Reviewerly/exHarmony huggingface dataset:
Deep Learning for Face Expressions Detection: Enhanced Recurrent Neural Network with Long Short Term Memory

In recent years, deep learning neural frameworks have been given significant attention in programming development, especially in machine learning, machine vision and artificial intelligence (AI). The ability to detect faces has inspired many researchers because a human face shows great dissimilarities in form and figure due to changes in position and expression in different situations. This research aims to produce a method of applying recurrent neural network (RNN) designs using long short-term memory (LSTM) to identify facial expressions. The proposed method involves an improved RNN that uses LSTM to increase the effectiveness of the feature extraction process using input sets which regenerate the input-data from the features. The accuracy and computing time of this technique were studied. With LSTM-RNNs, the results show that the design gives enhanced outcomes compared with other methods, including most image/video face detection methods. The efficiency evaluation of LSTM-RNNs in images and in video frame series shows that there are performance improvements of more than 5% compared with traditional neural networks.
A Synopsis of the WhatsApp-Based Instruction and ELT Pedagogy: Concept, Challenge and Strategy

Learning system is developing fast. The learning process has become easier than before. Technology has taken the place of an immensely important tool of the learning system. E-mails, Facebook, Instagram, Twitter, Imo, Line App and WhatsApp are some of the most important, common, social, immediate messaging tools. WhatsApp is generally used on the mobile phones. It is one of the most popular tool for immediate messaging, transferring ideas, knowledge and pictures for fun, and, of course, for learning. WhatsApp incorporates an ultimate solution for learners, teachers and teacher educators. It facilitates easier communication among the learners, teachers and teacher educators. It could be another venue for learners, teachers and teacher educators. Learning could be achieved faster through this channel. Technology has been used as a tool for instructions in many interesting ways. Students as well as instructors use mobile in various ways, and feel satisfied, feel to be there, feel to be with one another as well. Technology, in our mobile age, has been incorporated into almost all curricula. “Tandem learning may be found as more compatible than any other kind”. (Khan.2018, P-1) Hence, this paper is going to show how the WhatsApp could be a means of allocating as well as distributing educational resources and information to learners, teachers and teacher educators. Whether, it could be a strategy to enhance the learning process faster and easier. Whether, it could be an interesting experience for learners, teachers and teacher educators. Is it attainable? Is it time-friendly? I hope this research paper will accommodate the responses and will try to find a conclusion. WhatsApp is a mobile instant messaging App, which is used immensely for so many reasons. It is simple, popular and versatile. Therefore, the aim of the study is to discover the influence as well as the waves of the use of mobile, and especially, the ‘WhatsApp’ as a tool in boosting the process of ESL/EFL pedagogy.
Towards Universal Object Detection

Author(s): Cai, Zhaowei | Advisor(s): Vasconcelos, Nuno | Abstract: Object detection is one of the most important and challenging research topics in computer vision. It is playing an important role in our everyday life and has many applications, e.g. surveillance, autonomous driving, robotics, drone, medical imaging, etc. The ultimate goal of object detection is a universal object detector that can work very well in any case under any condition like human vision system. However, there are multiple challenges on the universality of object detection, e.g. scale-variance, high-quality requirement, domain shift, computational constraint, etc. These will prevent the object detector from being widely used for various scales of objects, critical applications requiring extremely accurate localization, scenarios with changing domain priors, and diverse hardware settings. To address these challenges, multiple solutions have been proposed in this thesis. These include an efficient multi-scale architecture to achieve scale-invariant detection, a robust multi-stage framework effective for high-quality requirement, a cross-domain solution to extend the universality over various domains, and a design of complexity-aware cascades and a novel low-precision network to enhance the universality under different computational constraints. All these efforts have substantially improved the universality of object detection, and the advanced object detector can be applied to broader environments.
Determination of Prospective Science Teachers’ knowledge levels about earthquake

Earthquake is the event that takes its source from the depths of the earth in its broadest sense and causes sudden vibrations that occur as a result of fractures within the earth's crust, causing waves to shake the earth's surface. In this study, it was aimed to determine the knowledge levels of prospective science teachers about earthquake. The sample of the study consists of 24 4th grade students studying in the Science Teaching Department of the Department of Mathematics and Science Education, Faculty of Education, Agri Ibrahim Cecen University. In this research, Earthquake Achievement Test taken from literature was used for prospective science teachers from literature. The obtained data were analyzed by descriptive analysis method. It was determined that the knowledge levels of prospective teachers were statistically significant, but the level of knowledge about what to do during an earthquake was insufficient.
Telescope: an interactive tool for managing large-scale analysis from mobile devices

Abstract Background In today's world of big data, computational analysis has become a key driver of biomedical research. High-performance computational facilities are capable of processing considerable volumes of data, yet often lack an easy-to-use interface to guide the user in supervising and adjusting bioinformatics analysis via a tablet or smartphone. Results To address this gap we proposed Telescope, a novel tool that interfaces with high-performance computational clusters to deliver an intuitive user interface for controlling and monitoring bioinformatics analyses in real-time. By leveraging last generation technology now ubiquitous to most researchers (such as smartphones), Telescope delivers a friendly user experience and manages conectivity and encryption under the hood. Conclusions Telescope helps to mitigate the digital divide between wet and computational laboratories in contemporary biology. By delivering convenience and ease of use through a user experience not relying on expertise with computational clusters, Telescope can help researchers close the feedback loop between bioinformatics and experimental work with minimal impact on the performance of computational tools. Telescope is freely available at https://github.com/Mangul-Lab-USC/telescope.
Parallax-Tolerant 360 Live Video Stitcher

This paper presents an open-source software implementation for real-time 360-degree video stitching. To ensure a seamless stitching result, cylindrical and content-preserving warping are implemented to dynamically correct image alignment and parallax, which may drift due to scene changes, moving objects, or camera movement. Depth variation, color changes, and lighting differences between adjacent frames are also smoothed out to improve visual quality of the panoramic video. The system is benchmarked with six 1080p videos, which are stitched into 4096×732 pixel output format. The proposed algorithm attains an output rate of 18 frames per second on GeForce GTX 1070 GPU and real-time speed can be met with a high-end GPU.
Free Heyting algebra endomorphisms: Ruitenburg’s Theorem and beyond

Abstract Ruitenburg’s Theorem says that every endomorphism f of a finitely generated free Heyting algebra is ultimately periodic if f fixes all the generators but one. More precisely, there is N ≥ 0 such that f N +2 = f N , thus the period equals 2. We give a semantic proof of this theorem, using duality techniques and bounded bisimulation ranks. By the same techniques, we tackle investigation of arbitrary endomorphisms of free algebras. We show that they are not, in general, ultimately periodic. Yet, when they are (e.g. in the case of locally finite subvarieties), the period can be explicitly bounded as function of the cardinality of the set of generators.
A low complexity Wyner-Ziv coding solution for Light Field image transmission and storage

Compressing Light Field (LF) imaging data is a challenging but very important task for both LF image transmission and storage applications. In this paper, we propose a novel coding solution for LF images using the well-known Wyner-Ziv (WZ) information theorem. First, the LF image is decomposed into a fourth-dimensional LF(4D-LF) data format. Using a spiral scanning procedure, a pseudo-sequence of 4D-LF is generated. This sequence is then compressed in a distributed coding manner as specified in the WZ theorem. Secondly, a novel adaptive frame skipping algorithm is introduced to further explore the high correlation between 4D-LF pseudo- sequences. Experimental results show that the proposed LF image compression solution is able to achieve a significant performance improvement with respect to the standard, notably around 54% bitrate saving when compared with the standard High Efficiency Video Coding (HEVC) Intra benchmark while requiring less computational complexity.
What is measured when a qubit measurement is performed on a multiqubit chip

We study how single-qubit dispersive readout works alongside two qubit coupling. To make calculations analytically tractable, we use a simplified model which retains core characteristics of but is discretised compared to dispersive homodyne detection. We show how measurement speed and power determine what information about the qubit(s) is accessed. Specifically we find the basis the measurement is closest to projecting onto. Compared to the basis gates are applied in, this measurement basis is modified by the presence of photons in the readout resonator.
Learning Resource Recommendation Based on Generalized Matrix Factorization and Long Short-Term Memory Model

Online learning is becoming increasingly popular in recent years. Personalized recommendation is particularly important for the development of online learning systems. Though LSTM model has been widely applied in various recommendations, it normally can't deal with the problem of sparse data. In this paper, we present a novel model for learning resource recommendation, named G-LSTM. Our model integrates the Generalized Matrix Factorization (GMF) with Long Short-Term Memory (LSTM) model. For evaluating our model, we prepare and analyze two datasets from Junyi Academy. Extensive experiments are conducted on the two datasets to verify the superiority of our model in both effectiveness and accuracy.
Transformative Computing in Knowledge Extraction and Service Management Processes

In this paper will be described a new methodology of service management processes. This issue will be classified by cognitive and transformative computing. One of the most important direction of proposed methods will be oriented for the Cloud services. New methodology based on signals from external sensors can be used at different levels of data analysis processes in distributed infrastructure. The impact of service management processes will be analysed and presented in research results.
Reasearch on comparison and analysis of the projector calibration

Projectors are widely used in high-precision industrial fields, and the accuracy of projector calibration is the key to achieve system accuracy. Therefore, it is necessary to perform high-precision calibration for the projector. This paper analyzes the pinhole mathematical model and classifies the calibration method of the projector: projector calibration method based on virtual mark points and projector calibration method based on phase-shifting technique. Then the principle and methods of each calibration method are summarized, the experiment is carried out and the calibration results are compared. Finally, the advantages and disadvantages of two calibration methods are analyzed, which has the certain guiding significance in the choice of projector calibration method.
Digit Recognition From Wrist Movements and Security Concerns with Smart Wrist Wearable IOT Devices

In this paper, we investigate a potential security vulnerability associated with wrist wearable devices.Hardware components on common wearable devices include an accelerometer and gyroscope, among other sensors.We demonstrate that an accelerometer and gyroscope can pick up enough unique wrist movement information to identify digits being written by a user.With a data set of 400 writing samples, of either the digit zero or the digit one, we constructed a machine learning model to correctly identify the digit being written based on the movements of the wrist.Our model's performance on an unseen test set resulted in an area under the receiver operating characteristic (AUROC) curve of 1.00.Loading our model onto our fabricated device resulted in 100% accuracy when predicting ten writing samples in real-time.The model's ability to correctly identify all digits via wrist movement and orientation changes raises security concerns.Our results imply that nefarious individuals may be able to gain sensitive digit based information such as social security, credit card, and medical record numbers from wrist wearable devices.
Universal Dependencies 2.5 Models for UDPipe (2019-12-06)

Tokenizer, POS Tagger, Lemmatizer and Parser models for 94 treebanks of 61 languages of Universal Depenencies 2.5 Treebanks, created solely using UD 2.5 data (http://hdl.handle.net/11234/1-3105). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_25_models .To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe .In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.
Search Space of Adversarial Perturbations against Image Filters

The superiority of deep learning performance is threatened by safety issues for itself.Recent findings have shown that deep learning systems are very weak to adversarial examples, an attack form that was altered by the attacker's intent to deceive the deep learning system.There are many proposed defensive methods to protect deep learning systems against adversarial examples.However, there is still lack of principal strategies to deceive those defensive methods.Any time a particular countermeasure is proposed, a new powerful adversarial attack will be invented to deceive that countermeasure.In this study, we focus on investigating the ability to create adversarial patterns in search space against defensive methods that use image filters.Experimental results conducted on the ImageNet dataset with image classification tasks showed the correlation between the search space of adversarial perturbation and filters.These findings open a new direction for building stronger offensive methods towards deep learning systems.
John Hopcroft's views on China's education

Professor John Hopcroft at Cornell University is a Turing Prize winner (1986) and an educator with more than 55 years of teaching experience. For the past 10 years, Hopcroft has been coming to China to give courses to undergraduate students at Shanghai Jiaotong University (SJTU) and has helped SJTU to improve the quality of computer-science education. He also chairs the Center on Frontiers of Computing Studies at Peking University (PKU), the Turing Class at PKU and the Hopcroft Center at Huazhong University of Science and Technology (HUST) in Wuhan, and is engaged in many other projects aiming to upgrade China's computer-science undergraduate education. Recently, NSR talked with Professor Hopcroft to learn his views on education in China.
Evolution of a graduate software engineering capstone course — a course review

In education projects, students sometimes have difficulty conveying their knowledge when asked to solve real problems especiallywhen a software product is generated as an outcome. Faculty accumulated nearly 12 years of experience running a graduatesoftware engineering capstone course perceived the need to better assist students on the learning process. Course adaptations arerequired, particularly considering the heterogeneity of student’s background and work experience, as well as the use of currenttechnologies and tools. The paper outlines an evolved graduate software engineering capstone course for part-time graduateprofessional students. The capstone course is intended to create a meaningful student experience while providing a productiveenvironment to apply knowledge learned from the program. Moreover, the new proposal emphasizes agile methodologies and codeas the primary artifact. Our main contribution is to present the evolution of this capstone course, from inception to coding, testingand deployment. The course revision includes new artifacts, such as a work breakdown structure and burndown chart, as means toimprove the course learning outcomes based on lessons learned and student experience. For evaluation purposes, we selectedgroups of most recent capstone course sections totaling 175 students. Two learning objectives of the program were evaluated:teamwork and critical thinking. Critical thinking was assessed via the project plan artifact. Teamwork was assessed throughdiscussion forums, team test plans and reports. The results were positive; however, they indicate that not all elements of projectplanning are present in the studio course.
Design of a DBN Hardware Accelerator for Handwritten Digit Recognitions

With the development of artificial intelligence, researches on speech recognition and deep learning become increasingly popular. In this paper, the deep belief network (DBN) hardware accelerator for handwritten digit recognition was implemented in TSMC 90nm CMOS process. Addition, the MNIST database is used as a functional verification for the proposed hardware architecture, and the accuracy of the proposed design is 97.3%. The gate count of the proposed design is 1,160k, and power consumption is 353mW at 73.6MHz. The energy efficiency of the proposed design is 1.7337GOPs/W.
Customizing JATS (Journal Article Tag Suite)

Document Type: The core JATS Document Type is a journal article and the ANSI/NISO JATS Tag Sets are journal article tag sets, which define XML elements and attributes to describe the content and/or the metadata of journal articles. Such articles may include: research articles; subject review articles; non-research articles; editorials; letters; product, software, and book reviews; obituaries, and the peer reviews or author responses included with an article. Although originally just for journal articles, JATS-based tag sets have been built for: books (BITS: Book Interchange Tag Suite), standards (NISO STS, ISO STS), technical reports, conference proceedings, magazines and newsletters, and even posters. Purpose: Provides common XML format to preserve the intellectual content of journal articles (independent of format of initial publication) Expected Uses: Conversion target, archival storage, and interchange Expected Users: Publishers, aggregators, vendors, web-hosts, libraries, and archives who produce, interchange, and store journal article content When: ANSI/NISO Z39.96-2019 JATS: Journal Article Tag Suite (current) Customization Mechanism: The JATS Journal Article Tag Sets are distributed in DTD form, XSD form, and RELAX NG form, but they are maintained as DTDs. The customization mechanism for DTDs is modularization and Parameter Entities, with customization-specific information overriding JATS-default information. This paper will describe, explain, and illustrate this mechanism. Specific customization samples are provided in the Appendix Sample JATS Customizations URL: https://jats.nlm.nih.gov/
Automated Feature Extraction of Hepatic US Image of Dog Using Image Processing

Image processing has been one of the key factors in the area of Biomedical engineering especially in the detection of diseases. Incorporation of engineering methods in medical science has paved way for many optimistic outcomes that are much easier, cost effective and less time consumer when compared to conventional techniques. Image processing is one such technique. Here, the Ultra Sonography (US) images of the canine affected with hepatic and splenic carcinoma is the source, of which the features of cancer affected area is found out. Image preprocessing, image segmentation and feature extraction are the three stages in image processing that aids in the extraction of different features of canine inclined towards such cancers. Weiner filter is used for image denoising and Contrast Limited Adaptive Histogram Equalization (CLAHE) is the method for enhancement of the US images. These two processes comprise of image preprocessing. Image segmentation is achieved using adaptive threshold method. Finally, Gray Level Co-occurrence Matrix (GLCM) is used to extract the features of the segmented area.
Woodpecker Mating Algorithm (WMA): a nature-inspired algorithm for solving optimization problems

Nature-inspired metaheuristic algorithms have been a topic of interest for researchers to solve optimization problems in engineering designs and real-world applications, due to their simplicity and flexibility. This paper presents a new nature-inspired search algorithm called Woodpecker Mating Algorithm (WMA) and applies it to challenging problems in structural optimization. The WMA is a population-based metaheuristic algorithm that mimics the mating behavior of woodpeckers. It was inspired by the drumming sound intensity. In WMA, the population of woodpeckers is divided into male and female groups. The female woodpeckers approach the male woodpeckers based on the intensity of their drum sound. An efficiency comparison was drawn between the WMA algorithm and other metaheuristic algorithms by employing 19 benchmark functions(including unimodal, multimodal and composite functions). Moreover, the performance of WMA is compared with 8 of the best meta-heuristic algorithms using 13 high dimensional multimodal and unimodal benchmark functions. The assessments and statistical results indicate that the WMA algorithm offers promising results and is capable of outperforming the most recent and popular algorithms proposed in the literature in most of the employed benchmark functions. Moreover, a statistically significant difference was observed compared to the other assessed algorithms. The proposed algorithm produced significant results for a non-convex, inseparable, and scalable problems.