Reviewerly/exHarmony huggingface dataset:
Robust Interacting Multiple Model Unscented Particle Filter for Navigation

In order to solve the problems of particle degradation and difficulty in selecting importance density function in particle filter algorithm, a robust interacting multiple model unscented particle filter algorithm is presented, which is based on the advantages of interacting multiple model and particle filter algorithms. This algorithm can use the unscented transformation to get the particles that contain the latest measurement information of each model and calculate the robust equivalent weight function. This robust factor is designed to adjust the estimation and variance, and the important distribution function adaptively obtained is closer to the true distribution. Then, the particles weights can be flexibly adjusted in real time by using Euclidean distance to improve the computational efficiency during the resampling process. In addition, this filter process can comprehensively describe the uncertainty of the statistics characteristic of observation noise between different models. The diversity of available particles is increased, and the filter precision is improved. The proposed algorithm is applied to the SINS/GPS integrated navigation system, and the simulation analysis results demonstrate that the algorithm can effectively improve the filter performance and the calculation precision in positioning of integrated navigation system; thus, it provides a new method for nonlinear model filter.
JUST-BPR: Identify Implicit Friends with Jump and Stay for Social Recommendation

Recommender constantly suffers from the problems of data sparsity and cold-start. As suggested by social theories, people often alter their ways of behaving and thinking to cater to social environments, especially their friends. For this reason, prior studies have integrated social relations into recommender systems to help infer user preference when there are few available data, which is known as social recommendation. However, explicit social relations are also sparse and meanwhile are usually noisy. To enhance social recommendation, a few studies identify more reliable implicit relations for each user over the user-item and user-user networks. Among these research efforts, meta-paths guided search shows state-of-the-art performance. However, designing meta-paths requires prior knowledge from domain experts, which may hinder the applicability of this line of research. In this work, we propose a novel social recommendation model (JUST-BPR) with a meta-path-free strategy to search for implicit friends. Concretely, We adopt the idea of 'Jump and Stay', which is a heterogeneous random walk technique, to social recommendation. Based on this idea, we manage to bypass the design of meta-paths and obtain high-quality implicit relations in a more efficient way. Then we integrate these implicit relations into an augmented social Bayesian Personalized Ranking model for top-N recommendation. Experiments on two real-world datasets show the superiority of the proposed method and demonstrate the differences between implicit friends discovered by meta-paths and JUST-BPR, respectively.
DIME: An Online Tool for the Visual Comparison of Cross-modal Retrieval Models

Cross-modal retrieval relies on accurate models to retrieve relevant results for queries across modalities such as image, text, and video. In this paper, we build upon previous work by tackling the difficulty of evaluating models both quantitatively and qualitatively quickly. We present DIME (Dataset, Index, Model, Embedding), a modality-agnostic tool that handles multimodal datasets, trained models, and data preprocessors to support straightforward model comparison with a web browser graphical user interface. DIME inherently supports building modality-agnostic queryable indexes and extraction of relevant feature embeddings, and thus effectively doubles as an efficient cross-modal tool to explore and search through datasets.
An assessment framework for development processes of safety-critical system functions applied to a model-based safety-critical middleware

This paper deals with an assessment framework to quantifying the impact on development costs by using the selective middleware approach (SMW) for safety-critical system functions. The development of safety-critical system functions is troublesome and time-consuming. Most of the effort is spent on the development and verification of functions such as redundancy management or master/slave management to provide a fault-tolerant platform behavior. The SMW provides an efficient development method for platform management as a middleware used for safety-critical system functions on IMA. It is based on a knowledge-based toolchain, which automatically derives the instance of the platform management middleware from a manually designed high-level system architecture model. In addition to the platform management software, the required design artifacts and verification artifacts are automatically generated. Since the SMW approach supports the software development process with highly automated process activities, it is investigated to what extent the SMW approach reduces the development effort. Therefore, a detailed assessment framework is developed, which covers and evaluates its impact on development phases. Hence, four evaluation views are defined according to the development process, project management process, and the qualification process. The emphasis of this paper is on the so-called project view. Thereby the benefit of the SMW can be identified versus the conventional method. In order to be able to make a quantitative assessment, a set of criteria was defined. For each criterion, a figure of merit is given to quantify the impact. It allows the comparison of different development approaches, e.g., the SMW approach against the conventional IMA development approach. The assessment framework with the project view is applied to evaluate the SMW approach based on the experiences of a high-lift system development. Finally, the efficiency of the SMW is quantified and presented to estimate the approximate profit for the user.
Smart [AR] Mini-Application: Engaging Citizens in Digital Placemaking Approach

As an initial step to building a smart city, municipalities have used a wide range of communication means (applications and platforms) to improve cities' services provisions. Collectively, this research has explored how designing, developing, and testing an augmented reality (AR) application for smartphones and tablets impacts citizens' understandings of the concept of the digital placemaking approach. From a practical point of view, we employed AR to provide interactions and information about proposed locations for future public art projects within Mashhad city in this research. We found that experts can be using AR as a powerful tool to evaluate the impact of this way on placemaking or planning to become a smart city. Consequently, this research discusses the potential of the Smart [AR] Mini-Application to address placemaking conceptual aims. In addition, this research concluded that using AR technology could advance knowledge of placemaking; and how this solution could serve as an interactive, dynamic process for more meaningful participatory methods. In this work, as noted within, the application makes use of Mapbox to provide outdoor AR, which enables users' devices to receive and send their updated locations. In the development phase of the application, RAD model outlines the methodology used to test the research hypotheses, followed by a presentation of the designed AR application and its results. Finally, Mashhad citizens were invited to test the Smart [AR] Mini-Application by placing a famous statue, finding each pinpoint marker and sharing the proposed place. Then the partcipants completed a questionnaire.
Commentary: critique of contesting control: how an excellent exemplar of artist-led HCI research surfaces unresolved issues

Contesting Control offers a wonderful example of how to execute artist-led HCI research. The work is excellent due to the clear and narrow focus on contested control and the design and execution of...
Research on the Activation of the Old City Zone from the Perspective of Community Construction -Taking Front of customs street in Macau as an example

Abstract Under the background of high urbanization, the living environment of the old city gradually failed to keep up with the pace of urban development. The old urban area carries the memory and culture of urban development. How to protect and activate the old urban area has become the focus and difficulty of urban renewal. Due to the nature of land and legal regulations in Macau, the historical city of Macau can completely retain many buildings with different styles in such a dense area. At the same time, it is also limited by the nature of Macau’s land and related laws, and the renovation of the old city is not satisfactory. This article mainly uses the literature search method and case study method to study the current situation of the protection and activation of Macau’s old urban areas. This article will take Front of customs street in Macau as an example to discuss how to realize the activation of the city’s old city from the perspective of community construction, with a view to preserving the context of the old city, improving the living environment, improving the quality of life of residents and promoting the sustainable development of the city.
Self-Learning Transformations for Improving Gaze and Head Redirection

Many computer vision tasks rely on labeled data. Rapid progress in generative modeling has led to the ability to synthesize photorealistic images. However, controlling specific aspects of the generation process such that the data can be used for supervision of downstream tasks remains challenging. In this paper we propose a novel generative model for images of faces, that is capable of producing high-quality images under fine-grained control over eye gaze and head orientation angles. This requires the disentangling of many appearance related factors including gaze and head orientation but also lighting, hue etc. We propose a novel architecture which learns to discover, disentangle and encode these extraneous variations in a self-learned manner. We further show that explicitly disentangling task-irrelevant factors results in more accurate modelling of gaze and head orientation. A novel evaluation scheme shows that our method improves upon the state-of-the-art in redirection accuracy and disentanglement between gaze direction and head orientation changes. Furthermore, we show that in the presence of limited amounts of real-world training data, our method allows for improvements in the downstream task of semi-supervised cross-dataset gaze estimation. Please check our project page at: this https URL
Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks

The forthcoming fifth-generation networks require improvements in cognitive radio intelligence, going towards more smart and aware radio systems. In the emerging radio intelligence approach, the empowerment of cognitive capabilities is performed through the adoption of machine learning techniques. This paper investigates the combined application of the convolutional and recurrent neural networks for the channel state information forecasting, providing a multivariate scalar time series prediction by taking into account the multiple factors dependence of the channel state conditions. Finally, the system performance has been analyzed in terms of prediction accuracy expressed as absolute deviation error and mean percentage error, in comparison with an alternative machine learning method recently proposed in the literature with the aim at solving the same prediction problem.
Enhancing Collaborative Filtering Recommendations for Web-based Learning Platforms with Genetic Algorithms

Web-based learning platforms are now offering a considerable amount of training options, exhibiting great variability, similar to the one encountered by a potential customer in electronic shops. To mitigate this, efficient and effective recommendations engines are needed, capable of satisfying the specific needs of each user, while achieving the best possible promotion of available online training. This paper discusses the design of a potential generic architecture for online education recommender systems, specifically targeted for promoting online courses and web-based learning material. From an algorithmic perspective, the system relies on item-based and user-based collaborative filtering approaches. It extends this approach with a genetic algorithm, thus increasing its potential impact. Overall, the paper paves the ground for the specification of generic principles governing the design of personalized online education platforms as well as identifying metrics for evaluating their performance.
WITHDRAWN: Upgrade of service industry structure based on FPGA and machine learning

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
On recursive temporal difference and eligibility traces

This work studies a new reinforcement learning method in the framework of Recursive Least-Squares Temporal Difference (RLS-TD). Differently from the standard mechanism of eligibility traces, leading to RLS-TD(λ), in this work we show that the forgetting factor commonly used in gradient-based estimation has a similar role to the mechanism of eligibility traces. We adopt an instrumental variable perspective to illustrate this point and we propose a new algorithm, namely - RLS-TD with forgetting factor (RLS-TD-f). We test the proposed algorithm in a Policy Iteration setting, i.e. when the performance of an initially stabilizing controller must be improved. We take the cart-pole benchmark as experimental platform: extensive experiments show that the proposed RLS-TD algorithm exhibits larger performance improvements in the largest portion of the state space.
A Lexical Dependency Probability Model for Mongolian Based On Integration of Morphological and Syntactic Features

Abstract Syntactic parsing is an important topic in the field of Mongolian language information processing. Compared with English and Chinese dependency parsing, Mongolian dependency parsing is still at the beginning stage. Mongolian syntactic parsing lack of Treebank resources seriously, under such conditions, a high quality syntactic parser cannot be developed by statistical methods simply. Aiming at the characteristics that Mongolian language has rich morphological features, this paper presented a rule and statistics-based dependency parsing model using Mongolian Dependency Treebank as training and evaluation data. The morphological and syntactic rules are represented using complex features and unification operations. The statistical model is represented using lexical dependent probability. This model has now achieved accuracies of 77.18%, 69.42% and 95.44% for the unlabelled annotation score, the labeled annotation score and the head word annotation score respectively.
Vector Programming Using Generative Recursion

Vector programming is an important topic in many Introduction to Computer Science courses. Despite the importance of vectors, learning vector programming is a source of frustration for many students. Much of the frustration is rooted in discovering the source of bugs that are manifested as out-of-bounds indexing. The problem is that such bugs are, sometimes, rooted in incorrectly computing an index. Other times, however, these errors are rooted in mistaken reasoning about how to correctly process a vector. Unfortunately, either way, all too often beginners are left adrift to resolve indexing errors on their own. This article extends the work done on vector programming using vector intervals and structural recursion to using generative recursion. As for problems solved using structural recursion, vector intervals provide beginners with a useful framework for designing code that properly indexes vectors. This article presents the methodology and concrete examples that others may use to build their own CS1 modules involving vector programming using any programming language.
Human motion sequence recognition based on correlation feature selection and multilayer perceptron

Aiming at the problems of large dimension and low recognition efficiency of human action sequence recognition, a recognition algorithm based on correlation feature selection and multi-layer perceptron is proposed. Use Kinect to obtain continuous images of human movements. By extracting key frames of human movements and calculating human joint angles, key features of human movements are extracted. Through the correlation feature selection algorithm, the most effective feature subset is selected from a group of features to reduce the dimension of feature space and reduce the calculation amount of classification recognition. Finally, the multi-layer perceptron is used as the classifier, and the BP algorithm is used to adjust the network weights to realize the classification and recognition of human action sequences. Through the classification and recognition experiments of human action sequences, it is proved that the recognition algorithm based on correlation feature selection and multi-layer perceptron has a high recognition rate. The recognition rate of the algorithm proposed in this paper is as high as 96.667%.
A Case Study and Fraud Rate Prediction in e-Banking Systems Using Machine Learning and Data Mining

Recently banking sector of Bangladesh is undergoing in a revolutionizing change. Over the last few years, Bangladesh’s banking industry has achieved remarkable momentum. Especially radical change has come in e-banking and mobile banking sectors. Because of convenience, easy to use, time saving and less complexity, both educated and uneducated people are using those facilities. At the same time, fraudulent activity is also rising rapidly. It is noticed that fraudsters use scary tactics and emotional manipulation to obtain sensitive or confidential customer information instead of coding-based hacking process. As a result, cyber security is the main challenge for the banking sector in Bangladesh. The purpose of the research is to determine the key factors behind increasing fraudulent activities. Concurrently, this study focuses on the relationship between lack of awareness and likeliness to be affected by fraud. In order to acquire the specified purpose of this study, several investigations were conducted on primary and secondary data. Results show that there is a strong correlation between lack of awareness and likeliness to be affected by fraud. 76% people have no idea about e-banking and mobile banking fraud. Furthermore, our findings show that 86.3% of victims of e-banking or mobile banking fraud had no prior knowledge of this type of fraud. Simultaneously, 13.7% of victims in those sectors had prior knowledge of fraud. It is obvious that, behind this type of fraud, lack of knowledge and awareness can be a major fact.
Compensating for the Lack of Extra Training Data by Learning Extra Representation

Outperforming the previous state of the art, numerous deep learning models have been proposed for image classification using the ImageNet database. In most cases, significant improvement has been made through novel data augmentation techniques and learning or hyperparameter tuning strategies, leading to the advent of approaches such as FixNet, NoisyStudent, and Big Transfer. However, the latter examples, while achieving the state-of-the-art performance on ImageNet, required a significant amount of extra training data, namely the JFT-300M dataset. Containing 300 million images, this dataset is 250 times larger in size than ImageNet, but is publicly unavailable, while the model pre-trained on it is. In this paper, we introduce a novel framework, Extra Representation (ExRep), to surmount the problem of not having access to the JFT-300M data by instead using ImageNet and the publicly available model that has been pre-trained on JFT-300M. We take a knowledge distillation approach, treating the model pre-trained on JFT-300M as well as on ImageNet as the teacher network and that pre-trained only on ImageNet as the student network. Our proposed method is capable of learning additional representation effects of the teacher model, bolstering the student model’s performance to a similar level to that of the teacher model, achieving high classification performance even without extra training data.
Research on Object Tracking Algorithm Based on KCF

To solve the tracking failure problem of the Kernelized Correlation Filter (KCF) algorithm due to occlusion, the KCF-AO algorithm which uses the confidence of the response graph to make judgments on the tracking results of each frame is proposed in this paper. When the judgment is occlusion, the template update is stopped; When it is judged that the target is lost, the A-KAZE feature point matching algorithm and the normalized correlation coefficient matching algorithm are used to complete the re-detection of the target, and the position information is returned to the KCF to continue tracking. The partial sequence of the OTB100 data set is selected for testing. The experimental results show that the algorithm in this paper can find the target and track it accurately in the case of occlusion and target loss.
Generating Adversarial Examples for Sentiment Classifier of Chinese Sentences

Studies have shown that deep learning models are vulnerable to adversarial examples, which cause incorrect predictions by adding imperceptible perturbations into normal inputs. The same characteristic goes for the sentiment orientation classification models. If the input text for the models contains perturbing information such as typos or special symbols, the inputs could misled the models. The adversarial examples reflect the diversity of text features, and the flaws of the sentiment orientation classification models can be found by adversarial attacks. The adversarial examples can be used to train the sentiment orientation classification models to improve the robustness of the model. This paper proposes to generate adversarial examples of Chinese sentences by replacing one of the characters in the word with similar Chinese characters in a black-box manner. The experimental results show that the adversarial examples generated by this method cost less and visually closer to the normal text.