Reviewerly/exHarmony huggingface dataset:
Analysis of a single-mode waveguide at sub-terahertz frequencies as a communication channel

We study experimentally the transmission of an electro-magnetic waveguide in the frequency range from 160 to 300 GHz. Photo-mixing is used to excite and detect the fundamental TE10 mode in a rectangular waveguide with two orders-of-magnitude lower impedance. The large impedance mismatch leads to a strong frequency dependence of the transmission, which we measure with a high-dynamic range of up to 80 dB, and with high frequency-resolution. The modified transmission function is directly related to the information rate of the waveguide, which we estimate to be about 1 bit per photon. We suggest that the results are applicable to a Josephson junction employed as a single-photon source and coupled to a superconducting waveguide to achieve a simple on-demand narrow-bandwidth free-space number-state channel.
Cross-Modal Feature Integration Network for Human Eye-Fixation Prediction in RGB-D Images

With the advent of convolutional neural networks, research progress in visual saliency prediction has been impressive. While integrating features at different stages from the backbone network is important, feature extraction itself is equally relevant. A network may lose representative information during feature extraction. We address the loss of spatial information and perform a fusion of features extracted from RGB and depth data for eye-fixation prediction. Specifically, we propose an asymmetric feature extraction network comprising an edge guidance module (EGM) and a feature integration module (FIM) that processes RGB-D images. Edge guidance supports the extraction of spatial information, while feature integration merges features from RGB images and the corresponding depth maps. We obtain the eye-fixation prediction maps by linearly fusing the features from the backbone network with those optimized using the two modules. Experimental results on NCTU and NUS, two benchmark datasets for RGB-D saliency prediction, verify the effectiveness and high-performance of the proposed network compared with similar methods.
Controller design by symbolic regression

A novel method of empirical controller design is introduced with the potential to produce exotic controller forms. The controllers in this method are derived by symbolic regression (SR) to be in equation form, hence, they are legible in how the controller output is computed as a function of loop variables. Because SR is computationally costly due to its extensive search of controller space, requiring evaluation of millions, if not billions, of candidate controllers, the candidate controllers cannot be evaluated in closed-loop due to the high cost of simulation associated with such evaluation. This paper offers a recourse to this closed-loop evaluation by allowing evaluations to be performed algebraically. To this end, a method of inverse solution is introduced that estimates the plant input for a desired plant output. This estimated plant input is then used as the target output for candidate controllers that can be readily evaluated algebraically based on the available time series of loop variables associated with the desired plant output. Unlike traditional control design which relies on closed-loop performance metrics to provide controller performance guarantees, the proposed open-loop approach sacrifices such guarantees in favor of new controller forms that it may yield. Therefore, the fidelity, as controllers, of candidate controllers need to be verified post-design. For this purpose, the candidate controllers are first evaluated as controllers in closed-loop simulation. Once verified by simulation, they need to be validated for closed-loop stability, as demonstrated for one of the studied cases.
Optimal sparse eigenspace and low-rank density matrix estimation for quantum systems

Quantum state tomography, which aims to estimate quantum states that are described by density matrices, plays an important role in quantum science and quantum technology. This paper examines the eigenspace estimation and the reconstruction of large low-rank density matrix based on Pauli measurements. Both ordinary principal component analysis (PCA) and iterative thresholding sparse PCA (ITSPCA) estimators of the eigenspace are studied, and their respective convergence rates are established. In particular, we show that the ITSPCA estimator is rate-optimal. We present the reconstruction of the large low-rank density matrix and obtain its optimal convergence rate by using the ITSPCA estimator. A numerical study is carried out to investigate the finite sample performance of the proposed estimators.
Efficiency of evolutionary search for analog filter synthesis

Design automation presents a trade off between: using expert knowledge to restrict the possible solutions examined; or spending time searching through many, possibly ineffective, solutions. Incorrect assumptions or misapplication of constraints can miss otherwise superior results. Automated search of possible solutions has been shown effective, limited by the available computational resources. This paper presents an evolutionary search method for finding optimized circuit topologies and component values. An example analog filter problem is exhaustively enumerated, to test the efficiency of different stochastic search methods in finding globally optimal solutions. Evolutionary methods are shown to be efficient for this problem. Impacts of varying parameters and techniques of evolutionary search are compared for a more complex asymmetric bandpass filter problem. Genetic Algorithm and Evolutionary Strategy methods are found to have similar performance. Hybrid evolutionary methods using Differential Evolution for component value optimization are found to be more efficient with limited: component count; or computational resources. The hybrid method used is shown to scale to more complex problems without changing parameters. Proposed comparison metrics, normalized for experimental variables, show the efficiency of this work improves upon published benchmarks. This is achieved without restricting the synthesized topologies to known structures, producing novel results more effective than prior works.
Sentiment Analysis of Big-Data in Healthcare: Issue and Challenges

For a long time, the vital part of data gathering behavior depends on the opinion of the other people has been solved. Due to the continuous growth of popularity and availability of opinions from the various social sites. These are personal blogs and online feedback/reviews sites when there is a lot of data generated by user's opinions is available freely; have important information that can be very helpful for any organization and consumer as well. Thus new challenges and opportunities are emerging and data scientists to absorb important information from a large amount of data. Now they are using different types of tools and technologies to made decisions? Thus data science helps us to make decisions very fast and in a better way. Data science is very pure science which gives us all detail in bulk. In this paper, we describe the different needs, issues and challenges, and solutions of the problem of big data (sentiments analysis) which possess the result of data science.
How Good Is This Merged Ontology?

With the growing popularity of semantics-aware integration solutions, various ontology merging approaches have been proposed. Determining the success of these developments heavily depends on suitable evaluation criteria. However, no comprehensive set of evaluation criteria on the merged ontology exists so far. We develop criteria to evaluate the merged ontology. These criteria cover structure, function and usability of the merged ontology by evaluating General Merge Requirements (GMR)s, analyzing the intended use and semantics, and considering the ontology and entity annotation, respectively. We demonstrate the applicability of our criteria by providing empirical tests.
An automatic reading method for pointer meter based on one-stage detector

Nowadays pointer meters are widely used in industries. In this paper, we propose a novel automatic reading method for pointer meters. Different from previous works that adopt two-stage detectors for detection, our approach integrates two components: a one-stage detector NeighborNet (Neighbor Network) and an angle reading module. NeighborNet is a simple but effective framework that can generate keypoint detection and meter detection with higher accuracy and less inference time. Specifically, we construct a neighborhood module, which significantly improves the accuracy of keypoint detection, thereby improving the accuracy of meter readings. The prediction generated by NeighborNet are then delivered into the angle reading module to obtain the final meter reading. Experiments validate the effectiveness of our automatic reading method and also prove that our method is robust to different types of meters.
Teaching Agile Software Development Using Agile Methods: A Case Study

Today, the dramatic impact of digital transformation on business, education and private life are becoming more apparent each day. Considering the effect of major environmental events such as the covid-19 pandemic in 2020, technology has become even more influential. In this context, the very rapid developments in technologies such as cloud computing, internet of things, machine learning, artificial intelligence and 5G and consequently the need for continuous upskilling of expert human resources have made the adaptation of traditional learning and teaching methods to this new human condition a vital issue. While the aforementioned phenomenon is being experienced, a talent gap is observed between the competencies of the graduates of Computer Science Engineering, Software Engineering, Industrial Engineering and other related departments and industry expectations as a result of the traditional education approaches of the universities. The shortening of the lifespan of the produced vocational knowledge day by day continues to increase this gap. For this reason, it has become more important than ever that higher education institutions have an approach that is more suitable for this dynamic environment. In this study, it has been examined whether the agile methods, which have become widespread within software industry in the last 15 years, have similar potential when used as a training method. During the study, 49 students from the engineering faculty of a Turkish University participated in the "Agile Software and Product Development" course. The curriculum design, learning experiences, student performance considerations and the other results have been shared. In this course, 3 Sprints are designed with 8 Agile Project Teams that are self-organized with the Scrum framework. Continuous team learning opportunities were evaluated and the difficulties encountered in applying "agility" approach to students while developing innovative ideas and the effect of agile method on the performance of learning were examined. The study is considered to provide useful information for universities, faculty members, agile coaches, students, and firms and professionals in the Information and Communication Technologies industry.
Cascaded RBF-CBiLSTM for Arabic Named Entity Recognition

In this paper, an Arabic dataset known as ANERCorp is classified into three name entities: Person, Location, and Organization. This classification process aims to design a model that used to facilitate the searching tasks of the Arabic named entities. The classification process was done by proposing a hybrid model which consists of Radial Basis Function (RBF) cascaded with a sequential Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (BiLSTM). The performance of the proposed model was compared with stand-alone machine learning models: Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), RBF, CNN, and BiLSTM. In addition, the performance of the hybrid model was compared with models from the literature. The attained results show that the proposed model outperforms the other stand-alone models and the models from the literature in term of precision, recall, and F1-score.
Performance Evaluation of Linux Operating Systems

Recently, Linux has undergone a significant progress and since it provides many helpful features to companies and home users, it has become one of the most commonly used operating systems in IT industry. Considering the popularity of available Linux distributions, three Linux desktop distributions were selected for further evaluation. The idea of this paper is to evaluate performance and compare three different Linux distributions and their influence on processor, memory, graphics system and disk drive performance. Measurements were performed with a three different benchmarking tools specialized for a specific computer component. All Linux operating system distributions were installed on the same desktop computer and based on the achieved performance measurement results it was concluded that the best results were achieved by using Pop!_OS 20.04. Linux distribution, which is very surprising because it is a relatively new distribution on the market.
The validation of a full-field deformation analysis of an aircraft panel: A case study

The increased use of optical measurement techniques in industrial environments has the potential to increase knowledge and creates an opportunity for a more comprehensive validation of computational predictions. In this paper, a quantitative validation methodology is applied to a 1 m × 1 m panel from an aircraft fuselage subject to compression and torsion, in order to evaluate the predicted response of the panel. A test matrix with four loading cases, namely pre-buckling and post-buckling compression with and without torsion, was used to demonstrate the capabilities of the validation methodology on the industrial component. The out-of-plane displacement fields were analysed with the aid of image decomposition and a validation process was successfully performed using a quantitative metric. The feature vectors, obtained through image decomposition, representing the surface curvature of the physical and virtual specimens were analysed to assess the similarity of the component’s overall curvature. Then, the feature vectors representing measured and predicted displacements for the four loading cases were used to analyse the deformed shapes and conduct a validation process for the simulation outcomes. The predictions of the deformation of the fuselage panel were found to have a high probability of representing the measured data.
Unsupervised Anomaly Detection Based on Data Augmentation and Mixing

When performing tasks using deep learning, the quantity and quality of data are important. However, in unsupervised anomaly detection, it is difficult to obtain a large amount of high-quality training data. Hence, data are typically extended via rotation, translation, and cutting. However, the typical data augmentation method may generate data far from the original image. To obtain various high-quality training data, we partially use AugMix, a data augmentation method that mixes the original image with images obtained via image processing, such as by rotating the original image. Using this data augmentation, new data can be generated while retaining the features of the original image as much as possible, and the diversity of training data can be enhanced, compared with performing the same image processing. Consequently, the effectiveness of the proposal method for improving the accuracy of unsupervised anomaly detection is confirmed.
A Hierarchical <i>K</i>-Means-Assisted Scenario-Aware Reconfigurable Convolutional Neural Network

The superiority of convolutional neural network (CNN) has been proven in various object recognition tasks and has received much attention. However, the modern CNN approaches usually that assume the testing data and the training data belong to an identical category. Therefore, the current CNN approaches are not efficient for some applications with multisource data (i.e., the input data come from different sources), such as remote sensing scene. To increase the adaptability of the involved CNN approach, we first propose a K-means-assisted scenario-aware reconfigurable convolutional neural network (KASR-CNN) mechanism. The KASR-CNN is composed of a fully convolutional autoencoder-based K-means clustering (FCA-KC) and a reconfigurable convolutional neural network (RCNN), which are used to perform the coarsegrained classification and fine-grained classification to the input data, respectively. Furthermore, a Lego-like architecture design methodology is proposed to reduce the design complexity and improve computing flexibility. To show the adaptability, the KASR-CNN mechanism has been applied to different CNN models. In addition, the KASR-CNN has been verified on the Xilinx Zynq-ZC706 field-programmable gate array (FPGA) and implemented with TSMC 40-nm technology. Compared with the conventional approaches, the proposed KASR-CNN can help the involved CNN model to improve 3.73%-36.65% classification accuracy with only 0.38%-0.74% area overhead.
Two Families of Entanglement-Assisted Quantum MDS Codes from Cyclic Codes

With entanglement-assisted (EA) formalism, arbitrary classical linear codes are allowed to transform into EAQECCs by using pre-shared entanglement between the sender and the receiver. In this paper, based on classical cyclic MDS codes by exploiting pre-shared maximally entangled states, we construct two families of q-ary entanglement-assisted quantum MDS codes $$ \left[\left[\frac{q^2+1}{a},\frac{q^2+1}{a}-2\left(d-1\right)+c,d;c\right]\right] $$ , where q is a prime power in the form of am + l, and a = (l2 + 1) or $$ a=\frac{\left({l}^2+1\right)}{5} $$ . We show that all of q-ary EAQMDS have minimum distance upper limit much larger than the known quantum MDS (QMDS) codes of the same length. Most of these q-ary EAQMDS codes are new in the sense that their parameters are not covered by the codes available in the literature.
Ultrasonic Biometric Authentication System with Contact Gesture Sensing

The Qualcomm Technologies® Fingerprint Sensor based on piezoelectric ultrasound technology has achieved great success in commercialization. Here, we presented the concept of contact gesture sensing with the Quacomm fingerprint sensor system. Based on levels of detected contact force, two different algorithms are developed to sense the gesture. With light touch and swipe, 2D FFT with radon transform followed by normalized cross-correlation is used to identify the rotation and translation. With heavy touch and swipe, the wavelet transform is used to estimate the localized deformation, and further to identify the shear-induced gesture based on fingerprint images.
EdgeBooster: Edge-Assisted Real-Time Image Segmentation for the Mobile Web in WoT

Combining image segmentation with Web technology lays a good foundation for lightweight, cross-platform, and pervasive Web artificial intelligence applications, and further improves the capability of Web-of-Things (WoT) applications. However, no matter whether we use a Web real-time communication media server for advanced processing that views camera inputs as a video stream, or transfer continuous camera frames to the remote cloud for processing, we are unable to obtain a satisfactory real-time experience due to high resource consumption and unacceptable latency. In this article, we present EdgeBooster, a computational-efficient architecture that leverages a common edge server to minimize the communication costs, accelerates the camera frame segmentation, and guarantees an acceptable segmentation accuracy with the prior knowledge. EdgeBooster provides real-time segmentation by developing parallel technology that enables segmentation on slices of a camera frame and using presegmentation based on superpixels to accelerate the graph-based segmentation. It also introduces recent DNN-based segmentation results as the prior knowledge to improve the performance of the graph-based segmentation, especially in nonideal scenes, such as dark light and weak contrast. Finally, it creates a pure frontend segmentation that can provide continuous and stable services for mobile users in unstable networks, such as a weak network or with an unstable edge server. The experimental results show that EdgeBooster is able to achieve a considerable accuracy for the mobile Web, running at no less than 30 frames per second in real scenes.
A Neural Embedding for Source Code: Security Analysis and CWE Lists

In this paper, we design a technique for mapping the source code into a vector space and we show its application in the recognition of security weaknesses. By applying ideas commonly used in Natural Language Processing, we train a model for producing an embedding of programs starting from their Abstract Syntax Trees. We then show how such embedding is able to infer clusters roughly separating different classes of software weaknesses. Even if the training of the embedding is unsupervised and made on a generic Java dataset, we show that the model can be used for supervised learning of specific classes of vulnerabilities, helping to capture some features distinguishing them in code. Finally, we discuss how our model performs over the different types of vulnerabilities categorized by the CWE initiative.
Threat Objects Detection in Airport using Machine Learning

Airports operate under high demands and strive to satisfy many diverse, interrelated performance goals. Airport areas, such as security and safety also refer to detecting hazardous materials inside passengers’ luggage. This topic is independent to the size of the airport and presents a high importance. The detection of threat objects using X-ray luggage scan images is a big part of aviation security. Currently most screenings are still based on the manual detection of potential threat objects, and rely heavily on the human experts. Artificial intelligence is often used to describe machines that mimic cognitive human functions such as learning and problem solving. At this moment, the most interesting branch of artificial intelligence is automatic learning, allowing computers to learn to solve problems on their own. Machine learning (ML) made a series of discoveries that once seemed impossible, such as: recognizing the human voice, search engines, filtering out unsolicited messages, recognizing human face, medical diagnostics, creating realistic photos of people (using a set of photos, the algorithm creates new faces, practically inventing them), analyzing the stock market, recognizing objects, etc. The paper presents the implementation of a threat objects recognition system using the convolutional neural network and specialized libraries in ML such as OpenCV and Keras with TensorFlow. The topics presented are the development of the convolutional neural network, used data augmentation techniques, tests and detection rate using X-ray images.
GitHub Label Embeddings

GitHub repository issues can be “tagged” with labels to provide better understanding, organization, classification and to make information retrieval easier for both users and project managers. GitHub provides nine default labels and allows users to create, edit, and delete labels to fit the project maintainers' management goals. Such labels can, for example, help users to find open source projects that are open for new collaborators since they are able to search for the default label good first issuein GitHub's search engine. However, such a mechanism would be more powerful if the platform knew semantically similar customized labels and also reaches projects with them. In this study, we investigate two NBNE-based approaches and another based on Word2Vec algorithm to represent labels as embeddings (i.e., as vectors on a multidimensional space), so that semantically similar labels get closer. As a result, we found that Word2Vec is better indicated for this task, although it actually deserves further investigation.