Reviewerly/exHarmony huggingface dataset:
Multi-Level Clustering-Based Outlier’s Detection (MCOD) Using Self-Organizing Maps

Outlier detection is critical in many business applications, as it recognizes unusual behaviours to prevent losses and optimize revenue. For example, illegitimate online transactions can be detected based on its pattern with outlier detection. The performance of existing outlier detection methods is limited by the pattern/behaviour of the dataset; these methods may not perform well without prior knowledge of the dataset. This paper proposes a multi-level outlier detection algorithm (MCOD) that uses multi-level unsupervised learning to cluster the data and discover outliers. The proposed detection method is tested on datasets in different fields with different sizes and dimensions. Experimental analysis has shown that the proposed MCOD algorithm has the ability to improving the outlier detection rate, as compared to the traditional anomaly detection methods. Enterprises and organizations can adopt the proposed MCOD algorithm to ensure a sustainable and efficient detection of frauds/outliers to increase profitability (and/or) to enhance business outcomes.
Prior Visual Relationship Reasoning For Visual Question Answering

Visual Question Answering (VQA) is a representative task of cross-modal reasoning where an image and a free-form question in natural language are presented and the correct answer needs to be determined using both visual and textual information. One of the key issues of VQA is to reason with semantic clues in the visual content under the guidance of the question. In this paper, we propose Scene Graph Convolutional Network (SceneGCN) to jointly reason the object properties and their semantic relations for the correct answer. The visual relationship is projected into a deep learned semantic space constrained by visual context and language priors. Based on comprehensive experiments on two challenging datasets: GQA and VQA 2.0, we demonstrate the effectiveness and interpretability of the new model.
A Privacy-Preserving Personalized Service Framework through Bayesian Game in Social IoT

It is enormously challenging to achieve a satisfactory balance between quality of service (QoS) and users’ privacy protection along with measuring privacy disclosure in social Internet of Things (IoT). We propose a privacy-preserving personalized service framework (Persian) based on static Bayesian game to provide privacy protection according to users’ individual security requirements in social IoT. Our approach quantifies users’ individual privacy preferences and uses fuzzy uncertainty reasoning to classify users. These classification results facilitate trustworthy cloud service providers (CSPs) in providing users with corresponding levels of services. Furthermore, the CSP makes a strategic choice with the goal of maximizing reputation through playing a decision-making game with potential adversaries. Our approach uses Shannon information entropy to measure the degree of privacy disclosure according to the probability of game mixed strategy equilibrium. Experimental results show that Persian guarantees QoS and effectively protects user privacy despite the existence of adversaries.
Robust Real-Time Hand Gestural Recognition for Non-Verbal Communication with Tabletop Robot Haru

In this paper, we present our work in close-distance non-verbal communication with tabletop robot Haru through hand gestural interaction. We implemented a novel hand gestural understanding system by training a machine-learning architecture for real-time hand gesture recognition with the Leap Motion. The proposed system is activated based on the velocity of a user's palm and index finger movement, and subsequently labels the detected movement segments under an early classification scheme. Our system is able to combine multiple gesture labels for recognition of consecutive gestures without clear movement boundaries. System evaluation is conducted on data simulating real human-robot interaction conditions, taking into account relevant performance variables such as movement style, timing and posture. Our results show robustness in hand gesture classification performance under variant conditions. We furthermore examine system behavior under sequential data input, paving the way towards seamless and natural real-time close-distance hand-gestural communication in the future.
Rotation-Robust Intersection over Union for 3D Object Detection

In this paper, we propose a Rotation-robust Intersection over Union ( $$\textit{RIoU}$$ ) for 3D object detection, which aims to learn the overlap of rotated bounding boxes. In most existing 3D object detection methods, the norm-based loss is adopted to individually regress the parameters of bounding boxes, which may suffer from the loss-metric mismatch due to the scaling problem. Motivated by the IoU loss in the axis-aligned 2D object detection which is invariant to the scale, our method jointly optimizes the parameters via the $$\textit{RIoU}$$ loss. To tackle the uncertainty of convex caused by rotation, a projection operation is defined to estimate the intersection area. The calculation process of $$\textit{RIoU}$$ and its loss function is robust to the rotation condition and feasible for back-propagation, which only comprises basic numerical operations. By incorporating the $$\textit{RIoU}$$ loss with the conventional norm-based loss function, we enforce the network to directly optimize the $$\textit{RIoU}$$ . Experimental results on the KITTI, nuScenes and SUN RGB-D datasets validate the effectiveness of our proposed method. Moreover, we show that our method is suitable for the detection task of 2D rotated objects, such as text boxes and cluttered targets in the aerial images.
Ultrafast coherent excitation of a <sup>40</sup>Ca<sup>+</sup> ion

Fast entangling quantum gates can significantly enhance the performance of a trapped-ion quantum computer. In pursuit of implementing a fast two-qubit gate, we investigate the coherent excitation of a 40Ca+ ion with a train of picosecond pulses resonant to the 4S1/2 4P3/2 transition. The optical pulse train is derived from a mode-locked, stabilized optical frequency comb. We implement two techniques to characterize the pulse-ion interaction and show how all requirements can be met for an implementation of a fast phase gate operation.
An Efficient Container Management Scheme for Resource-Constrained Intelligent IoT Devices

Virtualization is an essential feature in the IoT-resource-constrained environment due to which the service providers are facing challenges to minimize the energy consumption by IoT devices. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption of IoT devices, which are an integral part of the modern data centers. A lot of research work has focused on efficient management of energy consumption by virtue of virtual machine consolidation. The existing virtualization techniques may not be suitable for this problem due to high computational overhead. As containers have been recently getting much popularity to encapsulate fog services, so they are the best candidate to handle this problem, especially for intelligent IoT devices. Keeping the focus on all these issues, in this article, we propose an energy-efficient container migration scheme by migrating the container from the source host server to the destination host server to meet the container's resource requirement. We used a novel approach to find the best destination host for container placement to solve host overload or underload problems using the best-fit container placement technique. The results obtained on the benchmark data set with respect to various performance evaluation metrics prove the efficacy of the designed scheme in comparison to the other existing state-of-the-art schemes.
Iterative Feature Transformation for Fast and Versatile Universal Style Transfer

The general framework for fast universal style transfer consists of an autoencoder and a feature transformation at the bottleneck. We propose a new transformation that iteratively stylizes features with analytical gradient descent (Implementation is open-sourced at https://github.com/chiutaiyin/Iterative-feature-transformation-for-style-transfer ). Experiments show this transformation is advantageous in part because it is fast. With control knobs to balance content preservation and style effect transferal, we also show this method can switch between artistic and photo-realistic style transfers and reduce distortion and artifacts. Finally, we show it can be used for applications requiring spatial control and multiple-style transfer.
When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans

In order to collaborate safely and efficiently, robots need to anticipate how their human partners will behave. Some of today’s robots model humans as if they were also robots, and assume users are always optimal. Other robots account for human limitations, and relax this assumption so that the human is noisily rational. Both of these models make sense when the human receives deterministic rewards: i.e., gaining either $\$100$ or $\$130$ with certainty. But in real-world scenarios, rewards are rarely deterministic. Instead, we must make choices subject to risk and uncertainty—and in these settings, humans exhibit a cognitive bias towards suboptimal behavior. For example, when deciding between gaining $\$100$ with certainty or $\$130$ only 80% of the time, people tend to make the risk-averse choice—even though it leads to a lower expected gain! In this paper, we adopt a well-known Risk-Aware human model from behavioral economics called Cumulative Prospect Theory and enable robots to leverage this model during human-robot interaction (HRI). In our user studies, we offer supporting evidence that the Risk-Aware model more accurately predicts suboptimal human behavior. We find that this increased modeling accuracy results in safer and more efficient human-robot collaboration. Overall, we extend existing rational human models so that collaborative robots can anticipate and plan around suboptimal human behavior during HRI. CCS Concepts • Mathematics of computing → Probabilistic inference problems; • Computing methodologies → Cognitive robotics; Theory of mind. ACM Reference Format: Minae Kwon*, Erdem Biyik*, Aditi Talati†, Karan Bhasin‡, Dylan P. Losey*, Dorsa Sadigh*. 2020. When Humans Aren’t Optimal: Robots that Collaborate with Risk-Aware Humans. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’20), March 23–26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3319502.3374832
A novel truncated nonconvex nonsmooth variational method for SAR image despeckling

Speckle reduction is a fundamental problem in coherent imaging systems. In this paper, to suppress the speckle in SAR images, we propose a novel truncated nonconvex nonsmooth model. It incorporates a truncated nonconvex regularization term and an I-divergence fidelity term. The truncated ℓpnorm (0<p<1) regularization can better recover neat edges and simultaneously prevent contrast reduction artefact. The I-divergence fidelity term is used to suppress the multiplicative noise effectively. We also propose an efficient algorithm based on variable-splitting and alternating direction method of multipliers (ADMM) method to solve the model. Compared to state-of-the-art speckle suppression methods, intensive experimental results on a variety of SAR images show the superiority of the proposed method qualitatively and quantitatively.
Transformation Consistency Regularization – A Semi-supervised Paradigm for Image-to-Image Translation

Scarcity of labeled data has motivated the development of semi-supervised learning methods, which learn from large portions of unlabeled data alongside a few labeled samples. Consistency Regularization between model’s predictions under different input perturbations, particularly has shown to provide state-of-the art results in a semi-supervised framework. However, most of these method have been limited to classification and segmentation applications. We propose Transformation Consistency Regularization, which delves into a more challenging setting of image-to-image translation, which remains unexplored by semi-supervised algorithms. The method introduces a diverse set of geometric transformations and enforces the model’s predictions for unlabeled data to be invariant to those transformations. We evaluate the efficacy of our algorithm on three different applications: image colorization, denoising and super-resolution. Our method is significantly data efficient, requiring only around 10–20% of labeled samples to achieve similar image reconstructions to its fully-supervised counterpart. Furthermore, we show the effectiveness of our method in video processing applications, where knowledge from a few frames can be leveraged to enhance the quality of the rest of the movie.
Learning quantum Hamiltonians from single-qubit measurements

In the Hamiltonian-based quantum dynamics, to estimate Hamiltonians from the measured data is a vital topic. In this work, we propose a recurrent neural network to learn the target Hamiltonians from the temporal records of single-qubit measurements, which does not require the ground states and only measures single-qubit observables. It is applicable on both time-independent and time-dependent Hamiltonians and can simultaneously capture the magnitude and sign of Hamiltonian parameters. Taking the Hamiltonians with the nearest-neighbor interactions as numerical examples, we trained our recurrent neural networks to learn different types of Hamiltonians with high accuracy. The study also shows that our method has good robustness against the measurement noise and decoherence effect. Therefore, it has widespread applications in estimating the parameters of quantum devices and characterizing the Hamiltonian-based quantum dynamics.2 MoreReceived 7 January 2021Revised 5 April 2021Accepted 29 April 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023246Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum measurementsQuantum tomographyPhysical SystemsArtificial neural networksQuantum Information
QPLEX: Duplex Dueling Multi-Agent Q-Learning

We explore value-based multi-agent reinforcement learning (MARL) in the popular paradigm of centralized training with decentralized execution (CTDE). CTDE has an important concept, Individual-Global-Max (IGM) principle, which requires the consistency between joint and local action selections to support efficient local decision-making. However, in order to achieve scalability, existing MARL methods either limit representation expressiveness of their value function classes or relax the IGM consistency, which may suffer from instability risk or may not perform well in complex domains. This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function. This duplex dueling structure encodes the IGM principle into the neural network architecture and thus enables efficient value function learning. Theoretical analysis shows that QPLEX achieves a complete IGM function class. Empirical experiments on StarCraft II micromanagement tasks demonstrate that QPLEX significantly outperforms state-of-the-art baselines in both online and offline data collection settings, and also reveal that QPLEX achieves high sample efficiency and can benefit from offline datasets without additional online exploration.
An Economic Analysis of Difficulty Adjustment Algorithms in Proof-of-Work Blockchain Systems

The design of the difficulty adjustment algorithm (DAA) of the Bitcoin system is vulnerable as it dismisses miners' strategic responses to policy changes. We develop an economic model of the Proof-of-Work based blockchain system. Our model allows miners to pause operation when the expected reward is below the shutdown point. Hence, the supply of aggregate hash power can be elastic in the cryptocurrency price and the difficulty target of the mining puzzle. We prove that, when the hash supply is elastic, the Bitcoin DAA fails to adjust the block arrival rate to the targeted level. In contrast, the DAA of another blockchain system, Bitcoin Cash, is shown to be stable even when the cryptocurrency price is volatile and the supply of hash power is highly elastic. We also provide empirical evidence and simulation results supporting the model's prediction. Our results indicate that the current Bitcoin system might collapse if a sharp price reduction lowers the reward for mining denominated in fiat money. While this crisis can be prevented through the upgrading of DAA, we also discuss that a large fraction of miners may disagree with upgrading the DAA because they can obtain a larger expected profit from an unstable DAA.
4 Impact of Deep Neural Learning on Artificial Intelligence Research

Deep learning techniques have had a huge impact on artificial intelligence research. They have improved upon the traditional machine learning techniques where human expertise was required for feature engineering. By removing one human factor, they have moved us one step forward in the field of artificial intelligence. They have not entirely removed humans, though. They are required for designing the architectures and cleaning the data. Deep learning techniques have managed to achieve breakthrough results in domains such as speech recognition, machine translation, image recognition, and object detection. This chapter gives a brief overview of various deep learning techniques being used today. Techniques that make deep learning more effective have been described. Some interesting applications have also been covered.
From nonequilibrium Green's functions to quantum master equations for the density matrix and out-of-time-order correlators: Steady-state and adiabatic dynamics

We consider a finite quantum system under slow driving and weakly coupled to thermal reservoirs at different temperatures. We present a systematic derivation of the quantum master equation for the density matrix and the out-of-time-order correlators. We start from the microscopic Hamiltonian and we formulate the equations ruling the dynamics of these quantities by recourse to the Schwinger-Keldysh nonequilibrium Green's function formalism, performing a perturbative expansion in the coupling between the system and the reservoirs. We focus on the adiabatic dynamics, which corresponds to considering the linear response in the ratio between the relaxation time due to the system-reservoir coupling and the time scale associated to the driving. We calculate the particle and energy fluxes. We illustrate the formalism in the case of a qutrit coupled to bosonic reservoirs and of a pair of interacting quantum dots attached to fermionic reservoirs, also discussing the relevance of coherent effects.
From Multitask Gradient Descent to Gradient-Free Evolutionary Multitasking: A Proof of Faster Convergence

Evolutionary multitasking, which solves multiple optimization tasks simultaneously, has gained increasing research attention in recent years. By utilizing the useful information from related tasks while solving the tasks concurrently, improved performance has been shown in various problems. Despite the success enjoyed by the existing evolutionary multitasking algorithms, still there is a lack of theoretical studies guaranteeing faster convergence compared to the conventional single task case. To analyze the effects of transferred information from related tasks, in this article, we first put forward a novel multitask gradient descent (MTGD) algorithm, which enhances the standard gradient descent updates with a multitask interaction term. The convergence of the resulting MTGD is derived. Furthermore, we present the first proof of faster convergence of MTGD relative to its single task counterpart. Utilizing MTGD, we formulate a gradient-free evolutionary multitasking algorithm called multitask evolution strategies (MTESs). Importantly, the single task evolution strategies (ESs) we utilize are shown to asymptotically approximate gradient descent and, hence, the faster convergence results derived for MTGD extend to the case of MTES as well. Numerical experiments comparing MTES with single task ES on synthetic benchmarks and practical optimization examples serve to substantiate our theoretical claim.
Traffic-Sign Recognition Using Deep Learning

Traffic-sign recognition (TSR) has been an essential part of driver-assistance systems, which is able to assist drivers in avoiding a vast number of potential hazards and improve the experience of driving. However, the TSR is a realistic task that is full of constraints, such as visual environment, physical damages, and partial occasions, etc. In order to deal with the constrains, convolutional neural networks (CNN) are accommodated to extract visual features of traffic signs and classify them into corresponding classes. In this project, we initially created a benchmark (NZ-Traffic-Sign 3K) for the traffic-sign recognition in New Zealand. In order to determine which deep learning models are the most suitable one for the TSR, we choose two kinds of models to conduct deep learning computations: Faster R-CNN and YOLOv5. According to the scores of various metrics, we summarized the pros and cons of the picked models for the TSR task.
An integrated augmented reality surgical navigation platform using multi-modality imaging for guidance

An integrated augmented reality (AR) surgical navigation system that potentially improves intra-operative visualization of concealed anatomical structures. Integration of real-time tracking technology with a laser pico-projector allows the surgical surface to be augmented by projecting virtual images of lesions and critical structures created by multimodality imaging. We aim to quantitatively and qualitatively evaluate the performance of a prototype interactive AR surgical navigation system through a series of pre-clinical studies. Four pre-clinical animal studies using xenograft mouse models were conducted to investigate system performance. A combination of CT, PET, SPECT, and MRI images were used to augment the mouse body during image-guided procedures to assess feasibility. A phantom with machined features was employed to quantitatively estimate the system accuracy. All the image-guided procedures were successfully performed. The tracked pico-projector correctly and reliably depicted virtual images on the animal body, highlighting the location of tumour and anatomical structures. The phantom study demonstrates the system was accurate to 0.55 ± 0.33mm. This paper presents a prototype real-time tracking AR surgical navigation system that improves visualization of underlying critical structures by overlaying virtual images onto the surgical site. This proof-of-concept pre-clinical study demonstrated both the clinical applicability and high precision of the system which was noted to be accurate to &lt;1mm.