Reviewerly/exHarmony huggingface dataset:
Two-step feedback preparation of entanglement for qubit systems with time delay

Quantum entanglement plays a fundamental role in quantum computation and quantum communication. Feedback control has been widely used in stochastic quantum systems to generate given entangled states since it has good robustness, where the time required to compute filter states and conduct filter based control usually cannot be ignored in many practical applications. This paper designed two control strategies based on the Lyapunov method to prepare a class of entangled states for qubit systems with a constant delay time. The first one is bang bang like control strategy, which has a simple form with switching between a constant value and zero, the stability of which is proved. Another control strategy is switching Lyapunov control, where a constant delay time is introduced in the filter-based feedback control law to compensate for the computation time. Numerical results on a two qubit system illustrate the effectiveness of these two proposed control strategies.
Effect of Dropout layer on Classical Regression Problems

In the last decade, deep learning architectures have provided good accuracy as they become deeper and wider in addition to other theoretical improvements. However, despite their current success, they initially faced with overfitting issue that limits their usage. The first practical and usable solution to overfitting in deep neural networks is a simple approach known as the dropout. Dropout is a regularization approach that randomly drops connections from earlier layers during training of neural nets. Dropout is a widely used technique, especially in image classification, speech recognition and natural language processing tasks, where features created by earlier layers are mostly redundant. Usage of the dropout layer in other tasks is largely unexplored. In this study, we seek an answer to question if the dropout layer is also useful for classical regression problems. A 3 layer deep learning net with a single dropout layer with various dropout levels tested on 8 real regression datasets. According to the experiments, the dropout layer does not help over fitting.
MorphFace: A Hybrid Morphable Face for a Robopatient

Physicians use pain expressions shown in a patient's face to regulate their palpation methods during physical examination. Training to interpret patients’ facial expressions with different genders and ethnicities still remains a challenge, taking novices a long time to learn through experience. This letter presents MorphFace: a controllable 3D physical-virtual hybrid face to represent pain expressions of patients from different ethnicity-gender backgrounds. It is also an intermediate step to expose trainee physicians to the gender and ethnic diversity of patients. We extracted four principal components from the Chicago Face Database to design a four degrees of freedom (DoF) physical face controlled via tendons to span <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim\!85\%$</tex-math></inline-formula> of facial variations among gender and ethnicity. Details such as skin colour, skin texture, and facial expressions are synthesized by a virtual model and projected onto the 3D physical face via a front-mounted LED projector to obtain a hybrid controllable patient face simulator. A user study revealed that certain differences in ethnicity between the observer and the MorphFace lead to different perceived pain intensity for the same pain level rendered by the MorphFace. This highlights the value of having MorphFace as a controllable hybrid simulator to quantify perceptual differences during physician training.
Conceptualizing the use of the term financial risk by non-academics and academics using twitter messages and ScienceDirect paper abstracts

A text mining technique, based on an Application Programming Interface (API) request—using narrative data from Twitter™ and ScienceDirect™—was used to identify how non-academics and academics conceptualize and evaluate sentiment indicators associated with the term financial risk in their communications. It was determined that unlike the day-to-day uses of the term—all of which tend to focus predominately on the business and technology aspects of risk taking—the academic definition of the term is expressed broadly. It was also determined that the term was mainly associated with negative emotions in daily conversations, whereas the term tended to be used in a positive way in research paper abstracts. Results from this study suggest that the way financial risk is conceptualized and applied in real-life settings primarily represents negative emotional contexts, while academic papers tend to represent positive emotional contexts. Information presented in this paper can help educators, researchers, and policy makers better understand the way non-academics objectively and subjectively evaluate and describe financial risk. This information may help lead to better investor educational interventions and decision outcomes.
Discriminative shared transform learning for sketch to image matching

Sketch to digital image matching refers to the problem of matching a sketch image (often drawn by hand or created by a software) against a gallery of digital images (captured via an acquisition device such as a digital camera). Automated sketch to digital image matching has applicability in several day to day tasks such as similar object image retrieval, forensic sketch matching in law enforcement scenarios, or profile linking using caricature face images on social media. As opposed to the digital images, sketch images are generally edge-drawings containing limited (or no) textural or colour based information. Further, there is no single technique for sketch generation, which often results in varying artistic or software styles, along with the interpretation bias of the individual creating the sketch. Beyond the variations observed across the two domains (sketch and digital image), automated sketch to digital image matching is further marred by the challenge of limited training data and wide intra-class variability. In order to address the above problems, this research proposes a novel Discriminative Shared Transform Learning (DSTL) algorithm for sketch to digital image matching. DSTL learns a shared transform for data belonging to the two domains, while modeling the class variations, resulting in discriminative feature learning. Two models have been presented under the proposed DSTL algorithm: (i) Contractive Model (C-Model) and (ii) Divergent Model (D-Model), which have been formulated with different supervision constraints. Experimental analysis on seven datasets for three case studies of sketch to digital image matching demonstrate the efficacy of the proposed approach, highlighting the importance of each component, its input-agnostic behavior, and improved matching performance.
Lolisa: Formal Syntax and Semantics for a Subset of the Solidity Programming Language in Mathematical Tool Coq

This article presents the formal syntax and semantics for a large subset of the Solidity programming language developed for the Etheruem blockchain platform based on our resent work about developing a general, extensible, and reusable formal memory (GERM) framework and an extension of Curry-Howard isomorphism, denoted as execution-verification isomorphism (EVI). This subset is denoted as Lolisa, which, to our knowledge, is the first mechanized and validated formal syntax and semantics developed for Solidity. The formal syntax of Lolisa adopts a stronger static type system than Solidity for enhanced type safety. In addition, Lolisa not only includes nearly all the syntax components of Solidity, such as mapping, modifier, contract, and address types, but it also contains general-purpose programming language features, such as multiple return values, pointer arithmetic, struct, and field access. Therefore, the inherent compatibility of Lolisa allows Solidity programs to be directly translated into Lolisa with a line-by-line correspondence without rebuilding or abstracting, and, in addition, the inherent generality of Lolisa allows it to be extended to express other programming languages as well. To this end, we also present a preliminary scheme for extending Lolisa to other languages systematically.
Preparation and study of the entanglement of the Schrödinger cat state on the ibmq-melbourne quantum computer

We study the entanglement between a certain qubit and the remaining system in the Schr\"odinger cat state prepared on the ibmq-melbourne quantum computer. The protocol, which we use for this purpose, is based on the determination of the mean value of spin corresponding to a certain qubit. We explore the dependence of the entanglement on a parameter of the Schr\"odinger cat state which consists of different numbers of qubits. In addition, we explore the entanglement of each qubit with the remaining system in the maximum entangled Schr\"odinger cat state.
Regularization graph convolutional networks with data augmentation

Recently, there is a lot of progress in designing effective convolutional schemes on irregular graph structures. These graph convolutional networks mostly solve the semi-supervised learning problem on the graph where the number of labeled nodes typically is very small and the number of unlabeled nodes is very large. In the standard GCN, only labeled nodes will enter the loss function; unlabeled data will not get into the loss function. The model will be unstable due to the unconstrained nature of unlabeled nodes. This will lead to overfitting and negatively affect the generalization ability of the model. To solve this problem, we propose to regularize the graph convolutional networks using data augmentation. We provide the details of the regularization GCN and SGC model and design rich data augmentation schemes including individual features perturbation, individual edge addition, and combination of them. To verify the performance of our method, we conduct experiments on standard benchmarks and explore the intensity effect of different data augmentation strategies. The experimental results on the node classification tasks demonstrate that our method can outperform baseline methods.
Reconstruction regularized low-rank subspace learning for cross-modal retrieval

With the rapid increase of multi-modal data through the internet, cross-modal matching or retrieval has received much attention recently. It aims to use one type of data as query and retrieve results from the database of another type. For this task, the most popular approach is the latent subspace learning, which learns a shared subspace for multi-modal data, so that we can efficiently measure cross-modal similarity. Instead of adopting traditional regularization terms, we hope that the latent representation could recover the multi-modal information, which works as a reconstruction regularization term. Besides, we assume that different view features for samples of the same category share the same representation in the latent space. Since the number of classes is generally smaller than the number of samples and the feature dimension, therefore the latent feature matrix of training instances should be low-rank. We try to learn the optimal latent representation, and propose a reconstruction based term to recover original multi-modal data and a low-rank term to regularize the learning of subspace. Our method can deal with both supervised and unsupervised cross-modal retrieval tasks. For those situations where the semantic labels are not easy to obtain, our proposed method can also work very well. We propose an efficient algorithm to optimize our framework. To evaluate the performance of our method, we conduct extensive experiments on various datasets. The experimental results show that our proposed method is very efficient and outperforms the state-of-the-art subspace learning approaches.
Recent Trends in Big Data Ingestion Tools: A Study

In big data era, data is flooding in at unparalleled, inflexible rate making collection and processing of data a bit hard and unmanageable without using appropriate data handling tools. Selecting the correct tool to meet the current as well as future requirement is a heavy task, and it became more strenuous with lack of awareness of all the available tools of this area. With right tools, one can rapidly fetch, import, process, clean, filter, store, and export data from variety of sources with different frequency as well as capacity of data generation. A comprehensive survey and comparative study of performance, merits, demerits, usage of various ingestion tools in existence for frequent data ingestion activities (keeping volume, variety, velocity, and veracity in mind) have been presented in this paper.
A 500-Fps Pan-Tilt Tracking System With Deep-Learning-Based Object Detection

In this letter, we propose a fast mirror-drive pan-tilt target tracking system that can robustly track an object whose appearance varies in a complex background at 500 fps. By assuming a small image displacement between frames, which is a property of high-frame rate vision, we develop an fast object tracking algorithm by hybridizing the convolutional-neural-network (CNN) based object detection with template-matching (TM) based tracking operating at hundreds of frames per second (fps). For object tracking with high-speed visual feedback, the proposed tracking algorithm can remarkably reduce dozens-of-milliseconds-latency in the CNN-based object detection by simultaneously executing TM-based tracking for several images at consecutive frames within a few milliseconds. In the proposed pan-tilt tracking system, when the current tracked objects are occluded or out of the camera view, it can recognize objects to be newly tracked with CNN-based object detection at the rate of 33 fps with acceleration using graphic processing units (GPUs). Controlling the pan-tilt tracking system via visual feedback at 500 Hz, fast moving objects can be robustly tracked at the center of the camera view. The effectiveness of our method was experimentally demonstrated via several results when fast-moving pre-learned objects, such as toy cars were tracked in complex backgrounds.
Data Augmentation and Feature Selection for Automatic Model Recommendation in Computational Physics

Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the physical system. For such classification tasks, labeled training data come from numerical simulations and generally correspond to physical fields discretized on a mesh. Three challenging difficulties arise: the lack of training data, their high dimensionality, and the non-applicability of common data augmentation techniques to physics data. This article introduces two algorithms to address these issues: one for dimensionality reduction via feature selection, and one for data augmentation. These algorithms are combined with a wide variety of classifiers for their evaluation. When combined with a stacking ensemble made of six multilayer perceptrons and a ridge logistic regression, they enable reaching an accuracy of 90% on our classification problem for nonlinear structural mechanics.
A Human Error Based Approach to Understanding Programmer-Induced Software Vulnerabilities

Many security incidents can be traced back to software vulnerabilities, which can be described as security-related defects/bugs in the code that can potentially be exploited by the attackers to perform unauthorized actions. An analysis of vulnerability data disseminated by organizations such as NIST' s National Vulnerability (NVD) and SANS Institute shows that a majority of vulnerabilities can be traced back to a relatively small set of root causes mostly related to the repeated mistakes by the programmers. That is, programmers exhibit a pattern of erroneous coding practices or behavior which lead to vulnerable code. Cognitive Psychologists have long been studying these erroneous behavior patterns and have termed them as human cognition failures or simply, human errors. The primary goal of this paper is to propose a classification for the most frequently observed human errors committed by the programmers (the commitment of a human error can lead to injection of one or more security defects/bugs). Such a classification can be useful for software development organizations as they can train developers on the human errors so that developers can avoid committing the human errors themselves, thereby reducing the chances of vulnerability injection in their code.
Designing Information Security Governance Recommendations and Roadmap Using COBIT 2019 Framework and ISO 27001:2013 (Case Study Ditreskrimsus Polda XYZ)

The use of technology has applied in all areas of Polri's duties. However, the use of this technology does not yet have a level of capability in information security management. For this reason, it is necessary to design recommendations and an ideal information governance roadmap based on COBIT 2019 and ISO/IEC 27001: 2013 concerning Information Security Management Systems (ISMS). The design is carried out based on six stages in the Design Science Research Methodology (DSRM) in the form of identify problems and motivate, define objects of a solution, design and development, demonstration, evaluation, and communication. By mapping ISO/IEC 27001: 2013 into COBIT 2019, 29 domains of the 2019 COBIT core model selected which became the basis for designing and assessing the level of information security management capability at Ditreskrimsus Polda XYZ. The formulation of recommendations considered the assessment results. It produced the model of organizational structure, human resources, and policies and procedures that must be applied to Ditreskrimsus Polda XYZ in the form of a roadmap starting in 2021-2025 in managing information security. This research contributes to producing an information security governance design.
A Hybrid Virtual Cloud Learning Model During the COVID-19 Pandemic

The COVID-19 pandemic has affected the educational systems worldwide, leading to the near-total closures of schools, universities, and colleges. Universities need to adapt to changes to face this crisis without negatively affecting students' performance. Accordingly, the purpose of this study is to identify and help solve to critical challenges and factors that influence the e-learning system for Computer Maintenance courses during the COVID-19 pandemic. The paper examines the effect of a hybrid modeling approach that uses Cloud Computing Services (CCS) and Virtual Reality (VR) in a Virtual Cloud Learning Environment (VCLE) system. The VCLE system provides students with various utilities and educational services such as presentation slides/text, data sharing, assignments, quizzes/tests, and chatrooms. In addition, learning through VR enables the students to simulate physical presence, and they respond well to VR environments that are closer to reality as they feel that they are an integral part of the environment. Also, the research presents a rubric assessment that the students can use to reflect on the skills they used during the course. The research findings offer useful suggestions for enabling students to become acquainted with the proposed system's usage, especially during the COVID-19 pandemic, and for improving student achievement more than the traditional methods of learning.
Deep Template-based Object Instance Detection

Much of the focus in the object detection literature has been on the problem of identifying the bounding box of a particular class of object in an image. Yet, in contexts such as robotics and augmented reality, it is often necessary to find a specific object instance-a unique toy or a custom industrial part for example-rather than a generic object class. Here, applications can require a rapid shift from one object instance to another, thus requiring fast turnaround which affords little-to-no training time. What is more, gathering a dataset and training a model for every new object instance to be detected can be an expensive and time-consuming process. In this context, we propose a generic 2D object instance detection approach that uses example viewpoints of the target object at test time to retrieve its 2D location in RGB images, without requiring any additional training (i.e. fine-tuning) step. To this end, we present an end-to-end architecture that extracts global and local information of the object from its viewpoints. The global information is used to tune early filters in the backbone while local viewpoints are correlated with the input image. Our method offers an improvement of almost 30 mAP over the previous template matching methods on the challenging Occluded Linemod [3] dataset (overall mAP of 50.7). Our experiments also show that our single generic model (not trained on any of the test objects) yields detection results that are on par with approaches that are trained specifically on the target objects.
EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

Multimodal large-scale datasets for outdoor scenes are mostly designed for urban driving problems. The scenes are highly structured and semantically different from scenarios seen in nature-centered scenes such as gardens or parks. To promote machine learning methods for nature-oriented applications, such as agriculture and gardening, we propose the multimodal synthetic dataset for Enclosed garDEN scenes (EDEN). The dataset features more than 300K images captured from more than 100 garden models. Each image is annotated with various low/high-level vision modalities, including semantic segmentation, depth, surface normals, intrinsic colors, and optical flow. Experimental results on the state-of-the-art methods for semantic segmentation and monocular depth prediction, two important tasks in computer vision, show positive impact of pre-training deep networks on our dataset for unstructured natural scenes. The dataset and related materials will be available at https://lhoangan.github.io/eden.
Dispersion cancellation in a quantum interferometer with independent single photons

A key technique to perform a proper quantum information processing is to get a high visibility quantum interference between independent single photons. One of the crucial elements that affects the quantum interference is a group velocity dispersion that occurs when the single photons pass through a dispersive medium. We theoretically and experimentally demonstrate that an effect of group velocity dispersion on the two-photon interference can be cancelled if two independent single photons experience the same amount of pulse broadening. This dispersion cancellation effect can be generalized to a multi-path linear interferometer with multiple independent single photons. As multi-path quantum interferometers are at the heart of quantum communication, photonic quantum computing, and boson sampling applications, our work should find wide applicability in quantum information science.