Reviewerly/exHarmony huggingface dataset:
Learning opportunities during informal science learning to promote understanding of fluids mechanics

This study focuses on existing learning opportunities in designing informal science learning with a specific pedagogical approach. 349 students were selected as the sample of this study, in two STEM activities during international Scout STEM Camporee. This study has successfully developed a module for informal learning activity and conducted a survey to study the learning opportunities. Post and pre-test were conducted. The post-test was conducted to investigate the understanding of science concept during the designing stage with the students’ ability to make inference. Descriptive analysis and Pearson correlation were used as the data analysis methods. The finding shows that most of the students did have an initial idea of science concepts such as buoyancy, density, weight of mini Titanic, and Archimedes’ principle. However, their ability to make inference and observation is weakly correlated and not significant. This study only able to shows that students are capable to produce observations and inferences that differ in nature during informal science learning. As a conclusion, for informal science learning, it is important for educators to consider the outcomes for students’ learning because the opportunities for students to tap into their prior knowledge from self-determination can be easily achieved within this setting. This study proposes important measures during informal science learning environment to maximize the learning opportunities.
Illumination Parameter Estimation of Outdoor Scene Using BP Neural Network

At present, augmented reality technology is a research hotspot, and its main purpose is to insert virtual objects into the real scene around the user in real time. One of the difficulties of this technology is how to make virtual objects have the same lighting effect with the real environment. Under the condition of outdoor Scene, it is difficult to simulate the erratic weather and model the complex objects. Therefore, it is relatively difficult to estimate the outdoor illumination, and there are few studies. We propose a novel method of illumination parameter estimation of outdoor scene based on BP neural network using the basis image decomposition model. Under supervised learning, this method can predict the outdoor illumination parameters of the input images by training the initial data set using the colour constancy constraint. This parameter is used to render virtual objects to achieve the illumination consistency of virtual and real objects. The algorithm does not require any 3D geometric information of the scene, and the materials and textures of objects. It also does not require special objects or special surfaces in the environment. Experiments show that the basis images and illumination coefficients obtained by this method can accurately reconstruct the original image. The virtual object can be rendered according to the light coefficients, thereby realizing seamless integration of virtual objects and real scenes. on mobile devices.
Designing a Mixed Reality Cognitive Orthosis to Support Independence of Older Adults from the Dementia Continuum

The societal and economic impacts of people living with dementia are significant. A possibility to mitigate these impacts are to support their independence at home through cognitive orthosis. Mixed-reality headsets are an emerging technology that come with technological benefits that could be useful in this context. The objective of this research is to propose a first design for a mixed-reality headset cognitive orthosis software to support independence at home of this population. The software architecture is defined as part of a user centred design methodology. The software has been developed during six iterations, each concluded by expert or cognitive walkthrough evaluations, with eight sessions in total. The software has been designed around the following five concepts: (1) finite state machine; (2) transitions between assistances; (3) assistances; (4) interaction surfaces; (5) scenarios. Five themes emerged from the evaluations: (1) gradation; (2) give the control to the user; (3) attract the user's attention; (4) discoverability; (5) interactions. Several opportunities of development are discussed, including usability testing with the population.
Effective Imbalance Learning Utilizing Informative Data

The class imbalance problem that is caused by unequal data distribution usually results in poor performance, and it has attracted increasing attention in the research community. The challenge of the problem is the difficulty to extract sufficient information from the minority class. As a result, the classifier converges to a sub-optimal state. While methods based on resampling and reweighing the cost for different classes are the common strategies to address the problem, there are still numerous issues with these methods such as under- or over-sampling that may remove necessary information or introduce noise, respectively; and reweighing may result in an inappropriate cost matrix. To address the above shortcomings, in this paper, an enhanced approach based on informative samples is proposed. In our approach, the classifier can indicate which class a sample is closer to by comparing it with boundary samples. The informative samples include the samples from both positive and negative samples located around the boundary. Finally, our experiments show that our proposed method outperforms state-of-the-art algorithms by 18% on $$F_1$$ score.
ES4D: Accelerating Exact Similarity Search for High-Dimensional Vectors via Vector Slicing and In-SSD Computation

Searching top-k nearest neighbor (kNN) based on the vector similarity is a common problem in many domains. Unlike approximate kNN search, exact kNN needs to check a large portion of the dataset, if not the entire set. When the data volume is large, the dataset cannot fit in the main memory and has to be stored on a storage device such as flash drive. ES4D is a kNN search platform implemented near-data on the solid-state drive (SSD). ES4D accelerates kNN search by using two levels of early termination, using pre-clustering of the dataset vectors and vector sharding. ES4D incorporates several optimization techniques to aid such early terminations. Using near-data processing, ES4D further enhances performance and reduces energy consumption. Performing the kNN search on the SSD side not only improves energy efficiency but also enables ES4D to optimize the physical page placement of dataset vectors on SSD to maximize the search throughput. We evaluated ES4D using various vector datasets. ES4D achieves 1.9× search performance improvement over existing exact kNN search mechanisms. Also, by off-loading the distance calculation to the SSD side, ES4D reduces energy consumption for kNN search by 8.1×.
Subspace-based Feature Alignment for Unsupervised Domain Adaptation

Autonomous agents need to perceive the world in a robust way, such that the shift in data distribution does not lead to faulty perception results. When agents cannot be trained with abundant data, agents may need to operate on real world environments while trained on simulated data, and suffer from domain shift. This paper proposes an effective and robust unsupervised domain adaptation (UDA) method that can resolve these situations. In the UDA setup, we are given a labeled source domain and an unlabeled target domain that share the same set of classes but are sampled from different distributions. This domain shift prevents agents which employ deep neural networks from generalizing well on the target domain. Recent methods adopt the strategy of self-training the networks with pseudo labeled target samples. However, falsely labeled samples cause negative transfer and deteriorate generalization of a network. to reduce negative transfer we propose an algorithm that can filter the pseudo labels, and use the filtered labels to align the domains in the feature space. The samples whose labels have not passed the filtering process can be used as an index to tune the hyperparameters of our method. Across various benchmarks, we validate the performance of our method. Especially, our method achieves strong performance on the synthetic-to-real adaptation scenario.
Task-adaptive Few-shot Learning on Sphere Manifold

Few-shot learning aims to learn generalized knowledge from limited data, which is a challenging study. Metric-based few-shot learning is the most widely studied direction, and most metric-based methods focus on how to learn a more robust feature embedding space or distance metric for classification. Few-shot learning is trained with a large number of tasks. Most existing methods do not realize that the role of the same sample in different learning tasks may be different, which will affect the effectiveness of classification. This paper proposes a task-adaptive feature adjustment method to obtain new features with task context information and more discrimination to solve this problem. We also introduce the Sphere manifold into few-shot learning, use the manifold metric instead of the Euclidean metric, and deduce the gradient optimization on the manifold. We propose a novel few-shot learning method with an excellent performance by combining these two innovations. We conduct several experiments on the few-shot learning benchmark datasets to demonstrate the advancement of our methods. The experimental results have significantly improved over the baseline and are highly competitive with the mainstream and effective methods.
EndGraph: An Efficient Distributed Graph Preprocessing System

Graph processing mainly includes two stages, namely, preprocessing and algorithm execution. Most previous proposals for performance enhancement of graph processing systems focus on the algorithm execution stage, and simple ignore the preprocessing overhead. However, in this work, we argue that the cost of preprocessing can not be ignored since the preprocessing time is much longer than the algorithm execution time in state-of-the-art systems.We propose EndGraph, a distributed graph preprocessing system, to improve preprocessing performance. Firstly, for graph partitioning, we find existing systems either assign imbalanced preprocessing workloads or spend too much time on graph partitioning. Hence, EndGraph proposes a novel chunk-based partition algorithm to balance preprocessing workloads and achieve theoretical lower bound of time complexity. Secondly, for graph construction (converting data layout from edge array to adjacency list), existing systems use counting sort, which is not efficient for computation and communication. EndGraph employs a novel two-level graph construction method by carefully decoupling the graph construction into intra-machine and inter-machine construction. Our extensive evaluation results show that, compared with five state-of-the-art systems, LFGraph, PowerLyra, PowerGraph, D-Galois, and Gemini, EndGraph can improve the preprocessing performance up to 35.76 ×(from 4.72×). To show the generality of EndGraph, we integrate it with D-Galois and Gemini, and it improves the end-to-end (including preprocessing and algorithm execution) graph processing performance up to 7.44× (from 2.96×).
Tutorial: Threat Modeling of Cloud-based Solutions

The tutorial aims to train the participants to apply a threat modeling process to identify potential threats to given cloud-based systems and prioritize countermeasures.
Dynamic Clustering Federated Learning for Non-IID Data

Federated learning (FL) aims to raise a multi-client collaboration approach in the case of local data isolation. In particular, the clients with non-IID data frequently participate in or leave the federated learning training process asynchronously, resulting in dynamic federated learning (DFL) scenario, which attracts more and more attention. Indeed, an effective DFL solution has to address the following two challenges: 1) Statistical Dynamics. The distributions of local data from clients are always non-IID and the global data distribution is dynamic due to the participation or departure of clients. 2) Expiration Dynamics. After clients leave the federated training process, their historical updated models have a certain validity to reuse in subsequent rounds but it is hard to quantify this validity. In this paper, we first consider clustering the clients with similar data distribution to make them much closer to IID and concentrating on the training the models in each cluster. Then we analyze the changing trend of model validity named model quality and define one suitable function to describe expiration dynamics. As a solution, we propose Dynamic Clustering Federated Learning (DCFL) framework to improve federated learning on non-IID data in DFL. Specifically, DCFL follows the client-server architecture as the standard FL. On the client side, the local devices calculate the related information of the local data distribution for client clustering. On the server side, we design two strategies for the challenges above. We propose dynamic clustering aggregation strategy (including a dynamic clustering algorithm and a two-stage aggregation) by dynamically clustering clients and then aggregating the local models to overcome Statistical Dynamics. Besides, we propose expiration memory strategy by reusing the historical models and then adjusting to model quality of historical models as the basis for model aggregation to overcome Expiration Dynamics. Finally, we conduct extensive experiments on public datasets, which demonstrate the effectiveness of the DCFL framework.
Medical Image Lossy Compression With LSTM Networks

Medical images have a larger size when compared to normal images. There arises a problem in the storage as well as in the transmission of a large number of medical images. Hence, there exists a need for compressing these images to reduce the size as much as possible and also to maintain a better quality. The authors propose a method for lossy image compression of a set of medical images which is based on Recurrent Neural Network (RNN). So, the proposed method produces images of variable compression rates to maintain the quality aspect and to preserve some of the important contents present in these images.
Anomaly Detection in Investors' Activities and Transactions via Rule-Based and Advanced Technique

Anomaly detection is an essential and challenging task to prevent cyberattacks, which can severely impact a company in financial, reputational, and legal categories. This issue is crucial for ensuring investors' safety in investment applications. In our case, the purpose of anomaly detection is to identify the standard behavior patterns of the application users and to detect situations far from it at the right time. There are situations of high importance for investment applications, such as password change, logging in at different locations, and high-volume cash flow or trading activities. It is crucial to immediately detect suspicious transactions and activities and take the necessary precautions to protect the users and the company's assets and reputation. In this study, we have defined anomaly scenarios based on the transactions and investor login metadata acquired from the investment application. Automatic detection of anomalies was studied by applying rule-based models (moving average and binning) and machine learning models (KNN, SVM, and Logistic Regression). As a result, we can determine anomalies in the flowing data and generate logs to notify the authorities. the flowing data and generate logs to notify the authorities
Manifold Learning-based Frequency Estimation for extracting ENF signal from digital video

Using electrical network frequency (ENF) for video forensics has been intensely studied in recent years. The ENF signal found in videos has twice the electrical frequency (100 Hz or 120 Hz), whereas frame rates of common videos are relatively low (around 30 Hz). To extract ENF signal from video, state-of-the-art works exploit the rolling shutter effect. However, this method has a constraint that the region affected by the flickering light has to be large enough to contain all the information which light leaves at the pixels. As these regions are only part of the scene in many cases, it is hard to take advantage of the rolling shutter effect. In this paper, we propose a novel method to extract ENF signals by topological approach without utilizing the rolling shutter effect. Based on the fact that the topological representation of the possible outcomes is in the form of a closed-loop, we obtain the phase angles of each frame using manifold learning. We convert the phase angles into the frequency values based on the prior knowledge about the nominal frequency of ENF and the frame rate of the video. We tested two different manifold learning algorithms (i.e., UMAP and t-SNE) and compared the result with the state-of-the-art works, and t-SNE shows the best performance achieving root-mean-square error (RMSE) of 0.00036 Hz.
The Distinctive Pathologies of U.S. and European Approaches to Free Speech

This paper explores the distinctive pathologies of U.S. and European approaches to free speech via comparison of some key features of the U.S. Supreme Court’s First Amendment doctrine and the European Court of Human Rights (ECtHR) caselaw applying Article 10 of the European Convention on Human Rights. On the U.S. side, modern First Amendment law has effectively guarded against state censorship, particularly viewpoint-based censorship, and has cleared space for a diverse array of private speech platforms to operate in the marketplace of ideas. But First Amendment law’s single-minded focus on state suppression leads it to over-police certain state actions whose threats to free expression values seem minimal and to under-police certain private actions whose threats to free expression values seem substantial. On the European side, ECtHR Art. 10 doctrine maintains more space for European states to regulate speech to correct market failures and ameliorate inequalities in private power, but tends to impose insufficient limits on content-based state censorship and sometimes over-zealous mandates for content-based private censorship. Given the harms associated with some aspects of U.S. and European free speech law, civil libertarians are left with the options of picking their poison or bridging the divide.
Rational Polynomial Image Magnification Constrained by Feature

The quality of the image edge plays a crucial role in the visual effect of the image, and the existing image magnification methods keep the edge of the image insignificant. To this end, an image magnification method is proposed to construct a rational polynomial surface to fit the image. First, for each pixel, taking the edge of the image as the constraint, a local fitting quadratic polynomial surface patch which can better preserve the edge features of the image is constructed. Then, according to the approximation accuracy of each surface patch to adjacent images, a rational weight function is constructed. On each square grid, the weighted combination of four quadratic polynomial surfaces generates a rational polynomial surface patch, and all rational polynomial surface patches are combined to form an overall surface. The error of the overall surface at the edge of the image is relatively large, so the error surface is constructed. The overall surface is corrected to improve the accuracy of the overall surface and the ability to preserve edge features. The experimental results compared with the other seven methods show that, for the four commonly used comparison image sets, the average PSNR and SSIM values of the magnified images generated by this method are the highest in set5, set14 and Urban100, and are the second highest in BSD100. For the three images used to compare the effect of edge preservation, proposed method has the best edge preservation ability, so the visual effect is the best. In addition, the complexity and algorithm speed of proposed method belong to the same order of magnitude as Bicubic, which is suitable for real-time image magnification processing, and is significantly faster than the other six methods.
Distributions of Subsurface Anomaly Patterns Using Machine Learning Tools

Determination of the subsurface structural distribution patterns is a vibrant approach to the development and strategic of minerals exploration. This paper, therefore, addressed the applicability of supervised Machine Learning tools, (MLt), using real datasets from the Euler Depth Solutions, (EDS), to train the Neural Network Pattern Recognition Techniques, NNPRT), and adequately map the subsurface structural patterns within the study area. The approach uses data mining technique to extract the desirable data from interpreted aeromagnetic datasets readable for machine to determine the Neural Network Patterns, (NNP) that takes both a six input and trained the datasets; one hundred hidden neutrons and delivered a six-output model with a high degree of precision. The significance of these techniques is in the correct characterisation of subsurface geological structures through generation of insights into the subsurface anomaly, thereby helping in better decisions for minerals prospecting. The choice of the techniques was informed by their usefulness in managing large volume of datasets.
GraHyAlt—A Computer Program for the Graphical Presentation of the Hydrothermal Alteration Induced Effects in Geochemical Parameters of Volcanic Rocks

The computer program GraHyAlt is developed for the Graphical presentation of the Hydrothermal Alteration induced effects in geochemical parameters of volcanic rocks. These parameters are mobility of the chemical elements, concentration and mass changes of the elements, which are extensively used in the studies related to the hydrothermal alteration. As these studies are directly or indirectly associated with mineralization processes, and mineral exploration, the exactness of the data is needed to avoid any possible misinterpretations. This can be achieved by the reliable computer programs for estimation of the reliable data. Considering their importance in the hydrothermal alteration studies, the program GraHyAlt is developed for the following plots: (1) concentrations plots of the major element oxides, REE and trace elements in the rocks of the present study, and in their precursor or reference fresh rock; (2) binary plot for the immobile elements (TiO2 versus Zr); (3) concentration and mass changes of the elements and (4) alteration box plot. Each one of the plots in this program is validated successfully by applying it to the reliable data set from the literature, and the obtained results are comparable to those reported in the literature.
Network Security Monitoring by Combining Semi-Supervised Learning and Active Learning

In network intrusion and network security monitoring, there is massive data. When using supervised learning method directly, it will cost lots of time to collect labeled samples, which is expensive. In order to solve this issue, this paper adopts an active learning model to detect network intrusion. First, massive unlabeled samples are used to establish a weighted support vector data description model. Then, the most valuable samples are used to improve the performance of network intrusion by combining with active learning, which utilizes labeled samples and unlabeled samples to extend the weighted support data description model in a semi-supervised learning method. The experimental results show that the active learning can utilize minor labeled sample to reduce the cost of manual labeling work, which is more suitable for an actual network intrusion detection environment.