Reviewerly/exHarmony huggingface dataset:
Applications of IoT-Driven Multi-Sensor Fusion and Integration of Dynamic Data-Driven Symbiotic Machine Learning

The internet of things is enabling new applications, especially in the field of biomedical systems. Such internet of things systems can not only use an existing infrastructure, but also build their own network for data exchange. By linking multiple distributed sensors, complex interpretations of data and identification of scenarios can be realized based on sensor fusion. This allows new relationships to be captured and interpreted. Data fusion is thus a key concept for object, motion, and scenario identification based on sensor values. Here, the authors first introduce specific applications of sensor fusion. Driven by the increase of pandemic work from home, this paper describes an IoT-based posture monitoring. The selection of different sensor types can be made on an application-specific basis using the flexible IoT platform, providing a toolbox. Second focus is the presentation of a concept for the integration of process models into the data fusion process. This model-based data fusion extends the concepts of the data fusion information group (DFIG) model.
BERT and ULMFiT Ensemble for Personality Prediction from Indonesian Social Media Text

Predicting personality is a growing topic in the field of natural language processing. The study of personality prediction has been proven to benefit the development of recommender systems and automated personality assessments by previous studies. Additionally, the widespread usage of social media in Indonesia such as Twitter has served as a potential source of data for developing such models. Existing personality prediction models has explored the implementation of both traditional machine learning models and deep learning models, with the latter proven to perform better with more data. Despite so, there is not much development of deep learning-based personality prediction models in the domain of Indonesian text. We propose a deep learning-based ensemble model based on Universal Language Model Fine Tuning (ULMFiT) and Bidirectional Encoder Representations from Transformers (BERT), the state-of-the-art pre-trained models for text classification. The model is trained with Indonesian tweets labelled with Five Factor Model (FFM) personality traits along with the users' metadata. The model is tested with five predetermined scenarios to prove the robustness of its performance. The final evaluation of the model proves that it has successfully achieved optimal performance of 76.41 % macro-average accuracy and 75.55% macro-average F-1 score, exceeding the state-of-the-art BERT and ULMFiT
A Visual Affordance Reasoning Network Based on Graph Attention

Visual affordance studies what kind of interaction is possible and whether the interaction is reasonable in the current environment from an image/video. When inferring affordances of objects, semantics and relations of objects in the environment should be considered, and graph is usually used for modeling the environment context for object. Considering the weight of edge in graph describes the amount of contributed information between objects during affordance reasoning, this paper proposes VAR-Net (Visual Affordance Reasoning Network) which models the weights as graph attention coefficients and learns the weights based on objects’ semantic and visual features implying their affordances. VAR-Net achieves higher accuracy on COCO-Tasks and ADE-Affordance datasets. Experiments also explain the meaning of edge weights in VAR-Net. For a definite affordance, an object commits it more, the edges linking from it to other objects have larger weights and vice versa, which makes objects’ features distinguishable for inferring affordances.
Personalized Avatars Without Agentic Interaction: Do They Promote Learning Performance and Sense of Self in a Teaching Context? A Pilot Study

Virtual agents are becoming promising opportunities for augmenting human-human interaction in the analog world in a diversity of domains such as health care or education. This experimental study explores the impact of virtual self- and virtual other presence on e-learning performance in a virtual classroom setting. In addition, the relevance of virtual teachers is explored. The classroom setting is designed to simulate to study either alone with one's self-representative avatar or together with virtual classmates. Participants could take part in an online course thought by a virtual teacher. First results show that the mere representation of the virtual Self or of virtual Others by static avatars without any functionality of the avatars to serve as interactive agents has no differential impact on the learner's immediate learning performance, even when the avatars representing the Self are self-chosen by the participants. However, the online course taught by a humanoid avatar as virtual teacher was positively related with the learning performance. This suggests that embodying teachers as virtual educators in online learning could help improve the learner's cognitive performance with or without virtual presence of one's Self or peers.
On Calibration of Graph Neural Networks for Node Classification

Graphs can model real-world, complex systems by representing entities and their interactions in terms of nodes and edges. To better exploit the graph structure, graph neural networks have been developed, which learn entity and edge embeddings for tasks such as node classification and link prediction. These models achieve good performance with respect to accuracy, but the confidence scores associated with the predictions might not be calibrated. That means that the scores might not reflect the ground-truth probabilities of the predicted events, which would be especially important for safety-critical applications. Even though graph neural networks are used for a wide range of tasks, the calibration thereof has not been sufficiently explored yet. We investigate the calibration of graph neural networks for node classification, study the effect of existing post-processing calibration methods, and analyze the influence of model capacity, graph density, and a new loss function on calibration. Further, we propose a topology-aware calibration method that takes the neighboring nodes into account and yields improved calibration compared to baseline methods.
Comparing Execution Traces of Jupyter Notebook for Checking Correctness of Refactoring

Jupyter Notebook is a popular tool for writing data analysis programs. Prior work showed that Jupyter Notebook users often duplicate their python code to try their hypothesis quickly. While such code clones can be removed by Extract Function refactoring later, users have to check that the output of a notebook is unaffected by the refactoring. However, users may not be able to compare execution results of a notebook before and after refactoring because non-textual output in Jupyter Notebook are fragile; for example, each of executions produce non-identical graphical images even though they look the same. To address this issue, we propose a method to automatically compare API calls to execute a Jupyter Notebook in addition to the textual output, while ignoring non-textual output. Our key assumption is that the same API calls with the same parameters produce the same results even if their details are non-identical. To demonstrate the effectiveness of the approach, we implemented an automatic tool for Jupyter Notebook that extracts a function from code clones and automatically checks the correctness. Using the tool, we have extracted functions from 3,995 cells in 520 Jupyter Notebook files. 142 out of 520 Notebook files are executable. Our tool compared API calls to check the correctness for 88 Notebook files, while a simple textual comparison could check 22 Notebook files.
An Implementation of Quantum Machine Learning Technique to Determine Insurance Claim Fraud

Quantum Machine Learning (QML) is an upcoming and active research field that uses quantum computing concepts for machine learning. This paper presents QML techniques for detecting fraud in housing insurance claims. Insurance fraud detection is challenging as the patterns may be complex. Therefore, various techniques and methods are employed to identify suspicious claims and prevent business losses due to these fraudulent claims. This paper explores Quantum Support Vector Machine (QSVM) techniques and feature engineering, feature selection, and parameter tweaking to identify fraudulent housing insurance claims. In addition, it compares Quantum SVM with classical SVM. Finally, this paper focuses on detecting property insurance claims fraud by using machine learning techniques.
Centralized-Equivalent Pairwise Estimation with Asynchronous Communication Constraints for two Robots

Collaboratively estimating the state of two robots under communication constraints is challenging regarding computational complexity and statistical optimality. Previous work only achieves practical solutions by either disregarding parts of the measurements or imposing a communication overhead, being non-optimal or not entirely distributed, respectively. In this work, we present a centralized-equivalent but dis-tributed approach for pairwise state estimation where two agents only communicate when they meet. Our approach utilizes elements from wave scattering theory to efficiently and consistently summarize (pre-compute) past estimator information (i.e., state evolution and uncertainty) between encounters of two agents. This summarized information is then used in a joint correction step taking into account all past information of each agent in a statistically correct way. This novel approach enables us to distribute the pre-computations of both state evolution and uncertainties on the agents and reconstruct the centralized-equivalent system estimate with very few computations once the agents meet again while still applying all measurements from both agents on both estimates upon encounter. We compare our approach on a real-world dataset against a state of the art collaborative state estimation approach.
Person Re-identification with a Cloth-Changing Aware Transformer

Cloth-Changing person re-identification is a challenging problem for its huge intra-class variance caused by changing clothes. In this paper, we focus on learning cloth-insensitive features without any cues from external models. Specifically, we introduce a novel Cloth-Changing Aware Transformer(CCAT) to learn identity-relevant cues. The transformer encoder utilizes Cloth Information Embedding(CIE) to encode outfits information and automatically focuses on the identity-discriminative feature. The tokens from the transformer encoder are then fed to the Cloth-Insensitive Consistency Learning(CICL) decoder to learn cloth-irrelevant features from pairwise pedestrian images. Besides, we impose Intra-class Constraint Learning(ICL) to pull the global tokens from the encoder closer. In this way, the model learns the identity-relevant and cloth-insensitive features. Extensive experimental results on three cloth-changing person ReID datasets demonstrate that our proposed algorithm can extract highly robust feature representations of cloth-changing persons, and it outperforms the state-of-the-art cloth-changing person ReID approaches. Our method achieves 87.4% and 69.7% on R-1 accuracy on the latest released LTCC [28] and PRCC [40], which outperforms previous methods with a large margin. The code is available at CCAT.
Research on the Online and Offline Mixed Teaching System of Dance in the Mobile Information Age

As mobile internet technology continues to develop and society enters the mobile information age, the education sector has also responded to the development of the times by ushering in information-based teaching. The blended teaching model, which uses teaching resources provided by online teaching platforms to improve the quality of offline classroom teaching and break the limitations of learning in time and space, responds to the needs of the mobile information era. Based on the Spring MVC framework, the online and offline mixed teaching system of dance effectively separates the client-side presentation from the server-side logic processing, which can improve code reusability and maintainability. The system functions are divided into teacher user subsystem, student user subsystem and system administrator subsystem according to user roles, which facilitates system implementation and permission allocation, reflecting the characteristics of high cohesion and low coupling.
Differential Dynamic Trees for Interactive Image Segmentation

The required number of users’ actions and the response time can critically affect user experience during interactive image segmentation. In this work, we revisit a recent graph-based algorithm, namely Dynamic Trees (DT), which has shown to be more effective than several well-established methods from the literature of graph-based image segmentation. DT solves segmentation by growing optimum-path trees rooted at seed pixels, such that the arc weights are estimated on the fly from image properties of the growing trees, defining the objects as optimum-path forests rooted at their internal seeds. Depending on the application (e.g., 3D medical images), the response time to correct segmentation by adding and removing seeds can seriously compromise the method’s efficiency. We present a differential dynamic trees (DDT) algorithm that adds and removes trees updating optimum paths only in the required regions of the image. We demonstrate that the DDT algorithm can preserve the high effectiveness of DT, being one order of magnitude faster than DT. The experiments also show the advantages of DDT over those well-established counterparts.
Towards Scalable and Efficient Client Selection for Federated Object Detection

Various computer vision techniques based on deep neural networks have been proposed to detect objects accurately and fast. However, due to the privacy, security and communication bandwidth restrictions of diverse participating parties, it is sometimes prohibitive to train such models on a centralized machine. Federated Learning (FL) provides a promising solution to learn a model from decentralized data. Despite the advances in FL, the diversity of client regions in which they operate and the Non-IID nature of the crowdsourced datasets reduces the accuracy of object detection models significantly. In this paper, we introduce a novel FL object detection system to efficiently train models with heterogeneous client datasets. We propose lightweight client selection methods to learn object detection models faster. Our client selection methods based on the object data distribution at clients achieves up to 74% reduction in required federated rounds compared to conventional approaches. We further extend this method by leveraging the metadata of the training images (e.g., location, direction, depth), to select clients which maximize the coverage of diverse geographical regions. We report on extensive experiments with real datasets.
A novel Tri-training algorithm based on Evidence Theory

In many machine learning tasks, it is usually difficult to obtain enough labeled samples. Semi-supervised classification(SSC) trains a few labeled samples and a large amount of unlabeled samples to obtain a better learning model, which has attracted a lot of research attention. Traditional semi-supervised methods may encounter uncertainty problems and information loss when dealing with those samples having ambiguous class belongingness. In this paper, the uncertainties encountered in semi-supervised learning are addressed using the Evidence Theory, and semi-supervised learning methods based on belief functions are proposed. The proposed methods label the unlabeled data with belief modeling. They can effectively use limited supervised information to facilitate the classification process. Experimental results based on benchmark data sets show that the proposed approaches can effectively exploit unlabeled data and perform better compared with prevailing approaches.
Retail Commodity Image Recognition based on WS-DAN

Computer vision technology has landed very widely in daily life, business, security, and other fields, especially the application represented by image recognition technology has penetrated into all aspects of life. In the business scenario of brand owners, the efficiency and convenience brought by commodity recognition technology is also particularly prominent, which can bring cost reduction and efficiency increase for enterprises. Especially in the retail goods industry, the use of image recognition technology can greatly improve productivity levels. However, the number of retail goods categories is huge. The differences in the packaging of similar goods may be very subtle, such as the difference in logo position, the difference in the label text, or even the difference in the color of a small area. These differences are difficult to be accurately identified in general image recognition. Ordinary image recognition technology cannot meet the demand, so it is necessary to apply fine-grained image recognition technology. In this paper, an mechanism of attention to guide data augmentation fine-grained neural networks image recognition, i.e. WS-DAN (Weakly Supervised Data Augmentation Network) is used for fine-grained recognition of retail goods. The model was trained and validated using the dataset provided by Baidu Al (Artificial Intelligence) studio, and experiments were carried out to investigate the impact of various factors on image recognition accuracy and the difficulty in recognizing products by category.
A Comparative Analysis of Image Steganography Based on Discrete Wavelet Transform (DWT) and Exploiting Modification Direction (EMD)

The art of hiding the existing data over a covering medium which may be an image or any other media file is called steganography. In this paper, we will try to review major steganographic methods that are existing until now for the various image formats. We will majorly do a comparative analysis between discrete wavelet transform (DWT)-based steganography and exploiting the modification direction (EMD) algorithm. In DWT-based steganography, we will try to conceal data in lower frequency bands of the image data which is less perceivable by the human eye, and in EMD-based steganography we will try to find an extraction function based on an algorithm for embedding data. For practical analysis, we will be using Matlab as a software tool that is robust while dealing with image processing.
SA-Q

Sentiment analysis has received constant research attention due to its usefulness and importance in different applications. However, despite the research advances in this field, most current tools suffer in prediction quality due to the inconsistencies in their results, i.e., intra- and inter-tool inconsistencies. This demonstration proposes a system for the evaluation of sentiment analysis quality namely SA-Q. The system allows the evaluation of inconsistency in sentiment analysis tools, the resolution of the inconsistency using state-of-the-art methods and the recommendation of relevant sentiment analysis tool for any type of data set provided by the attendees. It allows the attendees to compare the tools. Moreover, we demonstrate that SA-Q evaluates the consistency of tools on two levels (intra-tool and inter-tool). Through various scenarios, we showcase the challenges of inconsistency resolution, demonstrate the usefulness of the proposed system and the recommendations that can be given to the attendees for their datasets. We demonstrate that SA-Q system has practical utility in many areas of industrial applications for better decision making. This demonstration shows promising research areas for data management, NLP, and machine learning communities by adopting and drawing inspiration from truth inference methods to create more robust tools and improve the tool's scalability.
Neural Greedy Pursuit for Feature Selection

We propose a greedy algorithm to select <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> important features among <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> input features for a non-linear prediction problem. The features are selected one by one sequentially, in an iterative loss minimization procedure. We use neural networks as predictors in the algorithm to compute the loss and hence, we refer to our method as neural greedy pursuit (NGP). NGP is efficient in selecting <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> features when <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N\ll P$</tex> , and it provides a notion of feature importance in a descending order following the sequential selection procedure. We experimentally show that NGP provides better performance than several feature selection methods such as DeepLIFT and Drop-one-out loss. In addition, we experimentally show a phase transition behavior in which perfect selection of all <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> features without false positives is possible when the training data size exceeds a threshold.
A comparative study of credit card fraud detection using machine learning

Credit card fraud is one of the worldwide problems which affects everyone. Credit card fraud detection is a distribution issue with the aim of automatically and adaptively categorizing genuine and fraudulent transactions. Any malicious behavior causing financial loss to the other party is classifying as fraud. For example, in poor nations, the use of digital currency, or even plastic money, is on the rise it has a history of defrauding people. They have a track record of scamming others. In recent years, credit card fraud has increased. Customers and institutions all over the world are paying billions of dollars. Fraudsters continue to thrive despite the multiple fraud-prevention devices in place. In this study report, we're seeking to come up with new ways to swindle people. As a result, combatting these scams demands the implementation of a sophisticated fraud detection system. Fraud is not only detected, but also prevented by the system. The systems must be able to learn from past fraud schemes and adapt to new ones. The notion of credit card fraud, as well as the many types of fraud, were discussed in this study. Several fraud detection methods, such as logistic regression, decision trees, and random forests, were studied. Existing and proposed theories for credit card fraud are carefully scrutinized, and these strategies are tested using quantitative metrics such as accuracy and discovery rate. The system showed a high level of fraud detection, equal classification, a high Matthews correlation coefficient, and a false alarm level. The crime of stealing sensitive information, supplanting, grazing or stealing data on the part of the merchant, lost or stolen cards, producing fake or counterfeit cards, making a real site, and removing or replacing a magnetic line on the card keeping user information are all examples of credit card fraud. The study came to the conclusion that existing models have flaws and proposes a new technique for fixing them. Obstacles to fraud detection are expected to change and metamorphosize into hidden impediments in the future, based on how fraudsters do these illicit behaviors.