Reviewerly/exHarmony huggingface dataset:
Construction of Hybrid English Courses Based on Mobile Software Assisted Teaching in Chinese Universities

Developing "Internet + Education" and promoting the sharing of high-quality educational resources are the major trends of national educational reformation and development in China. As a compulsory course for undergraduates, it is necessary to reform the traditional mode of English teaching in order to better meet the needs of cultivating internationalized and practical talents in the Internet era. Therefore, based on the project of "Seminar Course" in one Chinese university, this paper explores the construction of Hybrid English Courses by employing Superstar Platform which is a mobile learning software. It aims to establish a new teaching mode which could improve undergraduates' language use ability in higher education in China.
Chinese Text Classification Method Based on BERT Word Embedding

In this paper, we enhance the semantic representation of the word through the BERT pre-training language model, dynamically generates the semantic vector according to the context of the character, and then inputs the character vector embedded as a character-level word vector sequence into the CapsNet.We builted the BiGRU module in the capsule network for text feature extraction, and introduced attention mechanism to focus on key information.We use the corpus of baidu's Chinese question and answer data set and only take the types of questions as classified samples to conduct experiments.We used the separate BERT network and the CapsNet as a comparative experiment. Finally, the experimental results show that the model effect is better than using one of the models alone, and the effect is improved.
Sequence Generation Network Based on Hierarchical Attention for Multi-Charge Prediction

The application of multi-label text classification in charge prediction aims at forecasting all kinds of charges related to the content of judgment documents according to the actual situation, which plays a vital role in the judgment of criminal cases. Existing classification algorithms have high accuracy for the single-charge prediction, but their accuracy for the multi-charge prediction is low. To solve this problem, in this paper we introduce a novel hierarchical nested attention structure model with relevant law article information to predict the multi-charge classification of legal judgment documents. By considering the correlation between different charges, the accuracy of multi-charge prediction is greatly improved. Experimental results on real-world datasets demonstrate that our proposed model achieves significant and consistent improvements over other state-of-the-art baselines.
Identifying Influential Spreaders in a Social Network (While Preserving Privacy)

Abstract In order to disseminate information in a social network, it is important to first identify the influential spreaders in the network. Using them as the seed spreaders, the aim is to ensure that the information is cascaded throughout the network. The traditional approach to identifying influential nodes is to determine the top- r ranked nodes in accordance with various ranking methods such as PageRank, k -Shell decomposition, ClusterRank and VoteRank. In the current work, we study the problem of ranking the nodes when the underlying graph is distributedly held by a set of individuals, who consider their share of the data as private information. In particular, we design efficient secure multiparty computation (MPC) protocols for k -Shell decomposition, PageRank and VoteRank. For improved efficiency, we employ the oblivious RAM construct in conjunction with efficient data-oblivious graph data structures. We are the first to propose a secure variant of the VoteRank algorithm. We prove that the proposed protocols are asymptotically more efficient and have lower runtime in practice than the previous best known MPC protocols for computing k -Shell decomposition and PageRank centrality scores.
The Effect of Linguistic Parameters in CLIR Performance.

This paper will detail how IARPA’s MATERIAL Cross-Language Information Retrieval (CLIR) program investigated certain linguistic parameters to guide language choice, data collection and partitioning, and understand evaluation results. Discerning which linguistic parameters correlated with overall performance enabled the evaluation of progress when different languages were measured, and also was an important factor in determining the most effective CLIR pipeline design, customized to handle language-specific properties deemed necessary to address.
Computing aesthetics of concrete user interfaces

Since aesthetics of a graphical user interface have become a sub-factor of the ISO 25010 standard on software quality, organisations start wondering how to practically evaluate this property in a consistent way. To address this challenge, we present a process for computing aesthetics at the level of a Concrete User Interface instead of Final User Interface to make it platform independent. This process consists of performing the following steps: reverse engineer a final user interface into its concrete equivalent, optionally edit it, and automatically compute ten aesthetic metrics at the level of concrete user interfaces.
Augmented Reality Technologies for Interior Design Planning using a Simultaneous Localization and Mapping Method

Abstract Planning the placement of furniture in a room is an important planning process, with proper planning, the placement of furniture can be more efficient in the use of space, as well as from an aesthetic point of view. During this time the process can be done by using a sketch or other manual method. In this research, we use augmented reality technology to plan furniture placement in a room. We use the Markerless technique in the implementation of this AR system, and use the Simultaneous Localization and Mapping (SLAM) technique to determine the geospatial point where the furniture will be the desired layout. The results of this application ifound that good minimum distance in detecting SLAM on the ground is 60 cm and can only be seen up to a maximum position of 45 ° from the Z axis of the camera’s initial position when tracking. This application runs on the Android mobile platform Introduction.
TACC: Topology-Aware Coded Computing for Distributed Graph Processing

This article proposes a coded distributed graph processing framework to alleviate the communication bottleneck in large-scale distributed graph processing. In particular, we propose a topology-aware coded computing (TACC) algorithm that has two novel salient features: (i) a topology-aware graph allocation strategy, and (ii) a coded aggregation scheme that combines the intermediate computations for graph processing while constructing coded messages. The proposed setup results in a trade-off between computation and communication, in that increasing the computation load at the distributed parties can in turn reduce the communication load. We demonstrate the effectiveness of the TACC algorithm by comparing the communication load with existing setups on both Erdös-Rényi and Barabási-Albert type random graphs, as well as real-world Google web graph for PageRank computations. In particular, we show that the proposed coding strategy can lead to up to 82% reduction in communication load and up to 46% reduction in overall execution time, when compared to the state-of-the-art and implemented on the Amazon EC2 cloud compute platform.
Hilo-wear: Exploring Wearable Interaction with Indoor Air Quality Forecast

The quality of air in office spaces can have far-reaching impacts on the well-being and productivity of office workers. We present a system, called Hilo, that can monitor the level of carbon dioxide (CO2) in an office and provide a fairly accurate forecast of its evolution in the next few minutes. The main objective is to inform and to support the users in taking preventive actions when a harmful level of CO2 is predicted. We elicited three main elements of such prediction — Risk, Temporal Proximity, and Certainty, and explored alternative ways of displaying indoor CO2 forecast through these elements. To evaluate our prototypes, we conducted a preliminary user study, in which three interfaces on Apple Watch were tested by 12 participants (within-subjects, a total of 36 sessions). In this paper, we describe the results of this study and discuss implications for future work on how to create an engaging interaction with the users about the quality of air in offices and particularly its forecast.
Generalized Entropy Loss Function in Neural Network: Variable’s Importance and Sensitivity Analysis

Artificial neural networks are powerful tools for data analysis and are particularly suitable for modelling relationships between variables for best prediction of an outcome. A large number of error functions have been proposed in the literature to achieve a better predictive power of a neural network. Only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine various characteristics of the $$ q $$-generalized function based on Tsallis entropy as an alternative loss measure in neural networks. To achieve this goal, we will explore various methods that can be used to interpret supervised neural network models. These methods can be used to visualize the model using the neural network interpretation diagram, assess the importance of variables by disaggregating the model’s weights (Olden’s and Garson’s algorithms) and perform a sensitivity analysis of model responses to input variables (Lek’s profile).
Chinese relation extraction based on lattice network improved with BERT model

Relation classification is a basic and important task in the field of natural language processing(NLP). There are already many researches on English dataset, but the researches on Chinese dataset are very few. Due to the particularity of Chinese language, most existing methods suffer from the two main problems of segmentation error and polysemy. To sum up, the problem of segmentation error can be solved fairly well by many models, take lattice model for example, which can segment Chinese word precisely. But the problem of polysemy has not received enough attention. In this paper, we take advantage of BERT model to deal with the problem of polysemy. The experimental results show that our model achieves good result and outperforms baseline model.
‘Eternal Recurrence’: development of a 3D water curtain system and real-time projection mapping for a large-scale systems artwork installation

‘Eternal Recurrence’ is a large-scale installation work consisting of a 3D water curtain and projection mapping systems.A 3D water curtain, water drop generation and control program, fog screen and computer program for recognizing visitors were developed for this work. The water curtain, implemented using an array of solenoid valves, creates ephemeral sculptures in real time; the continuous evolution of shapes and images is used as a metaphor of Eternal Recurrence. We developed a method of controlling the solenoid valves that avoids problematic vertical shape distortion caused by gravity and noisy satellite water droplets around the main water drops. The motion of the falling water drops at the centre of the installation is mirrored by the motion of baby-like shapes projected on to the floor and ceiling of the space in which this work is installed. This gives the impression of a full or completed circle. Visitors enter the exhibition through a fog screen, which acts as an entrance port to this immersive experience. ‘Eternal Recurrence’ is a convergence of digital images (3D shape projections) with natural phenomena (falling water drops), which are carefully blended and controlled. As an example of systems art, the installation embodies a process in which all of the separate elements are seamlessly blended through a mechanical–electrical–computer system. The creation and experience of this harmonious system help us to reconsider our existence and networked relations in contemporary society.
Support Vector Machine Classification Algorithm Based on Relief-F Feature Weighting

Aiming at the problem that the existing support vector machines only consider the importance of samples and ignore the importance of features on the classification results, this paper proposes a support vector machine method based on Relief-F feature weighting. This method first uses the Relief-F feature weighting algorithm to calculate the weight value of each feature, and then uses the feature weight value to weight the inner product in the support vector machine kernel function. This method effectively avoids the influence of weakly correlated features or uncorrelated features on the support vector machine classification results. Training and verification are performed on the data provided by the UCI public data set; experimental results show that the method can improve the classification accuracy of the classifier, reduce the number of support vectors, and have better robustness and classification ability than traditional SVM.
Robust Manifold Embedding for Face Recognition

Flexible manifold embedding (FME) has been recognized as an effective method for face recognition by integrating both class label information from labeled data and manifold structure information of all data. In order to achieve better performance, this particular method usually requires sufficient samples to make manifold smooth. However, it is often hard to provide enough samples for FME in practice. In view of facial symmetry, we utilize left/right mirror face images to address the deficiency of samples in manifold embedding. These mirror images enable to reflect variations of illuminations, or poses or both them that the original face images cannot provide. Therefore, we propose a robust manifold embedding (RME) algorithm in this paper, which can fully use the class label information and correctly capture the underlying manifold structure. The proposed RME algorithm integrates two complementary characteristics of the label fitness and the manifold smoothness. Moreover, the original face images and its left/right mirror images are jointly used in the learning of RME, which shows better robustness against the variations of both illuminations and poses. Extensive experiments on several public face databases demonstrate that the proposed RME algorithm is promising for higher recognition accuracy than other compared methods in reference.
SAN : Scale-Space Attention Networks

Deep neural networks (DNNs), especially convolutional neural networks (CNNs), have been effective in various data-driven applications. Yet, DNNs suffer from several major challenges; in particular, in many applications where the input data is relatively sparse, DNNs face the problems of overfitting to the input data and poor generalizability. This brings up several critical questions: "Are all inputs equally importantƒ" "Can we selectively focus on parts of the input data in a way that reduces overfitting to irrelevant observationsƒ" Recently, attention networks showed some success in helping the overall process focus onto parts of the data that carry higher importance in the current context. Yet, we note that the current attention network design approaches are not sufficiently informed about the key data characteristics in identifying salient regions in the data. We propose an innovative robust feature learning framework, scale-invariant attention networks (SAN), that identifies salient regions in the input data for the CNN to focus on. Unlike the existing attention networks, SAN concentrates attention on parts of the data where there is major change across space and scale. We argue, and experimentally show, that the salient regions identified by SAN lead to better network performance compared to state-of-the-art (attentioned and non-attentioned) approaches, including architectures such as LeNet, VGG, ResNet, and LSTM, with common benchmark datasets, MNIST, FMNIST, CIFAR10/20/100, GTSRB, ImageNet, Mocap, Aviage, and GTSDB for tasks such as image/time series classification, time series forecasting and object detection in images.
Implementation of Average Filter and Median Filter for OCR Pre Processing of Incoming Letters Image

Abstract Letters are one of the tools to communicate through writing and are useful for conveying information. STMIK STIKOM Indonesia campus (called STIKI Indonesia) receives letters from other institutions reaching an average of 50 letters a month, an automatic system is needed that can help manage the incoming letters and can determine the purpose of the letter. OCR (Optical Character Recognition) is one of the ways that can be used to find a text contained in an image, but to get good results in the OCR process requires a pre-process in order to reduce noise in letter images, combining average filters and median filters be an option because it succeeded in reducing noise and smoothing the image of the organization’s stamp and signature that piled on the name of the sender of the letter. The results obtained from OCR after going through pre-processing are an array of words from letters. The accuracy achieved based on the date of the letter, letter number, subject of the letter and the sender of the letters from 50 test images reached 90%.
Android-Based Chicken Development Monitoring Application

Abstract Eggs are one of the staples of Indonesian people’s food. Based on data from the Central Statistics Agency (BPS), it was found that the total national egg consumption in 2018 was 2,766,760 tons. As for the total production of 2,968,954 tons, so there was an excess of 202,195 tons of national stock. Even though there is excess stock, the current market price is unstable due to seed restrictions, so the chicken is slow to produce. In addition, the weather in the environment around the cage also affects the productivity of laying hens. In linda chicken farms, the problems faced are the same as those above, weather factors affect the health and productivity of chickens. If there is an unstable temperature and weather many chickens will experience pain and may even die. In this case, breeders sometimes do not know how the temperature and the weather are housed, whether or not the chicken needs it. With this Kancer system, breeders can monitor the development of laying hens and cages. Starting from the active or not the chicken is producing, changes in temperature, humidity and notification if there is rain. Microcontroller device that is installed in a cage and connected to a wifi network will send data temperature, humidity and rain detection to the server, then from the server the data will be processed and then sent to the application. And to monitor the productivity of each chicken, the kancer system utilizes the qrcode that has been stored per-chicken data, then through the kancer qrcode application is scanned and the application will display chicken data ranging from age to the number of eggs produced by the chicken.
Feature Extraction of Cultural Gene Image Based on PCA Method

Feature extraction technology is an important field in computer vision and image processing. It extracts the image information by using computer software, and then process these information appropriately. Finally, an image of the feature points and the corresponding feature values will be got. This paper introduced the main principles of the PCA feature extraction method, and analyzed the advantages of feature extraction in the study of cultural genes. Matlab software was used to carry out the experiments of feature extraction and recognition on the Miao costume image cultural gene data set. The effects of using original image recognition and feature extraction were compared. In final, effectiveness of the algorithm was verified and the significance of feature extraction technology in the study of cultural gene was proved.
Mixed Reality Simulation of High-Endurance Unmanned Aerial Vehicle with Dual-Head Electromagnetic Propulsion Devices for Earth and Other Planetary Explorations

One of the major limitations of existing unmanned aerial vehicles is limited flight endurance. In this study, we designed an innovative uninterrupted electromagnetic propulsion device for high-endurance missions of a quadcopter drone for the lucrative exploration of earth and other planets with atmospheres. As an airborne platform, this device could achieve scientific objectives better than state-of-the-art revolving spacecraft and walking robots, without any terrain limitation. We developed a mixed reality simulation based on a quadcopter drone and an X-Plane flight simulator. A computer with the X-Plane flight simulator represented the virtual part, and a real quadcopter operating within an airfield represented the real part. In the first phase of our study, we developed a connection interface between the X-Plane flight simulator and the quadcopter ground control station in MATLAB. The experimental results generated from the Earth’s atmosphere show that the flight data from the real and the virtual quadcopters are precise and very close to the prescribed target. The proof-of-concept of the mixed reality simulation of the quadcopter at the Earth atmosphere was verified and validated through several experimental flights of the F450 spider quadcopter with a Pixhawk flight controller with the restricted endurance at the airfield location of Hangang Drone Park in Seoul, South Korea. We concluded that the new generation drones integrated with lightweight electromagnetic propulsion devices are a viable option for achieving unrestricted flight endurance with improved payload capability for Earth and other planetary explorations with the aid of mixed reality simulation to meet the mission flight path demands. This study provides insight into mixed reality simulation aiming for Mars explorations and high-endurance missions in the Earth’s atmosphere with credibility using quadcopter drones regulated by dual-head electromagnetic propulsion devices.
Systematic survey of compression algorithms in medical imaging

To meet the requirements of communication networks and storage capacity, medical images are required to be compressed before storage and transmission. Therefore different compression techniques have been developed with an objective of reduction in data size as per recommendation in medical imaging. During the past few decades, a series of advanced developments have been made in medical image compression algorithms to enhance the efficiency of storage and transmission bandwidth. However, each compression algorithm has its own features and limitations and cannot be applied in every case. Consequently, this chapter presents the basic fundamentals as well as recent advancements in compression of medical imaging. The chapter concludes with a summary, a set of medical imaging compression algorithms, made available to a wider community of academics and practitioners for future directions.
Ensembles of criteria for determining the number of homogeneous groups in a combined batch of industrial production

Abstract An article proposes a procedure for ensembles of criteria formation for determine automatically a number of clusters for dividing a combined batch of industrial production. It is shown that these ensembles of criteria can improve the accuracy of the separation of combined batches of industrial production into homogeneous ones.