Reviewerly/exHarmony huggingface dataset:
Compact global association based adaptive routing framework for personnel behavior understanding

Personnel behavior understanding under complex scenarios is a challenging task for computer vision. This paper proposes a novel Compact model, which we refer to as CGARPN that incorporates with Global Association relevance and Adaptive Routing Pose estimation Network. Our framework firstly introduces CGAN backbone to facilitate the feature representation by compressing the kernel parameter space compared with typical algorithms, effectively lowering the calculation capacity and consumption. The framework integrates the Global Association information between keypoints, and learns the correlation between high-dimensional feature parameters. ARPN introduced by our structure is established to sufficiently excavate the resembling properties of outcome concealed in the network, adaptively achieving remarkable performance by selecting compatible paths for optimization. Meanwhile, Parametric Content Similarity NMS (PCSNMS) is developed where detailed information on proposal boxes is associated. Comparative experiments (datasets on FLIC, MPII, etc.) with CNN-based counterparts have empirically demonstrated the effectiveness and competitiveness of the model in perspective of accuracy, memory consumption, and computation perplexity. Our model contributes to an efficient and feasible framework of human behavior apprehension.
Improving the speed of variational quantum algorithms for quantum error correction

We consider the problem of devising a suitable Quantum Error Correction (QEC) procedures for a generic quantum noise acting on a quantum circuit. In general, there is no analytic universal procedure to obtain the encoding and correction unitary gates, and the problem is even harder if the noise is unknown and has to be reconstructed. The existing procedures rely on Variational Quantum Algorithms (VQAs) and are very difficult to train since the size of the gradient of the cost function decays exponentially with the number of qubits. We address this problem using a cost function based on the Quantum Wasserstein distance of order 1 ($QW_1$). At variance with other quantum distances typically adopted in quantum information processing, $QW_1$ lacks the unitary invariance property which makes it a suitable tool to avoid to get trapped in local minima. Focusing on a simple noise model for which an exact QEC solution is known and can be used as a theoretical benchmark, we run a series of numerical tests that show how, guiding the VQA search through the $QW_1$, can indeed significantly increase both the probability of a successful training and the fidelity of the recovered state, with respect to the results one obtains when using conventional approaches.
Handwritten Arabic Character Recognition for Children Writ-ing Using Convolutional Neural Network and Stroke Identification

Automatic Arabic handwritten recognition is one of the recently studied problems in the field of Machine Learning. Unlike Latin languages, Arabic is a Semitic language that forms a harder challenge, especially with variability of patterns caused by factors such as writer age. Most of the studies focused on adults, with only one recent study on children. Moreover, much of the recent Machine Learning methods focused on using Convolutional Neural Networks, a powerful class of neural networks that can extract complex features from images. In this paper we propose a convolutional neural network (CNN) model that recognizes children handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of the Arabic characters written by children, and 97% on Arabic Handwritten Character Dataset. The results showed a good improvement over the proposed model from the Hijja dataset authors, yet it reveals a bigger challenge to solve for children Arabic handwritten character recognition. Moreover, we proposed a new approach using multi models instead of single model based on the number of strokes in a character, and merged Hijja with AHCD which reached an averaged prediction accuracy of 96%.
Twitter Data Sentiment Analysis of COVID-19 Vaccination using Machine Learning

The expansion of the web is accelerating, which helps encourage the creation of fresh ideas. In today's internet era, we must suggest techniques to filter out various information. Social media sentiment analysis based on Twitter data can monitor the real-time monitoring of the COVID-19 vaccine. In this way, relevant organizations or governments can take proactive steps to address misinformation and inappropriate behaviour around the COVID-19 vaccine, which threatens the success of the national vaccination campaign. The purpose of this research is to determine if there is a link between how people feel about the COVID-19 vaccine on Twitter and how many people actually get vaccinated against it. This study uses the COVID-19 All Vaccines Tweet dataset sourced from Kaggle. This research Identifies public sentiment, emotion, word usage, and trend of all filtered tweets. The results show that there are 31% positive tweets, 10% negative tweets, and 58% neutral tweets. Tweets with neutral subjective valence tend to cluster in the middle of the polarity scale (between -1 and +1), whereas tweets with strong subjective valence are spread across the scale.
Web 2.0 Tools Supported Innovative Applications in Science Education Based on the Context-Based Learning Approach

The research aims to design and develop innovative materials supported by Web 2.0 tools to offer solutions to the problems of science and technology teachers in science education based on a context-based learning approach. For this purpose, the design and development research method were used to test this informative material's design, development, and validity. In the design, students and teachers are considered a common user group. Aside from the literature review, Web 2.0 tool design methods were used in the design process, and the resulting designs were presented. It is considered that innovative materials supported by Web 2.0 tools in science education based on context-based learning practices will enrich science teaching environments and support much more meaningful learning. Innovative technology applications were attempted to be presented to our teachers and students in Science and Technology Teaching using the designs realized within the scope of the research.
A Novel Cluster Matching-Based Improved Kernel Fisher Criterion for Image Classification in Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) is a popular approach to reducing distributional discrepancies between labeled source and the unlabeled target domain (TD) in machine learning. However, current UDA approaches often align feature distributions between two domains explicitly without considering the target distribution and intra-domain category information, potentially leading to reduced classifier efficiency when the distribution between training and test sets differs. To address this limitation, we propose a novel approach called Cluster Matching-based Improved Kernel Fisher criterion (CM-IKFC) for object classification in image analysis using machine learning techniques. CM-IKFC generates accurate pseudo-labels for each target sample by considering both domain distributions. Our approach employs K-means clustering to cluster samples in the latent subspace in both domains and then conducts cluster matching in the TD. During the model component training stage, the Improved Kernel Fisher Criterion (IKFC) is presented to extend cluster matching and preserve the semantic structure and class transitions. To further enhance the performance of the Kernel Fisher criterion, we use a normalized parameter, due to the difficulty in solving the characteristic equation that draws inspiration from symmetry theory. The proposed CM-IKFC method minimizes intra-class variability while boosting inter-class variants in all domains. We evaluated our approach on benchmark datasets for UDA tasks and our experimental findings show that CM-IKFC is superior to current state-of-the-art methods.
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research

Large Language Models (LLMs) generalize well across language tasks, but suffer from hallucinations and uninterpretability, making it difficult to assess their accuracy without ground-truth. Retrieval-Augmented Generation (RAG) models have been proposed to reduce hallucinations and provide provenance for how an answer was generated. Applying such models to the scientific literature may enable large-scale, systematic processing of scientific knowledge. We present PaperQA, a RAG agent for answering questions over the scientific literature. PaperQA is an agent that performs information retrieval across full-text scientific articles, assesses the relevance of sources and passages, and uses RAG to provide answers. Viewing this agent as a question answering model, we find it exceeds performance of existing LLMs and LLM agents on current science QA benchmarks. To push the field closer to how humans perform research on scientific literature, we also introduce LitQA, a more complex benchmark that requires retrieval and synthesis of information from full-text scientific papers across the literature. Finally, we demonstrate PaperQA's matches expert human researchers on LitQA.
Self-adaptive Privacy Concern Detection for User-Generated Content

To protect user privacy in data analysis, a state-of-the-art strategy is differential privacy in which scientific noise is injected into the real analysis output. The noise masks individual’s sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some mechanisms to protect data privacy in different scenarios, most of the existing studies assume uniform privacy concerns for all individuals. Consequently, putting an equal amount of noise to all individuals leads to insufficient privacy protection for some users, while over-protecting others. To address this issue, we propose a self-adaptive approach for privacy concern detection based on user personality. Our experimental studies demonstrate the effectiveness to address a suitable personalized privacy protection for cold-start users (i.e., without their privacy-concern information in training data).
Ensembles of feedforward-designed convolutional neural networks

An ensemble method that fuses the output decision vectors of multiple feedforward-designed convolutional neural networks (FF-CNNs) to solve the image classification problem is proposed in this work. To enhance the performance of the ensemble system, it is critical to increasing the diversity of FF-CNN models. To achieve this objective, we introduce diversities by adopting three strategies: 1) different parameter settings in convolutional layers, 2) flexible feature subsets fed into the Fully-connected (FC) layers, and 3) multiple image embeddings of the same input source. Furthermore, we partition input samples into easy and hard ones based on their decision confidence scores. As a result, we can develop a new ensemble system tailored to hard samples to further boost classification accuracy. Experiments are conducted on the MNIST and CIFAR-10 datasets to demonstrate the effectiveness of the ensemble method.
Data-driven Approach for Quality Evaluation on Knowledge Sharing Platform

In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live). Extensive experiments demonstrate the effectiveness of the proposed method. Furthermore, we introduce a dataset of Zhihu Live as an open resource for researchers in related areas. This dataset will facilitate the development of new methods on knowledge sharing services quality evaluation.
Software Multiple-Level Change Detection Based on Two-Step MPAT Matching

During software evolution, change detection plays an important role in software maintenance. For example, according to the changes, developers need to verify whether the evolved contents are consistent with the evolution plan, and making a regression testing plan. Right now, the text-based method and the AST-based matching method are widely used to detect changes. However, the text-based method cannot identify updating and renaming, so the accuracy of its detecting result is not high enough; the AST-based matching method only reflects the changes between files, so its detecting result is incomplete for understanding software evolution from an overall perspective. In order to solve the above problems, we propose a software multiple-level change detection method based on two-step MPAT (multilevel program analysis tree) matching, which detects changes between two programs from multiple levels to improve accuracy and proposes comprehensive change information. Our approach consists of three steps. Firstly, we construct MPAT for each program. Secondly, we implement the two-step matching algorithm to detect changes based on the two MPATs. Thirdly, these changes are classified and clustered. Finally, we develop ChangeAnalyzer to implement our approach, and conduct our experiments on six projects to evaluate the accuracy, performance and usefulness of ChangeAnalyzer.
Learning from Higher-Layer Feature Visualizations

Driven by the goal to enable sleep apnea monitoring and machine learning-based detection at home with small mobile devices, we investigate whether interpretation-based indirect knowledge transfer can be used to create classifiers with acceptable performance. Interpretation-based indirect knowledge transfer means that a classifier (student) learns from a synthetic dataset based on the knowledge representation from an already trained Deep Network (teacher). We use activation maximization to generate visualizations and create a synthetic dataset to train the student classifier. This approach has the advantage that student classifiers can be trained without access to the original training data. With experiments we investigate the feasibility of interpretation-based indirect knowledge transfer and its limitations. The student achieves an accuracy of 97.8% on MNIST (teacher accuracy: 99.3%) with a similar smaller architecture to that of the teacher. The student classifier achieves an accuracy of 86.1% and 89.5% for a subset of the Apnea-ECG dataset (teacher: 89.5% and 91.1%, respectively).
Evaluation Study of Linear Combination Technique for SVM related Time Series Forecasting

Time series forecasting and SVM are widely used in many domains, for example, smart city and digital services.Focusing on SVM related time series forecasting model, in this paper we empirical investigate the performance of eight linear combination techniques by using M3 competition dataset which includes 3003 time series.The results reveals that the "forecast combination puzzle" is not exist for combining SVM related forecasting model as the simple average is almost the worst combination technique.
Aspect-Oriented System Coupling Metric and its Validation

Background: Aspect-oriented programming promises to enhance the extensibility and reusability of code through the removal of tangled and crosscutting code. Determining the degree of coupling for Aspect- Oriented Systems (AOSs) would assist in the quantification of various software attributes and hence improve quality. Objective: The research aims to present a novel Aspect-oriented System Coupling Metric (COAO), that calculates the coupling for the complete aspect-oriented system as a whole, based on the properties of elements and the relationships among them. Methods: The process of defining a metric essentially requires a clear, unambiguous definition of primary and relevant concepts related to Aspect-Oriented Programming. As such, first and foremost, novel definitions of basic concepts such as system, element, relation, module, and attribute are specified concerning Aspect- Oriented Programming. Subsequently, a metric for Aspect-Oriented System Coupling is proposed. Subsequently, the proposed metric is validated theoretically against Braiand properties for coupling of software systems. Finally, an illustration for calculation of the proposed metric is demonstrated using an exemplary aspect-oriented system. Results: The findings reveal that the proposed Aspect-Oriented Coupling metric conforms to the five Property- Based software engineering measurements given by Braiand et al. for coupling. This indicates that the proposed metric for the Aspect-oriented System Coupling metric COAO is a valid metric for measuring coupling in Aspect-oriented Software Systems. Conclusion: Results of validation along with the supportive illustration show that single metric to assess coupling for the complete Aspect-oriented Software System is theoretically sound and also easies the calculation of coupling of a software system.
A Hierarchical Decoding Model For Spoken Language Understanding From Unaligned Data

Spoken language understanding (SLU) systems can be trained on two types of labelled data: aligned or unaligned. Unaligned data do not require word by word annotation and is easier to be obtained. In the paper, we focus on spoken language understanding from unaligned data whose annotation is a set of act-slot-value triples. Previous works usually focus on improve slot-value pair prediction and estimate dialogue act types separately, which ignores the hierarchical structure of the act-slot-value triples. Here, we propose a novel hierarchical decoding model which dynamically parses act, slot and value in a structured way and employs pointer network to handle out-of-vocabulary (OOV) values. Experiments on DSTC2 dataset, a benchmark unaligned dataset, show that the proposed model not only outperforms previous state-of-the-art model, but also can be generalized effectively and efficiently to unseen act-slot type pairs and OOV values.
Predicting Graph Operator Output over Multiple Graphs

A growing list of domains, in the forefront of which are Web data and applications, are modeled by graph representations. In content-driven graph analytics, knowledge must be extracted from large numbers of available data graphs. As the number of datasets (a different type of volume) can reach immense sizes, a thorough evaluation of each input is prohibitively expensive. To date, there exists no efficient method to quantify the impact of numerous available datasets over different graph analytics tasks. To address this challenge, we propose an efficient graph operator modeling methodology. Our novel, operator-agnostic approach focuses on the inputs themselves, utilizing graph similarity to infer knowledge about them. An operator is executed for a small subset of the available inputs and its behavior is modeled for the rest of the graphs utilizing machine learning. We propose a family of similarity measures based on the degree distribution that prove capable of producing high quality models for many popular graph tasks, even compared to modern, state of the art similarity functions. Our evaluation over both real-world and synthetic graph datasets indicates that our method achieves extremely accurate modeling of many commonly encountered operators, managing massive speedups over a brute-force alternative.
A meta-learning method for histopathology image classification based on LSTM-model

The rapid development of meta-learning methods enables the generalized classification of histopathology images with only a handful of new training images. Meta-learning is also named as learning to learn. In this study, we propose a LSTM-model based meta-learning framework for the histopathology image classification. We apply the DoubleOpponent (DO) neurons to model the texture patterns of histopathology images. And the LSTM-model is utilized for the optimization of the meta-learning algorithm to classify the histopathology images. Experiment results on real dataset demonstrated that the proposed method leads in all the measures, namely, recall, precision, F-measure and accuracy.
Real time streaming of 8K 360 degree video to mobile VR headsets

In this demo, we showcase how to stream 8K 360° video to commodity mobile devices without pre-processing or viewpoint prediction using our MEC-VR system. Users can freely select 360° video from YouTube and immediately become immersed in the video of 8K resolution using a Samsung GearVR compatible smartphone. Our system can support zoom in and zoom out while saving up to 80% bandwidth.
The $$\ell _{2,1}$$-Norm Stacked Robust Autoencoders via Adaptation Regularization for Domain Adaptation

Domain adaption aims to promote the learning tasks in target domain by using the knowledge from source domain whose data distribution is different from target domain. How to reconstruct more robust and high-level feature space to reduce the discrepancy between source and target domains is the crucial problem in domain adaptation. Recently, deep learning methods have shown promising results on learning new representations. However, in most of the previous work, the local geometry structure of data is not taken into account, which is benefit for constructing more robust feature space. Therefore, we propose a novel algorithm termed $$\ell _{2,1}$$ -norm stacked robust autoencoders via adaptation regularization (SRAAR) to learn better feature representations for domain adaptation. More specifically, we incorporate an effective manifold regularization into the objective function to preserve the local geometry structure of data. In addition, label information is used to compute the maximum mean discrepancy (MMD) in order to reduce the distance between source and target domains. Experimental results show the effectiveness of proposed approach.
CUPCF: combining users preferences in collaborative filtering for better recommendation

How to make the best decision between the opinions and tastes of your friends and acquaintances? Therefore, recommender systems are used to solve such issues. The common algorithms use a similarity measure to predict active users’ tastes over a particular item. According to the cold-start and data sparsity problems, these systems cannot predict and suggest particular items to users. In this paper, we introduce a new recommender system that is able to find user preferences and based on it, provides the recommendations. Our proposed system called CUPCF is a combination of two similarity measures in collaborative filtering to solve the data sparsity problem and poor prediction (high prediction error rate) problems for better recommendation. The experimental results based on MovieLens dataset show that, combined with the preferences of the user’s nearest neighbor, the proposed system error rate compared to a number of state-of-the-art recommendation methods improved. Furthermore, the results indicate the efficiency of CUPCF. The maximum improved error rate of the system is 15.5%, and the maximum values of Accuracy, Precision and Recall of CUPCF are 0.91402, 0.91436 and 0.9974, respectively.