Reviewerly/exHarmony huggingface dataset:
Optimization with delay-induced bifurcations

Optimization is finding the best solution, which mathematically amounts to locating the global minimum of some cost function. Optimization is traditionally automated with digital or quantum computers, each having their limitations and none guaranteeing an optimal solution. Here, we conceive a principle behind optimization based on delay-induced bifurcations, which is potentially implementable in non-quantum analog devices. Often, optimization techniques are interpreted via a particle moving in multi-well energy landscape and to prevent confinement to a non-global minima they should incorporate mechanisms to overcome barriers between the minima. Particularly, simulated annealing digitally emulates pushing a fictitious particle over a barrier by random noise, whereas quantum computers utilize tunneling through barriers. In our principle, the barriers are effectively destroyed by delay-induced bifurcations. Although bifurcation scenarios in nonlinear delay-differential equations can be very complex and are notoriously difficult to predict, we hypothesize, verify, and utilize the finding that they become considerably more predictable in dynamical systems, where the right-hand side depends only on the delayed variable and represents a gradient of some potential energy function. By tuning the delay introduced into the gradient descent setting, thanks to global bifurcations destroying local attractors, one could force the system to spontaneously wander around all minima. This would be similar to noise-induced behavior in simulated annealing but achieved deterministically. Ideally, a slow increase and then decrease of the delay should automatically push the system toward the global minimum. We explore the possibility of this scenario and formulate some prerequisites.
Managing Trajectories and Interactions During a Pandemic

COVID-19 has brought about substantial social, economic and health related burdens, motivating different control measures from policy makers worldwide. Contact tracing plays a pivotal role in the COVID-19 era. However, contact tracing is by nature entirely retrospective: it can only identify contacts of known or suspected cases. Our proposed system is prospective, aiming to 'create' networks that will ultimately make contact tracing and pandemic management easier. As contact tracing seeks to reconstruct the underlying interaction network, we can improve the process by reducing the complexity of contact network structure; we introduce a method for reducing contact network complexity through strategic scheduling. The method functions through pairwise comparison of individual trajectories in a coordinate space of activities, locations, and time intervals. We demonstrate the method through a simulated scenario where individuals (students) register for activities using a mobile application in a campus. The application then applies our algorithm to provide individuals with schedules that reduce the complexity of the overall network, without compromising individual privacy.
Channel-Adaptive Quantization for Wireless Federated Learning

As a popular distributed machine learning based on stochastic gradient decent (SGD), federated learning enables edge devices to compute stochastic gradients and then upload them to an edge server for global AI-model updating. However, there are two problems to be faced in federated learning. First, the transmission of high -dimensional stochastic gradients from edge devices to edge server causes serious communication bottleneck. Second, the fading and noise of wireless channels may distort the transmitted stochastic gradients. To address these issues, we adopt scaler quantization for SGD by taking dynamics of wireless channels into account and analyze the influence of wireless channels and quantization on the convergence rate. Then, the quantization error and channel effects are characterized in received signal-to-noise ratio (SNR). We formulate and then solve an optimization problem of sum-SNR maximization by quantization resources allocation, for the goal of making the quantization and transmission as reliable as possible. Simulations show that our scheme can outperform the benchmark schemes.
Research on Teaching Evaluation System of Higher Vocational Colleges Based on OKR and Big Data

In the era of “Internet =” in order to meet the needs of society, vocational colleges are developing rapidly, but the problems of backward teaching evaluation methods and low enthusiasm of teachers are gradually exposed. In order to improve this phenomenon, this paper proposes a teaching evaluation method based on OKR (Objectives and Key Results) concept and big data technology. This method uses real teaching data and big data technology to build a teaching evaluation system that is truly suitable for higher vocational colleges. The results of data analysis show that using this method can improve teachers' initiative, improve the quality of education and teaching, and standardize the development of higher vocational colleges.
Best-approximation error for parametric quantum circuits

In Variational Quantum Simulations, the construction of a suitable parametric quantum circuit is subject to two counteracting effects. The number of parameters should be small for the device noise to be manageable, but also large enough for the circuit to be able to represent the solution. Dimensional expressivity analysis can optimize a candidate circuit considering both aspects. In this article, we will first discuss an inductive construction for such candidate circuits. Furthermore, it is sometimes necessary to choose a circuit with fewer parameters than necessary to represent all relevant states. To characterize such circuits, we estimate the best-approximation error using Voronoi diagrams. Moreover, we discuss a hybrid quantum-classical algorithm to estimate the worst-case best-approximation error, its complexity, and its scaling in state space dimensionality. This allows us to identify some obstacles for variational quantum simulations with local optimizers and underparametrized circuits, and we discuss possible remedies.
Automatic Machine Learning: An Exploratory Review

Automatic Machine Learning (AutoML) is also an exploratory region that has procured almost all complexes recently. However, the different methodologies followed by specialists and what has been unveiled by the accessible work is neither appropriately reported nor extremely clear because of the distinctions in the methodologies. While designing machine learning frameworks, AutoML serves as a bridge among different degrees of competence and aids the data science measure. Although a wide range of strategies is used to address this, there is no target link between these methods. AutoML is a start-to-finish strategy for automating the model development workflow without the need for external assistance. In the introduction session, this paper discussed the AutoML process, needs, difficulties, and benefits. AutoML methods, as well as Providers, have been explored inside the second session. Described Unified or AI Platform for different kinds for AutoML user in the third session. The fourth session is a case study of PayPal's e-commerce application, that analyzes both Automatic Machine Learning and Unified AutoML to get even more accurate results when using the unified AI platform, and the last session includes the conclusion.
A Motion‐guided Interface for Modeling 3D Multi‐functional Furniture

Abstract While non‐expert 3D design systems are helpful for performing conceptual design, most existing works focused on modeling static objects. However, the 3D modeling interfaces can include more interactions between the user and the models that are dynamic (and can be interacted with). In this paper, we propose a 3D modeling system for the conceptual design of interactable multi‐functional furniture. Our contribution is in the design and development of a motion‐guided interface. The key idea is that users should create interactable furniture components as if they are interacting with them with their hands. We conducted a preliminary user study to explore users’ preferred hand gestures for creating various dynamic furniture components, implemented a 3D modeling system with the preferred gestures as a basis for the motion‐guided user interface, and conducted an evaluation user study to demonstrate that our user interface is user‐friendly and efficient for novice designers to perform conceptual furniture designs.
Privacy Guarantees for Cloud-based State Estimation using Partially Homomorphic Encryption

The privacy aspect of state estimation algorithms has been drawing high research attention due to the necessity for a trustworthy private environment in cyber-physical systems. These systems usually engage cloud-computing platforms to aggregate essential information from spatially distributed nodes and produce desired estimates. The exchange of sensitive data among semi-honest parties raises privacy concerns, especially when there are coalitions between parties. We propose two privacy-preserving protocols using Kalman filter and partially homomorphic encryption of the measurements and estimates while exposing the covariances and other model parameters. We prove that the proposed protocols achieve satisfying computational privacy guarantees against various coalitions based on formal cryptographic definitions of indistinguishability. We evaluate the proposed protocols to demonstrate their efficiency using data from a real testbed.
Bat algorithm for multilevel image thresholding based on Otsu and Kapur’s entropy

Abstract Multilevel image thresholding has attracted plenty of attention in the past decades. Otsu and Kapur’s entropy-based methods are often applied to search the optimal bi-thresholding. These techniques are also suitable for multilevel thresholds. However, it takes a lot of computation to solve the multilevel threshold problem. To address this problem, in this paper, a recently proposed bat algorithm is used to find the appropriate multilevel thresholds, in which Otsu and Kapur’s entropy is regarded as its fitness functions. Evaluation of image segmentation effect is performed using the peak-to-signal ratio (PSNR) and structural similarity (SSIM) index. The experiment results show that Otsu based method is more suitable for multi-level threshold image segmentation.
Against the leans: Overcoming spatial disorientation through galvanic vestibular stimulation

Leans illusion is a type of Spatial Disorientation (SD) that pilots often experience which can adversely affect flight performance. For pilots’ flight safety, research on how to effectively overcome SD such as leans illusion is important. The purpose of this study is to identify the overcoming effect of Galvanic Vestibular Stimulation (GVS) technology on leans illusion. Twenty-one Air Force pilots participated in a flight simulation experiment where leans illusion was induced through a specialized SD simulator. In the with-GVS condition, GVS was given during the roll-out phase. Data was analyzed using roll angle error and subjective SD scales by two conditions (with-GVS, without-GVS). Results showed that both the roll angle error and the subjective SD scale scores were found to be lower in the with-GVS condition than in the without-GVS condition. This study suggests that the use of GVS technology can potentially contribute in overcoming leans illusion.
A Cost-Efficient Resource Provisioning and Scheduling Approach for Deadline-Sensitive MapReduce Computations in Cloud Environment

The use of cloud services to process a large amount of data is growing and demands for scalable, reliable, and highly available services in cloud environments are raising. The demands and the urge for developing these facilities have made parallel computing more appealing. Cloud providers offer various types of Virtual Machines (VMs) that are compatible with parallel processing and the clients should pay for their hourly usage. The price varies based on the type, the number and the hiring time of the VMs. A daily price fluctuation timetable has been proposed and scaling the number of VMs on that helps to schedule the computations to meet both deadline and cost minimization goals. It becomes critical to select appropriate VMs and distribute workload efficiently across them. Therefore, the computations and the VMs require being well-managed, scheduled and monitored to meet the deadline while minimizing the total hiring cost. To address these concerns, this paper formulated the problem to calculate the total hiring cost before and during the computations. The execution time and the total cost are calculated based on the application's input size and the required type and the number of VMs. We worked on two applications as sample benchmarks to identify the best approach to choose and manage the VMs to compute them. Our results indicate that among different available approaches for hiring VMs, identifying the most affordable approach leads to minimizing the cost signiflcantly.
Fault-Aware Robust Control via Adversarial Reinforcement Learning

Robots have limited adaptation ability compared to humans and animals in the case of damage. However, robot damages are prevalent in realworld applications, especially for robots deployed in extreme environments. The fragility of robots greatly limits their widespread application. We propose an adversarial reinforcement learning framework, which significantly increases robot robustness over joint damage cases in both manipulation tasks and locomotion tasks. The agent is trained iteratively under the joint damage cases where it has poor performance. We validate our algorithm on a three-fingered robot hand and a quadruped robot. Our algorithm can be trained only in simulation and directly deployed on a real robot without any fine-tuning. It also demonstrates exceeding success rates over arbitrary joint damage cases.
Romulus: Lighweight AEAD from Tweakable Block Ciphers

In this chapter, we present the NIST lightweight finalist, Romulus. It is a family of lightweight, very efficient, and highly-secure algorithms; including, but not restricted to, NAE (Romulus-N) and MRAE (Romulus-M) schemes.
Image augmentation using GAN models in Computer Vision

The efficiency of deep learning algorithms will increase when it is trained on a large size of data. Over fitting problems will also be solved working on a large dataset. To collect a huge quantity of data for training a model is more challenging job. Collecting data will take more time as well as resources. The data augmentation technique will increase the diversity of data that to be trained on deep learning algorithms. This will also help in not collecting new data. This led to the need for generative models. When the set of training data is given to this generative model, it will learn to produce the same statistical data as the set of training data. GANs are used in many fields including computer vision, medical, agriculture etc. In this paper along with the traditional GANs two architecture variant and two loss-variant GANs are reviewed and experiments are conducted to generate images. Detailed reviews on the performance metrics of GAN models are also discussed and observed that FID plot doesn’t give much insight to compare the generated images. Despite of the significant success achieved till date, applying GAN model to real time dataset has some challenging problems. Challenges faced while training GANs including mode collapse, image quality and vanishing gradients are discussed.
The Product Cybersecurity Organization

The purpose of this chapter is to motivate the development of a Product Cybersecurity Organization for a medical device manufacturer (MDM). This chapter should be of interest to those in the executive management of MDMs who want to understand the organizational implications of cybersecurity—in terms of the processes, tools, and people they need to develop/procure/recruit, respectively. Medical device cybersecurity recruiters and hiring managers looking to hire cybersecurity talent for their teams might find some of the discussions in this chapter on hiring and retaining cybersecurity engineers to be useful.
Automatic Foreground Detection at 784 FPS for Ultra-High-Speed Human–Machine Interactions

Human-machine interactive systems show increasing demand for analysing fast moving objects in high-frame-rate videos. Robust foreground detection, which is able to reduce large amount of redundant background data from high-frame-rate video, becomes the essence to achieve ultra-high-speed human-machine interactions. This paper proposes a local spatial propagation based background model generation, a local linear illumination correction based background model update, and a regional central coordinates and edge keypoints constrained foreground region reselection. The three proposals make up a robust and hardware-friendly foreground detection method. Experimental results prove that the proposed hardware-friendly algorithm achieves high accuracy and robustness on various kinds of challenging cases. Meanwhile, the hardware implementation utilizes little hardware resources and achieves realtime processing of high-frame-rate (784 frame/second) video with the delay less than 1 ms/frame in image processing core. In addition, a practical system is implemented by combing a PC, a high-speed camera and a field programmable gate array (FPGA) for realworld applications. This work will significatively promote the development and application of high-speed human machine interaction. A demo of the proposed vision system working at 784 FPS is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://wcms.waseda.jp/em/5f84f75136a6</uri> . Note to Practitioners—This paper was motivated by the problem of high-frame-rate video contains large amount of redundant background pixels which makes ultra-high-speed human-machine interactions inaccessible. Existing approaches are mainly focused on designing complex background models, but processing speed, which is the most important issue for ultra-high-speed human-machine interactions, has received relatively little attention. This paper suggests a robust and hardware-friendly foreground detection algorithm which has been implemented as a hardware system by using an FPGA, a high-frame-rate camera, and a PC. We show that the hardware implementation utilizes less hardware resources and achieves real-time processing speed of 784 FPS with the delay less than 1 ms/frame in the image processing core. This work is a pioneering attempt of ultra-high-speed foreground detection, which will significatively speed up the wide applications of ultra-high-speed human machine interactions.
Scalable Hardware Architecture for Invertible Logic with Sparse Hamiltonian Matrices

We introduce a scalable hardware architecture for large-scale invertible logic. Invertible logic has been recently presented that can realize bidirectional computing probabilis-tically based on Hamiltonians with a small number of non-zero elements. In order to store and compute the Hamiltonians efficiently in hardware, a sparse matrix representation of PTELL (partitioned and transposed ELLPACK) is proposed. A memory size of PTELL can be smaller than that of a conventional ELL by reducing the number of paddings while parallel reading of non-zero values are realized for high-throughput operations. As a result, the proposed scalable invertible-logic hardware based on PTELL is designed on Xilinx KC705 FPGA board, which achieves two orders of magnitude faster than an 8-core CPU implementation.
Have You Been Framed and Can You Prove It?

This work addresses the potential for a frameup attack through the use of a programmable USB e.g., a ‘Rubber Ducky’ to plant false evidence on someone else's computer. The aim is to determine who performed these actions, the human or the Rubber Ducky. Experiments were undertaken where a human interacted with a computer and a Rubber Ducky performed the same actions using identical computers, with identical baseline configurations, to detect differences in the artifacts left behind in each case. Forensics images generated from each experiment were analysed using forensics tools. Our findings pose the question can a programmable USB device be used to masquerade as a human, and can the forensic analyst or legal counsel make informed decisions about the provenance of any artifacts identified, as the expert may not be able to differentiate between the actions of the human user or the programmable USB, which could lead to a miscarriage of justice. This work alerts investigators and experts to the potential presence of a programmable USB device, and presents some artifacts that show that a programmable USB could have carried out these actions, which might prevent an innocent individual being wrongfully convicted of a crime they did not commit.
A Robustness Evaluation of Concept Drift Detectors against Unreliable Data Streams

Machine learning algorithms have been widely used in IoT application systems for predicting labels of input data streams from IoT sensors. Since data streams are not always stationary in real-world scenarios, underlying data distribution changes may cause a deterioration of the prediction performance that is known as concept drift. Existing concept drift detection methods often assume that complete and true labels for detecting the prediction errors are always available immediately after the prediction. However, such an assumption is not realistic in real IoT application systems. This paper experimentally investigates the robustness of six representative concept drift detectors against unreliable data streams containing the data with error labels and the data with no labels. Robustness is evaluated in terms of the average detection delay and precision by increasing the ratios of error-labeled and missing-label data in a synthetic data stream. Our experimental results show that Cumulative Sum (CUSUM), Page-Hinkley (PH), and Drift Detection Method (DDM) achieve relatively stable performances when the ratio of error-labeled data is less than 40%. With respect to the drift detection efficiency, CUSUM can be regarded as the most robust detector among other detectors tested in our experiments.