Reviewerly/exHarmony huggingface dataset:
SSEMGAT: Syntactic and Semantic Enhanced Multi-Layer Graph Attention Network for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis aims to identify the sentiment polarity of specific aspects appearing in a given sentence or review. The model based on graph structure uses a dependency tree to link the aspect word with its corresponding opinion word and achieves significant results. However, for some sentences with ambiguous syntactic structure, it is difficult for the dependency tree to accurately parse the dependencies, which introduces noise and degrades the performance of the model. Based on this, we propose a syntactic and semantic enhanced multi-layer graph attention network (SSEMGAT), which introduces constituent trees in syntactic features to compensate for dependent trees at the clause level, exploiting aspect-aware attention in semantic features to assign the attention weight of specific aspects between contexts. The enhanced syntactic and semantic features are then used to classify specific aspects of sentiment through a multi-layer graph attention network. Accuracy and Macro-F1 are used as evaluation indexes in the SemEval-2014 Task 4 Restaurant and Laptop dataset and the Twitter dataset to compare the proposed model with the baseline model and the latest model, achieving competitive results.
Chaos in popular metaheuristic optimizers – a bibliographic analysis

AbstractThis paper presents an overview of the history and recent efforts in combining chaos theory and evolutionary computation techniques. Various algorithms from the evolutionary computation domain, also known as metaheuristic algorithms, have been successfully enhanced with chaotic components in the past. Numerous ways to incorporate chaos have been examined, and many impressive results have been reported. Implementations of discrete chaotic maps such as Lozi, Hénon, and logistic map as generators of chaotic pseudo-random sequences for controlling evolution operators in metaheuristics have achieved significant popularity. In this survey, we focus on the research field itself and perform a bibliographical analysis to show how broad and active is nowadays the research field of chaos-enhanced metaheuristics and what are some of the most recent works published.Keywords: Evolutionary computingchaosmetaheuristicgenetic algorithmdifferential evolutionparticle swarm optimizationMaths:: 65P2000-02 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://www.webofscience.com, Search date: October 14th, 2022.2 https://github.com/P-N-Suganthan/CEC2016.3 http://numbbo.github.io/workshops/.Additional informationFundingThis work was supported by the Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2023/004, and further by the resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
Efficient quantum simulation of electron-phonon systems by variational basis state encoder

Digital quantum simulation of electron-phonon systems requires truncating infinite phonon levels into $N$ basis states and then encoding them with qubit computational basis. Unary encoding and the more compact binary/Gray encoding are the two most representative encoding schemes, which demand $\mathcal{O}(N)$ and $\mathcal{O}(\log{N})$ qubits as well as $\mathcal{O}(N)$ and $\mathcal{O}(N\log{N})$ quantum gates respectively. In this work, we propose a variational basis state encoding algorithm that reduces the scaling of the number of qubits and quantum gates to both $\mathcal{O}(1)$ for systems obeying the area law of entanglement entropy. The cost for the scaling reduction is a constant amount of additional measurement. The accuracy and efficiency of the approach are verified by both numerical simulation and realistic quantum hardware experiments. In particular, we find using one or two qubits for each phonon mode is sufficient to produce quantitatively correct results across weak and strong coupling regimes. Our approach paves the way for practical quantum simulation of electron-phonon systems on both near-term hardware and error-corrected quantum computers.
A new paradigm for artificial intelligence based on group equivariant non-expansive operators

The recent frantic surge of machine learning and, more broadly, of artificial intelligence (AI) brings to light old and new open issues, and among them, the so-called eXplainable artificial intelligence (XAI) – AI that humans can understand – as opposed to black-box learning systems where even their designers cannot explain AI decisions. One of the major XAI questions is how to design transparent learning systems that incorporate prior knowledge. These topics are becoming more relevant and pervasive as AI systems become more unfathomable and entangled with human factors. Recently a new paradigm for XAI has been introduced in literature, based on group equivariant non-expansive operators (GENEOs), which are able to inject prior knowledge in a learning system. Hence, the use of GENEOs dramatically reduces the number of unknown parameters to be identified and the size of the related training set, providing both computational advantages and an increased degree of interpretability of the results. Here we will illustrate the main characteristics of GENEOs and the encouraging results already obtained on a couple of industrial case studies.
Parity-encoding-based quantum computing with Bayesian error tracking

Abstract Measurement-based quantum computing (MBQC) in linear optical systems is promising for near-future quantum computing architecture. However, the nondeterministic nature of entangling operations and photon losses hinder the large-scale generation of graph states and introduce logical errors. In this work, we propose a linear optical topological MBQC protocol employing multiphoton qubits based on the parity encoding, which turns out to be highly photon-loss tolerant and resource-efficient even under the effects of nonideal entangling operations that unavoidably corrupt nearby qubits. For the realistic error analysis, we introduce a Bayesian methodology, in conjunction with the stabilizer formalism, to track errors caused by such detrimental effects. We additionally suggest a graph-theoretical optimization scheme for the process of constructing an arbitrary graph state, which greatly reduces its resource overhead. Notably, we show that our protocol is advantageous over several other existing approaches in terms of the fault-tolerance and resource overhead.
Sentiment analysis algorithm using contrastive learning and adversarial training for POI recommendation

Finding a suitable POI based on the user's needs and intentions is a complex decision-making process. Obtaining valuable information from the vast amount of social media data and using it for travel recommendations is a challenging issue. Traditional POI recommendation algorithms do not fully take into account the true feelings of customers about tourist attractions implied in social media data because they usually require a large amount of tagged travel commentary data. This study presents an aspect-based sentiment analysis model and POI recommendation method to accurately capture sentiment information contained in social media data with a small amount of tagged data. The pre-training model BERT is used to obtain the embedded representation of words that fuse the semantic information of the text. Using contrastive learning, point clusters belonging to the same class in the embedded space of words are pulled together, and sample clusters from different classes are separated. The potential relationship between comment ratings and their impact on user perception is analyzed, and the best performance formula for the loss function is determined. The test accuracy and F1-Score of the model in the experiment improved by 13.03% and 12.23%, respectively, compared to the BERT base model. POI recommendation validation is performed using a variety of recommendation algorithms. The experimental results show that the addition of aspect-based sentiment attributes can effectively improve the accuracy of recommendations.
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. These features are rarely observed in previous vision-language models. However, the technical details behind GPT-4 continue to remain undisclosed. We believe that the enhanced multi-modal generation capabilities of GPT-4 stem from the utilization of sophisticated large language models (LLM). To examine this phenomenon, we present MiniGPT-4, which aligns a frozen visual encoder with a frozen advanced LLM, Vicuna, using one projection layer. Our work, for the first time, uncovers that properly aligning the visual features with an advanced large language model can possess numerous advanced multi-modal abilities demonstrated by GPT-4, such as detailed image description generation and website creation from hand-drawn drafts. Furthermore, we also observe other emerging capabilities in MiniGPT-4, including writing stories and poems inspired by given images, teaching users how to cook based on food photos, and so on. In our experiment, we found that the model trained on short image caption pairs could produce unnatural language outputs (e.g., repetition and fragmentation). To address this problem, we curate a detailed image description dataset in the second stage to finetune the model, which consequently improves the model's generation reliability and overall usability. Our code, pre-trained model, and collected dataset are available at https://minigpt-4.github.io/.
Motion Capture Benchmark of Real Industrial Tasks and Traditional Crafts for Human Movement Analysis

Human movement analysis is a key area of research in robotics, biomechanics, and data science. It encompasses tracking, posture estimation, and movement synthesis. While numerous methodologies have evolved over time, a systematic and quantitative evaluation of these approaches using verifiable ground truth data of three-dimensional human movement is still required to define the current state of the art. This paper presents seven datasets recorded using inertial-based motion capture. The datasets contain professional gestures carried out by industrial operators and skilled craftsmen performed in real conditions in-situ. The datasets were created with the intention of being used for research in human motion modeling, analysis, and generation. The protocols for data collection are described in detail, and a preliminary analysis of the collected data is provided as a benchmark. The Gesture Operational Model, a hybrid stochastic-biomechanical approach based on kinematic descriptors, is utilized to model the dynamics of the experts' movements and create mathematical representations of their motion trajectories for analysis and quantifying their body dexterity. The models allowed accurate the generation of human professional poses and an intuitive description of how body joints cooperate and change over time through the performance of the task.
A novel sequential structure for lightweight multi-scale feature learning under limited available images

Although multi-scale feature learning can improve the performances of deep models, its parallel structure quadratically increases the model parameters and causes deep models to become larger and larger when enlarging the receptive fields (RFs). This leads to deep models easily suffering from over-fitting issue in many practical applications where the available training samples are always insufficient or limited. In addition, under this limited situation, although lightweight models (with fewer model parameters) can effectively reduce over-fitting, they may suffer from under-fitting because of insufficient training data for effective feature learning. In this work, a lightweight model called Sequential Multi-scale Feature Learning Network (SMF-Net) is proposed to alleviate these two issues simultaneously using a novel sequential structure of multi-scale feature learning. Compared to both deep and lightweight models, the proposed sequential structure in SMF-Net can easily extract features with larger RFs for multi-scale feature learning only with a few and linearly increased model parameters. The experimental results on both classification and segmentation tasks demonstrate that our SMF-Net only has 1.25M model parameters (5.3% of Res2Net50) with 0.7G FLOPS (14.6% of Res2Net50) for classification and 1.54M parameters (8.9% of UNet) with 3.35G FLOPs (10.9% of UNet) for segmentation but achieves higher accuracy than SOTA deep models and lightweight models, even when the training data is very limited available.
AppSniffer: Towards Robust Mobile App Fingerprinting Against VPN

Application fingerprinting is a useful data analysis technique for network administrators, marketing agencies, and security analysts. For example, an administrator can adopt application fingerprinting techniques to determine whether a user’s network access is allowed. Several mobile application fingerprinting techniques (e.g., FlowPrint, AppScanner, and ET-BERT) were recently introduced to identify applications using the characteristics of network traffic. However, we find that the performance of the existing mobile application fingerprinting systems significantly degrades when a virtual private network (VPN) is used. To address such a shortcoming, we propose a framework dubbed AppSniffer that uses a two-stage classification process for mobile app fingerprinting. In the first stage, we distinguish VPN traffic from normal traffic; in the second stage, we use the optimal model for each traffic type. Specifically, we propose a stacked ensemble model using Light Gradient Boosting Machine (LightGBM) and a FastAI library-based neural network model to identify applications’ traffic when a VPN is used. To show the feasibility of AppSniffer, we evaluate the detection accuracy of AppSniffer for 150 popularly used Android apps. Our experimental results show that AppSniffer effectively identifies mobile applications over VPNs with F1-scores between 84.66% and 95.49% across four different VPN protocols. In contrast, the best state-of-the-art method (i.e., AppScanner) demonstrates significantly lower F1-scores between 25.63% and 47.56% in the same settings. Overall, when normal traffic and VPN traffic are mixed, AppSniffer achieves an F1-score of 90.63%, which is significantly better than AppScanner that shows an F1-score of 70.36%.
Enhancing context representations with part-of-speech information and neighboring signals for question classification

Abstract Question classification is an essential task in question answering (QA) systems. An effective and efficient question classification model can not only restrict the search space for answers, but also guide the QA system in selecting the optimal knowledge base and search strategy. In recent years, self-attention mechanism has been widely used in question classification for its strength of capturing global dependencies. However, it models all signals with weighted averaging, which is prone to overlooking the relation of neighboring signals. Furthermore, recent research has revealed that part-of-speech (POS) information can be used to determine and reinforce the semantics in sentence representation. In this paper, we propose a POS-aware adjacent relation attention network (POS-ARAN) for question classification, which enhance context representations with POS information and neighboring signals. To consider the local context, we propose an adjacent relation attention mechanism, which incorporates a Gaussian bias via a dynamic window to revise the vanilla self-attention mechanism. Thus, it can capture both the long-term dependency and local representation of semantic relations among words in different sentences. In addition, a POS-aware embedding layer is proposed, which helps to locate the appropriate headwords by syntactic information. Extensive experiments are conducted on Experimental Data for Question Classification (EDQC) dataset and Yahoo! Answers Comprehensive Questions and Answers 1.0, the results demonstrate that our model significantly outperforms the existing methods, achieving 95.59% in coarse-grained level accuracy and 92.91% in fine-grained level accuracy, respectively.
Towards a Catalogue of Self-Sovereign Identity Design Patterns

Self-sovereign identity (SSI) is a user-centric, decentralised identity approach that provides a means for identification, authentication, and authorisation without the involvement of external entities, responsible for identity provisioning and management in current centralised and federated approaches. In general, the basic building blocks of an SSI system include decentralised identifiers, verifiable credentials, identity wallets, a verifiable data registry, and three main actors: issuer, identity holder, and verifier. Even though the SSI field is dominated by proposals, SSI systems can be implemented in different ways, which is reflected in the absence of a well-defined architecture. Thus, the best implementation is still a matter of research, the requirements of the individual system, and its field of application. However, well-designed and implemented systems are crucial to avoiding failures, speeding up the development process, ensuring high quality, and the broader adoption of SSI solutions. Hence, the main objective of this study was to identify design patterns and good practices of the SSI ecosystems by reviewing and analysing the literature, technical documentation, and existing SSI implementations. Therefore, the study is built on existing knowledge, and presents a comprehensive catalogue of thirty-five SSI design patterns that can serve as a starting point for a possible SSI system design.
Readability Indices Structure and Optimal Features

The work considers formal structure and features of the readability indices widely employed in various information and education fields, including theory of communication, cognitive psychology, linguistics, and multiple applications. In spite of the importance and popularity of readability indices in practical research, their intrinsic properties have not yet been sufficiently investigated. This paper aims to fill this gap between the theory and application of these indices by presenting them in a uniform expression which permits analyzing their features and deriving new properties that are useful in practice. Three theorems are proved for relations between the units of a text structure. The general characteristics are illustrated by numerical examples which can be helpful for researchers and practitioners.
Accelerating the variational quantum eigensolver using parallelism

Abstract Quantum computers are getting larger and larger, but device fidelities may not be able to keep up with the increase in qubit numbers. One way to make use of a large device that has a limited gate depth is to run many small circuits simultaneously. In this paper we detail our investigations into running circuits in parallel on the Rigetti Aspen-M-1 device. We run two-qubit circuits in parallel to solve a simple instance of the Hubbard model using the variational quantum eigensolver (VQE). We present results for running up to 33 circuits in parallel (66 qubits), showing that with the use of error mitigation techniques it is possible to make use of, and gain a real-time speedup from, parallelisation on current quantum hardware. We obtain a speedup by 18× for exploring the VQE energy landscape, and by more than 8× for running VQE optimisation.
Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers

In the realm of robotics and interactive systems, gender recognition is a crucial problem. Considering the several uses it has in security, web search, human-computer interactions, etc., gender recognition from facial photos has garnered a lot of attention. The need to use and enhance gender recognition techniques is felt more strongly today due to a significant development in the design of facial recognition systems. Relatively speaking to other approaches, the progress gained in this area thus far is not exceptional. Thus, a novel method has been adopted in this study to improve accuracy in comparison to earlier research. To create the best rate of accuracy and efficiency in the suggested method of this research, we choose a minimal set of characteristics. Testing on the FERET and UTK-Face datasets reveals that our suggested algorithm has a lower degree of inaccuracy. In this article, the input image of the person's face is pre-processed to extract the right features from the face once the person's face has been recognized. Gender separation is achieved using Multi-class Support Vector Machine (SVM) Classifiers after features from normalized images have been extracted using Histogram Oriented Gradient (HOG), Gabor Filters, and Speeded Up Robust Features (SURF), as well as their combination to select the most appropriate feature from them as input for gender classification. As a feature reduction feature, the Principal Component Analysis (PCA) algorithm is also employed. Using the proposed approach, 98.75% gender recognition precision has been accomplished on the FERET database and a runtime performance of 0.4 Sec. on the UTK-Face database, 97.43% gender recognition accuracy has been accomplished and a runtime performance of 0.5 Sec.
Convolutional autoencoder based on latent subspace projection for anomaly detection

Image anomaly detection (AD) is widely researched on many occasions in computer vision tasks. High-dimensional data, such as image data, with noise and complex background is still challenging to detect anomalies under the situation that imbalanced or incomplete data are available. Some deep learning methods can be trained in an unsupervised way and map the original input into low-dimensional manifolds to predict larger differences in anomalies according to normal ones by dimension reduction. However, training a single low-dimension latent space is limited to present the low-dimensional features due to the fact that the noise and irreverent features are mapped into this space, resulting in that the manifolds are not discriminative for detecting anomalies. To address this problem, a new autoencoder framework is proposed in this study with two trainable mutually orthogonal complementary subspaces in the latent space, by latent subspace projection (LSP) mechanism, which is named as LSP-CAE. Specifically, latent subspace projection is used to train the latent image subspace (LIS) and the latent kernel subspace (LKS) in the latent space of the autoencoder-like model respectively, which can enhance learning power of different features from the input instance. The features of normal data are projected into the latent image subspace, while the latent kernel subspace is trained to extract the irrelevant information according to normal features by end-to-end training. To verify the generality and effectiveness of the proposed method, we replace the convolutional network with the fully-connected network contucted in the real-world medical datasets. The anomaly score based on projection norms in two subspace is used to evaluate the anomalies in the testing. Consequently, our proposed method can achieve the best performance according to four public datasets in comparison of the state-of-the-art methods.