Reviewerly/exHarmony huggingface dataset:
Analysis of task degree of English learning based on deep learning framework and image target recognition

Task degree has become one of the important indicators to measure students’ English learning intensity and learning quality, and the difference in task degree has different effects on students’ English learning. In order to realize the task recognition of English classroom teaching, combined with the characteristics of deep learning, this study combines the actual situation of English classroom teaching to analyze, and distinguishes characters through student positioning and feature recognition. Moreover, this paper combines the characteristics of English learning scoring to judge students’ learning situation, and designs a shallow convolutional neural network based on TensorFlow architecture for identifying images and uses GPU training acceleration to solve the problem of training time-consuming in the face of large data volume. In addition, the task results feedback is evaluated by scoring method, and the performance of the algorithm is analyzed by experiments. By setting the category of sensitive targets, this paper can perceive the results according to the target location and mark the sensitive targets in the input scene image. The research results show that the method proposed in this paper has certain effects.
Unconstrained workout activity recognition on unmodified commercial off-the-shelf smartphones

Smartphones have become an essential part of our lives. Especially its computing power and its current specifications make a modern smartphone even more powerful than the computers NASA used to send astronauts to the moon. Equipped with various integrated sensors, a modern smartphone can be leveraged for lots of smart applications. In this paper, we investigate the possibility of using a unmodified commercial off-the-shelf (COTS) smartphone to recognize 8 different workout exercises. App-based workout has become popular in the last few years. People do not need to go to the gym to practice. The advantage of using a mobile device is, that you can practice anywhere at anytime. In this work, we turned a COTS smartphone to an active sonar device to leverage the echo reflected from exercising movement close to the device. By conducting a test study with 14 participants performing these eight exercises, we show first results for cross person evaluation and the generalization ability of our inference models on unseen participants. A bidirectional LSTM model achieved an overall F1 score of 88.86 % for the cross subject case and 79.52 % for the holdout participants evaluation. Similar good results can be achieved by a VGG16 fine-tuned model in comparison to a 2D-CNN architecture trained from scratch.
A preliminary quantitative EEG study on Augmented Reality Guidance of Manual Tasks

Augmented reality head mounted display devices (HMDs) provide user's natural view of the real world with enhanced experience through optical superposition of virtual data.Manual-task guidance applications exploiting these systems are particularly suited in computer-aided-surgery.However, the typical working distance of commercial devices is higher than user reachable space, limiting the purpose of manualtask guidance.Specifically, known issues such as the "vergenceaccomodation-conflict" and the "focus-rivalry" may lead to visual fatigue and mental workload worsening task performance.Here, we exploit EEG recordings during a "connecting-the-dots" task performed with and without AR to evaluate the mental workload associated with AR-related visual fatigue.First, we quantify the reduction of users' performance based on starting and end points gap errors.Then, we investigate the effects on AR usage on cortical activity through the analysis of EEG power and Frontal Alpha Asymmetry (FAA) index.Although preliminary, our results suggest that mental workload associated with AR usage may derive from enhanced difficulty associated with the task.Furthermore, a shift in FAA from controlateral to ipsilateral regions seems to confirm this hypothesis.
Face recognition based on improved VGGNET convolutional neural network

Currently, many famous convolutional neural networks depend on massive training samples and high-performance computers. To solve this problem, this paper improves the deep learning algorithm VGGNet for image classification and proposes a face recognition method called MicroFace. MicroFace uses CASIA WebFace database as the testing and training samples. Our research shows that compared with the original algorithm, the improved algorithm reduces the parameters, improves the pooling function, and increases the number of convolution kernels, which not only decreases the dependence on massive training samples and high-performance computers, but also achieves 96% recognition rate with good recognition performance and certain practicability.
Educational Responses to the COVID-19 Outbreak in South Korea

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D2D: Learning to find good correspondences for image matching and manipulation

We propose a new approach to determining correspondences between image pairs under large changes in illumination, viewpoint, context, and material. While most approaches seek to extract a set of reliably detectable regions in each image which are then compared (sparse-to-sparse) using increasingly complicated or specialized pipelines, we propose a simple approach for matching all points between the images (dense-to-dense) and subsequently selecting the best matches. The two key parts of our approach are: (i) to condition the learned features on both images, and (ii) to learn a distinctiveness score which is used to choose the best matches at test time. We demonstrate that our model can be used to achieve state of the art or competitive results on a wide range of tasks: local matching, camera localization, 3D reconstruction, and image stylization.
SGP-DT

Semantic Genetic Programming (SGP) approaches demonstrated remarkable results in different domains. SGP-DT is one of the latest of such approaches. Notably, SGP-DT proposes a dynamic-target approach that combines multiple GP runs without relying on any form of crossover. On eight well-known datasets SGP-DT achieves small RMSE, on average 25% smaller than state-of-the-art approaches.
A Comparison of Pre-trained Word Embeddings for Sentiment Analysis Using Deep Learning

Santosh Kumar, P. Yadav, Rakesh Bahadur Dhavale, Sunita VikrantThe public opinion expressed on review or blogging sites and social networking platforms can be the source for the extraction of very critical information related to feelings and emotions of mass towards the subject matter in the field of commerce and governance. Natural Language Processing (NLP) and Artificial Intelligence can be used for sentiment analysis of this textual information. For text processing, NLP applications nowadays rely on pre-trained embeddings derived from large corpora such as news collection and web crawlers. There are many pre-trained word embeddings available. However, no study found which compares the accuracy achieved using these embeddings. In this paper, we worked on different kinds of word embeddings (pre-trained and untrained) and derived a comparison concerning accuracy for sentiment analysis applications using Deep Learning (DL) models. We found that the deep learning models perform better with pre-trained embeddings compared to Keras default (untrained) embedding.
Design of Attendance Checking Management System for College Classroom Students Based on Fingerprint Recognition

In view of the situation that the traditional way of is checking in attendance by teachers in the daily classroom in Colleges and universities, exist such as answering on behalf of others, wasting class time, low efficiency which are not conducive to the management of students' attendance. The independent pressure fingerprint recognition modules of SM-2B series launched by Hangzhou Zhongzheng Company are redeveloped against the background of the present situation of students' classroom attendance in Tianjin Tianshi College. The fingerprint attendance system for college students is designed by combining VB, VC++ programming language and MySQL database. The fingerprint registration and attendance checking management platform was established on the classroom teacher's computer, and the attendance information was stored in the database which ever can be inquired on the server. The test results show that the attendance system can query and manage the students' attendance conveniently, and the system is stable and scalable.
Peak Detection Enhancement in Autonomous Wearable Fall Detection

Fall Detection (FD) has drawn the attention of the research community for several years. A possible solution relies on on-wrist wearable devices including tri-axial accelerometers performing FD autonomously. This type of approaches makes use of an event detection stage followed by some pre-processing and a final classification stage. The event detection stage is basically performed using thresholds or a combination of thresholds and finite state machines. In this research, a novel event detection is proposed avoiding the use of user predefined thresholds; this fact represents the main contribution of this study. It is worth noticing that avoiding the use of thresholds make solutions more general and easy to deploy. Moreover, a new set of features are extracted from a time window whenever a peak is detected, classifying it with a Neural Network. The proposal is evaluated using the UMA Fall, one of the publicly available simulated fall detection data sets. Results show the improvements in the event detection using the new proposal, outperforming the base line method; however, the classification stage still needs improvement. Future work includes introducing a finite state machine in the event detection method, adding extra features and a pre-classification of the post-peak interval and a better training configuration of the Neural Networks.
An Efficient Image Categorization Method With Insufficient Training Samples

Image classification is an important part of pattern recognition. With the development of convolutional neural networks (CNNs), many CNN methods are proposed, which have a large number of samples for training, which can have high performance. However, there may exist limited samples in some real-world applications. In order to improve the performance of CNN learning with insufficient samples, this article proposes a new method called the classifier method based on a variational autoencoder (CFVAE), which is comprised of two parts: 1) a standard CNN as a prior classifier and 2) a CNN based on variational autoencoder (VAE) as a posterior classifier. First, the prior classifier is utilized to generate the prior label and information about distributions of latent variables; and the posterior classifier is trained to augment some latent variables from regularized distributions to improve the performance. Second, we also present the uniform objective function of CFVAE and put forward an optimization method based on the stochastic gradient variational Bayes method to solve the objective model. Third, we analyze the feasibility of CFVAE based on Hoeffding's inequality and Chernoff's bounding method. This analysis indicates that the latent variables augmentation method based on regularized latent variables distributions can generate samples fitting well with the distribution of data such that the proposed method can improve the performance of CNN with insufficient samples. Finally, the experiments manifest that our proposed CFVAE can provide more accurate performance than state-of-the-art methods.
Modern student: Digital existence

The main purpose of the study was to identify the most stable and common ideas about modern students, about the features of their digital culture. The collection of primary information about the attitude of students to digital technologies, their use in everyday life, educational and professional activities was carried out by means of a questionnaire survey of the bachelor students of Volgograd State Technical University. The collection of primary information involved obtaining data on the students’ attitude to digital technologies, their use in everyday life and educational and professional activities. According to the research data and the results of the study, the authors made conclusions about the digital existence of modern students of a technical University, and presented recommendations for the training of digital engineers.
Software Architecture for Blockchain-based Trade Certificate Systems

Various kinds of certificates are used in international trade, and it is important for trade participants to know about their authenticity and status. One example are phytosanitary certificates, which record that exported plant-based materials meet biosecurity requirements of destination countries. An electronic "ePhyto" certificate system is being developed to be operated by the United Nations (UN). An ePhyto certificate is transmitted from the national plant protection organisation (NPPO) of the exporting country to the NPPO of the importing country. The UN system provides a secure communication channel between pre-registered NPPOs. However, industry participants who are not connected to the UN system are not able to confirm the authenticity of an ePhyto certificate, and whether that ePhyto certificate has been revoked or reissued. In this paper, we describe a blockchain-based system and its software architecture that could augment the emerging ePhyto certificate system by adding a tamper-proof for public users to check the authenticity and status of ePhyto certificates.
Mixed-code text analysis for the detection of online hidden propaganda

Internet-based communication systems have become an increasing tool for spreading misinformation and propaganda. Though mechanisms adept in tracking unwarranted information and messages exist, users have devised different methods to avoid scrutiny and detection. One of such method is the use of mixed-code language. Mixed code is text written in an unconventional form combining different languages, symbols, scripts and shapes, with the aim to make it difficult to detect due to its custom approach and its ever changing aspects. Utilizing special characters to substitute for alphabets, which makes it readable to humans but nonsensical to machine. The intuition is that a substituted alphabet should resemble the shape of the intended alphabet. In this context, the paper explores the possibility of identifying such mixed code texts with special characters by proposing an approach to normalize them and determine if it contains propaganda elements. As a consequence, a tailored algorithm in combination with a deep learning models for character selection is defined and presented. The results gathered from its experimentation are discussed and the achieved performances are compared with the related works1.
Ensuring Food Safety Through Blockchain

Food safety is a most highlighted area in today’s fast-food world as many health problems such as diarrhea, vomiting, typhoid, and food poisoning can be happened by consuming those contaminated packed foods. Hence, every possible detail of a packed food item must be placed in a supply chain management (SCM) to trace the path of the food item before delivery. Blockchain technology can be used to track the records of the journey of a packed food item from the manufactured time to the delivery time of the packet. In this paper, we have proposed a model based on blockchain technology (BT) to trace food items through various stages of supply chain management. Security analysis discussed in the paper proved that this chain will achieve the desired level of security in this area.
Calibration method for the electrically tunable lens based on shape-changing polymer

In this paper, a calibration method for the camera system with electrically tunable lens (ETL) based on shape-changing polymer (SCP) is proposed to improve the accuracy, robustness and practicality of the system. The camera model of the ETL based on SCP is proposed based on the analyses of its optical properties. The calibration strategy, including initial estimation of camera parameters and bundle adjustment is presented. To eliminate the influence of temperature on ETL in machine vision applications, a real-time temperature compensation method is proposed. The proposed method makes use of the existing calibration hardware without adding new components to the system. Both simulations and experiments are conducted to evaluate the effectiveness and accuracy of the proposed camera model and calibration method. The measurement error with the proposed calibration method is below 20 microns at high magnification, whose measurement accuracy is improved by five times than the existing method at high magnification. With the proposed calibration method for the camera system with ETL based on SCP, the calibration workload is reduced and accurate calibration at high magnification is achieved. It also benefits the development of autofocusing 3D measurement technology.
Towards a Context-Free Machine Universal Grammar (CF-MUG) in Natural Language Processing

In natural language processing, semantic document exchange ensures unambiguity and shares the same meaning for documents sender and receiver cross different natural languages (e.g., English to Chinese), this difference makes the translation between natural languages becomes complex and inaccurate. This paper proposed a novel framework of Context-Free Machine Universal Grammar which consists of local mode (sender and receiver) and mediation mode (Machine Universal Language) based on the concept of collaboration, the framework improves semantic unambiguity and accuracy in crossing language document, meanwhile makes document computer-readable through unique ID for each word or phrase. More importantly, inspired by grammatical case in linguistics, a novel Machine Universal Grammar provides a universal grammar that accepts all coming languages and improves semantic accuracy in natural language processing.
Development of a reliable supply chain system using blockchain

Traceability and food quality are significant challenges in realizing a reliable food supply chain. The reliability of data in supply chains is one of the critical factors. Ensuring transparency, integrity, and availability is the primary requirement for establishing a proper supply chain network. Blockchain is a distributed structure of immutable records that are chained together to form blocks. It provides a guarantee of storing the data correctly and reliably. Smart contracts, which are self-executing contracts containing the terms of the agreement between the entities involved, provide utility for automation of reputation calculation with the transactions. Reputation systems allow participants to rate each other, thus building trust through reputation. The present reputation systems have bounded scrutiny and lack granularity; hence they are not ideal for supply chains. In this work, we propose a reliable supply chain framework using blockchain and smart contracts. It uses a consortium blockchain network to trace communication between the participants and to calculate reputation scores dynamically. Rewards and penalties are assigned to the participants of the supply chain network based on the food product quality involved in the trade. The network participants have defined roles and the access permissions govern who can access the ledger. An immutable ledger stores all the transactions occurring in the network. Any change in one block will reflect in the consecutive blocks, which ensures the data is reliable and secure. The proposed system is implemented using Hyperledger Composer. The proposed framework is evaluated in terms of throughput and latency for varying asset size and batch size using the benchmarking tool Caliper. Results show that the security and reliability provided by the proposed framework justify the overheads in contrast to a trading model that does not include a blockchain network.
Prediction of Wheat Rust Diseases Using Data Mining Application

Stem rust caused by Puccinia graminis f.sp.tritici and stripe rust caused by Puccinia striiformis are the most destructive wheat rust diseases when environment conditions are favorable in regions where wheat crops are grown.An early prediction mechanism can play a great role in forecasting the occurrence of the rust disease.It assists proactive control and early decision making.However, in the absence of prediction mechanism of wheat rust disease hurried wheat production yield loss more.Hence, to overcome these issues, this study was conducted to develop wheat stripe rust and stem rust diseases prediction model using data mining application.The meteorological and disease data for the year 2010 to 2019 of national meteorological agency southern Oromia region Bale Robe service center and Oromia Seed enterprise Sinana farm unit II were obtained and used for the study.Two bread wheat varieties, namely Kakaba and Danda'a were involved in the study.Fist, daily meteorological data mean values of three-day consecutive interval were computed.Then, mean value of meteorological data has been integrated with disease incidence and severity date observed during the most critical infection period (mid-august to November-30) was used to develop the model.WEKA software machine learning tool with J48 decision tree algorithm was used for data preprocess and experiments.It is open source software containing various machine learning algorithms for data mining tasks.The study results showed that, stripe rust predictive model trained and tested with using training set test option achieved accuracy 75.70% and stem rust predictive model achieved accuracy 90.045% with cross-validation fold 10 test option.The results found were much promised to forecast occurrence of wheat stripe and stem rust disease.
Interpretable Probabilistic Password Strength Meters via Deep Learning

Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produce an opaque security estimation that fails to fully support the user during the password composition. In the present work, we move the first steps towards cracking the intelligibility barrier of this compelling class of meters. We show that probabilistic password meters inherently own the capability to describe the latent relation between password strength and password structure. In our approach, the security contribution of each character composing a password is disentangled and used to provide explicit fine-grained feedback for the user. Furthermore, unlike existing heuristic constructions, our method is free from any human bias, and, more importantly, its feedback has a clear probabilistic interpretation. In our contribution: (1) we formulate the theoretical foundations of interpretable probabilistic password strength meters; (2) we describe how they can be implemented via an efficient and lightweight deep learning framework suitable for client-side operability.
Calibration of the Pressure Sensor Device with the Extreme Learning Machine

The measurement accuracy of the pressure sensor devices is affected by the intrinsic factors and environment parameters. This paper applies the Extreme Learning Machine (ELM) method to the calibration of the pressure sensors. The proposed ELM calibration method can compensate the system errors caused by the temperature and voltage fluctuation, because the ELM is able to approximate any non-linear functions. The experimental results of the calibration from real pressure sensor product showed that, the accuracy and stability of the proposed method is better than that of the BP and SVM based counterparts.