Reviewerly/exHarmony huggingface dataset:
Detection of Breast Lesion Using Improved GLCM Feature Based Extraction in Mammogram Images

A classic mammogram image processing method commonly illustrates of mammogram image pre-processing, segmentation, feature extraction and classification techniques. Feature extraction is a significant measure in Computer Assisted Diagnosis (CAD) of Breast cancer abnormalities. Feature Extraction is the method of shrinking the dimension of image data by finding an essential detail as of the segmented image. The visual substance of a segmented image portions can be confined using this procedure. Feature extraction is one of the Image retrieval methods for achieving higher accuracy. In this paper an Improved Gray Level Co-occurrence Matrix (GLCM) Feature based Extraction with shape and Tamura features are discussed. Tamura features are extracted from Optimized Kernel Fuzzy Clustering Algorithm (OKFCA) which is one of the segmentation methods. This paper proposed Improved GLCM Feature Based Extraction algorithm to obtain the trained features which will be used for further processing in future. Experimental results of 322 images were evaluated from Mammographic Image Analysis Society (MIAS) database and used to evaluate the feature extraction process.
Design and Development of an Internet of Things (IoT)-Based Anti-Theft System in Museum Cultural Relics Using RFID

Art colonnades and museums all over the world are the first option for individuals to visit for the enhancement of the cultural life of people. To ensure their safety, museums have established numerous cultural security measures. Traditional strategies do not obstruct their pace entirely. They only use a computer in the museum to check individuals at the entrance and exit. Therefore, the authors proposed a gallery anti-stealing device created on the internet-of-things (IoT) technology that ensures security through passive readers/writers of RFID. Radio frequency identification (RFID) remains a system that practices isolated data storing and recovery and offers object tracking with a unique identity code. The system then sends sound and light warning information, while the photographic camera structure is triggered to capture a picture at the same time. The recognition of the accuracy in the hardware component of the device can be additionally enhanced by the use of this technology to increase the safety of museum equipment.
Evaluation Methodologies for Code Learning Tasks

There has been a growing interest in developing machine learning (ML) models for code learning tasks, e.g., comment generation and method naming. Despite substantial increase in the effectiveness of ML models, the evaluation methodologies, i.e., the way people split datasets into training, validation, and testing sets, were not well designed. Specifically, no prior work on the aforementioned topics considered the timestamps of code and comments during evaluation (e.g., examples in the testing set might be from 2010 and examples from the training set might be from 2020). This may lead to evaluations that are inconsistent with the intended use cases of the ML models. In this paper, we formalize a novel time-segmented evaluation methodology, as well as the two methodologies commonly used in the literature: mixed-project and cross-project. We argue that time-segmented methodology is the most realistic. We also describe various use cases of ML models and provide a guideline for using methodologies to evaluate each use case. To assess the impact of methodologies, we collect a dataset of code-comment pairs with timestamps to train and evaluate several recent code learning ML models for the comment generation and method naming tasks. Our results show that different methodologies can lead to conflicting and inconsistent results. We invite the community to adopt the time-segmented evaluation methodology.
Gradient-Enhanced Multifidelity Neural Networks for High-Dimensional Function Approximation

Abstract In this work, a novel multifidelity machine learning (ML) algorithm, the gradient-enhanced multifidelity neural networks (GEMFNN) algorithm, is proposed. This is a multifidelity extension of the gradient-enhanced neural networks (GENN) algorithm as it uses both function and gradient information available at multiple levels of fidelity to make function approximations. Its construction is similar to the multifidelity neural networks (MFNN) algorithm. The proposed algorithm is tested on three analytical functions, a one, two, and a 20 variable function. Its performance is compared to the performance of neural networks (NN), GENN, and MFNN, in terms of the number of samples required to reach a global accuracy of 0.99 of the coefficient of determination (R2). The results showed that GEMFNN required 18, 120, and 600 high-fidelity samples for the one, two, and 20 dimensional cases, respectively, to meet the target accuracy. NN performed best on the one variable case, requiring only ten samples, while GENN worked best on the two variable case, requiring 120 samples. GEMFNN worked best for the 20 variable case, while requiring nearly eight times fewer samples than its nearest competitor, GENN. For this case, NN and MFNN did not reach the target global accuracy even after using 10,000 high-fidelity samples. This work demonstrates the benefits of using gradient as well as multifidelity information in NN for high-dimensional problems.
Modeling Worlds in Text

We provide a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives -- or text-adventure games -- are partially observable environments structured as long puzzles or quests in which an agent perceives and interacts with the world purely through textual natural language. Each individual game typically contains hundreds of locations, characters, and objects -- each with their own unique descriptions -- providing an opportunity to study the problem of giving language-based agents the structured memory necessary to operate in such worlds. Our dataset provides 24198 mappings between rich natural language observations and: (1) knowledge graphs that reflect the world state in the form of a map; (2) natural language actions that are guaranteed to cause a change in that particular world state. The training data is collected across 27 games in multiple genres and contains a further 7836 heldout instances over 9 additional games in the test set. We further provide baseline models using rules-based, question-answering, and sequence learning approaches in addition to an analysis of the data and corresponding learning tasks.
BLOCKCHAIN BASED CERTIFICATE VERIFICATION USING ETHEREUM AND SMART CONTRACT

Blockchain technology allows the formation of a distributed record of a digital event in a decentralized manner where no third-party controls data and related transactions. This technology was used early for value transfer but now it has a wide range of applications in various fields such as healthcare, banking, the internet of things and many more. In the education sector, it gives numerous chances for decentralized management of records in educational institutions. Certificates distributed in colleges or universities are mostly hard copy. Students submit these certificates while applying for jobs at public or private sectors, where all these certificates are needed to be verified manually, it is very time-consuming process. There can be incidents where students may produce the fake certificate and it is difficult to identify them. This problem of fake academic certificates has been a longstanding issue in the academic community. There are chances that some may have produce the certificate which is not legit and that may get unnoticed by the verifier during the verification process. Because of the above situation, ineligible candidate will get a chance illegally. The key issues in Certificate verification for Workplace, banks and other businesses are in storage, retrieval and access to data with security. Blockchain technology can be enforced to solve these troubles in storing and accessing of data. This technology provides a common shared platform from where to store, retrieve and access documents securely. The very nature of the technology is in the distributed, shared, open ledgers, verifiable by all. This problem can be solved by storing the digital certificates on the Blockchain.
Variational Bayesian Inference: Message Passing Schemes and Streamlined Multilevel Data Analysis

Mean-field variational Bayes (MFVB) is a deterministic technique for approximating intractable integrals arising in Bayesian inference. They are typically used for making approximate inference for parameters in complex statistical models. Most of its foundational literature and applications are in Machine Learning. However, in the age of “Big Data”, and by extension large sample sizes, MFVB has become an important tool in Statistics.The approximating schemes afforded by MFVB rely on heavy algebraic derivations across the model. The emergence of Big Data has resulted in more complex statistical models, making the process of formulating an MFVB algorithm cumbersome. Fortunately, the MFVB updating scheme can be simplified by representing the parameters of the statistical model in a probabilistic graph. The derivations are made more efficient by decomposing the required computations into calculations that are local to each node in the graph.We focus on constructing variational Bayesian inference algorithms based on a modularised format known as variational message passing (VMP), which is founded upon the notion of messages passed between fragments on a factor graph. Primitive functions, which represent the localised messages over factor graph fragments, are derived and can be called upon for direct implementation into arbitrarily large statistical models. The MFVB and VMP approaches result in superficially different algorithms, but converge to identical posterior density function approximations because they are founded upon the same optimisation problem. For complex statistical models, VMP has the advantage that the iterative updates are adjusted into a modularised format by taking advantage of the localised computations afforded by variational Bayesian methods. The resulting algorithm is a sequence of fragment-based functions that represent a compartmentalisation of the required algebra and computer coding.Despite the computational convenience of VMP algorithms over their MFVB counterparts, the speed of both classes is limited for multilevel data models, such as Gaussian response linear mixed models. Statistical inference on such models requires standard matrix operations, such as inversion and matrix-vector multiplication, on sparse matrices, which are difficult to achieve efficiently. Furthermore, computational storage issues restrict the size of such models. Streamlined matrix algebraic results are necessary for implementing fast frequentist and variational Bayesian inference, which is not inhibited by storage-greedy sparse matrix operations, on multilevel data models.This thesis develops factor graph fragment functions that can be used to build complex statistical models and achieves streamlined matrix algebraic derivations for multilevel data analysis.
First Place Solution of KDD Cup 2021 OGB Large-Scale Challenge Graph-Level Track

In this technical report, we present our solution of KDD Cup 2021 OGBLarge-Scale Challenge - PCQM4M-LSC Track. We adopt Graphormer and ExpC as ourbasic models. We train each model by 8-fold cross-validation, and additionallytrain two Graphormer models on the union of training and validation sets withdifferent random seeds. For final submission, we use a naive ensemble for these18 models by taking average of their outputs. Using our method, our teamMachineLearning achieved 0.1200 MAE on test set, which won the first place inKDD Cup graph-level track.
A Hybrid Classifier for Detection of Online Spam Reviews

The Social web contains enormous content, in which higher range of online users believe the Online Reviews for their decision-making before any online purchase or using services. The online reviews written are all not true and some of them are Spam. Data Mining Classification helps in finding the Review as Spam or Ham. Many Text Classification Algorithms are existing and it has been proved that these classifiers can be improved when Hybrization of classifiers is performed. This research work focuses on developing hybrid classifiers for spam review detections. Hybridization of classification is performed as Classification-Classification process by using NaiveBayes, K Nearest Neighbor(KNN) as base classifiers in stage 1 and Support Vector Machine (SVM) Classifier in stage 2. Amazon-based and Yelp Review Datasets are used for implemenation and the Accuracy of the proposed Hybrid classifier improves the performance of the classifier from 89.04 to 93.50% of Accuracy.
Fast Style Transfer

Neural style transfer (NST) is a computer vision technique that takes two images – a content image and a style reference image – and blends them together so that the resulting output image retains the core elements of the content image but appears to be painted in the style of the style reference image. The output image from a NST network is called a pastiche. A pastiche is a work of visual art, literature, theater or music that imitates the style (or character) of the work of one or more other artists. Unlike a parody, a pastiche celebrates rather than mocks the work it imitates.
Learning to Collocate Fashion Items from Heterogeneous Network Using Structural and Textual Features

This research presents a new framework for collocating fashion items from heterogeneous network using structural and textual features. Specifically, we construct a fashion heterogenous network, and extract structural features of fashion items from the heterogenous network by utilizing a GATNE model. Then we propose a fashion collocation model based on the fusion of structural and textual features. Given item pairs, their textual features achieved by a Siamese network and structural features achieved by the GATNE model in advance are fused to generate new features. Our framework was examined on a large-scaled clothing item set. The experiment results demonstrate that our proposed framework is effective in the task of fashion collocation.
Semantic-Based Sentence Recognition in Images Using Bimodal Deep Learning

The accuracy of computer vision systems that understand sentences in images with text can be improved when semantic information about the text is utilized. Nonetheless, the semantic coherence within a region of text in natural or document images is typically ignored by state-of-the-art systems, which identify isolated words or interpret text word by word. However, when analyzed together, seemingly isolated words may be easier to recognize. On this basis, we propose a novel “Semantic-based Sentence Recognition” (SSR) deep learning model that reads text in images with the help of understanding context. SSR consists of a Word Ordering and Grouping Algorithm (WOGA) to find sentences in images and a Sequence-to-Sequence Recognition Correction (SSRC) model to extract semantic information in these sentences to improve their recognition. We present experiments with three notably distinct datasets, two of which we created ourselves. They respectively contain scanned catalog images of interior designs and photographs of protesters with hand-written signs. Our results show that SSR statistically significantly outperforms baseline methods that use state-of-the-art single-word-recognition techniques. By successfully combining both computer vision and natural language processing methodologies, we reveal the important opportunity that bi-modal deep learning can provide in addressing a task which was previously considered a single-modality computer vision task.
Heterogeneous Graph Attention Networks for Learning Diverse Communication

Multi-agent teaming achieves better performance when there is communication among participating agents allowing them to coordinate their actions for maximizing shared utility. However, when collaborating a team of agents with different action and observation spaces, information sharing is not straightforward and requires customized communication protocols, depending on sender and receiver types. Without properly modeling such heterogeneity in agents, communication becomes less helpful and could even deteriorate the multi-agent cooperation performance. We propose heterogeneous graph attention networks, called HetNet, to learn efficient and diverse communication models for coordinating heterogeneous agents towards accomplishing tasks that are of collaborative nature. We propose a Multi-Agent Heterogeneous Actor-Critic (MAHAC) learning paradigm to obtain collaborative per-class policies and effective communication protocols for composite robot teams. Our proposed framework is evaluated against multiple baselines in a complex environment in which agents of different types must communicate and cooperate to satisfy the objectives. Experimental results show that HetNet outperforms the baselines in learning sophisticated multi-agent communication protocols by achieving $\sim$10\% improvements in performance metrics.
DRVI: Dual Refinement for Video Interpolation

The quality of a video clip is considered to be poor if the resolution or the frame rate is low. Video interpolation is thus introduced to enhance video quality and provide a better viewing experience to users. However, there are still some challenges, like the blur caused by motion changes. In this paper, we introduce a dual refinement technique for video interpolation (DRVI). It has three main steps, namely flow refinement, frame synthesis, and Haar refinement. The flow refinement can generate accurate bi-directional flows, which are more suitable for frame interpolation tasks. The Haar refinement uses the Discrete Wavelet Transform (DWT). It can preserve information in different frequency domains and also speed up the learning process. We also add an arbitrary time approximation module to allow multi-frame generation. The number of learnable parameters in our model is much less than existing methods; still, it has excellent performance. Our method is trained on Vimeo90K (Xue et al., 2019) and tested on three well-known datasets to demonstrate its effectiveness.
The Cognitive Loads and Usability of Target-based and Steering-based Travel Techniques

Target and steering-based techniques are two common approaches to travel in consumer VR applications. In this paper, we present two within-subject studies that employ a prior dual-task methodology to evaluate and compare the cognitive loads, travel performances, and simulator sickness of three common target-based travel techniques and three common steering-based travel techniques. We also present visual meta-analyses comparing our results to prior results using the same dual-task methodology. Based on our results and meta-analyses, we present several design suggestions for travel techniques based on various aspects of user experiences.
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult. In this work, we consistently evaluate and compare three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multi-agent learning tasks. Our experiments serve as a reference for the expected performance of algorithms across different learning tasks, and we provide insights regarding the effectiveness of different learning approaches. We open-source EPyMARL, which extends the PyMARL codebase~\citep{samvelyan19smac} to include additional algorithms and allow for flexible configuration of algorithm implementation details such as parameter sharing. Finally, we open-source two environments for multi-agent research which focus on coordination under sparse rewards.
Detecting Changes in Covariance via Random Matrix Theory

A novel method is proposed for detecting changes in the covariance structure of moderate dimensional time series. This nonlinear test statistic has a number of useful properties. Most importantly, it is independent of the underlying structure of the covariance matrix. We discuss how results from Random Matrix Theory, can be used to study the behavior of our test statistic in a moderate dimensional setting (i.e., the number of variables is comparable to the length of the data). In particular, we demonstrate that the test statistic converges point wise to a normal distribution under the null hypothesis. We evaluate the performance of the proposed approach on a range of simulated datasets and find that it outperforms a range of alternative recently proposed methods. Finally, we use our approach to study changes in the amount of water on the surface of a plot of soil which feeds into model development for degradation of surface piping.
An Immersive Approach Based on Two Levels of Interaction for Exploring Multiple Coordinated 3D Views

Multiple coordinated views have often been used for visual analytics purposes over the last years. In this context, if the exploration of 2D visualizations is not an obstacle, adding an extra dimension can be an issue. The interaction with multiple 3D visualizations in 2D conventional displays lacks usability and does not guarantee the usefulness the extra dimension would provide. Immersive visualization techniques can potentially fulfill these gaps by providing 3D visualizations and novel 3D interactions simultaneously. In this paper, we propose a new approach for interacting with composite and multiple coordinated visualizations in immersive virtual environments. We use a 3D-WIMP-like concept, i.e., virtual cubes (Spaces), for encapsulating views, which the user can freely control in the virtual environment. Moreover, operations like “cloning” and “coordinated interactions” features provide a way for performing composed tasks. We compared our approach with a desktop version to evaluate its performance when dealing with composed tasks. A user study with 19 participants was conducted, and the results show that the immersive approach has advantages over the corresponding desktop version regarding interaction with multiple coordinated 3D views.
Semantic Systematicity in Connectionist Language Production

Decades of studies trying to define the extent to which artificial neural networks can exhibit systematicity suggest that systematicity can be achieved by connectionist models but not by default. Here we present a novel connectionist model of sentence production that employs rich situation model representations originally proposed for modeling systematicity in comprehension. The high performance of our model demonstrates that such representations are also well suited to model language production. Furthermore, the model can produce multiple novel sentences for previously unseen situations, including in a different voice (actives vs. passive) and with words in new syntactic roles, thus demonstrating semantic and syntactic generalization and arguably systematicity. Our results provide yet further evidence that such connectionist approaches can achieve systematicity, in production as well as comprehension. We propose our positive results to be a consequence of the regularities of the microworld from which the semantic representations are derived, which provides a sufficient structure from which the neural network can interpret novel inputs.