Reviewerly/exHarmony huggingface dataset:
OkenReader: ML-based classification of the reading patterns using an Apple iPad

Digital learning and professional training require processing of large volumes of information, mostly in the text format. The new information environment requires the development of new studying methods and, most importantly, a new tool for assessing its quality in order to adjust the strategy of compiling the educational content. Management and control of perception in reading can be performed with the use of technology, assessing attention, engagement, understanding, cognitive load and tiredness of readers. The article describes an early effort towards creation of a commercial technology based on a machine-learning (ML) algorithm that uses readers’ eye-tracking recording as a proxy for their cognitive state. The solution was realized on Ipad Pro with a standard iOS operating system, which provides opportunities for mass adoption in educational and training settings.
Global Feature Polishing Network for Glass-Like Object Detection

Glass object detection aims to detect and segment glass-like objects in an input image. Compared with other binary segmentation tasks, the transparent property of glass brings great challenges to glass object detection. This requires the model to capture more richer image global semantics and local detail information. In this work, we propose a novel global feature polishing network for glass object detection. We first design a global perception module for coarse localization by embedding a self-attention block on top of the backbone. Then we propose a global feature polishing module to establish long-distance semantic dependence between different pixels and a multi-scale refinement module to combine multi-level side-output features, which can well explore the missing object parts and also refine the false detection in previous layers. In addition, we build a challenging Window dataset for comprehensive evaluation and further research. Experimental results demonstrate that the proposed method performs favorably against state-of-the-art methods without any pre-processing and post-processing.
Adaptive Enhanced Generative Adversarial Network for MRI Reconstruction

Extracting effective feature information is very important for deep learning methods. However, most deep learning-based CS-MRI methods use the standard convolutional layers with square kernels for feature extraction, which is difficult to further improve the accuracy of MRI reconstruction under limited computational resources. In this paper, we propose the enhanced asymmetric convolution blocks (EACBs) and the selective asymmetric kernel blocks (SAKBs) to effectively enhance the feature extraction ability of the network. Further, we propose a novel adaptive enhanced generative adversarial network, termed as AEGAN, for high-precision MRI reconstruction, where the proposed EACBs and SAKBs are embedded in the network architecture of AEGAN. In the AEGAN, the proposed EACBs are used to enhance the corresponding skeleton of the square kernel, and the SAKBs are used to efficiently capture useful feature information by adaptively adjusting the size of receptive fields. Therefore, the combination of EACBs and SAKBs can extract rich feature information more effectively and efficiently. In further experiments, it can be demonstrated that the proposed AEGAN exceeds the state-of-the-art GAN-based CS-MRI methods with fewer model parameters.
Fast generation of optimized execution plans for parameterizable CNN accelerators

Generating an optimal execution plan for a given convolutional neural network (CNN) and a parameterizable hardware accelerator is a challenge.We present a framework that finds an execution plan that maximizes throughput for a given network and a specific configuration of our parameterizable accelerator. The framework first generates tiled dataflows for each layer, then maps the dataflows to the different independent hardware units using techniques borrowed from traditional list scheduling. Evaluated with a number of different networks and different hardware configurations, the presented framework clearly outperforms existing approaches in terms of speedup or schedule generation time.
A Fuzzy Deep Learning Approach to Health-Related Text Classification

Following the tremendous amounts of text generated in social networks and news channels, and gaining valuable and dependable insights from diverse sources of information is a tedious task. The challenge is increased during specific periods, for example, in a pandemic event like Covid-19. Existing text categorization methods, such as sentiment classification, aim to help people tackle this challenge by categorizing and summarizing the text content. However, the inherent uncertainty of user-generated text limits their efficiency. This paper proposes a novel architecture based on fuzzy inference and deep learning for sentiment classification that overcomes this limitation. We evaluate the proposed method by applying it to well-known health-related text datasets and comparing the accuracy with state-of-the-art methods. The results show that the proposed fuzzy fusion methods increase the accuracy compared to individual pretrained models. The model also provides an expressive architecture for health news classification.
Code-Switching ASR and TTS Using Semisupervised Learning with Machine Speech Chain

The phenomenon where a speaker mixes two or more languages within the same conversation is called code-switching (CS). Handling CS is challenging for automatic speech recognition (ASR) and text-to-speech (TTS) because it requires coping with multilingual input. Although CS text or speech may be found in social media, the datasets of CS speech and corresponding CS transcriptions are hard to obtain even though they are required for supervised training. This work adopts a deep learning-based machine speech chain to train CS ASR and CS TTS with each other with semisupervised learning. After supervised learning with monolingual data, the machine speech chain is then carried out with unsupervised learning of either the CS text or speech. The results show that the machine speech chain trains ASR and TTS together and improves performance without requiring the pair of CS speech and corresponding CS text. We also integrate language embedding and language identification into the CS machine speech chain in order to handle CS better by giving language information. We demonstrate that our proposed approach can improve the performance on both a single CS language pair and multiple CS language pairs, including the unknown CS excluded from training data.
Design of the English Teaching Network Resource Database under Cloud Environment

To concentrate a large number of teaching resources to meet the needs of foreign language teachers and students, this paper adopts the concept of hierarchical design, and designs a network resource library construction scheme under the cloud environment. We introduce and discuss the related technologies needed for the implementation of the system, and focus on the analysis of the application status of teaching resource platform based on Hadoop framework. We also use different database management systems to deal with structured and unstructured data respectively. Then the overall architecture of the system is constructed by MVC and web service mode, and the design and implementation methods of key modules are provided. In the system test, JSP + MySQL + Apache is used to complete the design process of the web interface of the teaching resource platform. Finally, combined with foreign language teaching events, the simple running test of the built cloud platform is performed and the designing process of the platform is optimized and summarized.
The significance of creativity development of students future engineers

The article is devoted to the important issue of developing creativity of future engineers at the university and the specifics of its interconnection with a creative environment. Based on the theoretical analysis of the research problem, a creative environment is considered as an important condition for the creativity development of future engineers at higher institutions. The main components of the creative environment creation process are highlighted in the article. In addition, a creative development project is developed. The creative environment is defined as a means of multi-factor determination of creative personality development and as a significant factor of a productive creative activity and active manifestations of the creative competence of future engineers.
A Breast Cancer CT Image Detection Algorithm Based on Improved Feature Pyramid Network

In medical diagnosis, medical imaging detection is often affected by training data and image backgrounds, which easily leads to lower detection accuracy. Aiming at the problem, a region convolution neural network breast cancer image detection algorithm based on improved feature pyramid structure is proposed. On the basis of the feature pyramid network structure, the bottom-up feature fusion is added, and the ideas of up-down fusion and bypass connection are adopted. And the fusion feature maps generate after two convolution operation. The improved algorithm is compared with the algorithms of original models for Mask R-CNN and ResNet-50 on the datasets DDSM (Digital Database for Screening Mammography). The experiment results show the correct detection rate of the improved algorithm is greatly enhanced in both normal and fuzzy positive sample images under the same feature extraction network and data set.
scrAPP: Enabling Reuse of Scrap Materials with a Smartphone

We introduce scrAPP, a smartphone app that helps designers reuse odd-shaped scrap sheet materials. scrAPP uses a smartphone camera to capture the contour of scrap materials providing precise measurements on the spot enabling users to quickly determine if their CAD models will fit on the material and to then place the models for cutting. This work contributes to the body of work focused on sustainability in digital fabrication through the development of tools for designers. The paper describes the rationale and user-centered approach to the development of scrAPP as well as insights into the needs of creative users gathered from an exploratory preliminary user study. A number of challenges in terms of technical implementation and user experience are explored, and conclusions on the future support for mobile material reuse are provided.
Principal Gradient Direction and Confidence Reservoir Sampling for Continual Learning

Task-free online continual learning aims to alleviate catastrophic forgetting of the learner on a non-iid data stream. Experience Replay (ER) is a SOTA continual learning method, which is broadly used as the backbone algorithm for other replay-based methods. However, the training strategy of ER is too simple to take full advantage of replayed examples and its reservoir sampling strategy is also suboptimal. In this work, we propose a general proximal gradient framework so that ER can be viewed as a special case. We further propose two improvements accordingly: Principal Gradient Direction (PGD) and Confidence Reservoir Sampling (CRS). In Principal Gradient Direction, we optimize a target gradient that not only represents the major contribution of past gradients, but also retains the new knowledge of the current gradient. We then present Confidence Reservoir Sampling for maintaining a more informative memory buffer based on a margin-based metric that measures the value of stored examples. Experiments substantiate the effectiveness of both our improvements and our new algorithm consistently boosts the performance of MIR-replay, a SOTA ER-based method: our algorithm increases the average accuracy up to 7.9% and reduces forgetting up to 15.4% on four datasets.
Conditions for advantageous quantum Bitcoin mining

Our aim is to determine conditions for quantum computing technology to give rise to security risks associated with quantum Bitcoin mining. Specifically, we determine the speed and energy efficiency a quantum computer needs to offer an advantage over classical mining. We analyze the setting in which the Bitcoin network is entirely classical except for a single quantum miner who has a small hash rate compared to that of the network. We develop a closed-form approximation for the probability that the quantum miner successfully mines a block, with this probability dependent on the number of Grover iterations the quantum miner applies before making a measurement. Next, we show that for a quantum miner that is “peaceful”, this success probability is maximized if the quantum miner applies Grover iterations for 16 minutes before measuring, which is surprising as the network mines blocks every 10 minutes on average. Using this optimal mining procedure, we show that the quantum miner outperforms a classical computer in efficiency (cost per block) if the condition Q < Crb is satisfied, where Q is the cost of a Grover iteration, C is the cost of a classical hash, r is the quantum miner's speed in Grover iterations per second, and b is a factor that attains its maximum if the quantum miner uses our optimal mining procedure. This condition lays the foundation for determining when quantum mining, and the known security risks associated with it, will arise.
Anomaly detection in indoor localization using Machine Learning

One of the most concerning topics in smart cities is the internet of things, namely wireless sensor networks in indoor localization. Wi-Fi with received signal strengths (RSSs) is one of the most used indoor localization techniques. It generates signal intensity irregularities in Wi-Fi RSSs owing to reflection, refraction, incursion, and channel noise. RSS values cannot be defined (the location of each unknown node must be accurately stated). Here, Asymmetrical, abnormal circumstances are present in the Wi-Fi indoor localization area. Matplotlib, numpy, pandas, ploty, scipy, seaborn, scikitlearn, wordcloud, statsmodels, and streamlit were utilized in this study.
Research on obstacle avoidance path planning of electrical control robot in unknown environment

Aiming at the problems of robot path planning with artificial potential field method, such as target unreachable and local minimum points, an improved artificial potential field method model is proposed. The obstacles in unknown environment are designed in the form of grid map, so that the robot can avoid obstacles and move towards the target through perception. Matlab GUI is used to set up a two-dimensional coordinate system environment including obstacles and target points, and the optimal trajectory movement simulation experiment is carried out from the initial position to the desired position in the plane map. Experiments show that the improved artificial potential field method can make the mobile robot navigate in unknown environment, avoid obstacles and find the appropriate path, so as to achieve the requirement of collision free. The simulation results are close to the expected results, which shows that this method can effectively improve the feasibility of path planning and the effectiveness of obstacle avoidance of mobile robot in unknown environment.
Genetic Optimization Algorithm for Construction and Realization Path of the Precision Funding for Colleges with Data Monitoring Model

Genetic optimization algorithm for construction and realization path of the precision funding for colleges with data monitoring model is defined in this paper. For the efficient designing of the robust model, considering information of the different individuals cannot be exchanged directly, because the relationship between genes and individuals is very close, so the crossover operation in genetic algorithm cannot be used here. In order to obtain the optimal solution of the multi-objective problem, it is necessary to then make necessary coordination and compromise when optimizing each sub-objective. With this assistance, this research work will further apply the model into the study of the path estimation. The systematic performance is validated with the test on the robustness and efficiency.
Stereo Music System Control using Vision based Static Hand Gesture Recognition

Human gesture plays a very crucial role for communication with the person and even with machines. Now a day human gesture is very famous for assisting deaf and dumb person but it can also be used to control machines in an efficient manner. This paper is taking the only example of the machine as a stereo music system for controlling it with the help of seven different human static hand gestures. This approach uses vision-based static hand gesture recognition. The wavelet transform is used for feature extractor from the image and neural network is used for classification purpose. This system is trained and tested with three different databases of images and thus the result is derived that is 97.14% which is highly satisfactory.
의사소통 중심 한국어 수업에서의 블렌디드 러닝의 활용 -슬랙(Slack)을 이용한 수업 사례 연구-

The purpose of this study is to examine the implementation of blended learning in a communicative Korean language class using an online communication platform, Slack. For this purpose, this paper looked into a case of Korean language course, described the activities based on the interaction types, and discussed the characteristics of blended learning with slack in communicative language class. Satisfaction survey was also conducted on the students. The blended learning environment implemented using Slack showed a very high level of efficiency in terms of ‘data sharing’, ‘mutifaceted communication path’, ‘course management’, and ‘task submission and feedback’. In addition, this study described and analyzed the activities in ‘teacher-individual student interaction’, ‘student-student interaction and teacher communicating with both students’, ‘teacher-group of students interaction’, ‘group-to-group interaction and teacher communicating with both groups’, and ‘interaction between the whole class and the teacher’ through electronic media. According to the satisfaction survey result, more than 90% of learners answered that they were satisfied with the blended learning used in this study, and the reasons cited were ‘easiness of task submission and receiving feedback’, and ‘accessibility on smartphone/laptop’. (University of Seoul)
Challenges and Obstacles of Distance Learning in Higher Education

The chapter characterizes the transformation of the learning process from full-time to distance learning on the example of Faculty of Economics, Matej Bel University in Slovakia, during the COVID-19 epidemic in the form of case study. The chapter analyses the strengths and weaknesses of this process during the summer semester 2019/2020 based on the secondary data and results of a questionnaire survey among faculty students realized in 2 rounds – in the first stage of transformation (April 2020) and after the end of summer semester (June 2020). It identifies the key steps that have (or had) to be taken to overcome the second wave of the COVID-19 pandemic and the key challenges for the future development of virtual learning as a full compensation of full-time form of study from the students' and faculty's perspective. This case is an example of smart education in crisis situations. The results show that distance learning can be a suitable complement to the fully presence learning process, and temporarily, it can replace it in emergency situations.
A Novel Model for Automatic Identification of Open Source Software License Terms

Open source software nowadays has become an important trend for software technology innovation and software industry development. The use and distribution of open source software will come with an open source license. Open source licenses can regulate the use of open source software and protect its intellectual property. However, the diversity of licenses makes it difficult for developers to correctly understand the license content and has aggravated the chance of illegally combining open source components. To alleviate this problem, existing methods mainly analyse license text manually. In this paper, a novel method is proposed for the automatic identification of open source software license terms. This method consists of three key components, license modeling for license terms extraction, topic model based on Latent Dirichlet Allocation to mine topics in licenses, and topics and terms mapping module for construction of their relationships. To evaluate the effectiveness and practicability of our model, we build a new Open Source License Dataset for model training and testing. Experimental results demonstrate that our approach can achieve a satisfactory solution for identifying license terms.
Construction of multi-source geospatial vector data association relation based on topic maps

Implicit correlations exist between multi-source geospatial data, the association relation can’t be displayed intuitively and retrieved effectively, which leads to the difficulty in data utilization and data sharing. Aimed at this situation, association relation topic maps is constructed using topic map tools Ontopia in this article. Besides, to increase the efficiency of building topic map, the automatic generation algorithm of association relation topic map which based on C# is proposed. The result of experiment shows that the association relation topic map can be constructed correctly by using Ontopia tools and automatic generation algorithm.
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

This work addresses the challenging task of LiDAR-based 3D object detection in foggy weather. Collecting and annotating data in such a scenario is very time, labor and cost intensive. In this paper, we tackle this problem by simulating physically accurate fog into clear-weather scenes, so that the abundant existing real datasets captured in clear weather can be repurposed for our task. Our contributions are twofold: 1) We develop a physically valid fog simulation method that is applicable to any LiDAR dataset. This unleashes the acquisition of large-scale foggy training data at no extra cost. These partially synthetic data can be used to improve the robustness of several perception methods, such as 3D object detection and tracking or simultaneous localization and mapping, on real foggy data. 2) Through extensive experiments with several state-of-the-art detection approaches, we show that our fog simulation can be leveraged to significantly improve the performance for 3D object detection in the presence of fog. Thus, we are the first to provide strong 3D object detection baselines on the Seeing Through Fog dataset. Our code is available at this http URL.
Training Bi-Encoders for Word Sense Disambiguation

Modern transformer-based neural architectures yield impressive results in nearly every NLP task and Word Sense Disambiguation, the problem of discerning the correct sense of a word in a given context, is no exception. State-of-the-art approaches in WSD today leverage lexical information along with pre-trained embeddings from these models to achieve results comparable to human inter-annotator agreement on standard evaluation benchmarks. In the same vein, we experiment with several strategies to optimize bi-encoders for this specific task and propose alternative methods of presenting lexical information to our model. Through our multi-stage pre-training and fine-tuning pipeline we further the state of the art in Word Sense Disambiguation.