Reviewerly/exHarmony huggingface dataset:
Research on judgment reasoning using natural language inference in Chinese medical texts

Machine reading comprehension (MRC) is a task used to test the degree to which a machine understands natural language by asking the machine to answer questions according to a given context. Judgment reasoning is one of MRC tasks which means that given a context and questions, let machine gives the true and false answers, for some real-world data, there will be another option of unknown. Considering the current research status, this paper uses natural language inference (NLI) models to further study this judgment reasoning task, which is mainly to judge the semantic relationship between two sentences. In our paper, we first explain how the NLI task can be used to train universal sentence encoding models in the judgment reasoning process and subsequently describe the architectures used in NLI task, which covers a suitable range of sentence encoders currently in use and take the bi-directional long short-term memory (BI-LSTM) model with max-pooling over the hidden representations as an example explained in this paper. After some comparative experiments, we have verified that our NLI models are effective strategies to improve the performance of judgment reasoning in Chinese medical texts, which can effectively improve the accuracy values.
Recognition and Alarm System with Deep Learning for Correctly Wearing a Face Mask

We have designed and implemented a detection and alarm contact tracing mask recognition access control system to wear a face mask correctly to improve Taiwan CDC 1922 SMS contact tracing system, which is difficult to be indeed executed on university campuses and lacks efficiency. Our system involved deep learning, the Internet of Things, 3D printing technologies to overcome the shortcomings of the current real link system. We optimized the deep learning model recognition accuracy and solved the imbalance problem of the dataset on this task. Thus, the model recognition achieved 88.89% in mAP@0.50 and 75.02% in Avg. IOU.
Multilabel Text Classification of Scientific Abstract

Srinivas, T. R. Rithvik, A. V. S. Mukherjee, Saswati In the current era, research and technological advancements are growing at a rapid pace in various disciplines. Thus, it becomes very exhaustive to identify, maintain and manually classify a large volume and collection of publications and articles. One of the main concerns of many students, researchers and professors is finding scientific papers and articles relevant to a particular area(s) of research or category. Classification of papers into the relevant categories makes the process easier. Although many researches have published their findings on scientific text classification, a majority of the work is focused on binary or multiclass classification. In a real-world scenario, research articles may belong to more than one or more categories at the same time. Thus, the paper aims to address these gaps by studying and evaluating various approaches for multilabel classification of scientific text. Various multilabel feature selection, multilabel classification and deep learning methods are studied, implemented and evaluated for the task in consideration using micro-averaged F1 score as the performance measure. This study will help researchers to exploit multilabel scientific text classification faster.
Real-Scenes Face Super-Resolution using a Lightweight Generative Adversarial Network

Although deep learning-based methods have achieved great success in the field of image super-resolution (SR), the SR algorithms still have some shortcomings, such as unsatisfactory results in reconstructing face images collected in real scenes, the model has a large amount of calculation and serious delays when running in a real-time scene. To solve the above problems, this paper proposes a SR algorithm for face images based on a lightweight Generative Adversarial Network (GAN). The algorithm in this paper constructs a real-scenes face datasets for training. Based on the ESRGAN, this study completes the lightweight reconstruction of the basic network inspired by MobileNetV3. Experimental results show that the algorithm is better than the existing mainstream SR algorithms in terms of image reconstruction time, model volume, and parameters amount. When the test image is a face image with an unknown degradation method collected in a real scene, the algorithm in this paper can also achieve the ideal reconstruction effect.
ARERec: Attentive Local Interaction Model for Sequential Recommendation

Previous sequential-recommendation methods have been able to capture patterns of item characteristics that interact with the user. However, they modeled user behavior using a whole interaction sequence, despite possible changes in a user&#x2019;s behavior over time, which can make some behaviors no longer relevant by the end of the user&#x2013;item interaction period. That is, each item representation was derived from the influences of all items in the whole sequence (i.e. global representation) without considering item-adjacency factors that can affect the item characteristics during the interaction period (i.e. local representation). Furthermore, these methods modeled only user behavior, ignoring item behavior, which can involve patterns of user characteristics for users who interact with the item. In this paper, we therefore propose a novel attentive local-interaction model for sequential recommendation called ARERec, which applies a region-embedding technique to both user and item historical sequences. The aim is to model user and item behavior over the user&#x2013;item interaction sequence while considering local representations that contain specific characteristics of both user and item in the sequence. In this way, information is derived for corresponding periods that reflect more-specific reasons behind the interaction in the sequence. Moreover, to account for ratings and neighbor-related factors, we adopt the concept of neighbor-based collaborative filtering in our predictions. One issue is that neighbors have the same similarity levels for all users, resulting in similar predictions, even though there may be different user-specific information. To address this problem, we apply a multi-head attention mechanism to personalize each neighbor based on the user&#x2019;s characteristics. Extensive experiments on three datasets demonstrate that ARERec consistently outperforms state-of-the-art sequential methods, including Recurrent Neural Networks and the attention-based methods (both unidirectional and bidirectional) HitRate and Normalized Discounted Cumulative Gain. Our experiments also show that ARERec provides superior results by considering interaction periods with local representation rather than the whole sequence using global representation.
A Fractal Uncertainty Principle for the Short-Time Fourier Transform and Gabor Multipliers

We study the fractal uncertainty principle in the joint time-frequency representation, and we prove a version for the Short-Time Fourier transform with Gaussian window on the modulation spaces. This can equivalently be formulated in terms of projection operators on the Bargmann-Fock spaces of entire functions. Specifically for signals in $L^2(\mathbb{R}^d)$, we obtain norm estimates of Daubechies’ time-frequency localization operator localizing on porous sets. The proof is based on the maximal Nyquist density of such sets, and for multidimensional Cantor iterates we derive explicit upper bound asymptotes. Finally, we translate the fractal uncertainty principle to discrete Gaussian Gabor multipliers.
Bitmap Vectorization Conversion Method in Plane Design

The existing Hough transform recognition has a large amount of calculation and low accuracy in the vectorization of plane graphics. To solve this problem, an improved Hough vectorization algorithm based on transformation is proposed in this paper. Firstly, the image vectorization edge detection operator is added to the preprocessing process to eliminate the noise. Then, the least square method and Hough exchange are combined for line recognition, and the generalized Hough transform is adopted to detect the circle to improve the recognition accuracy. Finally, the object-oriented operation software is designed by MATLAB, and the improved fitting method is used to vectorize a combined graph, and the vector graphs of them are obtained to verify the accuracy of this scheme.
Assessment of the State of the Geological Section at the Site of Railway Tunnel Construction Using Non-destructive Control Methods

The article presents the main results of research in the field of control of the geological section at the site of the transport tunnels. The relevance of the research is due to the difficult mining and geological conditions, the scale of such work in the country and the shortcomings of existing methods for assessing the geological environment. Purpose of the stady is a development of a set of methods for controlling a geological section to obtain reliable information that ensures effective decisions on the choice of mining technologies at the site of construction of transport tunnels. Objects: rock mass (geological environment), transport tunnel, lithotypes, rock layers, geomechanical contacts. Methods: geological survey, seismoacoustic control of the geological environment, geoelectric profiling of a mine roof, system analysis and synthesis of control results, complexing of geophysical technologies. Results. An objective assessment of the existing methods for assessing the state of the geological environment at the construction site of transport tunnels is given. An effective method for investigating a geological section in a mining area with the use of a rational complex of non-destructive testing methods is proposed. The quality of the assessment of the state of the geoenvironment required for the choice of the tunnel construction technology is ensured by a large-scale verification of the results in practice.
Is Deep Learning Good Enough for Software Defect Prediction?

Due to high impact of internet technology and rapid change in software systems; it has been a tough challenge for us to detect software defects with high accuracy. Traditional software defect prediction research mainly concentrates on manually designing features (e.g., complexity metrics) and inputting them into machine learning classifiers to distinguish defective code. To gain high prediction accuracy, researchers have developed several deep learning or high computational models for software defect prediction. However, there are several critical conditions and theoretical problems in order to achieve better results. This article explores the investigation of SDP using two deep learning techniques, i.e., SqueezeNet and Bottleneck models. We employed seven different open-source datasets from NASA Repository to perform this comparative study. We use F-Measure as a performance evaluator and found these methods statistically outperform eight state-of-the-art methods with mean F-Measure of 0.93±0.014, and 0.90±0.013, respectively. We found that these two methods are significantly more effective in terms of F-Measure over large and moderate size projects. But they are computationally expensive in terms of training time. As the size of projects is getting immense and sophisticated, such deep learning methods are worth applying.
An IPSO-SA algorithm based on feedback mechanism for smooth path planning of UAV

Abstract The traditional PSO algorithm has been widely used in the path planning of unmanned equipment, but its algorithm is faced with the problem of premature convergence, which is easy to fall into the local optimal solution. When the initial position is not suitable, it can’t even plan the path in complex environment. At the same time, its line trajectory does not meet the requirements of the path curvature of realistic unmanned equipment. To solve the above problems, this paper proposes a new path planning strategy for unmanned equipment, and designs a new path planning algorithm for unmanned aircraft by combining the improved particle swarm optimization (IPSO) algorithm with simulated annealing algorithm (SA). At the same time, it solves the two problems that PSO algorithm is easy to fall into local optimal solution and abandoning all possible feasible solution regions in the early stage because of an illegal path. The path planned by IPSO-SA algorithm is processed by cubic spline interpolation to solve the curvature problem of its motion path. In addition, in view of the problem that the initial point is not suitable in complex environment, this algorithm designs a feedback mechanism to correct the initial point in time. The effectiveness of the algorithm is verified by theoretical derivation and simulation experiments, that is, the algorithm only needs a small number of point sequences to plan the path of UAV in simple and complex environment, and has less search amount and space-time overhead.
Weakly supervised discourse segmentation for multiparty oral conversations

Discourse segmentation, the first step of discourse analysis, has been shown to improve results for text summarization, translation and other NLP tasks.While segmentation models for written text tend to perform well, they are not directly applicable to spontaneous, oral conversation, which has linguistic features foreign to written text.Segmentation is less studied for this type of language, where annotated data is scarce, and existing corpora more heterogeneous.We develop a weak supervision approach to adapt, using minimal annotation, a state of the art discourse segmenter trained on written text to French conversation transcripts.Supervision is given by a latent model bootstrapped by manually defined heuristic rules that use linguistic and acoustic information.The resulting model improves the original segmenter, especially in contexts where information on speaker turns is lacking or noisy, gaining up to 13% in F-score.Evaluation is performed on data like those used to define our heuristic rules, but also on transcripts from two other corpora.
Traffic Matrix Prediction with Attention-based Recurrent Neural Network

Traffic matrix (TM) shows the traffic volume of a network. Therefore, TM prediction is of great significance for network management. Attention mechanism has been successful in many sub-domains of machine learning, such as computer vision and natural language processing, and it performs particularly well on time series data. In this work, we first introduce attention mechanisms into the traffic matrix prediction field by proposing an attention-based deep learning model for traffic matrix prediction. This model is composed of two parts, encoder and decoder. We use a recurrent neural network (RNN) architecture as the encoder and our decoder has an attention layer and a linear layer. Attention mechanism allows the model to have better memory ability, so the model can concentrate on those important data regardless of distance. We also reduce the time consumption of our model using GPU-based parallel acceleration. Finally, we evaluate the effectiveness of our model on a real world TM dataset, and the results show our implementations on the proposed model perform better than the baseline models.
BERT-fused Model for Finnish-Swedish Translation

Translation between Finnish and Swedish is a common yet time-consuming and expensive task. In this paper, we train new neural machine translation models and compare them with publicly available tools for automatic translation of Finnish to Swedish. Furthermore, we analyze if fusing BERT models with traditional Transformer models produces better translations. We train a base Transformer and a large Transformer model using Fairseq and compare the results with BERT-fused versions of the models. Our large transformer model matches the state-of-the-art performance in Finnish-Swedish translation and slightly improves the BLEU score from 29.4 to 29.8. In our experiments, fusing the smaller Transformer model with a pre-trained BERT improves the quality of the translations. Surprisingly, the larger Transformer model in contrast does not benefit from being fused with a BERT model.
Prediction Algorithm of Physical Education Achievement Based on BP Neural Network

Achievement prediction is one of the important contents of the research on future development strategy of competitive sports. In this study, take the men's 800 meters final results in 2008 as an example, use BP neural network algorithm to explore the method of achievement prediction on competitive sports. Firstly, expounds the basic principle of BP neural network algorithm, and establish the BP neural network structure diagram; Then, introduces the training process and establish process of BP neural network model, and construct the training process flow chart; Finally, begin the prediction for the achievements of men's 800m based on BP neural network, and through error analysis to test the accuracy of the prediction. BP neural network has the characteristics of massively parallel, distributed processing, self-organizing, self-learning, also has the ability of nonlinear mapping, adaptive ability, generalization ability and fault tolerant ability.
A Logic for Iterated Belief Revision

Representing belief information is a fundamental problem in the field of belief revision. The AGM framework uses a deductively closed set of formulas, known as a theory, to represent the belief information of an agent, because the belief information of a rational agent should satisfy properties similar to a theory. However, in the iterated revision setting, the DP framework uses conditional beliefs like (φ | ψ) to represent such information, which is not natural, as conditional beliefs are not formulas and logical connections between them cannot be characterized clearly. In this paper, we propose a novel logic system for representing belief information under iterated revision as a theory in this logic system, which is more natural than the approach of the DP framework. We also prove the soundness and completeness of the logic system, such that it is readily usable for performing iterated revision. Finally, we showed that this logic system is powerful enough to represent general epistemic state as a theory, which is more general than the representation of the DP framework.
Research on Power Information Knowledge Extraction Model Based on BERT

Knowledge extraction is the basic step of constructing power information knowledge graph. Aiming at the problems of polysemy, poor context awareness and limited sentence types in the previous text processing methods in the power field, we propose a knowledge extraction method based on BERT from the characteristics of power information text. In the first part, BERT-BiGRU-CRF model is used to complete the named entity recognition of power information text. In the second part, BERT-BiGRU-attention model is used to extract the relation between entities. The model can improve the effect of named entity recognition and relation extraction in complex context, and can process diversified power information texts.
Cognitive Training Based on Human-Computer Interaction and Susceptibility to Visual Illusions. Reduction of the Ponzo Effect Through Working Memory Training

The current study aimed to test efficiency of cognitive training programs based on human-computer interaction and their influence on resistance to orientation visual illusions (Poggendorff, Zӧllner) and metric visual illusions (Ebbinghaus, Müller-Lyer, Ponzo). Moreover, we attempted to verify whether Witkin’s field dependence/independence style moderates the influence of cognitive training on visual illusion resistance. 250 participants aged 19-32 took part in the experiment. Three training groups were used: a working memory-training group, an attention-training group, and a perception-training group; a passive control group was also used. The groups were homogeneous in terms of gender, age, and proportion of field-dependent and field-independent individuals. All groups received about three weeks of adaptive cognitive training, consisting of 18 sessions of 30 minutes per day. The results showed that, in general, field-dependent participants appeared to be more susceptible to visual illusions than field-independent ones. In addition, it was demonstrated that working memory (WM) training is effective in reducing field-dependent participants’ susceptibility to the Ponzo illusion. The results are discussed from the perspective of the cognitive deficits hypothesis.
New Solutions to Biomedical Data Sharing: Secure Computation and Synthetic Data

An essential precondition for Artificial Intelligence applications in health is the possibility of having recourse to Big Data. The inconvenient truth, however, is that ever-growing quantities of biomedical data sit idle in fragmented silos, and their use is hindered, among other things, by the need to act in compliance with the General Data Protection Regulation. A recent H2020 Research and Innovation project, MyHealthMyData, highlighted two privacy-enhancing technologies which can provide new solutions for data sharing in health. The first is based on bringing the algorithms to the data in what is called the “visiting mode”, based on secure computation mechanisms, by which only the outcomes are released, not the original data. The second approach leverages the capacity of producing synthetic data through generative adversarial neural networks, adding a further non-reidentification guarantee by making use also of differential privacy. Synthetic data generation and secure computation may thus complement the GDPR by developing an ecosystem in which health data can safely provide the Big Data needed to fully make use of AI in medicine.
Adapting on Long-Tail Domains by High Quality Self-training for Object Detection

Domain shift is a critical challenge when we deploy object detectors to real world applications. Due to the data distribution misalignment between the source and target domains, the detector suffers from a significant performance drop on the target domain. In some situations, the target domain contains a large ratio of objects in a few categories. This long-tail distribution makes it even harder to transfer well from source to target. In this paper, we tackle this categorical distribution difference in domain adaptation using self-training, where the unlabeled images in target domain are tagged with pseudo-labels to train the detector itself. To promote the effect of self-training, we propose SEAT (Score Ensemble and Adaptive Threshold), where high quality pseudo-labeled samples are elected by comprehensively analyzing information from multiple aspects. We show that these high quality pseudo-labeled training samples play a critical role in dealing with categorical distribution difference. Experiments on public data sets show the effectiveness of our method.
Integrating Real-Time Vehicle and Watercraft Modeling and Simulation Tools for Analysis of Amphibious Operations

Amphibious operations are complex, multi-domain problems that occur in an unpredictable environment. As such, they require knowledge and understanding of the battlespace to be successful. This report describes the development and demonstration of a proof-of-concept tool that combines existing simulation capabilities for watercraft and ground vehicles. The new multi-domain co-simulation environment allows for the modeling and simulation of amphibious operations. During operation, the ship simulator half of the Ship-to-Shore proof-of-concept tool controls an amphibious vessel during its approach to the beach. Once at the shore, primary simulation control passes to the vehicle simulator. Then the ground vehicle is either manually or autonomously maneuvered from the vessel bay, across the beach, and further inland. This combined capability provides a novel environment for ship-to-shore mission assessment. This enables enhanced planning, rehearsal, and decision support prior to mission execution.