracle that returns a noisy version of the exact gradient. In this context, we propose a novel single-loop decentralized hybrid variance-reduced stochastic gradient method, called GT-HSGD, that outperforms the existing approaches in terms of both the oracle complexity and practical implementation. The GT-HSGD algorithm implements specialized local hybrid stochastic gradient estimators that are fused over the network to track the global gradient. Remarkably, GT-HSGD achieves a network topology-independent oracle complexity of $O(n^{-1}\epsilon^{-3})$ when the required error tolerance $\epsilon$ is small enough, leading to a linear speedup with respect to the centralized optimal online variance-reduced approaches that operate on a single node. Numerical experiments are provided to illustrate our main technical results.
App Developer Centric Trusted Execution Environment

ARM TrustZone is the de-facto hardware TEE implementation on mobile devices like smartphones. As a vendor-centric TEE, TrustZone greatly overlooks the strong protection demands and requirements from the App developers. Several security solutions have been proposed to enable the TEE-assisted isolation in the Normal World of ARM, attempting to balance the security and usability. However, they are still not full-fledged in serving Apps' needs. In this paper, we introduce LEAP, which is a lightweight App developer Centric TEE solution in the Normal World. LEAP offers the auto DevOps tool to help developers to prepare the codes running on it, enables isolated codes to execute in parallel and access peripheral (e.g. mobile GPUs) with ease, and dynamically manage system resources upon Apps' requests. We implement the LEAP prototype on the off-the-shelf ARM platform without any hardware change. We perform the comprehensive analyses and experiments to demonstrate that LEAP is efficient in design, comprehensive in support, and convenient in adoption.
Spectral Roll-off Points Variations: Exploring Useful Information in  Feature Maps by Its Variations

Useful information (UI) is an elusive concept in neural networks. A quantitative measurement of UI is absent, despite the variations of UI can be recognized by prior knowledge. The communication bandwidth of feature maps decreases after downscaling operations, but UI flows smoothly after training due to lower Nyquist frequency. Inspired by the low-Nyqusit-frequency nature of UI, we propose the use of spectral roll-off points (SROPs) to estimate UI on variations. The computation of an SROP is extended from a 1-D signal to a 2-D image by the required rotation invariance in image classification tasks. SROP statistics across feature maps are implemented as layer-wise useful information estimates. We design sanity checks to explore SROP variations when UI variations are produced by variations in model input, model architecture and training stages. The variations of SROP is synchronizes with UI variations in various randomized and sufficiently trained model structures. Therefore, SROP variations is an accurate and convenient sign of UI variations, which promotes the explainability of data representations with respect to frequency-domain knowledge.
Auto4D: Learning to Label 4D Objects from Sequential Point Clouds

In the past few years we have seen great advances in object perception (particularly in 4D space-time dimensions) thanks to deep learning methods. However, they typically rely on large amounts of high-quality labels to achieve good performance, which often require time-consuming and expensive work by human annotators. To address this we propose an automatic annotation pipeline that generates accurate object trajectories in 3D space (i.e., 4D labels) from LiDAR point clouds. The key idea is to decompose the 4D object label into two parts: the object size in 3D that's fixed through time for rigid objects, and the motion path describing the evolution of the object's pose through time. Instead of generating a series of labels in one shot, we adopt an iterative refinement process where online generated object detections are tracked through time as the initialization. Given the cheap but noisy input, our model produces higher quality 4D labels by re-estimating the object size and smoothing the motion path, where the improvement is achieved by exploiting aggregated observations and motion cues over the entire trajectory. We validate the proposed method on a large-scale driving dataset and show a 25% reduction of human annotation efforts. We also showcase the benefits of our approach in the annotator-in-the-loop setting.
"The Resistance of the Cantilever Bridge by the Use of Dual Resinous Cement"

Akira Kanda* Author Affiliations Department of Nutrition, Faculty of Health Sciences, Aomori University of Health and Welfare, Japan Received: July 04, 2022 | Published: July 13, 2022 Corresponding author: Akira Kanda, Department of Nutrition, Faculty of Health Sciences, Aomori University of Health and Welfare, 58-1 Mase, Hamadate, Aomori 030-8505, Japan DOI: 10.26717/BJSTR.2022.45.007151
Theoretical and methodological fundamentals of the concept "digital communication"

In this study, the focus is to better understand and explore the concept of "digital communication", to analyze the international experience of using digital technologies in public activities. Attention is drawn to the basic means of communication and information in the information society, and shows the need to modernize information institutions where everyone can find everything they need. The article examines the availability of information, and the speed of obtaining information with which it can be obtained, especially social networks, where there were younger people, and the change in the age limit to increase and now the age of users has changed. Explained as a global pandemic caused by the spread of coronavirus disease in the world has affected the use of the Internet, which is now used by children in kindergarten, schoolchildren, university students, workers, and the number of progressive people of retirement age is also growing.
Multi-Robot Gaussian Process Estimation and Coverage: A Deterministic  Sequencing Algorithm and Regret Analysis

We study the problem of distributed multi-robot coverage over an unknown, nonuniform sensory field. Modeling the sensory field as a realization of a Gaussian Process and using Bayesian techniques, we devise a policy which aims to balance the tradeoff between learning the sensory function and covering the environment. We propose an adaptive coverage algorithm called Deterministic Sequencing of Learning and Coverage (DSLC) that schedules learning and coverage epochs such that its emphasis gradually shifts from exploration to exploitation while never fully ceasing to learn. Using a novel definition of coverage regret which characterizes overall coverage performance of a multi-robot team over a time horizon $T$, we analyze DSLC to provide an upper bound on expected cumulative coverage regret. Finally, we illustrate the empirical performance of the algorithm through simulations of the coverage task over an unknown distribution of wildfires.
Mixup Without Hesitation

Mixup linearly interpolates pairs of examples to form new samples, which is easy to implement and has been shown to be effective in image classification tasks. However, there are two drawbacks in mixup: one is that more training epochs are needed to obtain a well-trained model; the other is that mixup requires tuning a hyper-parameter to gain appropriate capacity but that is a difficult task. In this paper, we find that mixup constantly explores the representation space, and inspired by the exploration-exploitation dilemma in reinforcement learning, we propose mixup Without hesitation (mWh), a concise, effective, and easy-to-use training algorithm. We show that mWh strikes a good balance between exploration and exploitation by gradually replacing mixup with basic data augmentation. It can achieve a strong baseline with less training time than original mixup and without searching for optimal hyper-parameter, i.e., mWh acts as mixup without hesitation. mWh can also transfer to CutMix, and gain consistent improvement on other machine learning and computer vision tasks such as object detection. Our code is open-source and available at https://github.com/yuhao318/mwh