Reviewerly/exHarmony huggingface dataset:
An STFT-LSTM System for P-Wave Identification

The identification of P-waves in data with substantial noises is essential in seismic phase recognition, which is the foundation of seismic localization and an essential part of automatic seismic monitoring and processing. We treat the identification of P-waves as a sequence classification problem and build a long short-term memory (LSTM) network to model the difference between P-waves and noises. LSTM is designed to process time series data and has the ability to model the connections among short and long time steps; such temporal changes are characteristic of P-waves. In addition to temporal changes, spectral characteristics also distinguish between P-waves and noises. To leverage this difference, we introduce the short-time Fourier transform (STFT) at the front-end of the network to retain the implicit information, as well as to reduce the burden of the network. The results show that a deep neural network with STFT fails to function when the P-waves are not strong, but in the proposed STFT-LSTM system, the average recall for the P-wave detection is 90%, and the precision is approximately 75%. These results suggest that the proposed STFT-LSTM system is capable of distinguishing P-waves from background noises.
Incivility Detection in Online Comments

Incivility in public discourse has been a major concern in recent times as it can affect the quality and tenacity of the discourse negatively.In this paper, we present neural models that can learn to detect name-calling and vulgarity from a newspaper comment section.We show that in contrast to prior work on detecting toxic language, fine-grained incivilities like namecalling cannot be accurately detected by simple models like logistic regression.We apply the models trained on the newspaper comments data to detect uncivil comments in a Russian troll dataset, and find that despite the change of domain, the model makes accurate predictions.
Research Trends in In-service Science Teacher Professional Development from 2012 to 2016

This paper presents a content analytic approach to research related to in-service science teachers' professional development.The research under consideration was published in four international science education journals and three international conference proceedings specialized in science education from 2012 through 2016.A total of 204 articles (44 published in journals and 160 published in conference proceedings) were examined to identify the fields of concentration within continuous professional development (CPD) programmes, which are; focus of professional development, Types of Professional development, research purposes, research tools, Types of the data, the context of the in-service CPD programmes.The result indicated that there is increasing emphasis on pedagogical content knowledge (PCK), inquiry, problem-solving and thinking.Most of the journals and conferences provide CPD as in-school and long-term training programmes subject to systematic evaluation.
Current Trends and Advances in Image Quality Assessment

Image quality assessment (IQA) is one of the constantly active areas of research in computer vision. Starting from the idea of Universal Image Quality Index (UIQI), followed by well-known Structural Similarity (SSIM) and its numerous extensions and modifications, through Feature Similarity (FSIM) towards combined metrics using the multi-metric fusion approach, the development of image quality assessment is still in progress. Nevertheless, regardless of new databases and the potential use of deep learning methods, some challenges remain still up to date. Some of the IQA metrics can also be used efficiently for alternative purposes, such as texture similarity estimation, quality evaluation of 3D images and 3D printed surfaces as well as video quality assessment.
Development and Comparison of Customized Voice-Assistant Systems for Independent Living Older Adults

Voice-controlled in-home personal assistants have a great potential to assist older adults. This paper explores the aspects of a human-computer interface design, specifically a voice assistant, to help older adults manage their personal health, especially in the case of chronic health conditions. In our previous work, we have built a web interface for caregivers to monitor older adults’ health changes based on in-home sensor data from motion sensors, bed sensors and depth sensors. Data collected from these sensors are stored in servers and processed using several algorithms to obtain health and activity parameters including gait, motion patterns, sleep, heart rate, and respiration rate, as well as to generate health alerts. The sensor system with automated health alerts and care coordination has been shown to help seniors maintain better functionality. In our current research project, we focus on developing a consumer interface for older adults and their family members that can provide health information on-demand, based on spoken queries. The health information is presented as both audio and visual displays and uses graphical visualizations and linguistic summaries of the sensor data trends and changes. The goal is to present data in a form that is simple to understand. To accomplish our objective of creating an easy-to-use-and-understand health data interface for older adults, we explore voice-controlled, in-home personal assistants as a solution. Two voice assistant platforms with displays have been selected for implementation and testing, namely, the Amazon Echo Show and the Lenovo Smart Display with Google Assistant.
Chinese Text Classification System on Regulatory Information Based on SVM

The rapid development of computer technology has brought a large amount of text information. This paper aims to classify the text in view of the increasing number of Chinese texts in the process of supervision and the increasing demand of processing Chinese texts, and to improve the efficiency of information query and management. In this paper, a Chinese text brake classification system based on support vector machine is designed and implemented. The text is represented mathematically by vector space model, and the classifier is trained to classify the text based on the principle of support vector machine. In this paper, the classification performance of the text system is tested and evaluated by using the file of regulatory information. The experimental results show that the classification accuracy of the text system is relatively high and has certain practicability. The system in this paper can improve the efficiency of information inquiry and management and make it possible to manage massive text information.
Application of Robotics in Higher Education in Industry 4.0 Era

As one of the most destructive technologies in the Industry 4.0 Era, robotics has been applied into higher educational practices.In this paper, we critically review the current situation of robotics application in higher education.Taking a quantitative research method, we survey both instructors and students to have a general view of how they perceive robotics in their teaching and learning process, respectively.We find no statistical differences by gender groups and country of origin for instructors and students.At last, we discuss obstacles hindering the development and wide popularization of robotics in high education institutions and put forward some tentative suggestions for the future spread of robotics in higher education.The findings of this paper would be of interest for educators, national policy makers and students.
Security testing of web applications

Web applications have become increasingly essential in many domains that operate on confidential data related to business. SQL injection attack is one of the most significant web application security risks. Detecting SQL injection vulnerabilities is essential for protecting the underlying web application. However, manually enumerating test cases is extremely challenging, if not impossible, given the potentially infinite number of user inputs and the likely nonexistence of one-to-one mapping between user inputs and malicious SQL statements. This paper proposes an automatic security test case generation approach to detect SQL injection vulnerabilities for web applications, following a search-based software engineering (SBSE) paradigm. Particularly, we propose a novel fitness function that evaluates the similarity between the SQL statements produced by feeding user inputs in the system under test and a known malicious SQL statement. For the search algorithm, we exploit differential evolution, which is robust in continuous optimization but it is under-investigated in SBSE. Based on three real-world web applications, we conduct experiments on 19 configurations that are of diverse forms of SQL statements and types of attacks. Results demonstrate that our approach is more effective, with statistical significance and high effect sizes, than the state-of-the-art.
High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks

Foreign object intrusion is a great threat to high-speed railway safety operations. Accurate foreign object intrusion detection is particularly important. As a result of the lack of intruding foreign object samples during the operational period, artificially generated ones will greatly benefit the development of the detection methods. In this paper, we propose a novel method to generate railway intruding object images based on an improved conditional deep convolutional generative adversarial network (C-DCGAN). It consists of a generator and multi-scale discriminators. Loss function is also improved so as to generate samples with a high quality and authenticity. The generator is extracted in order to generate foreign object images from input semantic labels. We synthesize the generated objects to the railway scene. To make the generated objects more similar to real objects, on scale in different positions of a railway scene, a scale estimation algorithm based on the gauge constant is proposed. The experimental results on the railway intruding object dataset show that the proposed C-DCGAN model outperforms several state-of-the-art methods and achieves a higher quality (the pixel-wise accuracy, mean intersection-over-union (mIoU), and mean average precision (mAP) are 80.46%, 0.65, and 0.69, respectively) and diversity (the Fréchet-Inception Distance (FID) score is 26.87) of generated samples. The mIoU of the real-generated pedestrian pairs reaches 0.85, and indicates a higher scale of accuracy for the generated intruding objects in the railway scene.
Estimation of manual wheelchair-based activities in the free-living environment using a neural network model with inertial body-worn sensors

Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU). Upper arm IMU data were collected as ten MWC users performed lab-based MWC-related ADLs. A neural network model was developed to classify data as non-propulsion activity, propulsion, or static, and validated for the lab-based data collection by video comparison. Six of the participants’ free-living IMU data were collected and the lab-based model was applied to estimate daily non-propulsion activity, propulsion, and static time. The neural network model yielded lab-based validity measures ≥0.87 for differentiating non-propulsion activity, propulsion, and static time. A quasi-validation of one participant’s field-based data yielded validity measures ≥0.66 for identifying propulsion. Participants’ estimated mean daily non-propulsion activity, propulsion, and static time ranged from 158 to 409, 13 to 25, and 367 to 609 min, respectively. The preliminary results suggest the model may be able to accurately identify MWC users’ field-based activities. The inclusion of field-based IMU data in the model could further improve field-based classification.
4D Light Field Superpixel and Segmentation

Superpixel segmentation of 2D images has been widely used in many computer vision tasks. Previous algorithms model the color, position, or higher spectral information for segmenting a 2D image. However, limited to the Gaussian imaging principle in a traditional camera, where each pixel is formed by summing lots of light rays from different angles, there is not a thorough segmentation solution to eliminate the ambiguity in defocus and occlusion boundary areas. In this paper, we consider the essential element of image pixel, i.e., rays in light space, and propose light field superpixel (LFSP) to eliminate the ambiguity. The LFSP is first defined mathematically and then two evaluation metrics, named LFSP self-similarity and effective label ratio, are proposed to evaluate the refocus-invariant and full-sliced properties of segmentation. By building a clique system containing 80 neighbors in light field, a robust refocus-invariant LFSP segmentation algorithm is developed. Experimental results on both synthetic and real light field datasets demonstrate the advantages over the current state of the art in terms of traditional evaluation metrics. Additionally, the LFSP self-similarity evaluations under different light field refocus levels show the refocus-invariance of the proposed algorithm. The full-sliced property of the proposed LFSP algorithm is verified by comparing it with the classical supervoxel algorithms. Finally, an LFSP-based application is demonstrated to show the effectiveness of LFSP in light field editing.
Blind Modulation Classification for Overlapped Co-Channel Signals Using Capsule Networks

Automatic modulation classification (AMC) for single signals has been widely studied in the past two decades. Meanwhile, the demand for multi-signal AMC is also increasing; however, single-signal AMC performs poorly and few methods were proposed for multi-signal AMC. To address this problem, a blind AMC method for overlapped co-channel signals applying capsule networks is proposed. Multiple transmitters transmit signals simultaneously and signals are received by a single-antenna receiver. The method handle signals at intermediate frequency directly, and is able to detect the number of signals automatically. Numerical results demonstrate the effectiveness of our proposed method, and excellent generalization ability provided by capsule network.
Time-frequency transforms of white noises and Gaussian analytic functions

A family of Gaussian analytic functions (GAFs) has recently been linked to the Gabor transform of Gaussian white noises [4]. This answered pioneering work by Flandrin [10], who observed that the zeros of the Gabor transform of white noise had a regular distribution and proposed filtering algorithms based on the zeros of a spectrogram. In this paper, we study in a systematic way the link between GAFs and a class of time-frequency transforms of Gaussian white noises. Our main observation is a correspondence between pairs (transform, GAF) and generating functions for classical orthogonal polynomials. This covers some classical time-frequency transforms, such as the Gabor transform and the Daubechies-Paul wavelet transform. It also unveils new windowed discrete Fourier transforms, which map white noises to fundamental GAFs. Moreover, we discuss subtleties in defining a white noise and its transform on infinite dimensional Hilbert spaces and its finite dimensional approximations.
Invisible Gold in Pyrite from Epithermal, Banded-Iron-Formation-Hosted, and Sedimentary Gold Deposits: Evidence of Hydrothermal Influence

“Invisible gold” in pyrite is defined as an Au solid solution of the pyrite lattice, sub-microscopic Au nanoparticles (NPs) in the pyrite, or other chemisorption complexes of Au. Because the relationship between the Au and As concentrations in pyrite could indicate the genesis of the deposit, the purpose of this study is to assess the micro-analytical characteristics of the Au–As relationship in pyrite from epithermal and hydrothermally affected sedimentary Au deposits by secondary ion mass spectrometry. The Au and As concentrations in pyrite vary from 0.04 to 30 ppm and from 1 to 1000 ppm, respectively, in the high-sulfidation Nansatsu-type epithermal deposits; these concentrations are both lower than those of the low-sulfidation epithermal Hishikari deposit. The Au concentrations in pyrrhotite and pyrite reach 6 and 0.3 ppm, respectively, in the Kalahari Goldridge banded-iron-formation-hosted gold deposit, and Au in pyrrhotite may sometimes exist as NPs, whereas As concentrations in pyrrhotite and pyrite are both low and lie in a narrow range from 6 to 22 ppm. Whether Au is present as NPs is important in ore dressing. The Au and As concentrations in pyrite from the Witwatersrand gold field range from 0.02 to 1.1 ppm and from 8 to 4000 ppm, respectively. The shape of the pyrite grains might prove to be an indicator of the hydrothermal influence on deposits of sedimentary origin, which implies the genesis of the deposits.
Can Home Use of Speech-Enabled Artificial Intelligence Mitigate Foreign Language Anxiety – Investigation of a Concept

The harmful effects of speech-related anxiety among learners and users of foreign language is well documented.Aiming to investigate the potential of reducing anxiety through ad libitum interaction with Artificial Intelligence chatbots, a controlled pilot study was conducted on East Asian staff of a large financial institution over a 4-week period, the sample of 40 divided between a test group using AI and the non-exposed subjects.Variety of measures were employed to gain as comprehensive an insight as warranted given the study limitations of a small sample, brief study period and relatively unsophisticated but freely available AI chatbot.The measures ranged from surveys of anxiety, attitudes toward daily chatbot usage, oral interviews and IELTS testing of English speaking ability.The results were cautiously encouraging: nearly universal endorsement of AI as a non-threatening interlocutor positively impacting subjects' confidence and to the extent limited by time constraint, enhancing individual components of the IELTS paradigm.Moreover, significant linear relationship between individual anxiety survey items and overall IELTS scores at baseline was observed, lending a measure of validity to the construct developed for the occasion.In summary, it is felt that subject to further development and refinement, conversationally enhanced AI chatbots hold a significant promise toward reducing speech-related anxieties and learning inhibitions of English as a foreign language and thus merit further investigation toward this objective.
How Good is Your Data? Investigating the Quality of Data Generated During Security Incident Response Investigations

An increasing number of cybersecurity incidents prompts organizations to explore alternative security solutions, such as threat intelligence programs.For such programs to succeed, data needs to be collected, validated, and recorded in relevant datastores.One potential source supplying these datastores is an organization's security incident response team.However, researchers have argued that these teams focus more on eradication and recovery and less on providing feedback to enhance organizational security.This prompts the idea that data collected during security incident investigations may be of insufficient quality for threat intelligence analysis.While previous discussions focus on data quality issues from threat intelligence sharing perspectives, minimal research examines the data generated during incident response investigations.This paper presents the results of a case study identifying data quality challenges in a Fortune 500 organization's incident response team.Furthermore, the paper provides the foundation for future research regarding data quality concerns in security incident response.
Decision Making Photonics: Solving Bandit Problems Using Photons

Decision making in dynamically changing uncertain environments is one of the most important elements in information and communications technology, in applications ranging from resource assignment in data centers to wireless communications and search functions. Here, we review our research on the efficient physical realization or acceleration of decision making using photonics. The problem of interest is the multi-armed bandit (MAB) problem, where the purpose is to maximize the total rewards in unknown environments that involve difficult tradeoffs in the so-called exploration-exploitation dilemma. We describe the principle of solving MAB problems by utilizing the wave-particle duality of single photons in which the probabilistic attributes of single light quanta are employed for exploration. The principle is transformed into ultrafast laser chaos, where the chaotically oscillating irregular time series provides fast and scalable decision making abilities. The problem becomes even more difficult when multiple players are involved, in the so-called competitive MAB (CMAB) problem, where the expected value is related to maximizing the social benefit and ensuring fairness among individuals. We demonstrate that entangled photons can resolve the CMAB problem. Theoretical studies on photonic decision making are also reviewed, showing that six entities in total interact with each other according to an octahedral structure.
Efficient Path Planning Method of USV for Intelligent Target Search

Abstract In order to accomplish a target search task safely and efficiently and make full use of prior information and real-time information, a path planning method of unmanned surface vehicle (USV) for intelligent target search is proposed. The overall strategy is divided into three parts: global path planning based on prior information, local path planning based on real-time information, and improved A* obstacle avoidance algorithm. Before the start of the task, the global path planning is carried out based on prior information such as the initial position of USV, the predicted position of the target and range of search area. After the start of the task, if USV finds suspicious targets, in order to further approach these suspicious targets, it will enter different local path planning modes according to the characteristics of these targets. During task execution, if obstacles are encountered, an improved A* obstacle avoidance algorithm is adopted. The simulation results show that the proposed method can improve the efficiency of target recognition and reduce the turning cost of USV when encountering obstacles. So, for USV intelligent target search, the proposed path planning method can save resources and improve search efficiency.
Object Detection and Classification from Thermal Images Using Region based Convolutional Neural Network

In recent years, object detection and classification has gained so much popularity in different application areas like face detection, selfdriving cars, pedestrian detection, security surveillance systems etc.The traditional detection methods like background subtraction, Gaussian Mixture Model (GMM), Support Vector Machine (SVM) have certain drawbacks like overlapping of objects, distortion due to smoke, fog, lightening conditions etc.In this paper, thermal images are used as thermal cameras capture the image by using the heat generated by the objects.Thermal camera images are not influenced by smoke and bad weather conditions which makes them a built-up apparatus in inquiry and safeguards or fire-fighting applications.These days, deep learning techniques are extensively used for detection and classification.In this paper, a comparative analysis has been done by applying Faster region based convolutional neural network on thermal images and visual spectrum images.The experimental results show that thermal camera images are better as compared to visible spectrum images.