Reviewerly/exHarmony huggingface dataset:
Better together: Extending JMP<sup>®</sup> with open‐source software

JMP is commercial software designed for interactive data analysis and exploration. JMP's high‐level, visual interface makes it an outstanding tool for teaching best practices, methods and model building techniques. JMP is also designed for extensibility, with features that allow the embedding of and deployment to open‐source packages and environments. In this paper, we will explore use cases that illustrate how JMP users can take advantage of the broad support and rapid pace of change of open‐source software while still benefiting from JMP's streamlined interface and robust capabilities.
Optical Polarization Rotation through Spin-Photon Entanglement in a Charged Quantum Dot-Micropillar System

We calculate the spin-induced optical polarisation rotation of a resonant ul-trashort pulse in intermediate and strongly-coupled dot-micropillar systems, showing good agreement with recent experiments and demonstrating onset of Rabi splitting of the cavity mode.
Personalized Retrieval in the Medical Domain: A NoSQL Solution Based on Ontology Building

Managing medical information in a Big Data context is a challenging task since searching for relevant information in a large volume of data needs advanced treatments. Medical data is a special type of data because it comes from different sources and in different formats and encapsulates medical knowledge. Personalized retrieval is necessary when it comes to medical data management. In fact, the patient’s medical record needs to be taken into account in order to offer relevant documents since it contains his/her medical history. The proposed approach offers an ontology building process based on the patient’s medical record. The built ontology is then used for personalized information retrieval as well as user similarity computation. The approach is composed of three layers: (1) Data layer, (2) Treatment layer and (3) Semantic layer and offers three treatments: (1) Ontology building, (2) Query reformulation and (3) User similarity computation. An application supporting all three layers has been implemented and it allowed an experimental evaluation of the proposal. The results show an improvement in the relevancy of returned medical documents.
SHE: An Interactive VR Short Film to Discover Who You Are

“SHE” is a VR interactive short film, and its story revolves around a boy who is struggling with gender identity and the difficulties he faces. This work uses multiple strategies for narrative guidance, such as audio, visual, light, eye contact, and interactive guidance, in a 360 environment to fully immerse viewers in the plot. We also built a haptic device to enhance the physical sensations of the VR experience. In sum, this work offers viewers the opportunity to explore and understand the indecision and moral conflicts experienced by the protagonist.
Information Exposure From Relational Background Knowledge on Social Media

While some users share large amounts of information, others share very little. However, even with limited amounts of sharing, users may still have high levels of exposure. Previous research has shown that for certain attributes like gender, adversaries can determine a target's hidden attribute value by taking a majority vote of its community or by finding others in the site population with similar profiles. However, for some attributes, these attacks fail because of the diversity of the attribute value in the community. In this paper, we present a new privacy attack - a relational background attack (RBA), where an adversary builds inference models for a hidden attribute of the target by using the target's relational background set. Doing this allows the adversary to build a "biased" model that captures the significant local features for inferring the hidden attribute. We empirically demonstrate the effectiveness of this attack on a special case of the relational background set (a local community) using a Twitter data set. We then consider the case when an adversary only has access to different subsets of the target's local community, and show that the attack can still be conducted effectively with certain approximations of the target's local community.
Realtime Customer Merchandise Engagement Detection and Customer Attribute Estimation with Edge Device

In this paper, a low-cost computer vision-based system has been developed which detects and monitors the interaction between customer and merchandise. Various computer vision technologies such as real-time object detection, object tracking and person identification have been used in this work. The proposed system has been divided into two subsystems. First, the customer and merchandise interaction detection system while second, the age-gender estimation server. The two subsystems establish their communication through TCP socket. Through extensive evaluation and experiments, our proposed lightweight and low-cost system successfully achieves high accuracy in detecting the interaction in real time.
Density-Attentive Head Detector for Crowd Counting

In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which significantly restricts its applications. Detection-based method counts each head with their accurate locations but works only in middle or low-level density scenes. In this paper, a joint learning method, named Density-attentive Head Detector (DAHD) is developed to overcome their respective shortcomings via a multi-task training procedure. Specifically, to guarantee the sensitivity of the detector to small or partially occluded heads, we carefully equip the detector with a density map which learned from regression module. Moreover, a novel Dilated Feature Pyramid Network (DFPN) is introduced to our method to enlarge the receptive field of convolutional kernel, bringing confirmative additional benefits to identify small heads. Experiments on the popular ShanghaiTech and Mall datasets confirm the improved performance of DAHD compared with the current detection-based approaches, and a comparable performance to regression-based approaches in term of counting.
Meta-learning Process Analytics for Adaptive Tutoring Systems

The effectiveness of the learning process depends to a large extent on the provision of an adequate instruction program. Individual approach to learners' needs posits the necessity to comply with the characteristics of their preferences and predispositions for learning, which ought to be considered in tutoring content delivery. Adapting the instruction material to a particular learner requires intelligent tutoring system (ITS). In turn, the adaptation mechanism itself is based on comprehensive data analytics derived from the learning environment and the learner's behaviour as well. The paper presents a new approach in which intelligent tutor can deliver a support on meta level of learning process through analytics and adaptability. Most commonly, the analytics and adaptation in ITS are targeted mainly at the instruction program, but since the individual approach assumes that each learner should be treated individually, we must consider replacing active role of a human teacher, by the functionality of the intelligent tutor. Therefore, the role of supervising the learning process depends on the user themselves. It means that the meta-learning process has to be the subject of analysis and adjustment. Users usually do not have enough knowledge about how to learn, and for that reason the task of monitoring learner's performance and adjusting all activities should be ensured by the system.
Research on modulation recognition of Communication Radar Based on spread spectrum technology

Abstract Aiming at the problem that the traditional radar signal recognition method needs to be completed under the noiseless condition, which leads to the low recognition accuracy of low SNR signal, the modulation recognition method of communication radar signal based on spread spectrum technology is studied. The phase difference and instantaneous frequency characteristics of communication radar signal are extracted after the spread spectrum processing of communication radar signal with direct sequence spread spectrum. The training sample is used to train PNN neural network, and the modulation recognition of communication radar signal is completed. Simulation experiments under different parameters show that the recognition method based on spread spectrum technology has an effective recognition rate higher than 90% for low SNR signal, which is superior to the traditional method and can be applied in practice.
Image Authentication based on Q-FMT via Blind Geometric Correction

In the proposed method, the quaternion Fourier Mellin transform (Q-FMT) is used in image hashing, due to its inherent properties of robustness against rotation, scaling, and translation. The input RGB color image is converted into a quaternion domain, which provides the spatial as well as chromatic information. The exemplar-based saliency detection approach and blind geometric distortion correction approach have been also used to identify the tampered regions, in the case of geometric distortions and tampering, that occur simultaneously. The experiment carried out on a large database and shows the proposed image hashing is robust to most digital operations and provides good discrimination. The receiver operating characteristic shows the efficacy of the proposed method.
Inverse-filter method to suppress pressure measurement distortion due to spatial averaging across a membrane hydrophone sensitive element

This paper reports an investigation of an inverse-filter method to correct for experimental underestimation of pressure due to spatial averaging across a hydrophone sensitive element. The spatial averaging filter (SAF) depends on hydrophone type (membrane, needle, or fiber-optic), hydrophone geometrical sensitive element diameter, transducer driving frequency, and transducer F number (ratio of focal length to diameter) [Wear, IEEE Trans UFFC 66, 318–339 (2019)]. The absolute difference between theoretical and experimental SAFs for 25 transducer / hydrophone pairs was 7% ± 3% (mean ± standard deviation). Empirical formulas based on SAFs allow for correction for hydrophone spatial averaging errors in peak compressional pressure (pc), peak rarefactional pressure (pr), pulse intensity integral (pii), mechanical index (MI), and spatial-peak-temporal-average intensity (Istpa). The formulas show, for example, that if a 3-MHz, F/2 transducer is driven to moderate nonlinear distortion and measured at the focal point with a 500-μm membrane hydrophone, then spatial averaging errors are approximately 16% (pc), 12% (pr and MI), and 24% (pii and Istpa). The formulas are based on circular transducers but also provide plausible upper bounds for spatial averaging errors for transducers with rectangular-transmit apertures, such as linear and phased arrays.
STAW: An Audio Watermarking in Short Time DCT for Copyright Protection

An audio watermarking scheme STAW in short time discrete cosine transform domain using spread spectrum method is proposed in this work. Here, we framed the audio signal in the time domain with overlapping segments which then transferred as DCT represented, according to binary watermark, we generated some pseudo-random float sequences as embedding message, which are orthonormal and energy uniform, since the DCT coefficients is not integer or binary. Additionally, in order to improve imperception an adaptive mechanism for embedding strength is proposed. The experimental results show that STAW provides high robustness against common audio attack as well as high imperceptibility.
Impediments in Mobile Forensics

In this digital era, the usage of mobile phones in daily life has become inextricable due to the facilities and the level of sophistication it offers. Proportionately, the crimes and offenses involving the mobile devices are growing in rapid speed. Whenever a crime occurs in a spot, the forensic team will arrive there to identify and locate the evidence of the criminals. If the crime involves digital equipment like computers and laptop, then digital forensic team will investigate and analyze the devices for digital evidence collection. These days, mobile phones have the capability to offer any kind of information and services digitally on top of the palm of the user. Anything is available on the hands with a single touch on the screen of the mobile devices. It also offers to the adversaries many digital services which are harmful to the societies. The fast-paced advancement in the digital front paves the way for many digital crimes. Hence, a new field, mobile forensics, emerges out to trace the evidence, but it faces many challenges due to the dynamic nature of the digital technologies.
Sleep disorder diagnosis expert system using certainty factor method

Abstract Sleep disorders is a disorder experienced by a person when the REM and NREM cycle don’t work normally. Prolonged sleep disorders can cause a variety of bad conditions such as depression, fatigue, reduce work, performance and endurance. It quickly offended till affect to our own safety. The latest study in 2018 conducted by Dr. Welly Sondakh, MPH in Jakarta revealed that there were 10% of people with insomnia from the Indonesian population or around 28 million people. The low public awareness of sleep disorders causes them to need alternative media, namely an expert system to diagnose sleep disorders. Expert system is a system that uses the knowledge of an expert to solve a problem in a particular field where the system is not limited by time and can be used by many people. The method used in this system is Certainty Factor as an inference engine to determine the results of the diagnosis based on the symptoms determined. The calculation of this method combines the MB (measure of increased belief) and MD (measure of increased disbelief) values obtained from experts. From 50 test data, 45 data were obtained so that the accuracy level reached 90%. This shows that the expert system of diagnosing sleep disorders is suitable for early diagnosis of sleep disorders.
Vulnerability in Information Technology and Computing- A Study in Technological Information Assurance

Information Assurance is the prime name for the security and privacy related affairs. It is responsible for the secure design, development and building of healthy sophisticated information systems. The technologies have become crucial for the development of content and information systems. Information Assurance is a new name in respect of Computing and IT Security; however, it has important significance as the area deals with both traditional and technological security related affairs. The IT Security primarily responsible for the computational secure systems whereas Information Assurance focuses not only on the design and development of secure systems but also policies, framework and regulations leading to secure information systems preparation. Among the technological space few common names are include vulnerabilities, virus, denial of services etc. Moreover, the vulnerabilities include the affairs of hardware, software, network, personal and physical site, organizational security systems etc. This paper talks about the basics of Information Assurance and allied affairs. Moreover, it talks about the vulnerabilities and affairs leading to computer access control, application security, authentication, authorization, aspects of data centric security, encryption, firewall etc. The paper also highlights the basic overview of the technologies and solution as well.
Structural and Tectonic Conditions as a Regional Geological Factor of Formation of the Environment in the European Part of Southern Russia

The role of geological conditions in the European part of Southern Russia was specified as a factorthat formed the Quaternary and Neogene complex of subaerial dispersion rocks. From the geoecological perspective, they can be considered as sources of man-made impact on the geological environment. An importantindicator of the ecological status of the territory is the pollution of rock and groundwater, which depends onphysical and chemical properties of dispersed ground rock, composing the soil strata. The level and nature ofsuch pollution depend on physical and chemical properties of dispersion rocks that were studied by means ofexperimental laboratory modeling using chemical, spectral, emission and atomic adsorption methods. Theadsorption properties of dispersed rocks lying at different depths were investigated. The dependence betweenthe number of plasticity and the number of heavy metals sorbed from experimental solutions was revealed bymodeling mass transfer processes in dispersed rocks.
Intelligent Measurement Modeling Using a Novel Multi-nonlinear Mapping Based Extreme Learning Machine Integrated with Partial Least Square Regression

Accurate intelligent measurement modeling plays a key role in complex process industries. However, establishing an accurate and robust measurement model tends to be more and more difficult because of the increasing complexity in terms of nonlinearity and collinearity of data. To solve this problem, a novel multi-nonlinear mapping based extreme learning machine integrated with partial least square regression is proposed in this paper. In the proposed model, two problems of nonlinearity and collinearity are effectively dealt with by using multi-nonlinear mapping and partial least square regression, respectively. For evaluating performance, empirical studies on a commonly used bench mark problem and a real-world application confirm that the presented method can obtain high accuracy and high stability performance for intelligent measurement.
Convolution Neural Networks Based Blind Quality Predictor

Abstract Image recognition focused on convolution neural networks (CNN) in various areas of computer vision and image processing has recently proved to deliver state of the art output. Nonetheless, applying deep CNN to an NR-IQA remains a long-standing challenge owing to crucial challenges, i.e. the absence of a testing framework. The CNN test has not yet been performed. In this paper suggested a NR-IQA system focused on CNN which can solve the problem effectively. The approach suggested the DIQA divides the NR-IQA into two stages: 1 analytical portion of the illusion, and 2 one subjective component of the visual system. In the first step, the CNN learns to forecast the objective error map and then in the second phase the algorithm is able to predict subjective values. In addition, we are introducing a reliability chart to supplement the inaccuracy of the objective error map in the homogenous area. To further improve the accuracy, two simple handmade features were used. Therefore, we suggest a way to show graphs of perception errors and examine what the deep CNN model has experienced. The DIQA received the most sophisticated precision on the different databases in the experiments.
RELATIONSHIP BETWEEN PHILOSOPHY AND COMPUTER SCIENCE

The article discusses the relationship between philosophy and computer science.
How to Design a School-Based Curriculum: Case of Bridge Rhyme

School-based curriculum refers to a diverse, selectable curriculum developed using the resources of local communities and schools through the scientific assessment of the needs of their students while ensuring the underlying quality of the national and local programs (Zhong, Cui, & Zhang, 2001). It coexists with the national and local curricula and is an indispensable part of the school curriculum system by providing the functions of adjustment, supplementation, and expansion (Wu, 2013). This chapter focuses on the design of a specific school-based curriculum. Taking the school-based course “Bridge Rhyme” from Maiyuqiao Primary School in Hangzhou, Zhejiang Province, as an example, we will examine the professional aspects of the course (the course syllabus and an overview of the implementation). Together, the four elements which are the course objectives, content, implementation and assessment answer the following questions: How do I want the students to turn out? What are the necessary materials and activities? How can I reach the goals? How do I know what they already know? In pondering these questions, we will learn how to design, implement, and assess a school-based curriculum, and also what distinguishes an excellent one from a mediocre one.
A Light-weight Convolutional Neural Network based Speech Recognition for Spoken Content Retrieval Task

Convolutional Neural Network has shown to achieve a state of the art performance in computer vision. They have also progressively become popular in speech recognition and other natural language processing tasks. In this study, we aim at designing a light-weight Convolutional Neural Network architecture for the under-resourced end-to-end speech recognition task. We present a carefully designed 1-dimensional Convolutional deep neural network architecture that could achieve reasonable accuracy to be cascaded with spoken content retrieval systems. We explored the usage of Convolutional Neural Networks with Connectionist Temporal Classification under resource-constrained conditions. The possibility of having an end-to-end system with the best decoding result keeping the network parameters and computational time minimum is also shown. The paper presents the results on the Amharic syllable-based end-to-end speech recognition system implementing the designed model. The architecture is trained and evaluated on ≈70 hours of Amharic read-speech, audiobooks, and multi-genre radio programs. On the development set, we report a character error rate of 12.60% and a syllable error rate of 27.28% without language-models integrated. Likewise, on the test set 18.38% character error rate and 27.71% syllable error rate is reached.
A Study on the Application of Hierarchical Corresponding Technique to TCM Chinese-English Electronic Dictionary

Under the background of Traditional Chinese Medicine (TCM) going out to the world, the transmission process of TCM has become faster and faster. Translation of TCM plays a vital role in the “going out” of TCM culture. An electronic (online) dictionary which can quickly and accurately query traditional Chinese medicine vocabulary or terms can provide a convenient tool for TCM translators. This paper focuses on the research and development of TCM Chinese-English electronic dictionaries with hierarchical correspondence technology in order to make contributions to the compilation of TCM electronic dictionary and the development of TCM translation. The application of hierarchical correspondence technology to entry process, the initial establishment of the lexicon has been completed, that is, the basic corpus has been established. And a specialized dictionary software that can be used to query TCM terms has been developed. The work to be done in the future is to perfect the dynamic corpus and combine it with Internet technology to upgrade it to the Internet (online dictionary).