Reviewerly/exHarmony huggingface dataset:
Investigating the impact of total quality management practices and Six Sigma processes to enhance the quality and reduce the cost of quality: the case of Dubai

The application of the TQM and Six Sigma in the plastic industry in Dubai is known for its commitment to quality products and competitive prices, making Dubai a world shopping destination in the world of plastics. Literature confirms the efficacy of TQM and Six Sigma to achieve quality and cost/price, efficiencies, competitiveness, consumer satisfaction and profitable businesses. The study results reveal among other things, almost a 1:1 relationship between TQM and Six Sigma, which would enable companies to carefully plan for their quality and costs in order to achieve total commitment to quality. The policy implication is to introduce the booming environmental factor into TQM and Six Sigma dynamics and calculus.
The future of quantum computing with superconducting qubits

For the first time in history, we are seeing a branching point in computing paradigms with the emergence of quantum processing units (QPUs). Extracting the full potential of computation and realizing quantum algorithms with a super-polynomial speedup will most likely require major advances in quantum error correction technology. Meanwhile, achieving a computational advantage in the near term may be possible by combining multiple QPUs through circuit knitting techniques, improving the quality of solutions through error suppression and mitigation, and focusing on heuristic versions of quantum algorithms with asymptotic speedups. For this to happen, the performance of quantum computing hardware needs to improve and software needs to seamlessly integrate quantum and classical processors together to form a new architecture that we are calling quantum-centric supercomputing. In the long term, we see hardware that exploits qubit connectivity in higher than 2D topologies to realize more efficient quantum error correcting codes, modular architectures for scaling QPUs and parallelizing workloads, and software that evolves to make the intricacies of the technology invisible to the users and realize the goal of ubiquitous, frictionless quantum computing.
Towards understanding knowledge distillation

Knowledge distillation, i.e. one classifier being trained on the outputs of another classifier, is an empirically very successful technique for knowledge transfer between classifiers. It has even been observed that classifiers learn much faster and more reliably if trained with the outputs of another classifier as soft labels, instead of from ground truth data. So far, however, there is no satisfactory theoretical explanation of this phenomenon. In this work, we provide the first insights into the working mechanisms of distillation by studying the special case of linear and deep linear classifiers. Specifically, we prove a generalization bound that establishes fast convergence of the expected risk of a distillation-trained linear classifier. From the bound and its proof we extract three keyfactors that determine the success of distillation: data geometry – geometric properties of the datadistribution, in particular class separation, has an immediate influence on the convergence speed of the risk; optimization bias– gradient descentoptimization finds a very favorable minimum of the distillation objective; and strong monotonicity– the expected risk of the student classifier always decreases when the size of the training set grows.
A Chaos-Based Image Encryption Technique Utilizing Hilbert Curves and H-Fractals

Image encryption is the most direct and effective technical means for protecting the security of image information.Based on the space filling property of the Hilbert curve and the infinite property of the H-geometric fractal, a new image encryption technique is proposed, which combines the pseudorandomness of a hyperchaotic system and the sensitivity to initial values.First, the hash value of a plaintext image is calculated using the secure hash algorithm 3 (SHA-3) as the initial value of the piece-wise linear chaotic map (PWLCM) and Rossler chaotic systems, which associates the key with the plaintext.In addition, the chaotic sequences that are generated by the chaotic systems are used to scramble the global pixel positions and the pixel values of the images, thereby disturbing the distribution of the pixel positions and the pixel values.Second, the Hilbert curve and H-fractal are alternately used to scramble the local pixel positions and diffuse the pixel values twice.Finally, the ciphertext feedback is used to further enhance the confusion and diffusion characteristics of the algorithm in order to achieve higher security.The experimental results and security analysis show that the encryption technique has enough key space to resist exhaustive attacks and can effectively resist statistical attacks, differential attacks, noise attacks, and cropping attacks.It can be used for military, judicial, and other privacy-related digital images secure storage and network security transmissions.
Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methods

This paper presents a system for online learning of human classifiers by mobile service robots using 3D LiDAR sensors, and its experimental evaluation in a large indoor public space. The learning framework requires a minimal set of labelled samples (e.g. one or several samples) to initialise a classifier. The classifier is then retrained iteratively during operation of the robot. New training samples are generated automatically using multi-target tracking and a pair of "experts" to estimate false negatives and false positives. Both classification and tracking utilise an efficient real-time clustering algorithm for segmentation of 3D point cloud data. We also introduce a new feature to improve human classification in sparse, long-range point clouds. We provide an extensive evaluation of our the framework using a 3D LiDAR dataset of people moving in a large indoor public space, which is made available to the research community. The experiments demonstrate the influence of the system components and improved classification of humans compared to the state-of-the-art.
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers

Deep neural networks have been shown to exhibit an intriguing vulnerability to adversarial input images corrupted with imperceptible perturbations. However, the majority of adversarial attacks assume global, fine-grained control over the image pixel space. In this paper, we consider a different setting: what happens if the adversary could only alter specific attributes of the input image? These would generate inputs that might be perceptibly different, but still natural-looking and enough to fool a classifier. We propose a novel approach to generate such ``semantic'' adversarial examples by optimizing a particular adversarial loss over the range-space of a parametric conditional generative model. We demonstrate implementations of our attacks on binary classifiers trained on face images, and show that such natural-looking semantic adversarial examples exist. We evaluate the effectiveness of our attack on synthetic and real data, and present detailed comparisons with existing attack methods. We supplement our empirical results with theoretical bounds that demonstrate the existence of such parametric adversarial examples.
Shuttling-based trapped-ion quantum information processing

Moving trapped-ion qubits in a microstructured array of radiofrequency traps offers a route toward realizing scalable quantum processing nodes. Establishing such nodes, providing sufficient functionality to represent a building block for emerging quantum technologies, e.g., a quantum computer or quantum repeater, remains a formidable technological challenge. In this review, the authors present a holistic view on such an architecture, including the relevant components, their characterization, and their impact on the overall system performance. The authors present a hardware architecture based on a uniform linear segmented multilayer trap, controlled by a custom-made fast multichannel arbitrary waveform generator. The latter allows for conducting a set of different ion shuttling operations at sufficient speed and quality. The authors describe the relevant parameters and performance specifications for microstructured ion traps, waveform generators, and additional circuitry, along with suitable measurement schemes to verify the system performance. Furthermore, a set of different basic shuttling operations for a dynamic qubit register reconfiguration is described and characterized in detail.
Quantum Solver of Contracted Eigenvalue Equations for Scalable Molecular Simulations on Quantum Computing Devices

The accurate computation of ground and excited states of many-fermion quantum systems is one of the most consequential, contemporary challenges in the physical and computational sciences whose solution stands to benefit significantly from the advent of quantum computing devices. Existing methodologies using phase estimation or variational algorithms have potential drawbacks such as deep circuits requiring substantial error correction or non-trivial high-dimensional classical optimization. Here we introduce a quantum solver of contracted eigenvalue equations, the quantum analogue of classical methods for the energies and reduced density matrices of ground and excited states. The solver does not require deep circuits or difficult classical optimization and achieves an exponential speed-up of the exact classical algorithms. We demonstrate the algorithm though computations on both a quantum simulator and two IBM quantum processing units.
Higher Education’s Microcredentialing Craze: a Postdigital-Deweyan Critique

As the value of a university degree plummets, the popularity of the digital microcredential has soared. Similar to recent calls for the early adoption of Blockchain technology, the so-called ‘microcredentialing craze’ could be no more than a fad, marketing hype, or another case of ‘learning innovation theater.’ Alternatively, the introduction of these compact skills- and competency-based online certificate programs might augur the arrival of a legitimate successor to the four-year university diploma. The thesis of this article is that the craze for microcredentialing reflects (1) administrative urgency to unbundle higher education curricula and degree programs for greater efficiency and profitability and (2) a renascent movement among industry and higher education leaders to reorient the university curriculum towards vocational training.
A Relation-Specific Attention Network for Joint Entity and Relation Extraction

Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may have overlapping entities. Most existing methods perform entity recognition followed by relation detection between every possible entity pairs, which usually suffers from numerous redundant operations. In this paper, we propose a relation-specific attention network (RSAN) to handle the issue. Our RSAN utilizes relation-aware attention mechanism to construct specific sentence representations for each relation, and then performs sequence labeling to extract its corresponding head and tail entities. Experiments on two public datasets show that our model can effectively extract overlapping triplets and achieve state-of-the-art performance.
Demoting Racial Bias in Hate Speech Detection

In current hate speech datasets, there exists a high correlation between annotators' perceptions of toxicity and signals of African American English (AAE).This bias in annotated training data and the tendency of machine learning models to amplify it cause AAE text to often be mislabeled as abusive/offensive/hate speech with a high false positive rate by current hate speech classifiers.In this paper, we use adversarial training to mitigate this bias, introducing a hate speech classifier that learns to detect toxic sentences while demoting confounds corresponding to AAE texts.Experimental results on a hate speech dataset and an AAE dataset suggest that our method is able to substantially reduce the false positive rate for AAE text while only minimally affecting the performance of hate speech classification.
Deep Learning with Gaussian Differential Privacy

Deep learning models are often trained on datasets that contain sensitive information such as individuals' shopping transactions, personal contacts, and medical records. An increasingly important line of work therefore has sought to train neural networks subject to privacy constraints that are specified by differential privacy or its divergence-based relaxations. These privacy definitions, however, have weaknesses in handling certain important primitives (composition and subsampling), thereby giving loose or complicated privacy analyses of training neural networks. In this paper, we consider a recently proposed privacy definition termed f-differential privacy [18] for a refined privacy analysis of training neural networks. Leveraging the appealing properties of f-differential privacy in handling composition and subsampling, this paper derives analytically tractable expressions for the privacy guarantees of both stochastic gradient descent and Adam used in training deep neural networks, without the need of developing sophisticated techniques as [3] did. Our results demonstrate that the f-differential privacy framework allows for a new privacy analysis that improves on the prior analysis [3], which in turn suggests tuning certain parameters of neural networks for a better prediction accuracy without violating the privacy budget. These theoretically derived improvements are confirmed by our experiments in a range of tasks in image classification, text classification, and recommender systems. Python code to calculate the privacy cost for these experiments is publicly available in the TensorFlow Privacy library.
Become Competent within One Day in Generating Boxplots and Violin Plots for a Novice without Prior R Experience

The boxplot is a powerful visualization tool of sampled continuous data sets because of its rich information delivered, compact size, and effective visual expression. The advantage of boxplots is not widely appreciated. Many top journals suggest that boxplots should be used in place of bar charts, but have been wrongly replaced by bar charts. One technical barrier to the usage of boxplots in reporting quantitative data is that bench scientists are not competent in generating boxplots, and are afraid of R, a programming tool. This tutorial provides an effective training material in that even a novice without prior R experience can become competent, within one day, in generating professional boxplots. The available R scripts for boxplots are very limited in scope and are aimed at specialists, and the bench scientists have difficulty in following these scripts. This tutorial provides extensive step-by-step R scripts and instructions, as well as 29 illustrations for customizing every detail of the boxplot structures. Basic R commands and concepts are introduced for users without prior R experiences, which can be skipped by audiences with R knowledge. Violin plots are the enhanced version of boxplots, and therefore, this tutorial also provides a brief introduction and usage of the R package vioplot with one additional illustration. While the protocol is prepared for the newbies and trainees it will be a handy tool for infrequent users, and may benefit the experienced users as well since it provides scripts for customizing every detail of boxplots.
Intel Quantum Simulator: a cloud-ready high-performance simulator of quantum circuits

Classical simulation of quantum computers will continue to play an essential role in the progress of quantum information science, both for numerical studies of quantum algorithms and for modeling noise and errors. Here we introduce the latest release of Intel Quantum Simulator (IQS), formerly known as qHiPSTER. The high-performance computing (HPC) capability of the software allows users to leverage the available hardware resources provided by supercomputers, as well as available public cloud computing infrastructure. To take advantage of the latter platform, together with the distributed simulation of each separate quantum state, IQS allows to subdivide the computational resources to simulate a pool of related circuits in parallel. We highlight the technical implementation of the distributed algorithm and details about the new pool functionality. We also include some basic benchmarks (up to 42 qubits) and performance results obtained using HPC infrastructure. Finally, we use IQS to emulate a scenario in which many quantum devices are running in parallel to implement the quantum approximate optimization algorithm, using particle swarm optimization as the classical subroutine. The results demonstrate that the hyperparameters of this classical optimization algorithm depends on the total number of quantum circuit simulations one has the bandwidth to perform. Intel Quantum Simulator has been released open-source with permissive licensing and is designed to simulate a large number of qubits, to emulate multiple quantum devices running in parallel, and/or to study the effects of decoherence and other hardware errors on calculation results.
Blockchain Technology: Emerging Applications and Use Cases for Secure and Trustworthy Smart Systems

Blockchain, also known as a distributed ledger technology, stores different transactions/operations in a chain of blocks in a distributed manner without needing a trusted third-party. Blockchain is proven to be immutable, which helps with integrity and accountability, and, to some extent, confidentiality through a pair of public and private keys. Blockchain has been in the spotlight after successful boom of the Bitcoin. There have been efforts to leverage salient features of Blockchain for different applications and use cases. This paper presents a comprehensive survey of applications and use cases of Blockchain technology for making smart systems secure and trustworthy. Specifically, readers of this paper can have thorough understanding of applications and use cases of Blockchain technology.
Increasing communication capacity via superposition of order

Classically, no information can be transmitted through a depolarising, that is a completely noisy, channel. We show that by combining a depolarising channel with another channel in an indefinite causal order---that is, when there is superposition of the order that these two channels were applied---it becomes possible to transmit significant information. We consider two limiting cases. When both channels are fully-depolarising, the ideal limit is communication of 0.049 bits; experimentally we achieve $(3.4{\pm}0.2){\times}10^{-2}$ bits. When one channel is fully-depolarising, and the other is a known unitary, the ideal limit is communication of 1 bit. We experimentally achieve 0.64${\pm}$0.02 bits. Our results offer intriguing possibilities for future communication strategies beyond conventional quantum Shannon theory.
GANplifying event samples

A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample. We show for a simple example with increasing dimensionality how generative networks indeed amplify the training statistics. We quantify their impact through an amplification factor or equivalent numbers of sampled events.
Machine Learning into Metaheuristics

During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this article, we will investigate different opportunities for using ML into metaheuristics. We define uniformly the various ways synergies that might be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem and low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic that need further in-depth investigations.
Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing, tracking, and forecasting (STF), for a class of targets subject to smooth motion processes, e.g., the target moves with nearly constant acceleration or affected by insignificant noises. Fundamentally different from the conventional Markov transition formulation, the state process is modeled by a continuous trajectory function of time (FoT) and the STF problem is formulated as an online data fitting problem with the goal of finding the trajectory FoT that best fits the observations in a sliding time-window. Then, the state of the target, whether the past (namely, smoothing), the current (filtering) or the near-future (forecasting), can be inferred from the FoT. Our framework releases stringent statistical modeling of the target motion in real time, and is applicable to a broad range of real world targets of significance such as passenger aircraft and ships which move on scheduled, (segmented) smooth paths but little statistical knowledge is given about their real time movement and even about the sensors. In addition, the proposed STF framework inherits the advantages of data fitting for accommodating arbitrary sensor revisit time, target maneuvering and missed detection. The proposed method is compared with state of the art estimators in scenarios of either maneuvering or non-maneuvering target.
Black-Box Quantum State Preparation without Arithmetic

Black-box quantum state preparation is an important subroutine in many quantum algorithms. The standard approach requires the quantum computer to do arithmetic, which is a key contributor to the complexity. Here we present a new algorithm that avoids arithmetic. We thereby reduce the number of gates by a factor of 286-374 over the best prior work for realistic precision; the improvement factor increases with the precision. As quantum state preparation is a crucial subroutine in many approaches to simulating physics on a quantum computer, our new method brings useful quantum simulation closer to reality.