Reviewerly/exHarmony huggingface dataset:
An Efficient Bayesian Neural Network for Multiple Data Streams

Spatial and temporal data such as multiple data streams often have concept drift problems, which refers to changes of the data distributions over time. Once concept drift occurs, a stationary machine learning predictor will probably be invalid because the testing data have a different data distribution from that of the training data. Recent studies in data streams aim to address this issue by concept drift adaptation techniques. Concept drift adaptation methods update the predictor by time and have been validated to provide accurate real-time prediction for a single data stream. However, handling multiple relevant data streams is still an unsolved challenge in this area, considering many real-world applications generate multiple data streams that are simultaneously evolving. To fill this gap, we here present a predicting network for multiple data streams, named MuNet. MuNet leverages the dependency between data streams to efficiently lower down the computational cost of real-time predictions for multiple data streams. In MuNet, an online-learned Bayesian neural network (BNN) is designed as a connector between streams. The BNN-connector can continuously use the real-time information from only one base stream to correct the stationary predictor for the other streams, which avoids the high cost caused by repeated adaptation.
Generalised Controller Design Using Continual Learning

In control systems applications, controllers for different plants are usually designed with different methods. Although plants may share common characteristics, these controllers are generally designed in isolation. The problem of continually learning a sequence of related tasks has been extensively studied recently. A challenge in continual learning is the phenomenon of catastrophic forgetting of knowledge of previous tasks which have been integrated into a neural network model. In this paper we evaluate the feasibility of modelling different controllers using continual learning. We explore regression versions of state-of-the-art methods and demonstrate that even the simplest continual learning approach decreases the overall Mean Average Error (MAE) by \(39\%\) of the MAE achieved by a non-continual strategy. Furthermore, a method based on dynamically expanding the network can achieve an overall MAE which is only \(18\%\) of the non-continual MAE. We also propose a set of new metrics that allow us to characterise the nature of catastrophic forgetting experienced while using different continual learning methods.
Management Information Systems for International Students in Indonesia and United States during Covid-19 in 2020

The purpose of this study is to provide an overview of management information systems relates to international students in Indonesia and the US during the COVID-19 pandemic in 2020. The Management Information Systems touched interpersonal communication between persons equipped with technology. Primary data collection techniques using interviews through the Zoom application revealed that communication through technology was essential during the COVID-19 epidemic. The results of the discussion showed that there was no significant change in management information systems in the learning processes of students in the two countries. One issue that was found, was adjusting to each other's time zones. The student interaction protocol with the campus has followed the rules set by the campus without exception. While some students required assistance with digital technology, the students feel support to use information systems because they already knew the conditions through the campus website that had been prepared.
An investigation of various Dehazing algorithms used on thermal infrared imagery for maritime surveillance systems

Maritime surveillance systems’ long-range capabilities are dependent on the quality of their images. Poor weather conditions such as haze, fog, or smog can severely hamper the ability to observe a thermal imaging scenario accurately, which affects the ability to detect, identify and track any object of interest within it. An image processing technique, known as dehazing is required in these types of systems. In this paper, state of the art image dehazing algorithms are used for long range thermal images (~5km, ~9km, ~15km) and their image restoration quality performances are compared based on qualitative metrics such as structural similarity (SSIM), peak signal to noise ratio (PSNR), and Feature similarity (FSIM). The results from this benchmark study can provide a suitable dehazing technique for maritime surveillance systems.
Sentence Semantic Matching with Hierarchical CNN Based on Dimension-augmented Representation

As a fundamental task in natural language processing, sentence semantic matching (SSM) is critical yet challenging due to difficulties in learning expressive sentence representation while capturing complex interactions between sentences. Recent work has shown the great potential of deep neural models in improving the performance of SSM task. However, existing work usually employs recurrent neural networks (RNNs) or 1D (one-dimensional) convolutional neural networks (CNNs) to learn sentence representation, leading to limited performance improvement. Benefiting from the multi-dimensional structure, 2D convolutional neural networks are expected to be more powerful to learn expressive sentence representation by capturing the implicit inter-sentence interactions and thus can further improve the performance of SSM. To this end, in this paper, we propose a novel sentence semantic matching model named Hierarchical CNN based on Dimension-augmented Representation (HiDR). In HiDR, first, bidirectional long short-term memory networks (LSTMs) are utilized to generate dimension-augmented representation for each of the input sentences; then, a hierarchical 2D CNN is devised to learn sentence representation while capturing the inter-sentence interactions, followed by a prediction layer based on sigmoid function to output the matching degree between sentences. To evaluate the performance of our proposed model, we conducted extensive experiments on two public real-world data sets. The empirical results show that HiDR has achieved remarkable results, which demonstrates either better or comparable performance w.r.t. BERT-based models.
Sparse Coding Driven Deep Decision Tree Ensembles for Nucleus Segmentation in Digital Pathology Images

Automating generalized nucleus segmentation has proven to be non-trivial and challenging in digital pathology. Most existing techniques in the field rely either on deep neural networks or on shallow learning-based cascading models. The former lacks theoretical understanding and tends to degrade performance when only limited amounts of training data are available while the latter often suffers from limitations for generalization. To address these issues, we propose sparse coding driven deep decision tree ensembles (ScD2TE), an easily trained yet powerful representation learning approach with performance highly competitive to deep neural networks in the generalized nucleus segmentation task. We explore the possibility of stacking several layers based on fast convolutional sparse coding-decision tree ensemble pairwise modules and generate a layer-wise encoder-decoder architecture with intra-decoder and inter-encoder dense connectivity patterns. Under this architecture, all the encoders share the same assumption across the different layers to represent images and interact with their decoders to give fast convergence. Compared with deep neural networks, our proposed ScD2TE does not require back-propagation computation and depends on less hyper-parameters. ScD2TE is able to achieve a fast end-to-end pixel-wise training in a layer-wise manner. We demonstrated the superiority of our segmentation method by evaluating it on the multi-disease state and multi-organ dataset where consistently higher performances were obtained for comparison against other state-of-the-art deep learning techniques and cascading methods with various connectivity patterns.
Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA

This letter explores a deep reinforcement learning (DRL) approach for designing image-based control for edge robots to be implemented on Field Programmable Gate Arrays (FPGAs). Although FPGAs are more power-efficient than CPUs and GPUs, a typical DRL method cannot be applied since they are composed of many Logic Blocks (LBs) for high-speed logical operations but low-speed real-number operations. To cope with this problem, we propose a novel DRL algorithm called Binarized P-Network (BPN), which learns image-input control policies using Binarized Convolutional Neural Networks (BCNNs). To alleviate the instability of reinforcement learning caused by a BCNN with low function approximation accuracy, our BPN adopts a robust value update scheme called Conservative Value Iteration, which is tolerant of function approximation errors. We confirmed the BPN's effectiveness through applications to a visual tracking task in simulation and real-robot experiments with FPGA.
Application programming interface recommendation according to the knowledge indexed by app feature mined from app stores

Abstract Application programming interfaces (APIs) play an important role in the increasingly competitive mobile application development industry, as they can greatly improve the efficiency of app development. However, finding proper APIs is often time‐consuming for the gap between the knowledge of APIs and app features. To solve this problem, we give an approach to summarize the wisdom of developers contained in the products in app stores and establish the system of API knowledge indexed by app features for the API recommendation. First, we extract features from the app descriptions and define the feature framework. Second, we parse the APK files of apps to gain the methods in code and APIs called by them and further introduce such API knowledge into the feature framework by utilizing method names as bridges. Finally, according to features in developers' queries, we locate corresponding feature nodes in the API knowledge system and recommend related API knowledge to developers. We conduct experiments based on 38,952 apps from five categories on Google Play, and the experimental results show that our approach has a good recommendation effect for the queries on app features.
NIR-VIS Heterogeneous Face Synthesis via Enhanced Asymmetric CycleGAN

Heterogeneous face image involves many fields, among which the near infrared (NIR) and visible (VIS) face image recognition is a hot field. NIR image has excellent imaging effect under extreme illumination conditions. Although few NIR images are available, unpaired image-to-image translation provides a solution for converting NIR-VIS to each other. However, NIR image contains relatively poor information, while VIS image contains relatively rich information, which results in asymmetry between NIR and VIS, and not suitable for unpaired translation based on two domains of the same complexity. In this paper, an enhanced asymmetric CycleGAN(EN-ASGAN) with edge retention module, auxiliary encoder module and generators equipped with different sizes is used to convert NIR-VIS face images. To validate the effective of EN-ASGAN, we conduct experiments on CASIA NIR-VIS 2.0 dataset. The experimental results are evaluated qualitatively and quantitatively and show that EN-ASGAN is effective for heterogeneous face generation in unpaired NIR-VIS images.
Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers

Hospital readmission is a major cost for healthcare systems worldwide. If patients with a higher potential of readmission could be identified at the start, existing resources could be used more efficiently, and appropriate plans could be implemented to reduce the risk of readmission. Therefore, it is important to predict the right target patients. Medical data is usually noisy, incomplete, and inconsistent. Hence, before developing a prediction model, it is crucial to efficiently set up the predictive model so that improved predictive performance is achieved. The current study aims to analyse the impact of different preprocessing methods on the performance of different machine learning classifiers. The preprocessing applied by previous hospital readmission studies were compared, and the most common approaches highlighted such as missing value imputation, feature selection, data balancing, and feature scaling. The hyperparameters were selected using Bayesian optimisation. The different preprocessing pipelines were assessed using various performance metrics and computational costs. The results indicated that the preprocessing approaches helped improve the model’s prediction of hospital readmission.
An Improved Dynamic Collaborative Filtering Algorithm Based on LDA

Currently, available collaborative filtering (CF) algorithms often utilize user behavior data to generate recommendations.The similarity calculation between users is mostly based on the scores, without considering the explicit attributes of the users with profiles, as these are difficult to generate, or their preferences over time evolve.This paper proposes a collaborative filtering algorithm named hybrid dynamic collaborative filtering (HDCF), which is based on the topic model.Considering that the user's evaluation of an item will change over time, we add a time-decay function to the subject model and give its variational inference model.In the collaborative filtering score, we generate a hybrid score for similarity calculation with the topic model.The experimental results show that this algorithm has better performance than currently available algorithms on the MovieLens dataset, Netflix dataset and la.fm dataset.
Designing a Partially Understandable Neural Network through Semantic Embedding

In recent years, the convolution neural network (CNN) has been successfully applied in numerous fields as a machine learning model. However, such neural models are still considered to be “black box” for most tasks. The fundamental issue underlying this problem is that the information and knowledge learned by a neural network during the training process are unknown and unpredictable. In this study, we attempted to design a partially understandable neural network through semantic embedding. Firstly, we selected several understandable feature extraction operators as expected information. Secondly, we embedded these operators into the hierarchical layers of a neural network at the beginning of its training process. Finally, these embedded operators were only involved in forward calculation and remained unchanged in the error back-propagation of the training process. We applied our method to the ResNet and DenseNet models in image classification tasks. In the experiments, our new models achieved almost the same performance as the original ones, but decreased performance was exhibited when shutting the embedded parts, and the features in the embedded parts were understandable. The experiments verified that the embedded parts not only contributed to the classification tasks, but also caused the neural network model to be partially understandable.
Perceiving Speech Aggression with and without Textual Context on Twitter Social Network Site

The Internet has become the leading place for the emergence and spread of aggressive behavior. All major social network sites today work in one way or another to maintain a respectful atmosphere within their communities. Besides all this work to harmonize communication, today speech aggression is still a common fact within internet-communication on social network sites. The paper explores speech aggression perception in internet-communication of Russian speaking Twitter users. We suppose that taking into account such factors as communicative situation and communicative context, when analyzing the fact of speech aggression implementation, will increase the accuracy of determining the nature (direction) of speech aggression in the process of speech communication on social network sites. Based on ten tweet stimuli selected we perform an empirical research recruiting 45 Russian speaking recipients and asking them to detect speech aggression in tweets without and within textual context. The results of the research argue that indeed, the presence of textual context influences the interpretation of the tweet as an offensive or a defensive aggressive speech act.
Tag-aware Attentional Graph Neural Networks for Personalized Tag Recommendation

Personalized tag recommender systems recommend a series of tags for items by leveraging users' historical records, which helps tag-aware recommender systems (TRS) to better depict user profiles and item characteristics. However, existing personalized tag recommendation solutions are insufficient to capture the collaborative signal hidden in the interactions among entities without considering reasonable correlations, since neighborhood messages are treated as the same weights when constructing graph-structured data, resulting in decreased accuracy in making recommendations. In this paper, we propose a Tag-aware Attentional Graph Neural Network (TA-GNN), which integrates the attention mechanism into tag-based graph neural networks to alleviate the above issues. Specifically, we extract the user-tag interaction and the item-tag interaction from the user-tag-item graph structure. For each interaction, we exploit the contextual semantics of multi-hop neighbors by leveraging attentional strategy on graph neural networks to discriminate the importance of different connected nodes. In this way, we effectively extract collaborative signals of neighborhood representations and capture the potential information in an explicit manner. Extensive experiments on three public datasets show that our proposed TA-GNN outperforms the state-of-the-art personalized tag recommendation baselines.
Learning Motion Planning Functions using a Linear Transition in the C-space: Networks and Kernels

Motion planning approaches aided by learning schemes have achieved relevant results in the community, particularly in terms of rendering new paths efficiently and adapting to new environments/situations through encoder-decoder frameworks and latent space configurations. This paper evaluates the feasibility of learning motion planning functions for robot manipulators using a linear transition of the configuration space. Our computational experiments involving a relevant set of learning architectures have shown the feasibility and the efficiency in finding motion planning functions that meet user-defined criteria. Our approach contributes to realizing the practical efficiency to tackle the learning-based motion planning problem. Due to the amenability to parallelization schemes, our approach is potential to tackle larger degrees of freedom.
Proposal of a Methodology for the Implementation of a Smart Campus

Nowadays, the transformation of a city to a smart city is of great interest to governments with the firm purpose of improving the quality of life of its citizens. Several initiatives have presented methodologies to be able to carry it out covering all the work areas of a city, such as transport, mobility, the economy, sustainability, health, and education, the latter being of great importance for this work. When we talk about education, we mean offering agile, automatic processes that give great benefit and satisfaction to the work that students and teachers do. Talking about a smart campus in the area of higher education leads us to present a methodological proposal for the creation of a smart university campus, taking advantage of all the management, administration, and education processes. The methodology presented has been the result of a related work research process focused on a higher education institution. It is made up of three phases: preliminary planning, a functional scheme, and the development and execution of the proposal, leaving solid foundations for the implementation of a prototype of a smart campus.
Validation and Data Processing in JSON Format

The article describes the mechanism of validation and data processing in JSON format in the form of a modified model, which by its rules will implement a universal approach to the process of data processing of arbitrary structure with the ability to implement its logic of validation and processing of nodes with any level of complexity. Each node of this data must have its own logic for checking their format and a certain logic of post-operation of the node state. An example of implementation of a modified model of processing input data of arbitrary structure in JSON format is given.
M-ary Hopfield Neural Network Based Associative Memory Formulation: Limit-Cycle Based Sequence Storage and Retrieval

In this paper, we examine Hopfield network composed of multi-state neurons for storing sequence data as limit cycles of the network. Earlier, we had presented uni-modal data - particularly text, speech and audio data storage and retrieval in bipolar Hopfield based associative memory architecture. We extended this to multi-modal data and we demonstrated that Hopfield can indeed work as content addressable memory for multi-modal data. This paper is a step towards realising a more wider definition of multi-modality. We present a M-ary Hopfield associative memory model for storing limit cycle data. The proposed system uses a dual weight learning mechanism to exhibit limit cycle behavior in which sequence data can be stored and retrieved. We particularly deal with a) sequence of images and b) movie clip data as instances of limit cycle data. We also propose and use a two stage firing mechanism to retrieve the stored sequence data from the limit cycles. We present a trade-off behavior between the number of cycles and length of cycles the network can store and we demonstrate that the network capacity is still of the order of network size i.e., O(N) for limit cycle data. This represents a first of its kind attempt for sequence storage and retrieval in Hopfield network as limit-cycles, particularly with image-sequence and movie-content data of real-world scales.
Weight Perturbation as a Method for Improving Performance of Deep Neural Networks

Learnable weights in very deep neural networks are not utilized in an efficient way i.e. the variance of weights in some non-performing layers is close to zero. One approach taken to mitigate this problem, has been the connection pruning, in which some of the weights in a network are masked with zeros, without negatively affecting the network performance. The drawback of such approach is that in the GPU implementations of neural networks, pruning the weights does not affect the network training or inference speed. In this paper we propose an alternative solution to the problem of inefficient utilization of network learning capability - perturbing the weights of the network during training with additive, zero-mean noise. It causes the variance of weights in non-performing layers to rise, and can improve the performance of neural networks. In this paper we show the effect of the weight perturbation on the performance of five well known network architectures, while testing them on the task of CIFAR100 classification.