Reviewerly/exHarmony huggingface dataset:
User Experience Design of Augmented Reality-based Mobile Learning Media for English Subjects through User-Centered Design Approach

Learning during the COVID-19 pandemic has changed and adapted to existing conditions in new ways. To maintain the existence of learning, the integration of ICT in learning is necessary. The integration of learning with ICT that is applied properly can increase the effectiveness of learning and make learning more fun. This study aims to design the user experience of learning media based on Mobile Learning with Augmented Reality technology to increase the motivation of high school students in learning English called ARCALIS. The approach used the User-Centered Design (UCD) approach. The UCD approach is an approach that uses human factors, knowledge, usability techniques, and an interactive approach to developing systems in system design and development. The resulting then tested with the assessment of learning media experts covering 3 aspects, namely Education, Program Display, and Technical Quality. The media validation assessment obtained an average value of 80% with a valid feasibility level without revision, which means that ARCALIS media is good to implement. In the future, ARCALIS can be developed in terms of material content, multimedia content, and learning evaluation with high-order thinking skills.
SA-InterNet: Scale-Aware Interaction Network for Joint Crowd Counting and Localization

Crowd counting and crowd localization are essential and challenging tasks due to uneven distribution and scale variation. Recent studies have shown that crowd counting and localization can complement and guide each other from two different perspectives of crowd distribution. How to learn the complementary information is still a challenging problem. To this end, we propose a Scale-aware Interaction Network (SA-InterNet) for joint crowd counting and localization. We design a dual-branch network to regress the density map and the localization map, respectively. The dual-branch network is mainly constructed with scale-aware feature extractors, which can obtain multi-scale features. To achieve mutual guidance and assistance of the two tasks, we design a density-localization interaction module by learning the complementary information. Our SA-InterNet can obtain accurate density map and localization map of an input image. We conduct extensive experiments on three challenging crowd counting datasets, including ShanghaiTech Part_A, ShanghaiTech Part_B and UCF-QNRF. Our SA-InterNet achieves superior performance to state-of-the-art methods.
An Intelligent Exploratory Approach for Product Recommendation Using Collaborative Filtering

Recommendation systems (RS) have become a hot topic in the study, intending to assist consumers in finding goods online by offering choices that closely match their interests. Recommending a product to customers exclusively based on a quantitative review may result in the recommendation of a product that is irrelevant. Various recommendation algorithms are used by online e-commerce companies like Amazon and Flipkart to offer different choices to different customers. Amazon now uses item-to-item collaborative filtering, which expands to enormous data sets and produces high-quality real-time suggestions. This type of filtering compares the users purchased and rated items to similar things, the results are then compiled into a user-friendly list of recommendations. The goal of this research is to create a product suggestion system for an e-commerce platform that is tailored to the preferences of customers. Collaborative Filtering is one of the methods for generating suggestions. Recommend products to consumers based on their previous purchases and the ratings left by other customers who purchased comparable things. This paper discusses a model-based collaborative filtering approach, which assists in the development of predictive items for a specific user by recognizing patterns based on preferences gleaned from various user data.
Simple Convolutional-Based Models: Are They Learning the Task or the Data?

Convolutional neural networks (CNNs) evolved from Fukushima's neocognitron model, which is based on the ideas of Hubel and Wiesel about the early stages of the visual cortex. Unlike other branches of neocognitron-based models, the typical CNN is based on end-to-end supervised learning by backpropagation and removes the focus from built-in invariance mechanisms, using pooling not as a way to tolerate small shifts but as a regularization tool that decreases model complexity. These properties of end-to-end supervision and flexibility of structure allow the typical CNN to become highly tuned to the training data, leading to extremely high accuracies on typical visual pattern recognition data sets. However, in this work, we hypothesize that there is a flip side to this capability, a hidden overfitting. More concretely, a supervised, backpropagation based CNN will outperform a neocognitron/map transformation cascade (MTC) when trained and tested inside the same data set. Yet if we take both models trained and test them on the same task but on another data set (without retraining), the overfitting appears. Other neocognitron descendants like the What-Where model go in a different direction. In these models, learning remains unsupervised, but more structure is added to capture invariance to typical changes. Knowing that, we further hypothesize that if we repeat the same experiments with this model, the lack of supervision may make it worse than the typical CNN inside the same data set, but the added structure will make it generalize even better to another one. To put our hypothesis to the test, we choose the simple task of handwritten digit classification and take two well-known data sets of it: MNIST and ETL-1. To try to make the two data sets as similar as possible, we experiment with several types of preprocessing. However, regardless of the type in question, the results align exactly with expectation.
WG4Rec

News recommendation plays an indispensable role in acquiring daily news for users. Previous studies make great efforts to model high-order feature interactions between users and items, where various neural models are applied (e.g., RNN, GNN). However, we find that seldom efforts are made to get better representations for news. Most previous methods simply adopt pre-trained word embeddings to represent news and also suffer from cold-start users.
MuSpel-Fi: Multipath Subspace Projection and ELM-Based Fingerprint Localization

This letter proposes a multipath subspace projection and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">extreme learning machine</i> (ELM)-based indoor fingerprint localization algorithm called MuSpel-Fi, where the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">channel state information</i> (CSI) is utilized as the raw data to establish fingerprints. In this algorithm, the CSI is firstly organized into a time-domain matrix and then is projected into a subspace. This processing not only preserves the channel multipath information as much as possible, but also reduces the data dimension. Based on the reduced dimension projected data, the ELM network is exploited to implement the fingerprint localization. Considering the limited performance of a single ELM network, multiple ELM networks are jointly optimized to improve the localization performance. The experimental results demonstrate that the proposed MuSpel-Fi algorithm has higher positioning accuracy than traditional ones.
Global Correlative Network for Person re-identification

Person re-identification (re-id) aims to retrieve the image which belongs to the same person from a set of images according to a few clues. The dataset images are usually from multiple cameras of different locations. Recently, local feature extraction has been one of the mainstream to handle this task. Recent methods show that exploration of part-level characteristics in person re-id can be helpful for its ability in capturing discriminative features. However, those works ignore the association among image regions, and it will lead to wrong retrieval results when not considering such clues. To address this limitation, we propose an effective Global Correlative Network (GCN) to jointly exploit the potential relationships between the features from semantic regions for more discriminative image representations. Specifically, we integrate the association between local areas and the global for discriminative region-aggregated features. With a relatively concise structure, a novel relation learning framework is proposed to enhance the representation with the contextual relations between the local and global perspectives. Experiments on Market-1501, DukeMTMC-reID, and CUHK03 show the effectiveness of our proposed method. Specifically, our method achieves a state-of-the-art rank-1 accuracy of 90.0%, 83.7%, 78.5% on the DukeMTMC-reID, CUHK03(Labeled), and CUHK03(Detected) datasets, so it sets a new state-of-the-art.
Online Ensemble Aggregation using Deep Reinforcement Learning for Time Series Forecasting

Both complex and evolving nature of time series structure make forecasting among one of the most important and challenging tasks in time series analysis. Typical methods for forecasting are designed to model time-evolving dependencies between data observations. However, it is generally accepted that none of them is universally valid for every application. Therefore, methods for learning heterogeneous ensembles by combining a diverse set of forecasts together appear as a promising solution to tackle this task. Several approaches, ranging from simple and enhanced averaging tactics to applying meta-learning methods, have been proposed to learn how to combine individual models in an ensemble. However, finding the optimal strategy for ensemble aggregation remains an open research question, particularly, when the ensemble needs to be adapted in real time. In this paper, we leverage a deep reinforcement learning framework for learning linearly weighted ensembles as a meta-learning method. In this framework, the combination policy in ensembles is modelled as a sequential decision making process, and an actor-critic model aims at learning the optimal weights in a continuous action space. The policy is updated following a drift detection mechanism for tracking performance shifts of the ensemble model. An extensive empirical study on many real-world datasets demonstrates that our method achieves excellent or on par results in comparison to the state-of-the-art approaches as well as several baselines.
An evolutionary correlation-aware feature selection method for classification problems

As global search techniques, population-based optimization algorithms have provided promising results in feature selection (FS) problems. However, their major challenge is high time complexity associated with the exploration of a large search space and consequently a large number of fitness function evaluations. Moreover, the interaction between features is another key issue in FS problems, directly affecting the classification performance through selecting correlated features. In this paper, an estimation of distribution algorithm (EDA)-based method is proposed with three important contributions. Firstly, as an extension of EDA, the proposed method in each iteration generates only two individuals competing based on a fitness function, evolving during the algorithm using our proposed update procedure. Secondly, we provide a guiding technique to determine the number of features to be selected for individuals in each iteration. As a result, the number of selected features in the final solution would be optimized during the evolution process. These two would lead to increasing the convergence speed of the algorithm. Thirdly, as the main contribution of the paper, in addition to considering the importance of each feature alone, the proposed method can consider the interaction between features, being able to deal with complementary features and consequently increase classification performance. To do this, we provide a conditional probability scheme that considers the joint probability distribution of selecting two features. The introduced probabilities successfully detect correlated features. Experimental results on a synthetic dataset with correlated features proved the performance of our proposed approach facing these types of features. Furthermore, the results on 13 real-world datasets obtained from the UCI repository showed the superiority of the proposed method in comparison with some state-of-the-art approaches. To evaluate the effectiveness of each feature subset, support vector machines are used as classifier. The efficiency analysis of the experimental results using two non-parametric statistical tests proved that the proposed method had significant advantages in comparison to other approaches.
Hulls of cyclic serial codes over a finite chain ring

In this paper, we explore some properties of hulls of cyclic serial codes over a finite chain ring and we provide an algorithm for computing all the possible parameters of the Euclidean hulls of that codes. We also establish the average pr-dimension of the Euclidean hull, where Fpr is the residue field of R, as well as we give some results of its relative growth.
Identification of Genuine from Fake Banknotes Using an Enhanced Machine Learning Approach

In this study, we propose a supervised learning problem to differentiate genuine from fake banknotes. The problem is reformulated as a regularized optimization problem whose fidelity term is the hinge loss function and the hypothesis space is constructed by a reproducing kernel Hilbert space (RKSH). The numerical results are realized by the Adam method, which shows her success in detecting counterfeit banknotes compared to the stochastic gradient descent (SGD) algorithm.
SaliencyBERT: Recurrent Attention Network for Target-Oriented Multimodal Sentiment Classification

As multimodal data become increasingly popular on social media platforms, it is desirable to enhance text-based approaches with other important data sources (e.g. images) for the Sentiment Classification of social media posts. However, existing approaches primarily rely on the textual content or are designed for the coarse-grained Multimodal Sentiment Classification. In this paper, we propose a recurrent attention network (called SaliencyBERT) over the BERT architecture for Target-oriented Multimodal Sentiment Classification (TMSC). Specifically, we first adopt BERT and ResNet to capture the intra-modality dynamics with the textual content and the visual information respectively. Then, we design a recurrent attention mechanism, which can derive target-sensitive visual representations, to capture the inter-modality dynamics. With recurrent attention, our model can progressively optimize the alignment of target-sensitive textual features and visual features and produce an output after a fixed number of time steps. Finally, we combine the loss of all-time steps for deep supervision to prevent converging slower and overfitting. Our empirical results show that the proposed model consistently outperforms single modal methods and achieves an indistinguishable or even better performance on several highly competitive methods on two multimodal datasets from Twitter.
Reinforcement learning-based radar-evasive path planning: a comparative analysis

Abstract This paper discusses the utilisation of deep reinforcement learning algorithms to obtain optimal paths for an aircraft to avoid or minimise radar detection and tracking. A modular approach is adopted to formulate the problem, including the aircraft kinematics model, aircraft radar cross-section model and radar tracking model. A virtual environment is designed for single and multiple radar cases to obtain optimal paths. The optimal trajectories are generated through deep reinforcement learning in this study. Specifically, three algorithms, namely deep deterministic policy gradient, trust region policy optimisation and proximal policy optimisation, are used to find optimal paths for five test cases. The comparison is carried out based on six performance indicators. The investigation proves the importance of these reinforcement learning algorithms in optimal path planning. The results indicate that the proximal policy optimisation approach performed better for optimal paths in general.
Social Group Query Based on Multi-Fuzzy-Constrained Strong Simulation

Traditional social group analysis mostly uses interaction models, event models, or other social network analysis methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interactions between members of the group. Generally, Graph Pattern Matching (GPM) technique is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this article, first, a social group query problem that needs to consider the trust between members of the group is proposed. Then, to solve this problem, a multi-fuzzy-constrained strong simulation matching model is proposed based on multi-constrained simulation, and a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence is proposed. Aiming at the inefficiency of the NTSS algorithm when pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matching edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm.
Krill

As a large number of emerging graph applications spread across different domains, the need for processing massive concurrent graph jobs (CGJs) is increasing. However, existing graph processing systems designed for a single job cannot efficiently tackle multiple CGJs, where they suffer from interfering memory access patterns and inefficient property management. In this paper, we introduce Krill, a compiler and runtime system for processing concurrent graph jobs. We propose an SAP model, which decouples graph structure, algorithm, and property. In the compiler, we propose leveraging the property buffer to easily write and manage property data. In the runtime system, we propose a novel technique named graph kernel fusion to reduce memory accesses, which fuses all the jobs and processes them as a whole. Experimental results show our system significantly reduces the number of memory accesses for CGJs by more than 6x compared with the baseline, and achieves up to 6.76x speedup with 3.84x shorter response latency than GraphM, the state-of-the-art concurrent graph processing system.
Image quality improvement of old and distorted artworks using fuzzy logic technique

The image quality of distorted and faded art works may be improved by increasing the contrast, decreasing the noise, or sharpening the edges of the image. Such operations can be performed in various ways, such as filtering the image, histogram equalization techniques, decorrelation stretch, fuzzy techniques, etc. In this paper the fuzzy logic technique has been used to restore the distorted tribal artworks. The results obtained by this process have been compared with different histogram equalization (HE) techniques. The results of fuzzy logic and HE techniques that are mentioned in this paper are obtained using MATLAB. Parameters like Absolute Mean Brightness Error (AMBE), Peak to Signal Noise Ratio (PSNR), and Normalised Mean Square Error (NMSE) are tabulated and compared for the mentioned techniques.
Notification privacy protection via unobtrusive gripping hand verification using media sounds

This work proposes a media sound-based authentication method to protect smartphone notification privacy unobtrusively, which wisely hides or presents sensitive content by verifying who is holding the phone. We show that media sounds, such as the melodies of notification tones (e.g., iPhone message and Samsung whistle) can be directly used to sense and verify the user's gripping hand. Because sounds and vibrations co-exist, we capture two novel responses via the smartphone mic and accelerometer to describe how the individual's contacting palm interferes with the signals in two different domains. Based on the two responses, we develop a convolutional neural network-based algorithm to verify the user. Moreover, because the smartphone sensors are all embedded on the same motherboard, we develop a cross-domain method to validate such hard-to-forge physical relationships among the mic, speaker and accelerometer. They prevent external sounds from cheating the system. Additionally, we consider the notification vibration as a special type of media sound, which also results in two responses, and extend our method to work in the silent mode. Extensive experiments with ten notification tones and four phone models show that our system verifies users with 95% accuracy and prevents replay sounds with 100% accuracy.
Hand Gesture Recognition using Continuous Wave (CW) Radar based on Hybrid PCA-KNN

Development in human-computer interactions (HCI) has increased over the last decades due to the widespread use of computing devices such as portable computers and smartphones. While the conventional keyboard and mouse are effective, their use can be limited, therefore the hand gesture is an alternative interaction method. The development of a hand gesture recognition system will certainly improve the existing HCI systems. In this study, a hand gesture recognition system was presented using commercially available continuous-wave (CW) radar combined with a classification algorithm based on k-nearest neighbours. The principal component analysis (PCA) is proposed to extract features and reduce dimensionality of hand gesture spectra. Performance validation based on the leave-one-out procedure shows that the proposed system has almost 100% classification accuracy.
On the (un‐)adoption of JavaScript front‐end frameworks

Abstract JavaScript is characterized by a rich ecosystem of libraries and frameworks. A key element in this ecosystem are frameworks used for implementing the front‐end of web‐based applications, such as Vue and React . However, despite their relevance, we have few works investigating the factors that drive the adoption—and un‐adoption—of front‐end‐based JavaScript frameworks. Therefore, in this article, we first report the results of a survey with 49 developers where we asked them to describe the factors they consider when selecting a front‐end framework. In the second part of the work, we focus on projects that migrate from one framework to another since JavaScript's ecosystem is also very dynamic. Finally, we provide a quantitative characterization of the migration effort and reveal the main barriers faced by the developers during this effort. Although not completely generalizable, our central findings are as follows: (a) popularity and learnability are the key factors that motivate the choice of front‐end frameworks in JavaScript; (b) from the 49 surveyed developers, one out of four have plans to migrate to another framework in the future; (c) the time spent performing the migration is greater than or equal to the time spent using the old framework in all studied projects. We conclude with a list of implications for practitioners, framework developers, tool builders, and researchers.