Reviewerly/exHarmony huggingface dataset:
Unsupervised Semantic Mapping for Healthcare Data Storage Schema

Data, information, and knowledge processing systems, in the domain of healthcare, are currently plagued by heterogeneity at various levels. Current solutions have focused on developing a standard-based, manual intervention mechanism, which requires a large number of human resources and necessitates the realignment of existing systems. State-of-the-art methodologies in the field of natural language processing and machine learning can help to partially automate this process, reducing the resource requirements and providing a relatively good multi-class-based classification algorithm. We present a novel methodology for bridging the gap between various healthcare data management solutions by leveraging the strength of transformer-based machine learning models, to create mappings between the data elements. Additionally, the annotated data, collected against five medical schemas and labeled by four annotators is made available for helping future researchers. Our results indicate, that for biased, dependent multi-class text classification, transformer-based models provide better results than linguistic and other classical models. In particular, the Robustly Optimized BERT Pretraining Approach (RoBERTa) provides the best schema matching performance by achieving a Cohen's kappa score of 0.47 and Matthews Correlation Coefficient (MCC) score of 0.48, with human-annotated data.
Visual-guided attentive attributes embedding for zero-shot learning

Zero-shot learning (ZSL) aims to learn a classifier for unseen classes by exploiting both training data from seen classes and external knowledge. In many visual tasks such as image classification, a set of high-level attributes that describe the semantic properties of classes are used as the external knowledge to bridge seen and unseen classes. While the attributes are usually treated equally by previous ZSL studies, we observe that the contribution of different attributes varies significantly over model training. To adaptively exploit the discriminative information embedded in different attributes, we propose a novel encoder–decoder framework with attention mechanism on the attribute level for zero-shot learning. Specifically, by mapping the visual features into a semantic space, the more discriminative attributes are emphasized with larger attention weights. Further, the attentive attributes and the class prototypes are simultaneously decoded to the visual space so that the hubness problem can be eased. Finally, the labels are predicted in the visual space. Extensive experiments on multiple benchmark datasets demonstrate that our proposed model achieves a significant boost over several state-of-the-art methods for ZSL task and comparative results for generalized ZSL task.
Optical direct write of Dolan–Niemeyer-bridge junctions for transmon qubits

We characterize highly coherent transmon qubits fabricated with a direct-write photolithography system. Multi-layer evaporation and oxidation allow us to change the critical current density by reducing the effective tunneling area and increasing the barrier thickness. Surface treatments before resist application and again before evaporation result in high-coherence devices. With optimized surface treatments, we achieve energy relaxation T1 times in excess of 80 μs for three dimensional transmon qubits with Josephson junction lithographic areas of 2 μm2.
Developing a Low-Order Statistical Feature Set Based on Received Samples for Signal Classification in Wireless Sensor Networks and Edge Devices

Classifying fluctuating operating wireless environments can be crucial for successfully delivering authentic and confidential packets and for identifying legitimate signals. This study utilizes raw in-phase (I) and quadrature-phase (Q) samples, exclusively, to develop a low-order statistical feature set for wireless signal classification. Edge devices making decentralized decisions from I/Q sample analysis is beneficial. Implementing appropriate security and transmitting mechanisms, reducing retransmissions and increasing energy efficiency are examples. Wireless sensor networks (WSNs) and their Internet of Things (IoT) utilization emphasize the significance of this time series classification problem. Here, I/Q samples of typical WSN and industrial, scientific and medical band transmissions are collected in a live operating environment. Analog Pluto software-defined radios and Raspberry Pi devices are utilized to achieve a low-cost yet high-performance testbed. Features are extracted from Matlab-based statistical analysis of the I/Q samples across time, frequency (fast Fourier transform) and space (probability density function). Noise, ZigBee, continuous wave jamming, WiFi and Bluetooth signal data are examined. Supervised machine learning approaches, including support vector machines, Random Forest, XGBoost, k nearest neighbors and a deep neural network (DNN), evaluate the developed feature set. The optimal approach is determined as an XGBoost/SVM classifier. This classifier achieves similar accuracy and generalization results, on unseen data, to the DNN, but for a fraction of time and computation requirements. Compared to existing approaches, this study’s principal contribution is the developed low-order feature set that achieves signal classification without prior network knowledge or channel assumptions and is validated in a real-world wireless operating environment. The feature set can extend the development of resource-constrained edge devices as it is widely deployable due to only requiring received I/Q samples and these features are warranted as IoT devices become widely used in various modern applications.
Meta‐heuristic optimization algorithm for predicting software defects

Abstract Software engineering companies strive to improve software quality by predicting software defects‐prone modules. Although various data mining methods have been developed, unstable accuracy rates are still critical issues owing to the imbalanced nature and high dimensionality of software defect datasets. To deal with this issue, we propose a spotted hyena, a novel meta‐heuristic optimization algorithm for predicting software defects. Support and confidence in classification rules are the basis of a multi‐objective fitness function that assists the spotted hyena algorithm in serving as a classifier by finding the fittest classification or standard rules among individuals. Experiments were conducted on four NASA software datasets, JM1, KC2, KC1, and PC3. The spotted hyena classifier provides an accuracy of 85.2, 84, 89.6, and 81.8%, respectively, for these datasets. These accuracy rates are better than those achieved using other popular data mining techniques. We also discuss other classification measures in connection with the experimental results, such as precision, recall, and confusion matrices, in connection with the experimental results. Moreover, the Gaussian mixture model is used to study the uncertainty quantification of the proposed classifier. The study proved the feasible performance of the spotted hyena classifier in four different case studies.
Improvement of JPEG XL Lossy Image Coding Using Region Adaptive DCT Block Partitioning Structure

The Joint Picture Expert Group (JPEG) committee has been standardizing next-generation image compression, called JPEG XL, to meet the specific needs for a responsive web, wide color gamut, and high dynamic range. JPEG XL supports lossy and lossless compression. A variable-sized discrete cosine transform (DCT) block is used for lossy compression. A block partitioning method is regarded as a critical function for the performance of JPEG XL. The current DCT block partitioning method used in JPEG XL is highly dependent on the compression rate and tends to assign small-sized DCT blocks to homogeneously textured regions (HTRs) having similar or regular patterns. We propose a region-adaptive DCT block partitioning method that assigns larger blocks to the HTR. The proposed method identifies the HTRs by using a combined metric employing a sum-modified Laplacian, zero-crossing, and colorfulness metric for measuring the region homogeneity. Objective, subjective, and visual comparison evaluations with the ten images recommended by the JPEG working group were provided to show the improvement in coding performance. The proposed method shows its superiority in terms of the compression efficiency evaluated using six objective metrics, subjective tests with 15 participants, visual comparison improvements in the HTR, and gains in the execution time.
Improving Evolutionary Algorithms by Enhancing an Approximative Fitness Function through Prediction Intervals

Evolutionary algorithms are a successful application of bio-inspired behaviour in the field of Artificial Intelligence. Transferring mechanisms such as selection, mutation, and recombination, evolutionary algorithms are capable of surmounting the disadvantages of traditional methods. Adjusting an evolutionary algorithm to a specific problem requires both, a good understanding of the problem and deep knowledge of the effects of choosing one or another operator in the algorithm. This becomes an especially difficult task when the fitness function is not analytically given - that is, exists only as an approximation, that is highly dependent on the present training data. We propose using prediction intervals to modify the fitness function such, that worse fitness values are less penalized if they occur in a poorly fitted area. We evaluate this with an example from material sciences as well as four standard benchmark algorithms for evolutionary algorithms using a Support Vector Regression for training the approximative fitness function and find that our approach outperforms the naive approximative function.
Detecting Facial Region and Landmarks at Once via Deep Network

For accurate and fast detection of facial landmarks, we propose a new facial landmark detection method. Previous facial landmark detection models generally perform a face detection step before landmark detection. This greatly affects landmark detection performance depending on which face detection model is used. Therefore, we propose a model that can simultaneously detect a face region and a landmark without performing the face detection step before landmark detection. The proposed single-shot detection model is based on the framework of YOLOv3, a one-stage object detection method, and the loss function and structure are altered to learn faces and landmarks at the same time. In addition, EfficientNet-B0 was utilized as the backbone network to increase processing speed and accuracy. The learned database used 300W-LP with 64 facial landmarks. The average normalized error of the proposed model was 2.32 pixels. The processing time per frame was about 15 milliseconds, and the average precision of face detection was about 99%. As a result of the evaluation, it was confirmed that the single-shot detection model has better performance and speed than the previous methods. In addition, as a result of using the COFW database, which has 29 landmarks instead of 64 to verify the proposed method, the average normalization error was 2.56 pixels, which was also confirmed to show promising performance.
Bias correction for linear discriminant analysis

Linear discriminant analysis (LDA) is perhaps one of the most fundamental statistical pattern recognition techniques. In this work, we explicitly present, for the first time, an asymptotically exact estimator of the LDA optimal intercept in terms of achieving the lowest overall risk in the classification of two multivariate Gaussian distributions with a common covariance matrix and arbitrary misclassification costs. The proposed estimator of the optimal bias term is developed based on the theory of random matrices of increasing dimension in which the observation dimension and the sample size tend to infinity while keeping their magnitudes comparable. The simple form of this estimator provides us with some analytical insights into the working mechanism of the bias correction in LDA. We then complement these analytical insights with numerical experiments. In particular, empirical results using real data show that insofar as the overall risk is concerned, the proposed bias-corrected form of LDA can outperform the conventional LDA classifier in a wide range of misclassification costs. At the same time, the superiority of the proposed form over LDA tends to be more evident as dimensionality or the ratio between class-specific costs increase.
One-visibility cops and robber on trees

The one-visibility cops and robber game is a variation of the classic cops and robber game, where one-visibility means that the information of the robber is known to all cops only when the distance between the robber and at least one cop is at most one. In this paper, we give a lower bound on the one-visibility copnumber of general trees. We present strategies to clear trees according to their structures. We propose a linear-time algorithm for computing the one-visibility copnumber of trees.
Can Accurate Future Bandwidth Prediction Improve Volumetric Video Streaming Experience?

Recently, the advancements in technologies have enabled volumetric media techniques to capture, encode, decode, and render videos in six degree-of-freedom (6DoF) in order to make the objects highly immersive, interactive, and expressive within the scene. This is enabled by using multiple cameras around the object(s). However, streaming 6DoF videos require a huge bandwidth and computational processing. As the end-user focuses on viewport-scenes, a large portion of the consumed bandwidth is mainly introduced due to unseen video-scenes. To fill this gap, it is imperative to predict the future head-movement (future viewport) of end-user in order to avoid the waste of network bandwidth and reduce computational processing power. In this paper, we propose a holistic architecture for the future viewport prediction using deep-neural-network (DNN)-based model. Specifically, our solution uses residual long-short-term-memory (RLSTM) architecture for accurate future viewport prediction. We confirm the effectiveness of our solution through trace-driven streaming experiments using a popular public dataset over four categories of DNN models: linear, dense, convolutional, and long-short-term-memory (LSTM). Experimental results show that our solution is able to achieve the lowest possible mean absolute error of ~ 0.01 compared to its competitor.
Measurement of the positions of light sensors on a mobile frame being tracked with a lighthouse localization system

The laser lighthouse is a nearly 40 year old concept that only recently had a practical incarnation in the HTC Vive VR kit base station, a single beacon that sweeps a light sheet at a fixed frequency like a lighthouse. It can be placed on a flat surface in any position from where it has a line of sight to a mobile frame equipped with light sensors and some computing power to record and process time stamps at the passage of the light sheet. From the recorded timestamp and the positions of the sensors in the mobile frame, the position of the frame in the (global) coordinate frame of the lighthouse can be established. Surface applicable light sensors have been announced for this purpose, that can turn any object into a trackable object with the laser lighthouse system. The accuracy of the positions of the sensors in the mobile frame is cardinal to the accuracy of the calculated position of the mobile frame. This presents a challenge as the positions of sensors on the surface of an object are not easy to obtain in a 3D coordinate frame. Even if they are available in a CAD design, manufacturing tolerances might cause the positions of the sensors to deviate from their designed positions. In this first paper on this subject, a method is proposed to measure the position of the sensors in the mobile frame using (hand-) measurement of the distance between a small number of designated features of the mobile frame only. The positions of the light sensors in the mobile frame are derived from the timestamps recorded by the light sensors in a procedure that subjects the mobile frame to constrained rotations. The efficacy of the proposed method is tested and validated experimentally. The sensor positions obtained with the method improve the accuracy of the localization of the mobile frame.
Designing an Educational Chatbot: A Case Study of CikguAIBot

This research aims to design a chatbot application to teach Artificial Intelligent (AI) in Malay language. CikguAIBot offers learners the possibility to learn and interact with an agent without the need for a teacher to be around. The development of CikguAIBot is based on the RAD model with the involvement of a number of experts and real users. The main focus of this paper is on the contents and flow design of the chatbot so that the objectives of the chatbot are achieved. Results from the expert review sessions were reported and a detailed evaluation strategy with the students is also included although the evaluation session is in the future plan. This research is expected to foster the usage of chatbot technology in supporting successful learning in Malaysia's education system.
Quantum-Inspired Estimation Of Distribution Algorithm To Solve The Travelling Salesman Problem

A novel Quantum-Inspired Estimation of Distribution Algorithm (QIEDA) is proposed to solve the Travelling Salesman Problem (TSP). The QIEDA uses a modified version of the W state quantum circuits to sample new solutions during the algorithm runtime. The algorithm behaviour is compared with other state-of-the-art population-based algorithms. QIEDA convergence is faster than other algorithms, and the obtained solutions improve as the size of the problem increases. Moreover, we show that quantum noise enhances the search of an optimal solution. Because quantum computers differ from each other, partly due to the topology that distributes the qubits, the computational cost of executing the QIEDA in different topologies is analyzed and an ideal topology is proposed for the TSP solved with the QIEDA.
DevOps Team Structures: Characterization and Implications

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context</i> : DevOps can be defined as a cultural movement to improve and accelerate the delivery of business value by making the collaboration between development and operations effective. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i> : This paper aims to help practitioners and researchers to better understand the organizational structure and characteristics of teams adopting DevOps. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method</i> : We conducted an exploratory study by leveraging in-depth, semi-structured interviews with relevant stakeholders of 31 multinational software-intensive companies, together with industrial workshops and observations at organizations’ facilities that supported triangulation. We used Grounded Theory as qualitative research method to explore the structure and characteristics of teams, and statistical analysis to discover their implications in software delivery performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i> : We describe a taxonomy of team structures that shows emerging, stable and consolidated product teams that are classified according to six variables, such as collaboration frequency, product ownership sharing, and autonomy, among others, as well as their implications on software delivery performance. These teams are often supported by horizontal teams (DevOps platform teams, Centers of Excellence, and chapters) that provide them with platform technical capabilities, mentoring and evangelization, and even temporarily may facilitate human resources. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion</i> : This study aims to strengthen evidence and support practitioners in making better informed about organizational team structures by analyzing their main characteristics and implications in software delivery performance.
Benchmarking AI Inference: Where we are in 2020

AI is continuing to emerge as an important workload across enterprise and academia. Benchmarking is an essential tool to understand its computational requirements and to evaluate performance of different types of accelerators available for AI. However, benchmarking AI inference is complicated as one needs to balance between throughput, latency, and efficiency. Here we survey current state of the field and analyze MLPerf Inference results, which represent the most comprehensive inference performance data available. Additionally, we present our own experience in AI inference benchmarking along with lessons learned in the process. Finally, we offer suggestions for the future we would like to see in AI benchmarking from a point of view of a datacenter server vendor.
Enhancing the Learning of Interval Type-2 Fuzzy Classifiers with Knowledge Distillation

Fuzzy Logic Systems (FLSs), especially Interval Type-2 (IT2) ones, are proven to achieve good results in various tasks, including classification problems. However, IT2-FLSs suffer from the curse of dimensionality problem, just like its Type-1 (T1) counterparts, and also training complexity since IT2-FLS have a large number of learnable parameters when compared to T1-FLSs. Deep learning (DL) architectures on the other hand can handle large learnable parameter sets for good generalizability but have their disadvantages. In this study, we present DL based approach with knowledge distillation for IT2-FLSs which transfers the generalizability features of deep models into IT2-FLS and increases its learning performance significantly by eliminating the problems that may arise from large input sizes and high rule counts. We present in detail the proposed approach with parameterization tricks so that the training of IT2-FLS can be accomplished straightforwardly within the widely employed DL frameworks without violating the definitions of IT2-FSs. We present comparative analysis to show the benefits of the inclusion knowledge distillation in the learning of IT2-FLSs with respect to rule number and input dimension size.
Improve the Accuracy of Fall Detection Based on Artificial Intelligence Algorithm

This work presents a fall detection system based on artificial intelligence. The system incorporates miniature wearable devices for fall detection. Fall detection is achieved by integrating a three-axis gyroscope and a three-axis accelerometer. The system gathers the differential data collected by the gyroscope and accelerometer, applies artificial intelligence algorithms for model training and constructs an effective model for fall detection. To provide easy wearing and effective position detection, it is designed as a small device attached to the user’s waist. Experiment results have shown that the accuracy of the proposed fall detection model is up to 98%, demonstrating the effectiveness of the model in real-life fall detection.