Reviewerly/exHarmony huggingface dataset:
Know Your Limits: Machine Learning with Rejection for Vehicle Engineering

New vehicle designs need to be tested in representative driving scenarios to evaluate their durability. Because these tests are costly, only a limited number of them can be performed. These have traditionally been selected using rules of thumb, which are not always applicable to modern vehicles. Hence, there is a need to ensure that vehicle tests are aligned with their real-world usage. One possibility for obtaining a broad real-world usage overview is to exploit the data collected by sensors embedded in production vehicles. But these do not produce the detailed data needed to derive the metrics computed using expensive sensors during testing. Therefore it is necessary to correlate the low-end sensor measurements available in production vehicles with the relevant metrics acquired using high-end sensors during testing. Machine learning is a promising avenue for doing this. The key challenge is that vehicles will be used "in the wild" in many scenarios that were not encountered in the controlled testing environment, and it is unlikely that learned models will perform reliably in these previously unseen environments. We overcome this challenge by allowing learned models to abstain from making a prediction when unexpected vehicle usage is identified. We propose a general framework that combines standard machine learning with novelty detection to identify previously unseen situations. We illustrate our framework's potential on data we collected from a large-scale road-roughness analysis use case. Empirically, our approach can identify novel road types in the wild and by doing so it yields better performance.
Neural Artistic Style Transfer Using Deep Neural Networks

Neural style transfer (NST) is a category of software algorithm that uses digital videos and images, adopting the look or viewing style of another video or image. Neural style transfer techniques use deep neural networks to perform image transformation. NST is a high-performance visualization process that allows us to use and thus replicate the style content of another style. It takes two pictures: image content and style reference, and we combine both together to get the resulting image, thus making it look like it was painted in style image style, despite keeping the main elements of the content image. In this paper, the proposed activity uses a convolutional neural network to transmit neural style.
On Heterogeneous Transfer Learning for Improved Network Service Performance Prediction

Transfer learning has been proposed as an approach for leveraging already learned knowledge in a new environment, especially when the amount of training data is limited. However, due to the dynamic nature of future networks and cloud infrastructures, a new environment may differ from the one the model is trained and transferred from. In this paper, we propose and evaluate an approach based on neural networks for heterogeneous transfer learning that addresses model transfer between environments with different input feature sets, which is a natural consequence of network and cloud re-orchestration. We quantify the transfer gain, and empirically show positive gain in a majority of cases. Further, we study the impact of neural-network architectures on the transfer gain, providing tradeoff insights for multiple cases. The evaluation of the approach is performed using data traces collected from a testbed that runs a Video-on-Demand service and a Key-Value Store under various load conditions.
i-RD: A Reverse Dictionary for English Idioms

A traditional forward dictionary provides the meanings of words. In contrast to this, a Reverse Dictionary provides semantically appropriate words for a phrasal description of a concept in mind. It addresses the needs of the language producers viz. speakers and writers. In this paper, we have addressed the problem of Reverse Dictionary in the specific context of English idioms. We have considered the idiomatic expressions in our study owing to the fact that their usage in speech/text creates a spectacular illustration of the speaker&#x0027;s/writer&#x0027;s thought process and is an effective means of communication. We have adopted the architecture of Wordster Reverse Dictionary and build the i-RD, a Reverse Dictionary for English idioms. Specifically, for a given description in natural language, the i-RD generates an apt idiom for usage. The evaluation carried out for our implementation reports an accuracy of 70&#x0025;. While this is satisfactory, we provide an insightful discussion on the limitations and future prospects of the current implementation.
Transformation of Educational Communication in the Context of Education Digitalization

The contribution investigates the problem of digital transformation of the educational environment based on humanistic, anthropological, axiological principles. Digital transformations of the educational environment are associated not only with the process of technological re-equipment, but also with systemic changes of an organizational, managerial, cultural nature, as well as with changes in the communication paradigm. The digital educational environment (DSP) of a modern university is a complex system of information and technical, educational, methodological and communication support. Educational communications ensure the interaction of subjects, the unification of conditions and means of learning, as well as the relationship with external information and educational environments. The condition for effective communication in the DSP is the search for innovative forms of communicative interaction that would provide feedback between the teacher and students; the purpose of the tutor in digital education is revealed. When mastering the digital format of training, teachers face a difficult problem – the need to include digital technologies in the learning process without losing the depth of the content of fundamental knowledge, without losing sight of the socializing and culture-creating function of education. The research results can be used in further studies on digitalization issues, for proposals and recommendations development.
Neural Networks

The ultimate goal of machine learning is to find a good set of parameters, so that the model can learn the mapping relationship fθ : x → y, x, y ∈ Dtrain from the training set and use the trained relationship to predict new samples. Neural networks belong to a branch of research in machine learning. It specifically refers to a model that uses multiple neurons to parameterize the mapping function fθ.
Chatbots for Learning: A Facebook Messenger ‘Bot’

The last two years have changed how students access formal and informal education. Due to the COVID-19 pandemic, language learners have had limited opportunities to interact actively with teachers and fellow students.However, chatbots can help to fill this gap. This paper describes an educational chatbot employed on Facebook Messenger as a supplement to direct teacher input. It was developed using Google’s Dialogflow, which allows teachers to build omnichannel chatbots using natural language understanding models.Specific branching (question and answer) was employed to limit the chatbot’s scope to specific language areas. Chatbots, if employed successfully, could have widespread applications in higher education institutions and shape language teaching in the future.
Laser Based Navigation in Asymmetry and Complex Environment

For collision-free navigation in unstructured and cluttered environments, deep reinforcement learning (DRL) has gained extensive successes for being capable of adapting to new environments without much human effort. However, due to its asymmetry, the problems related to its lack of data efficiency and robustness remain as challenges. In this paper, we present a new laser-based navigation system for mobile robots, which combines a global planner with reinforcement learning-based local trajectory re-planning. The proposed method uses Proximal Policy Optimization to learn an efficient and robust local planning policy with asynchronous data generation and training. Extensive experiments have been presented, showing that the proposed system achieves better performance than previous methods including end-to-end DRL, and it can improve the asymmetrical performance. Our analysis show that the proposed method can efficiently avoid deadlock points and achieves a higher success rate. Moreover, we show that our system can generalize to unseen environments and obstacles with only a few shots. The model enables the warehouse to realize automatic management through intelligent sorting and handling, and it is suitable for various customized application scenarios.
Explanation for KLM-Style Defeasible Reasoning

Explanation services are a crucial aspect of symbolic reasoning systems but they have not been explored in detail for defeasible formalisms such as KLM. We evaluate prior work on the topic with a focus on KLM propositional logic and find that a form of defeasible explanation initially described for Rational Closure which we term weak justification can be adapted to Relevant and Lexicographic Closure as well as described in terms of intuitive properties derived from the KLM postulates. We also consider how a more general definition of defeasible explanation known as strong explanation applies to KLM and propose an algorithm that enumerates these justifications for Rational Closure.
Web Service Classification Based on Reinforcement Learning and Structured Representation Learning

In recent years, the rapid growth of the number of web services on the Internet has made it difficult to discover services from a large number of web services and classify them efficiently, which poses a huge challenge for users to provide appropriate web services. The existing web service classification technology has problems such as sparse description text, insufficient consideration of attribute information and structural relationships, so the accuracy of web service classification can be further improved. In response to the above problems, this paper first proposes an information distillation model based on reinforcement learning. Reinforcement learning is introduced to delete redundant information and retain valid information. Then, the classification network classifies the distilled information and determines the reward based on the prediction result. Then, the reward is fed to reinforcement learning for gradient update and the classification network is updated inversely according to the classification result. It can effectively alleviate the inherent noise caused by the introduction of the attention mechanism. It can also learn classification tasks by identifying important words or task-related structures without displaying structural annotations (ie, artificially building models). Through comparative experiments on the real data set of Programmable Web, the results show that the proposed model achieves better results on the benchmark data set than other methods.
Ensembles of Long Short-Term Memory Experts for Streaming Data with Sudden Concept Drift

One of the challenges encountered when processing streaming data is a change of the data distribution, which is called concept drift. It has been shown that ensemble methods are effective in reacting to such a change. However, so far it has not been investigated how the architecture and configuration of the ensemble, as well as the properties of the scenario, influence the prediction accuracy if the ensemble members (experts) are Long Short-Term Memory networks with an internal state. This paper evaluates six ensemble architectures in several configurations with regards to their suitability for processing streaming data with sudden, recurring concept drift. The evaluation with a public dataset shows the impact of the architecture and configuration on the ensembles’ accuracies, as well as the influence of the concepts’ stability periods and the Long Short-Term Memory experts’ internal states under several conditions.
Signal Classification in Real-time Based on SDR using Convolutional Neural Network

In the modern complex battlefield environment, traditional conventional parameter measurement, fixed template matching and other methods are difficult to accurately identify unknown target signals and determine threats, and could not implement effective electronic attack. To solve the above problems, this paper designs a software radio system based on (Software-Defined Radio)SDR deep learning real-time signal classification. The system adopts the optimized convolutional neural network and uses training samples with different SNR(Signal-Noise Ratio) to enhance the robustness of the recognition network in various SNR environments. Finally, over the air test is realized by Pluto SDR platform, the accuracy of signal classification can reach at 96.83%. The system can complete the classification and recognition of typical radio signal modulation patterns in the battlefield. It has the advantages of high recognition accuracy and flexible deployment. It provides a new method and means to solve the difficulties of communication target reconnaissance and jamming.
An analytical code quality methodology using Latent Dirichlet Allocation and Convolutional Neural Networks

Recently, Code Quality (CQ) has become critical in a wide range of organizations and in many areas from academia to industry. CQ, in terms of readability, security, and testability, is a major goal throughout the software development process because it affects overall Software Quality (SQ) in terms of subsequent releases, maintenance, and updates. It is particularly important for the development of safety critical systems. Existing studies on CQ have several shortcomings in that they are based on incomplete information about the source code, and tend to focus on only one feature, which is likely to determine the performance of the model. Moreover, these considerations often limit obtaining high accuracy because there is no strong relationship between the input data and the output data. Thus, it is necessary to design an effective and efficient SQ measurement system for measuring multiple quality factors. To that end, we propose a deep learning framework that employed a Latent Dirichlet Allocation (LDA) with Convolutional Neural Networks (CNN), called CNN-LDA, to classify input data into topics that are related to CQ features and to identify hidden patterns and correlations in programming data. Three SQ metrics (i.e., readability, security, and testability) and machine learning techniques (e.g., random forest (RF) and support vector machine (SVM)) are taken into account to validate the proposed model. The proposed CNN-LDA outperformed its peers across the vast majority of datasets examined. The average overall F-measure for readability, security, and testability are 94%,94% and 93%. The average overall accuracy for readability, security, and testability are 93%,93% and 92%. The superiority of LDA-CNN over the other classifiers was very clear based on a Wilcoxon's non-parametric statistical test (α=0.05).
Outlier Detection Using K-Means Clustering with Minkowski-Chebyshev distances for Inquiry-Based Learning Results in Students Dataset

Outlier appears as an extreme value but often contains very important information, so it is necessary to be studied whether the data remains used or issued. Outlier detection is a hot topic for the study. Increasing new technologies and various applications cause increased requirements of outlier detection. The Outlier method is successfully applied in various fields, namely: economy, business, health, space, geology, and education. Implementation of an outlier of analysis in the education field is often applied to the evaluation of the learning model. Inquire -based learning model is an important component in educational renewal. Learning by this method encourages learners to learn mostly through their active involvement. This study aims to discuss outlier detection by using the K-Means Clustering method on the inquiry-based learning results in students. This study detects outliers with the K-means method using Minkowski-Chebyshev distance. The result of the proposed method will be compared with the extremes of standard deviation (ESD), Box-Plot, and K-Means Clustering using Euclidean distance. The outlier detection results using K-Means Clustering with the Minkowski-Chebyshev and Euclidean distance produce the same result that can detect 3 data as an outlier that is the student with the ID number 7 Exam Value 7.5, ID number 42 Exam Value 9.0, and ID number 72 with the value of 13.5. While the ESD method and Box-Plot are unable to detect any outlier.
Comprehensive Comparison of Post-Quantum Digital Signature Schemes in Blockchain

Blockchain is a distributed database that has gained much attention from researchers in recent years owing to its capacity to create transparent, redundant, and accountable connections in several application domains using asymmetric cryptography and digital signature. Nevertheless, blockchain systems are vulnerable to attacks from quantum computers using Shor's and Grover's algorithms. Post-quantum cryptography, according to several researchers, withstands quantum attacks. To address the problem, we examine the performance of various post-quantum digital signature algorithms in Blockchain and provide a comprehensive comparison in terms of computing time and memory usage in this study.
Adversarial Samples Generation Based on RMSProp

Adversarial sample attacks seriously threaten the security and robustness of deep learning models. There are three problems in state-of-the-art adversarial sample generation schemes: the gradient update step size needs to be manually selected, inaccurate gradient update direction and uncontrollable times of iterations. In order to solve these problems, Root Mean Square Prop optimization algorithm (RMSProp) is proposed, which is integrated with IFGSM and IFGM. This algorithm can be easily extended to other attacks, and to a certain extent it can alleviate the trade-off between white box attacks and deliverability. The algorithm proposed in this paper can generate non-targeted adversarial samples more efficiently and quickly. Experiments show that it can generate effective and robust adversarial samples against current mainstream convolutional neural network (CNN).
The Construction of Learning Diagnosis and Resources Recommendation System Based on Knowledge Graph

With the deepening integration of artificial intelligence, ICT in education is approaching to the stage of smart education, the main purpose of which is to realize learning personalization. This paper constructs an intelligent tutoring system to allow teacher establish the course knowledge model visually based on ontology. This system evaluates the learning situation of students using a test auto-generated by a global prediction accuracy optimization algorithm. The learning diagnosis module is implemented according to the learning situations of students and the structure analysis of knowledge graph based on node contribution. The resource recommendation module is implemented through the importance ranking of learning resources. The prototype system is constructed and the experiments are conducted. The results show that our approach can achieve personalized learning well in a certain range.
Facial Action Unit Detection Based on Teacher-Student Learning Framework for Partially Occluded Facial Images

Facial action unit (AU) detection is an important task in facial expression analysis. However, occlusion is a major hindrance in practical applications of AU detection as it interferes with extracting features from facial images and makes it difficult to capture the occurrence of AUs. To address this problem, we first construct a database for AU detection with occlusion by synthesizing occlusion objects such as bangs (i.e., fringe), glasses, and hands on facial images. Then we apply a teacher-student learning framework with two types of loss functions to AU detection for occluded facial images. To improve our model's robustness to occlusion, we propose a loss function for order regularization which considers the relationship between facial images as well as conventional distillation loss. The results of our experiment with our database for occluded images demonstrate that our method is effective for detecting AUs with occluded facial images.
On track to biopower? Toward a conceptual framework for user compliance in digital self-tracking

Digital self-tracking technologies, such as mobile applications and wearables have become commonplace, mediating users’ fitness and health management efforts by providing performance recommendations. While digital self-tracking technologies have been welcomed by some as useful tools in users’ pursuit of healthier and happier lives, they have also drawn criticisms, especially regarding body surveillance and control stemming from their embedded performance standards. In this article, we present our study of the experiences of users who regularly but casually engage with digital self-tracking technologies in order to identify factors that affect compliance with performance standards. Based on these data we propose a conceptual framework that brings together domain involvement, domain expertise, data literacy, and the tendency to anthropomorphize technology with performance standards and discuss possible relationships between these factors.
An Iterative Non-local Denoising Method of SAR Image Based on Multi-resolution

Synthetic aperture radar (SAR) is an advanced remote sensor, which can observe the earth’s surface in all weather conditions, widely used in military reconnaissance and disaster rescue. However, due to the coherent summation of the return echoes and the random electromagnetic interference, a SAR image will be significantly affected by the noise, reducing the readability of the image. To deal with this issue, in this paper, we propose an iterative non-local denoising method based on multi-resolution. First, the cascade downsampling is performed to get the multi-resolution sub-images. Then, the 2-D discrete cosine transform is applied to each fragment segmented from the sub-images. After that, grouping the similar fragments by using the pHash algorithm. And the basic denoising image can be obtained by performing collaborative filtering and aggregating. Finally, a denoising image can be acquired after iterating processing. Real airborne SAR data is used to validate the effectiveness of the proposed method.
Power Data Integration Method Based on Database-table Metadata Semantic

Abstract Aiming at the characteristic of power data with multiple sources, wide distribution and complex association, this paper proposes a power data integration method based on Database-Table Metadata Semantic (DTMS), which can realize the integration of grid data at the metadata layer. Firstly, based on the structure of the database, the semantic expression is established between database and table; Secondly, this paper combines the Jaccard distance similarity method and the edit distance similarity method to achieve semantic matching between metadata and technical terms, which can solve the shortcomings of the single similarity method. Finally, according to the characteristics of power data assets, a power data integration scheme based on DTMS is proposed for multi-source power data integration. Through experiments, the accuracy of the algorithm in this paper has reached 95.8%.