Reviewerly/exHarmony huggingface dataset:
Improved Aspect-level Sentiment Analysis Method based on Multi-Head Attention Mechanism

Abstract The purpose of the aspect-level sentiment analysis task is to analyze the sentiment orientation expressed by different aspects in the text. It has more fine-grained sentiment evaluation objects and is more in line with actual needs. Therefore, it has received extensive attention in recent years. At present, the model that combines the attention mechanism with the Recurrent Neural Network and its variants has gradually become the main method to solve aspect level sentiment analysis tasks. However, this type of method is limited by the deficiencies of the RNN itself, the training time is too long and the dependence between words decreases with the increase of distance. Even if the attention mechanism is added, the above problems will still exist. Therefore, this paper introduces the multi-head attention mechanism into the GCAE model (Gated Convolutional network with Aspect Embedding), and then proposes the GCAE-MHA model (Improved GCAE model based on Multi-Head Attention mechanism). This model models the context and specific aspects of the text sequence at the same time, and learns the interactive relationship between them. And finally uses the two together as the basis for sentiment classification. At the same time, in order to make up for the problem that the convolutional neural network can only extract the dependencies between local words, the GCAE-MHA model also uses the Dilated Convolutional Neural Network to replace it, which can extract the semantic information between long-distance words, and by setting different dilation rate to obtain a richer semantic feature. Finally, experiments are carried out on the SemEval2014 dataset and Twitter dataset. The experimental results show that the GCAE-MHA model can effectively improve the effect of aspect level sentiment analysis while ensuring the simplicity of the model network structure.
A Survey on Linear and Non-Linear Dimensionality Reduction Techniques

Dimensionality Reduction (DR) is gaining more attention these days as a result of the increasing need to efficiently handle large amounts of data. The applications of DR cover many fields like Medical, Geographical, E-Commerce, simulation, and many more. DR plays an important part in selecting essential features. So that it reduces the dimensions of the dataset. The Dimensionality Reduction techniques were used to select the most relevant features of face recognition, Speaker identification, image annotation, and many more. Dimensionality Reductions are performed using linear and non-linear techniques. In this paper, the comparison of linear and nonlinear dimensionality reduction techniques is given based on previous analysis. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Maximum Autocorrelation Factor (MAF), Slow Feature Analysis (SFA), Sufficient Dimension Reduction (SDR), Independent Component Analysis (ICA), Distance Metric Learning (DML), etc. are the most widely used linear dimensionality reduction techniques. The most commonly used nonlinear dimensionality reduction techniques are Locally Linear Embedding (LLE), Multidimensional scaling (MDS), Isomap, Hessian Locally Linear Embedding (HLLE), MVU, Diffusion Maps, Kernal PCA, Multilayer Autoencoder, Local Tangent Space Alignment (LTSA) and Laplacian Eigenmaps. This can be divided into Global nonlinear techniques and Local nonlinear techniques. This paper explains these techniques by pointing out flaws in current linear and nonlinear techniques and suggesting ways to improve the efficiency of linear and nonlinear dimensionality reduction techniques.
Estimating Contribution of Training Datasets using Shapley Values in Data-scale for Visual Recognition

In this paper, we propose a method to measure contributions of multiple datasets i.e. how much a specific dataset contributes to improve accuracy of the model. Our method is based on shapley value, of which purpose is to measure contribution by difference of the accuracy of the models. Unlike previous method, our method first converts the accuracy to data-scale measurements using fitted log curve. We calculate contributions in a fair way that each trials are evaluated not by its improvements of accuracy, but by the number of data needed to make the improvements. Our method can avoid overestimation of contributions in small data cases. To evaluate the proposed method, we trained models for Person Re-Identification tasks with combinations of datasets, and calculated contributions of each datasets. Results show that the proposed metrics can effectively reduce the over-estimations in small data cases, while the contributions maintain good properties such as local accuracy and additive law derived from shapley value definition. We also proposed normalization of shapley values in data-scale by its actual number of instances, which indicates intrinsic importance of a dataset per instance.
Design of Decision Support System for Food Selection for Diabetes Mellitus Type 2 Using Weighted Product Method (Case Study: Puskesmas Temon II Kulon Progo)

DM is known as cardiovascular disease, but DM is also one of the main causes of kidney disease and blindness under 65 years of age, as well as amputation. DM is also a cause of amputation (which is not caused by trauma), disability, and death. Another effect of diabetes is to reduce life expectancy by 5-10 years. The life expectancy of people with type 2 diabetes mellitus or non-insulin-dependent diabetes mellitus who suffer from serious mental illnesses, such as schizophrenia, is even 20% lower than the general population. The method to be used is the Weighted Product (WP) method. This method is used because the WP is one of the preferred methods for solving complex problems and can produce structured calculations. The ranking result states that the alternative Tempe Boiled with a value of 0.136 is the best suggestion for the user.
LAC: Latent Authorial Clustering of shorter texts

Authorial clustering of a corpus of documents results in grouping them so that each group encircles those written by the same author. LAC, the presented software, implements a novel approach towards authorial clustering of paragraph-long texts. It operates on documents modelled in low-dimensional language-independent topic-based vector spaces via a flexible command-line interface. Assessing the goodness of such representations and comparing them to other approaches are made easier with LAC. In addition, LAC facilitates studying semi-supervision for authorial clustering and how to best determine the number of authorial clusters k in a corpus.
Knowledge Transferred Fine-Tuning for Anti-Aliased Convolutional Neural Network in Data-Limited Situation

Anti-aliased convolutional neural networks (CNNs) introduce blur filters to intermediate representations in CNNs to achieve high accuracy. A promising way to build a new antialiased CNN is to fine-tune a pre-trained CNN, which can easily be found online, with blur filters. However, blur filters drastically degrade the pre-trained representation, so the fine-tuning needs to rebuild the representation by using massive training data. Therefore, if the training data is limited, the fine-tuning cannot work well because it induces overfitting to the limited training data. To tackle this problem, this paper proposes “knowledge transferred fine-tuning”. On the basis of the idea of knowledge transfer, our method transfers the knowledge from intermediate representations in the pre-trained CNN to the anti-aliased CNN while fine-tuning. We transfer only essential knowledge using a pixel-level loss that transfers detailed knowledge and a global-level loss that transfers coarse knowledge. Experimental results demonstrate that our method significantly outperforms the simple fine-tuning method.
Continuous trajectory planning based on learning optimization in high dimensional input space for serial manipulators

In order to generate trajectories continuously for serial manipulators with high dimensional degrees of freedom (DOFs) in a dynamic environment, a real-time trajectory planning method based on optimization and machine learning aimed at high dimensional inputs is presented. A learning optimization (LO) framework is established. Multiple criteria are defined to evaluate the performance quantitatively, and implementations with different sub-methods are discussed. In particular, a database generation method based on input space mapping is proposed for generating valid and representative samples. The methods presented are applied on a practical application—haptic interaction in virtual reality systems. The results show that the input space mapping method significantly elevates the efficiency and quality of database generation and consequently improves the performance of the LO. With the LO method, real-time trajectory generation with high dimensional inputs is achieved, which lays the foundation for robots with high dimensional DOFs to execute complex tasks in dynamic environments.
Multi-Scale Background Suppression Anomaly Detection In Surveillance Videos

Video anomaly detection has been widely applied in various surveillance systems for public security. However, the existing weakly supervised video anomaly detection methods tend to ignore the interference of the background frames and possess limited ability to extract effective temporal information among the video snippets. In this paper, a multi-scale background suppression based anomaly detection (MSBSAD) method is proposed to suppress the interference of the background frames. We propose a multi-scale temporal convolution module to effectively extract more temporal information among the video snippets for the anomaly events with different durations. A modified hinge loss is constructed in the suppression branch to help our model to better differentiate the abnormal samples from the confusing samples. Experiments on UCF Crime demonstrate the superiority of our MS-BSAD method in the video anomaly detection task.
Digital Resources and Digital Competence: A Cross-Sectional Survey of University Students of the Childhood Education Degree of the University of Jaén

This work is part of a broader investigation whose main objective is to determine the domain and preferences of the students of the Childhood Education Degree of the University of Jaén regarding digital competence. The research was focused on the “use of digital resources” dimension and was developed under a quantitative and descriptive methodology, of basic type and non-experimental cross-sectional design. We worked with a sample of 135 students from the four courses of the Childhood Education Degree of the University of Jaén, who answered a developed questionnaire voluntarily and anonymously. Data collection was carried out using a virtual questionnaire on the Google Form platform. In addition, the measurement instrument was considered valid and reliable, having obtained a Cronbach’s alpha coefficient (α) of 0.925 and a Kaiser–Meyer–Olkin sampling adequacy index (KMO) of 7.741. The results show that there are no significant differences based on the independent variables studied among most of the analyzed items. The use of ICT is beneficial for students’ participation in research.
Attention Mining Branch for Optimizing Attention Map

Attention branch networks (ABNs) can achieve high accuracy by visualizing the attention area of the network during inference and utilizing it in the recognition process. However, if the attention area does not highlight the target object to be recognized, it may cause recognition failure. While there is a method for fine-tuning the ABN using attention maps modified by human knowledge, it takes up a lot of labor and time because the attention map needs to be modified manually. In this paper, we propose a method that automatically optimizes the attention map by introducing an attention mining branch to the ABN. Our evaluation experiments show that the proposed method improves the recognition accuracy and obtains an attention map that appropriately focuses on the target object to be recognized.
Hate versus politics: detection of hate against policy makers in Italian tweets

Accurate detection of hate speech against politicians, policy making and political ideas is crucial to maintain democracy and free speech. Unfortunately, the amount of labelled data necessary for training models to detect hate speech are limited and domain-dependent. In this paper, we address the issue of classification of hate speech against policy makers from Twitter in Italian, producing the first resource of this type in this language. We collected and annotated 1264 tweets, examined the cases of disagreements between annotators, and performed in-domain and cross-domain hate speech classifications with different features and algorithms. We achieved a performance of ROC AUC 0.83 and analyzed the most predictive attributes, also finding the different language features in the anti-policymakers and anti-immigration domains. Finally, we visualized networks of hashtags to capture the topics used in hateful and normal tweets.
Investigating Potential Barriers to Cybersecurity Risk Management Investment in SMEs

Ignoring the importance of cybersecurity risk management in SMEs became an issue among decision-makers. Lack of strategic investment decision by SMEs could lead to further risks and vulnerabilities. The significant increase of cybersecurity threats might become a very difficult task for both SMEs and other parties in the future. The purpose of this paper is to identify potential barriers to cybersecurity risk management investment in SMEs based on conducting several interviews with decision-makers in the sector to understand the causes of this prevention. The authors aim to reveal the role the SMEs’ decision-makers are playing in addressing cybersecurity risks in recent years and to suggest avenues for further research. The paper follows a qualitative exploratory approach to identify those potential barriers. Starting with an invitation are distributed by e-mail, phone call, or social medial account, to hundreds of Saudi’s decision-makers in the SMEs sector, asking them to participate in this study. As a result, 20 participants from various SMEs business sectors have agreed to participate. Therefore, six potential barriers that play a key role in the prevention of cybersecurity risk management investment in SMEs, which are the financial capacity, the lack of awareness, the size of SMEs, the traditional commerce, the absence of risk standards, and the over-confidence of decision-makers. Some of these barriers impacts have been discussed before in different contexts. Yet, this study investigates these barriers impact on preventing SMEs from investing in CRM. Also, to the best of authors knowledge, none of previous research has combined these barriers together with an insight to measure their effect on managerial decision-making. Further research on cybersecurity risk management in SMEs is needed.
Improved Single Image Super-resolution Based on Compact Dictionary Formation and Neighbor Embedding Reconstruction

Single image super-resolution is one of the evolving areas in the field of image restoration. It involves reconstruction of a high-resolution image from available low-resolution image. Although lot of researches are available in this field, still there are many issues related to existing problems those are still unresolved. Here, this research work focuses on two aspects of image super-resolution. The first aspect is that the process of dictionary formation is improved by using lesser number of images while preserving maximum structural variations. The second aspect is that pixel value estimation of high-resolution image is improved by considering only those overlapping patches which are more relevant from the characteristics of image point of view. For this, all overlapping pixels corresponding to a particular location are classified whether they are part of smooth region or an edge. Simulation results clearly prove the efficacy of the algorithm proposed in this paper.
Deep Feature Fusion for Face Analytics

Data produced from a particular source often exhibit correlations with those arising from other sources. Common data sources include (i) sensors—that gather raw data, (ii) feature extractors—which process the raw data from sensors to generate features representing the original data, and (iii) evaluators- –which produce a score or a measure that conveys the likelihood of the provided features belonging to an application specific hypothesis.
Local Measurement-Induced Minimal Decoherence and its variants

Based on quantum measurement, the existing quantum correlation (QC) measures are divided into two categories, one class based on minimal entropy-like difference of the measurement fore-and-aft system density matrix, and the other based on minimal trace-like geometric distance of the measurement fore-and-aft system density matrix. Here we provide another class of QC measure, Local Measurement-Induced Minimal Decoherence (LMIMD) which indicates the simple and clear relationship between QC and quantum coherence. Its physical picture is intuitive and clear. These also indicate that the design realization of its experiment detection and specific potential applications with the superiorities would be much easier. In terms of some typical quantum systems, we investigate and analyze the advantages and disadvantages of the LMIMD and its variants Local Eigen-Measurement-Induced Minimal Decoherence (LEMIMD) and Local Eigen-Measurement-Induced Decoherence (LEMID) compared with the existing two classes of QC measure.
A Fuzzy Declarative Approach to Classify Unlabeled Short Texts Based on Automatically Constructed WordNet Ontologies

In this paper, we present an approach to categorizing short texts which only require, as user input, the category names defined using an ontology of terms automatically extracted from WordNet and modeled as a set of proximity equation. The use of a fuzzy extension of Prolog allows us to develop an efficient text classifier in which the classification method is explainable. The results of the experiments showed that the proposed method achieved a reasonable good performance.
XDIVINSA: eXtended DIVersifying INStruction Agent to Mitigate Power Side-Channel Leakage

Side-channel analysis (SCA) attacks pose a major threat to embedded systems due to their ease of accessibility. Realising SCA resilient cryptographic algorithms on embedded systems under tight intrinsic constraints, such as low area cost, limited computational ability, etc., is extremely challenging and often not possible. We propose a seamless and effective approach to realise a generic countermeasure against SCA attacks. XDIVINSA, an extended diversifying instruction agent, is introduced to realise the countermeasure at the microarchitecture level based on the combining concept of diversified instruction set extension (ISE) and hardware diversification. XDIVINSA is developed as a lightweight co-processor that is tightly coupled with a RISC-V processor. The proposed method can be applied to various algorithms without the need for software developers to undertake substantial design efforts hardening their implementations against SCA. XDIVINSA has been implemented on the SASEBO G-III board which hosts a Kintex-7 XC7K160T FPGA device for SCA mitigation evaluation. Experimental results based on non-specific t-statistic tests show that our solution can achieve leakage mitigation on the power side channel of different cryptographic kernels, i.e., Speck, ChaCha20, AES, and RSA with an acceptable performance overhead compared to existing countermeasures.
WarpingFusion: Accurate Multi-View TSDF Fusion with Local Perspective Warp

In this paper, we propose the novel 3D reconstruction framework, where the surface of a target object is reconstructed accurately and robustly from multi-view depth maps. A depth map of a moving object tends to have the spatially-varying perspective warps due to motion blur and rolling shutter artifacts. Incorporating those misaligned points from the views into the world coordinate leads to significant artifacts in the reconstructed shape. We address the mismatches by the patch-based depth-to-surface alignment using implicit surface-based distance measurement. The patch-based minimization finds spatial warps on the depth map fast and accurately with the global transformation preserved. The proposed framework efficiently optimizes the local alignments against depth occlusions and local variants thanks to the point to surface distance based on an implicit representation. The proposed method shows significant improvements over the other reconstruction methods, demonstrating efficiency and benefits of our method in the multi-view reconstruction.
An Effective Video Surveillance System by using CNN for COVID-19

An effective video surveillance system is a challenging task in the COVID-19 pandemic. Building a model proper way of wearing a mask and maintaining the social distance minimum six feet or one or two meters by using CNN approach in the COVID-19 pandemic, the video surveillance system works with the help of TensorFlow, Keras, Pandas, which are libraries used in Python programming scripting language used in the concepts of deep learning technology. The proposed model improved the CNN approach in the area of deep learning and named as the Ram-Laxman algorithm. The proposed model proved to build the optimized approach, the convolutional layers grouped as ‘Ram', and fully connected layers grouped as ‘Laxman'. The proposed system results convey that the Ram-Laxman model is easy to implement in the CCTV footage.
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains

Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. Though several approaches have been proposed, most existing few-shot learning (FSL) models assume that base and novel classes are drawn from the same data domain. When it comes to recognizing novel-class data in an unseen domain, this becomes an even more challenging task of domain generalized few-shot classification. In this paper, we present a unique learning framework for domain-generalized few-shot classification, where base classes are from homogeneous multiple source domains, while novel classes to be recognized are from target domains which are not seen during training. By advancing meta-learning strategies, our learning framework exploits data across multiple source domains to capture domain-invariant features, with FSL ability introduced by metric-learning based mechanisms across support and query data. We conduct extensive experiments to verify the effectiveness of our proposed learning framework and show learning from small yet homogeneous source data is able to perform preferably against learning from large-scale one. Moreover, we provide insights into choices of backbone models for domain-generalized few-shot classification.