Reviewerly/exHarmony huggingface dataset:
The Use of Virtual Reality at Lower Secondary Schools

This chapter examines the use of virtual reality (VR) at Swiss lower secondary schools. Many years of research appear to have produced only limited data on the effects of VR technology on children’s learning success. It is assumed that VR supports the learning theory of constructivism because it allows immersion, interaction, 3D representation, and adoption of multiple perspectives. To investigate the topic, a prototype has been developed for a “Nature and Technology” learning unit on the subject of plastics and their effects on the environment. Divided into five learning blocks, the learning unit provides students with knowledge about the composition, production, and recycling of plastics. In addition, it explores the related environmental consequences and possible solution strategies. Ideas for possible VR applications were developed for each of these blocks. This vision was discussed in four interviews with teachers. The resulting VR application was commented on and evaluated by 19 students of the same class. In a second phase, the teaching unit incorporating VR was compared to a conventional unit by means of a field experiment and two random sample tests measuring impact on the learning success of 82 students. Their learning success was not found to have improved significantly.
Enhanced sparse matrix approach in neural network algorithm for an effective intelligent classification system

The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners (physicians) to accelerate diagnosis and treatment processes. In this paper, the sparse matrix approach was incorporated in neural network learning algorithm for scalability, minimize higher memory usage/storage capacity, enhancing implementation time and speed up the analysis of the data. The proposed intelligent classification system maximizes the intelligently classification of data and minimizes the number of trends inaccurately identified. For robustness, the proposed method was tested with three different datasets, namely, Hepatitis, SPECT Heart and Cleveland Heart. Therefore, an attempt was made to determine the performance indicators efficacy. Compared to some similar existing methods, the approach presented achieves improved performance. The program used for implementation of the proposed model is MATLAB R2016a (version 9.0) and executed in the 4.0 GB RAM processor of PC Intel Pentium Quad Core N3700.
Ensemble Distilling Pretrained Language Models for Machine Translation Quality Estimation

Machine translation quality estimation (Quality Estimation, QE) aims to evaluate the quality of machine translation automatically without golden reference. QE can be implemented on different granularities, thus to give an estimation for different aspects of machines translation output. In this paper, we propose an effective method to utilize pretrained language models to improve the performance of QE. Our model combines two popular pretrained models, which are Bert and XLM, to create a very strong baseline for both sentence-level and word-level QE. We also propose a simple yet effective strategy, ensemble distillation, to further improve the accuracy of QE system. Ensemble distillation can integrate different knowledge from multiple models into one model, and strengthen each single model by a large margin. We evaluate our system on CCMT2019 Chinese-English and English-Chinese QE dataset, which contains word-level and sentence-level subtasks. Experiment results show our model surpasses previous models to a large extend, demonstrating the effectiveness of our proposed method.
Phase unwrapping with the convolutional neural network

Phase unwrapping is a classical signal processing problem, which refers to the recovery of the original phase value from the wrapped phase. Two dimensional phase unwrapping is widely used in optical measurement technology, such as digital holographic interferometry, fringe projection profilometry, synthetic aperture radar and many other applications. In this paper, a phase unwrapping method with the convolution neural network is proposed, and the feasibility is analyzed by numerical simulation. The convolution neural networks with different parameters are set up, and the phase screens used for the training set and testing set of convolution neural network are simulated with MATLAB software. The numerical simulation results show that the four convolution neural network models can be used for phase unwrapping, but the parameters have a significant impact on its accuracy.
A Real-Time Anomaly Detection Approach Based on Sparse Distributed Representation

As a hot topic in process industries, the problem of anomaly detection has been researched for years. A lot of model-based and data-based approaches were developed to monitor and diagnose faults. As known to us, the data-driven methods are more suitable for a modern industrial process that commonly associated with complex, coupled and large-scale subsystems. In such case, it is hardly to construct an exact model. In existing data-driven approaches, the statistics-based methodologies and the graph theory-based methodologies are typical technologies. But, a fatal flaw of them is online. Some of them works well in offline scenario, however, the performance of online is contrary to that such as Bayesian network. Thanks to our brain, the most complex and rigorous organ in nature copes with quantities of information every moment. A novel and intelligent idea called sparse distributed representation (SDR) has been proposed to encode each element of online data, which is inspired by the information processing way of cerebral cortex. In this paper, a further exploring on SDR is carried out. We propose a theoretical foundation for resolution that is a very important item for SDR to encode each digit exactly. In addition, we also provide a calculation method for its processing boundaries. Ultimately, we take this approach to detect real-time anomaly data like concept drift, and achieve good simulation performance.
Eye Tracking as a form of Alternate and Augmentative Communication for people with Speech, Language and Communication Needs

Diagnosing a disease or a condition has become easy and convenient for the clinician, in this digital era. However, the focus on rehabilitative medicine and recovery oriented technology is not as popular as diagnostics. The current trends in eye-tracking involve the tracking of the progression of cognitive disorders in the elderly patients. The tracking of eye motion can give deterministic data about the patient's ability to focus, socially interact, track moving objects (muscle movement), and so on. With the help of eye-tracking, the proposed system helps in designing an efficient system for alternative and augmentative communication (AAC), by utilizing a camera and a screen (with visual prompt display) to determine the accuracy, and speed of response, to categorize the efficiency of AAC method. The system in design can help to improve the latency of patient response and can help to determine the recovery status of the patient.
An Improved Level Set Method on the Multiscale Edges

The level set method can segment symmetrical or asymmetrical objects in real images according to image features. However, the segmentation performance varies with feature scale. In order to improve the segmentation effect, we propose an improved level set method on the multiscale edges, which combines the level set method with image multi-scale decomposition to form a unified model. In this model, the segmentation relies on multiscale edges, and the multiscale edges depend on multiscale decomposition. A novel total variation regularization is proposed in multiscale decomposition to preserve edges. The multiscale edges obtained by the multiscale decomposition are integrated into the segmentation process, and the object can be easily extracted from a proper scale. Experimental results indicate that this method has superior performance in precision, recall and F-measure, compared with relative edge-based segmentation methods, and is insensitive to noise and inhomogeneous sub-regions.
Hybrid ant colony optimization for even-2 satisfiability programming in Hopfield neural network

Restricted Boolean 2 Satisfiability (2SAT) is a variant of discrete constraint satisfaction problem, commonly being applied with Hopfield neural network (HNN) as logic programming. In recent years, the binary Ant colony optimization has been formulated to solve various satisfaction and optimization problem due to the power onlooker bee phase in attaining the global convergence. The core motivation of this research is to propose a hybrid Hopfield neural network (HNN) incorporated ACO in enhancing the learning phase of new type of 2SAT logic programming. The developed hybrid model will be compared with the other conventional learning method in hybrid HNNs models such as genetic algorithm (GA) and exhaustive search (ES). The simulations were conducted by training and testing the developed model and the other counterparts with randomized simulated data sets. Therefore, the results manifested the capability of ACO in improving the learning phase of HNN as compared with GA and ES under different complexities.
Cryptanalysis of GiophantusTM Schemes against Hybrid Attack

The hybrid attack was proposed by Howgrave-Graham in CRYPTO2007, which was originally designed for the cryptanalysis of NTRU cryptosystems. In this paper, based on Howgrave-Graham's attack model, we propose a simulator of hybrid attack to evaluate the hardness of the unique shortest vector problem. By a dynamical computation, our algorithm can trade off the cost between reduction and MitM, while both of them run in exponential time. Further, we adapt our simulator to Giophantus™, Giophantus+ and Giophantus- cryptosystems, proposed by Akiyama et al. in SAC2017, SCIS2019 and SCIS2020, respectively. Our analysis shows that by the hybrid attack, the security levels can be reduced by at most 19 bits for Giophantus' parameters proposed in NIST Post Quantum Cryptography (PQC) standardization 1st round submission. Meanwhile, the parameter sets of Giophantus+ and Giophantus- are secure against the hybrid attack.
Robust absolute 3D measurement using stereo cost-volume filtering for fringe orders

In fringe projection profilometry (FPP), the phase-shifting profilometry (PSP) is widely used in various fields such as industrial quality check and biomechanics. Among the methods of PSP for absolute phase retrieval, stereo phase unwrapping method, as a new-fashioned method, can eliminate the 2&pi; phase ambiguities and obtain a continuous phase map with a stereo vision system, retrieving the correct fringe period with geometry constraints and depth constraints and not needing to project any additional patterns. However, ensuring the stability of stereo phase unwrapping, the frequency of fringe patterns should be limited at about 20, which affects the accuracy of the 3D shape measurement. Furthermore, in order to enhance the performance of stereo phase unwrapping, stereo block matching based on the speckles as additional auxiliary information is used, which can distinguish several periodic candidate points but still not avoid mismatches, especially for the areas of the dark regions and the object edges where the fringe quality is low. To solve these problems, we proposed a robust absolute 3D measurement using stereo cost-volume filtering for fringe orders in this paper. In the proposed method, besides the stereo block matching, a matching cost filter is designed to aggregate the preliminary matching cost for the final cost which is used to obtain the fringe order map by WTA and then the absolute phase map. Then the more robust and more accurate 3D measurement is realized compared with traditional methods. The experimental results demonstrate that the proposed method can achieve robust absolute 3D measurement using stereo phase unwrapping.
ZeChipC: Time Series Interpolation Method Based on Lebesgue Sampling

In this paper, we present an interpolation method based on Lebesgue sampling that could help to develop systems based time series more efficiently. Our methods can transmit times series, frequently used in health monitoring, with the same level of accuracy but using much fewer data. Our method is based in Lebesgue sampling, which collects information depending on the values of the signal (e.g. the signal output is sampled when it crosses specific limits). Lebesgue sampling contains additional information about the shape of the signal in-between two sampled points. Using this information would allow generating an interpolated signal closer to the original one. In our contribution, we propose a novel time-series interpolation method designed explicitly for Lebesgue sampling called ZeChipC. ZeChipC is a combination of Zero-order hold and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation. ZeChipC includes new functionality to adapt the reconstructed signal to concave/convex regions. The proposed methods have been compared with state-of-the-art interpolation methods using Lebesgue sampling and have offered higher average performance.
Blind Super-Resolution with Kernel-Aware Feature Refinement

Numerous learning-based super-resolution (SR) methods that assume the blur kernel is known in advance or hand-crafted perform excellently on the synthesized images. While these methods fail to produce satisfactory results when applied to real-world images. Therefore, several blind SR methods are proposed, where the blur kernel is unavailable. However, they only consider the Gaussian blur kernel, which is not enough for real-world images. To address this problem, we propose to synthesize training data with real blur kernels estimated from real sensor images. Further, we find that models trained with the data synthesized with the estimated real blur kernels still cannot perform well enough without awareness of the blur kernel. Thus, we first propose a new kernel predictor (KP) to predict the blur kernel from the input low-resolution image. Then we design the kernel-aware feature refinement (KAFR) module to utilize the predicted blur kernel to facilitate the restoration of the high-resolution image. Extensive experiments on synthesized and real-world images show that the proposed method achieves superior performance against the state-of-the-art (SOTA) methods both on effectiveness and efficiency.
Article Reading Sequencing for English Terminology Learning in Professional Courses

Reading is one of the key methods to learn English, especially for the EFL (English as Foreign Language) students. For the purpose of assisting students in learning the English terminology of a professional (technical) course, this study aims at finding reading sequences among pre-collected articles and determining one ending article for each reading sequence. The topics or contents described in the articles are all highly related to the specific professional course. The determination of the ending article depends on the relations among the articles. The relation is defined by professional terminology overlap between articles. Floyd-Warshall algorithm and clustering concepts are utilized in the recommendation of the ending article and reading sequences. Brief simulation results of the algorithm are also illustrated in the paper.
Mono-LSDE: Lightweight Semantic-CNN for Depth Estimation from Monocular Aerial Images

In the last decade, with the advances in autonomous technologies, Unmanned Aerial Vehicles (UAVs) have been encountered a significant focus on several applications. With the complexity of the tasks performed by the UAVs, this addresses the necessity to obtain information about the surrounding environment. Estimating depth maps from monocular images is considered a key role when working small or micro UAVs, this is due to the Size, Weight, and Power (SWaP) constraints on these vehicles. Therefore, this paper proposed a lightweight Semantic Neural Network based on Encoder-Decoder architecture; to obtain a depth map from a monocular camera.The proposed method has been tested in several scenarios of complex environments, and the obtained results show its robustness and efficiency against different weather and light conditions, illustrating the functionality of the proposed method in real-time applications.
Generative Adversarial-Synergetic Networks for Anomaly Detection

Anomaly detection is an important and demanding problem in social harmony. However, due to the uncertainty, irregularity, diversity and scarcity of abnormal samples, the performance is often poor. This paper presents Generative Adversarial-Synergetic Networks (GA-SN) to improve the discriminative ability. It is built on the Adversarial Discriminant Network (ADN) for detecting anomaly and the Synergetic Generative Network (SGN) for extracting pivotal and discriminant feature. The ADN takes advantage of adversary structure between discriminator and the asymmetric generator. As an important hub of ADN and SGN, the asymmetric generator is combined with promotor by synergy in SGN. Different from other methods which based on generative adversarial network, our method pays more attention to the discriminant performance rather than the reconstruction performance. Our method was verified on MNIST and UCSD Ped2 dataset, where MNIST obtained higher values on both area under curve and F1-score, and 12.56% equal error rate is obtained on UCSD Ped2. The experimental results show the superiority of proposed method compared to existing methods. So the GA-SN combines synergy and adversarial relationship, which helps to improve discriminative performance in detection.
Using Algebra Graph Representation to Detect Pairwise-Constraint Software Faults

Automatic fault detection, localization, and repairing have always been a research hot spot. Because software faults can appear anywhere in a software, it is impossible to automatically repair all types of faults. One possible solution is to detect, localize, and fix a specific fault each time according to the fault signature pattern. Along this direction, we propose an approach that detects pairwise-constraint software faults using algebra graph representation. The approach takes a program and a pairwise-constraint as inputs and generates a fault report for programmers. It firstly generates a constraint control flow graph (CFG) that is labeled using pairwise constraint and then translates the constraint CFG as a path expression using algebra graph representation, which is an abstract path model for the program. Finally, it employs a detection algorithm to detect whether the program contains pairwise-constraint faults. We perform case studies to validate the effectiveness of our approach. The preliminary results show that the approach can detect pairwise-constraint software faults before software testing.
Architecture and Protocol to Optimize Videoconference in Wireless Networks

In the past years, videoconferencing (VC) has become an essential means of communications. VC allows people to communicate face to face regardless of their location, and it can be used for different purposes such as business meetings, medical assistance, commercial meetings, and military operations. There are a lot of factors in real-time video transmission that can affect to the quality of service (QoS) and the quality of experience (QoE). The application that is used (Adobe Connect, Cisco Webex, and Skype), the internet connection, or the network used for the communication can affect to the QoE. Users want communication to be as good as possible in terms of QoE. In this paper, we propose an architecture for videoconferencing that provides better quality of experience than other existing applications such as Adobe Connect, Cisco Webex, and Skype. We will test how these three applications work in terms of bandwidth, packets per second, and delay using WiFi and 3G/4G connections. Finally, these applications are compared to our prototype in the same scenarios as they were tested, and also in an SDN, in order to improve the advantages of the prototype.
LSTM-type Neural Network Implementation on a Processor Based on Neuromatrix and RISC Cores for Resource-Limited Applications

In this paper, we consider the implementation of a recurrent artificial neural network LSTM on the NM6407 digital signal processor (DSP) that is optimized for performing vector and matrix calculations. It contains two NeuroMatrix NMC4 cores, each of which includes RISC processor and vector coprocessor. The architectural features and processor resources are considered, as well as an assessment of its performance in the implementation of the LSTM network to solve a typical classification problem. The implementation of this type of network on the NM6407 tensor core accelerated computations by a factor of 15-350 compared to a scalar processor.
Implementation of a Novel Data Sharing Scheme Amongst Dynamic Group Users in Cloud

Centered on its inherent data sharing and low latency features, cloud infrastructure is considered as an effective alternative to conventional information technology. Cloud service providers (CSPs) like Amazon, are capable of providing cloud users with various services by integrating the powerful data centers. Users may utilize high-quality software to move their local data processing systems to cloud storage and save considerable investments in their local infrastructures. Annotation techniques by utilizing the pairs of meaning attributes are usually more descriptive, as they provide more details than the untyped methods. A new solution to use more concise queries with such annotations is the “pay-as-you-go” querying technique in data spaces.
Abusive Language Detection Using BERT Pre-trained Embedding

The rapid growth in social communication increases the importance of detecting toxic languages. However, detecting toxic language is difficult because of deliberately noisy words and lack of labeled data. These issues cause a low recall in toxic language detection. To address these, we utilized pre-trained BERT models for toxic language detection. We hypothesize pre-trained sub-words embeddings allow BERT models to quickly learn the meaning of obfuscation words and, hence improve the recall of the models on toxic language detection. Our results confirm this hypothesis and show that fine-tuned BERT models perform on a par with the state-of-the-art on the Twitter dataset and outperform the state-of-the-art on the Wikipedia dataset.