Reviewerly/exHarmony huggingface dataset:
BraggNet: Complex Photonic Integrated Circuit Reconstruction Using Deep Learning

We propose a deep learning model to reconstruct physical designs of complex coupled photonic systems, such as waveguide Bragg gratings, from their spectral responses for inverse design and fabrication diagnosis. Traditional reconstructing algorithms demand considerable computing resources at every query. Conversely, machine learning algorithms use most of the computing resources during the training process and provide effortless and orders-of-magnitude faster analysis in response to queries. This approach is demonstrated using silicon photonic grating-assisted, contra-directional couplers consisting of thousands of Bragg periods. The contra-directional couplers are modeled as coupled cavities, for which a transfer matrix model is used to generate a synthetic dataset comprising a strategic design parameter space. The free-form, architecture-independent model allows to include any geometries to the design parameter space. Upon proper training, the model achieves 1.4% mean absolute percentage error on device reconstruction and thus proves suitable for inverse design applications. To further show its potential for assessment of fabricated devices, another dataset is generated to emulate the fabrication conditions of a nominal design hindered by fabrication imperfections. The model is shown to reconstruct devices from experimental measurements with greater than 600-fold improvement in speed compared to the classical layer-peeling algorithm. This proves promising for data-driven processes required by Industry 4.0.
An Efficient VM Live Migration Through VM I/O Requests Using Live Scheduling Scheme (LSS)

Virtual machine (VM) live migration gets essential to execution in the open cloud platform, in order to permit snappy as well as straightforward work deferment scheduling between erratically located hubs, focused on superior and vitality productive use of server framework resources. Regardless, VM Input/Output (I/O) can altogether influence the presentation of VM live storage capacity migration because of conflict aimed at the common disk transfer speed and framework resources. In light of this perception, this paper recommends the Live Scheduling Scheme (LSS) to progress instantaneously the VM execution and migration effectiveness. Live Scheduling Scheme (LSS) lightens the I/O unsettling influence in the middle of VM I/O requests and relocation I/O appeals, by organizing the movement grouping as indicated by the process example of VM I/O demands. Moreover, it advances the store the board productivity by abusing the semantic subtleties from the VMs and relocation threads. The outcomes acquired as of following put together analyses concerning a lightweight model execution of Live Scheduling Scheme (LSS) show that this framework is amazingly robust, enlightening both the VM productivity and migration effectiveness considerably contrasted with prevailing schemes.
A Gaze-Based Unobstructive Information Selection by Context-Aware Moving UI in Mixed Reality

Mixed reality (MR) technology has been attracting attention in the automobile industry and logistics industry for work training and remote work support for newcomers. However, work support using MR technology has the problem that displaying too much information obstructs the user's field of vision and rather interferes with the work. Therefore, it is necessary to detect the user's intention and provide only the information that the user wants. In addition, the system should be able to detect naturally from the user's behavior without interrupting the work, which the user explicitly selects. To solve these problems, in this paper, we use the user's gaze to determine whether to display content by estimating whether or not they are looking at information. Another problem with gaze-based research in MR work support is that it is difficult to determine which space you are looking at in a space where virtual space and real space intersect. In order to overcome this problem, in this research, we grasp the user's behavior from the movement of the user's gaze, and we suggest a way to determine make the user interface (UI) the movement of the gaze that is unlikely to occur during that behavior to see if the user is looking at the UI. As a result of the experiment, we were able to promote the movement of gaze that is different from the characteristics of the movement of gaze during work with the proposed UI movement.
Angular Quantization Online Hashing for Image Retrieval

Online hash method with fast search mechanism and compact index structure plays a pivotal role. The inner product between label data has become one of the important means to measure the similarity between existing data and new data streams in online hashing methods. However, due to its discrete attributes and semantic gap, it often leads to a large amount of information loss. In this article, we propose a novel method called Angular Quantization Online Hashing (AQOH) to focus on learning compact binary codes with the help of cosine distance. Specifically, we propose an online hashing method for angular quantization, by minimizing the quantization error between the cosine similarity calculated from the original data and the generated binary code between the existing data and the new data stream. Further, within this framework, two effective algorithms to complete the optimization of the objective function to be designed, including continuous and discrete methods, respectively. Extensive experiments on various benchmark databases for online retrieval verify that our method outperforms many state-of-the art learning to hash methods.
An effective feature subset selection approach based on Jeffries-Matusita distance for multiclass problems

Jeffries-Matusita (JM) distance, a transformation of the Bhattacharyya distance, is a widely used measure of the spectral separability distance between the two class density functions and is generally used as a class separability measure. It can be considered to have good potential to be used for evaluation of the effectiveness of a feature in discriminating two classes. The capability of JM distance as a ranking based feature selection technique for binary classification problems has been verified in some research works as well as in our earlier work. It was found by our simulation experiments with benchmark data sets that JM distance works equally well compared to other popular feature ranking methods based on mutual information, information gain or Relief. Extension of JM distance measure for feature ranking in multiclass problems has also been reported in the literature. But all of them are basically rank based approaches which deliver the ranking of the features and do not automatically produce the final optimal feature subset. In this work, a novel heuristic approach for finding out the optimum feature subset from JM distance based ranked feature lists for multiclass problems have been developed without explicitly using any specific search technique. The proposed approach integrates the extension of JM measure for multiclass problems and the selection of the final optimal feature subset in a unified process. The performance of the proposed algorithm has been evaluated by simulation experiments with benchmark data sets in comparison with two other previously developed multiclass JM distance measures (weighted average JM distance and another multiclass extension equivalent to Bhattacharyya bound) and some other popular filter based feature ranking algorithms. It is found that the proposed algorithm performs better in terms of classification accuracy, F-measure, AUC with a reduced set of features and computational cost.
The Virtual Transformational Leadership Development Experience: Creating a Classroom of the Future

In response to a new set of challenges facing universities today—developing the next generation of leaders while integrating existing and emerging technologies to transform learning spaces—a team at Penn State University conducted research that culminated in the Virtual Transformational Leadership Development (VTLD) Experience, a course that addresses these challenges in a virtual format. The research examined the contribution of the arts, in conjunction with immersive technologies, towards promoting student engagement. Student interviews indicated that the creation of high impact practices revolving around the arts ultimately generates a memorable and involved experience that more frequently includes topics of social relevancy such as racial equity. Furthermore, the pairing of these methods with relevant and emerging technologies, such as artificial intelligence (AI) and virtual reality (VR), effectively creates an educational experience that more personally suits a student’s needs in this type of transformational course.
Adversarial bandit gene expression programming for symbolic regression

Gene expression programming (GEP) is a commonly used approach in symbolic regression (SR). However, GEP often falls into a premature convergence and may only reach a local optimum. To solve the premature convergence problem, we propose a novel algorithm based on an adversarial bandit technique, named AB-GEP. AB-GEP segments the mathematical space into many subspaces. It leverages a new selection method, AvgExp3, to enhance the population jump between segmented subspaces while maintaining the population diversity. AvgExp3 dynamically estimates a subspace by rewards generated from AB-GEP without any assumption about the distribution of subspace rewards, making AB-GEP choose the appropriate subspace that contains the correct results. The experimental evaluation shows that the proposed AB-GEP method can maintain the population diversity and obtain better results than canonical GEPs.
Polar Coordinates View on KM-Arcs

We study presentations of KM-arcs in polar coordinates. New characterizations on the point set of KM-arcs are obtained in terms of power sums and bilinear forms. Therefore, we provide purely algebraic criteria for the existence of KM-arcs. We also describe some constructions of KM-arcs which include known examples and describe their automorphism groups in this presentation.
Human pose estimation based on Improved High Resolution Network

Abstract Human pose estimation has become a hot problem in human-computer interaction and other intelligent application technologies, but the results obtained by previous deep learning networks are not very satisfactory when facing small-scale human instances. Therefore, in order to solve the problem of scale variation in human pose estimation, especially to pinpoint the keypoints of small-scale human instances, an improved High-Resolution Network (Improved HRNet) is proposed in this paper. The main improvement work in this paper is as follows: In this paper, a double attention mechanism is added in the forward transmission of the parallel sub-network, with the aim of assigning weights to the propagated information without changing the number of channels, assigning the information with high weights as useful information and reducing the interference caused by irrelevant information. In this paper, the network structure is validated using COCO dataset, and the average accuracy (Average Precision, AP) of Improved HRNet is 66.4, which is 2.3 higher than the average accuracy of High-Resolution Network (HRNet) with only 0.4% increase in parameters.
Real-time Detection of the More is Less Performance Anti-Pattern in MySQL Databases

This paper presents an approach for real-time detection of the More is Less software performance anti-pattern in MySQL databases. This project uses dynamic analysis paired with machine learning techniques to train a binary classifier on a collection of system-level metrics, database metrics, and MySQL status variables. After training, the classifier is loaded, at runtime, into the dynamic analysis code where the model can classify unlabeled data in real-time. The results of our approach show that the binary classifier can predict the More is Less software performance anti-pattern with 99.1% sensitivity.
A development cycle for automated self-exploration of robot behaviors

Abstract In this paper we introduce Q-Rock, a development cycle for the automated self-exploration and qualification of robot behaviors. With Q-Rock , we suggest a novel, integrative approach to automate robot development processes. Q-Rock combines several machine learning and reasoning techniques to deal with the increasing complexity in the design of robotic systems. The Q-Rock development cycle consists of three complementary processes: (1) automated exploration of capabilities that a given robotic hardware provides, (2) classification and semantic annotation of these capabilities to generate more complex behaviors, and (3) mapping between application requirements and available behaviors. These processes are based on a graph-based representation of a robot’s structure, including hardware and software components. A central, scalable knowledge base enables collaboration of robot designers including mechanical, electrical and systems engineers, software developers and machine learning experts. In this paper we formalize Q-Rock ’s integrative development cycle and highlight its benefits with a proof-of-concept implementation and a use case demonstration.
Data Augmented Network for Facial Landmark Detection

Facial landmark detection objects to locate some predefined points on human face images. Compared with previous method, the accuracy of facial landmark detection has a great improvement. However, there are still many problems need to be solved in the field of facial landmark detection, such as the impact of environment (extreme pose, occlusion conditions and blur) on detection accuracy and the balance between model size with accuracy. In order to solve the impact of these problems, we introduce an approach for this task through data augmentation and cascade regression. By using AugNet, based on image disentangled representations and generative adversarial networks, we could map a human face image into content space and style space. Then rebuild a set of images that the locations of facial landmarks could be controlled. In this paper, we suppose that facial landmarks, edges and other semantic information in human face are charged by content space, whereas textures and colors mainly refer to style space. At the same time, we propose a multi-stage cascaded facial landmark detection, known as GLNet, to tradeoff between the model size and accuracy. GLNet localizes facial landmarks with coarse-to-fine search. GLNet would be optimized with augmented dataset that generated by AugNet. Besides, we conduct several experiments in benchmark datasets 300W, WFLW and AFLW. Extensive experimental results demonstrate that our approach outperforms competing methods. Specifically, our proposed facial landmark detector with tiny model size and high precision is suitable for real-time applications.
Type Theory with Opposite Types: A Paraconsistent Type Theory

Abstract A version of intuitionistic type theory is extended with opposite types, allowing a different formalization of negation and obtaining a paraconsistent type theory ($\textsf{PTT} $). The rules for opposite types in $\textsf{PTT} $ are based on the rules of the so-called constructible falsity. A propositions-as-types correspondence between the many-sorted paraconsistent logic $\textsf{PL}_\textsf{S} $ (a many-sorted extension of López-Escobar’s refutability calculus presented in natural deduction format) and $\textsf{PTT} $ is proven. Moreover, a translation of $\textsf{PTT} $ into intuitionistic type theory is presented and some properties of $\textsf{PTT} $ are discussed.
CHART-ADAPT: Enabling actionable analytics at the critical care unit bedside

The increasing amount of complex patient data stored and collected in critical care units, alongside future use of genomic and proteomic data at the patient's bedside, means that state-of-the-art data analysis techniques have the potential to revolutionise personalised care. However, technical, organisational and practical challenges remain; tools and technologies are required to enable the bedside analysis of this big data and translate the often computationally complex results into meaningful clinical impact at the point of care. This work describes a technological framework, CHART-ADAPT, implemented in a real-world critical care setting, to stream patient data to a high performance computing platform capable of the live deployment of sophisticated algorithms and models and presenting results at the patient bedside in real-time. Evaluation consisted of a prospective observational study carried out in a single-centre, adult Neurointensive Care Unit on 831 patients. Further, CHART-ADAPT was used to answer two important Neurointensive Care clinical research questions. Over the period of a year, live patient data was processed and results of models successfully reintegrated with existing monitoring equipment. Median timings of data flow between components ranged from 0.04 to 341 seconds and model computation was 40 seconds. No adverse outcomes were noted on either the hospital network, or the Neurointensive Care service. Implementation challenges were overcome and previously unavailable models and techniques can now be used at the patient bedside. CHART-ADAPT enables actionable analytics in clinically meaningful timescales and significantly improves clinicians' ability to develop, and gain access to, state-of-the-art models.
MEC-based QoE Optimization for Adaptive Video Streaming via Satellite Backhaul

Recently, satellite communication has been introduced into terrestrial cellular networks as the backhaul link, which makes it feasible to offer cellular services in some rural and remote areas without mobile cellular network coverage. However, due to the limited bandwidth and expensive price of satellite communication, it is not economical to provide high-resolution video streaming service via satellite backhaul. In this paper, we present a Super-Resolution (SR)-based adaptive video streaming scheme in wireless network with satellite backhaul, which utilizes Mobile Edge Computing (MEC) server's computational capacity and SR algorithm to transmit high-quality video while saving satellite backhaul's bandwidth. Firstly, in order to eliminate SR algorithm's critical reliance on client devices' computational power, we introduce MEC server to tackle computationally expensive SR tasks. Specifically, we employ a multi-scale SR algorithm that can enlarge low-resolution video chunks with 3 scale factors (x2, x3, or x4) (i.e., zoom in the height and width of a video chunk to 2, 3, or 4 times). Next, in order to reduce the extra delay brought by the SR reconstruction process, a pipelined processing mechanism is proposed. Finally, considering the variation of network condition in Radio Access Network (RAN) and the heterogeneous computational capacity of MEC servers, we design a Reinforcement Learning (RL)-based adaption algorithm to dynamically choose a proper SR reconstruction scale factor. Performance evaluation over real-world network traces shows that the proposed scheme outperforms baseline schemes by 13%-63% in terms of Quality of Experience (QoE) under different computational capacities of MEC server.
Resolving synchronization conflicts in role-based multimodel-synchronization environments

The ability to collaboratively edit data in distributed environments is essential in our information-based industry. Typically users or systems concurrently access and modify data from different locations for different purposes. Each purpose might require its own representation and subset of the shared data (i.e., a model), for the editor to be productive. Consequently, a multi-model system results, which requires multi-directional synchronization. Although some approaches exist to realize such systems, none of these supports concurrent modifications. To overcome this limitation, we extend previous work on role-oriented model synchronization with a novel conflict detection and resolution approach. Role-oriented programming has been shown to be an adequate paradigm to realize multi-model systems, as it offers separation of concerns at the level of object collaborations and allows to express context-dependent behavior. We evaluate our approach using an employee data management case study and assess the introduced performance overhead.
An Adaptive Correlation Clustering-Based Recommender System for the Long-Tail Items

This is the study of long-tail problem of a recommender systems. The long tail has few ratings and is therefore difficult to use in recommendation systems. The approach presented in this paper separates the items according to their popularities. The head items are recommended based on their popularities. Long-tail items are correlation clustered according to their similarities. This method is applied on a subset of real-life dataset, and accuracy of rating prediction is studied. The diversity of recommendations is also ensured by including popular items from head part and also niche items from the long-tail part.
3D-Guided Face Manipulation of 2D Images for the Prediction of Post-Operative Outcome After Cranio-Maxillofacial Surgery

Cranio-maxillofacial surgery often alters the aesthetics of the face which can be a heavy burden for patients to decide whether or not to undergo surgery. Today, physicians can predict the post-operative face using surgery planning tools to support the patient's decision-making. While these planning tools allow a simulation of the post-operative face, the facial texture must usually be captured by another 3D texture scan and subsequently mapped on the simulated face. This approach often results in face predictions that do not appear realistic or lively looking and are therefore ill-suited to guide the patient's decision-making. Instead, we propose a method using a generative adversarial network to modify a facial image according to a 3D soft-tissue estimation of the post-operative face. To circumvent the lack of available data pairs between pre- and post-operative measurements we propose a semi-supervised training strategy using cycle losses that only requires paired open-source data of images and 3D surfaces of the face's shape. After training on "in-the-wild" images we show that our model can realistically manipulate local regions of a face in a 2D image based on a modified 3D shape. We then test our model on four clinical examples where we predict the post-operative face according to a 3D soft-tissue prediction of surgery outcome, which was simulated by a surgery planning tool. As a result, we aim to demonstrate the potential of our approach to predict realistic post-operative images of faces without the need of paired clinical data, physical models, or 3D texture scans.