Reviewerly/exHarmony huggingface dataset:
Gender differences in digital literacy among prospective physics teachers

Abstract The rapid development of digital technology has stimulated teachers with the number of competencies called digital literacy. The purpose of this study was to describe gender differences in digital literacy among prospective physics teachers. This research was conducted using a descriptive method involving 71 prospective physics teachers at a university in Tasikmalaya. They consisted of 31 males and 40 females with age 19-21 years old. Data was collected using valid digital literacy tests and interview. The collected data was processed by determining the average score of male and female students then analyzed through interview. The results showed that the mean of male’s digital literacy was higher than female. The males had a mean 57 (moderate category) and the females had a mean 53 (low category). There was a difference in digital literacy between males and females prospective physics teachers. These distinctions were influenced by their tendency in using digital technology. Some recommendations were delivered to prospective physics teachers, especially for females. They should effectively increase their digital technology use and appropriate training in the use of digital technology is needed to achieve a higher level of digital literacy.
Dynamic Graph Learning Convolutional Networks for Semi-supervised Classification

Over the past few years, graph representation learning (GRL) has received widespread attention on the feature representations of the non-Euclidean data. As a typical model of GRL, graph convolutional networks (GCN) fuse the graph Laplacian-based static sample structural information. GCN thus generalizes convolutional neural networks to acquire the sample representations with the variously high-order structures. However, most of existing GCN-based variants depend on the static data structural relationships. It will result in the extracted data features lacking of representativeness during the convolution process. To solve this problem, dynamic graph learning convolutional networks (DGLCN) on the application of semi-supervised classification are proposed. First, we introduce a definition of dynamic spectral graph convolution operation. It constantly optimizes the high-order structural relationships between data points according to the loss values of the loss function, and then fits the local geometry information of data exactly. After optimizing our proposed definition with the one-order Chebyshev polynomial, we can obtain a single-layer convolution rule of DGLCN. Due to the fusion of the optimized structural information in the learning process, multi-layer DGLCN can extract richer sample features to improve classification performance. Substantial experiments are conducted on citation network datasets to prove the effectiveness of DGLCN. Experiment results demonstrate that the proposed DGLCN obtains a superior classification performance compared to several existing semi-supervised classification models.
A Hybrid Of Watermark Scheme With Encryption To Improve Security Of Medical Images

With the recent research and development in the areas of communication, traditional diagnosis is being replaced by e-diagnosis in healthcare industry like teleconsulting, telesurgery and telediagnosis. The requirement of these applications is to exchange the medical images in 1s and 0s to another location in a secured manner. In this work, both robust and reversible watermarking is done and a three level security is provided. The medical image is divides into two major parts with giving importance to diagnose identification namely region of interest (ROI) and region of non-interest (RONI). Firstly in ROI Reversible data embedding, which embeds payload data in reversible manner in to binary image, since it is considered to be important by physician for making diagnosis. Therefore no changes are made in ROI image during watermarking. Secondly in RONI, Robust watermarking is done, so that it can withstand various intentional and unintentional attacks. In ROI region a two level decomposition is done by Haar wavelet and a watermark (patient's information seen on header) is embedded into LH1 and HL1 sub bands. Later, both watermarked images are combined and a single watermarked image is obtained. Thirdly, the resultant image is further processed by using AES algorithm which makes output image in an unreadable form, thus Providing high security. Further decryption process is done to retrieve the original image.
Survival analysis via hierarchically dependent mixture hazards

Hierarchical nonparametric processes are popular tools for defining priors on collections of probability distributions, which induce dependence across multiple samples. In survival analysis problems, one is typically interested in modeling the hazard rates, rather than the probability distributions themselves, and the currently available methodologies are not applicable. Here, we fill this gap by introducing a novel, and analytically tractable, class of multivariate mixtures whose distribution acts as a prior for the vector of sample-specific baseline hazard rates. The dependence is induced through a hierarchical specification of the mixing random measures that ultimately corresponds to a composition of random discrete combinatorial structures. Our theoretical results allow to develop a full Bayesian analysis for this class of models, which can also account for right-censored survival data and covariates, and we also show posterior consistency. In particular, we emphasize that the posterior characterization we achieve is the key for devising both marginal and conditional algorithms for evaluating Bayesian inferences of interest. The effectiveness of our proposal is illustrated through some synthetic and real data examples.
Generalized Bayes Quantification Learning under Dataset Shift

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Data mining techniques

This chapter introduces some of the most widely used techniques for data mining, including nearest-neighbor algorithm, k-mean algorithm, decision trees, random forests, Bayesian classifier, and others. Special techniques such as CURE and BFR for mining big data are also briefly introduced.
Visible-to-Thermal Transfer Learning for Facial Landmark Detection

There has been increasing interest in face recognition in the thermal infrared spectrum. A critical step in this process is face landmark detection. However, landmark detection in the thermal spectrum presents a unique set of challenges compared to in the visible spectrum: inherently lower spatial resolution due to longer wavelength, differences in phenomenology, and limited availability of labeled thermal face imagery for algorithm development and training. Thermal infrared imaging does have the advantage of being able to passively acquire facial heat signatures without the need for active or ambient illumination in low light and nighttime environments. In such scenarios, thermal imaging must operate by itself without corresponding/paired visible imagery. Mindful of this constraint, we propose visible-to-thermal parameter transfer learning using a coupled convolutional network architecture as a means to leverage visible face data when training a model for thermal-only face landmark detection. This differentiates our approach from models trained either solely on thermal images or models which require a fusion of visible and thermal images at test time. In this work, we implement and analyze four types of parameter transfer learning methods in the context of thermal face landmark detection: Siamese (shared) layers, Linear Layer Regularization (LLR), Linear Kernel Regularization (LKR), and Residual Parameter Transformations (RPT). These transfer learning approaches are compared against a baseline version of the network and an Active Appearance Model (AAM), both of which are trained only on thermal data. We achieve a 6.5% - 9.5% improvement on the DEVCOM ARL Multi-modal Thermal Face Dataset and a 4% improvement on the RWTH Aachen University Thermal Face Dataset over the baseline model. We show that LLR, LKR, and RPT all result in improved thermal face landmark detection performance compared to the baseline and AAM, demonstrating that transfer learning leveraging visible spectrum data improves thermal face landmarking.
Materializing knowledge bases via trigger graphs

The chase is a well-established family of algorithms used to materialize Knowledge Bases (KBs) for tasks like query answering under dependencies or data cleaning. A general problem of chase algorithms is that they might perform redundant computations. To counter this problem, we introduce the notion of Trigger Graphs (TGs), which guide the execution of the rules avoiding redundant computations. We present the results of an extensive theoretical and empirical study that seeks to answer when and how TGs can be computed and what are the benefits of TGs when applied over real-world KBs. Our results include introducing algorithms that compute (minimal) TGs. We implemented our approach in a new engine, called GLog, and our experiments show that it can be significantly more efficient than the chase enabling us to materialize Knowledge Graphs with 17B facts in less than 40 min using a single machine with commodity hardware.
Information Extraction from Invoices: A Graph Neural Network Approach for Datasets with High Layout Variety

Extracting information from invoices is a highly structured, recurrent task in auditing. Automating this task would yield efficiency improvements, while simultaneously improving audit quality. The challenge for this endeavor is to account for the text layout on invoices and the high variety of layouts across different issuers. Recent research has proposed graphs to structurally represent the layout on invoices and to apply graph convolutional networks to extract the information pieces of interest. However, the effectiveness of graph-based approaches has so far been shown only on datasets with a low variety of invoice layouts. In this paper, we introduce a graph-based approach to information extraction from invoices and apply it to a dataset of invoices from multiple vendors. We show that our proposed model extracts the specified key items from a highly diverse set of invoices with a macro \({F}_{1}\) score of 0.8753.
Supply Chain Fraud Prediction Based On XGBoost Method

It is very meaningful to build a model based on supply chain data to determine whether there is fraud in the product transaction process. It can help merchants in the supply chain avoid fraud, default and credit risks, and improve market order. In this paper, I propose a fraud prediction model based on XGBoost. The data set required to build the model comes from the supply chain data provided by DataGo. Compared with the model based on Logistic regression and the model of Gausian Naive bayes, the model proposed in this paper shows better classification ability. Specifically, the F1 score based on the Logistic regression model is 98.96, the F1 score based on the Gausian Naive bayes model is 71.95, and the F1 score value of the XGBoost-based model proposed in this paper is 99.31 in the experiment.
A blockchain-based collaborative training method for multi-party data sharing

In recent years, the construction of Space–Ground Integrated Network has been accelerated, connecting different types of networks in remote regions. The various devices are connected together, so that data that was difficult to communicate before can be used to train particular models together, giving birth to new service models. Privacy issues, however, remain a substantial concern affecting data sharing among multiple parties. Cooperative training methods such as federated learning usually require a centralized aggregator to aggregate the dispersed sub-models. In general, various privacy-preserving methods assume the aggregator as an honest-but-curious (HBC) role and cannot guarantee that the program can be executed correctly. In this paper, we propose a blockchain-based collaborative training method that uses the decentralized accounting technology of the blockchain to solve the trust problem between different participants. Through the anti-repudiation nature of the blockchain, it is ensured that the aggregation of the model is executed correctly. We designed a function encryption-based privacy preserving method in which the aggregator can only obtain the results of the aggregation model, and cannot access the models uploaded to the blockchain from other participants. Subsequently, a prototype system based on blockchain is developed to analyze and evaluate the time consumption of our proposed cooperative training method and function encryption module. The result of our experiments shows the feasibility of our cooperative training model.
Pharm‐AutoML: An open‐source, end‐to‐end automated machine learning package for clinical outcome prediction

Although there is increased interest in utilizing machine learning (ML) to support drug development, technical hurdles associated with complex algorithms have limited widespread adoption. In response, we have developed Pharm-AutoML, an open-source Python package that enables users to automate the construction of ML models and predict clinical outcomes, especially in the context of pharmacological interventions. In particular, our approach streamlines tedious steps within the ML workflow, including data preprocessing, model tuning, model selection, results analysis, and model interpretation. Moreover, our open-source package helps to identify the most predictive ML pipeline among defined search spaces by selecting the best data preprocessing strategy and tuning the ML model hyperparameters. The package currently supports multiclass classification tasks, and additional functions are being developed. Using a set of five publicly available biomedical datasets, we show that our Pharm-AutoML outperforms other ML frameworks, including H2O with default settings, by demonstrating improved predictive accuracy of classification. We further illustrate how model interpretation methods can be utilized to help explain the fine-tuned ML pipeline to end users. Pharm-AutoML provides both novice and expert users improved clinical predictions and scientific insights.
Examining Student Effort on Help through Response Time Decomposition

Many teachers have come to rely on the affordances that computerbased learning platforms offer in regard to aiding in student assessment, supplementing instruction, and providing immediate feedback and help to students as they work through assigned content.Similarly, researchers commonly utilize the large datasets of clickstream logs describing students' interactions with the platform to study learning.For the teachers that use this information to monitor student progress, as well as for researchers, this data provides limited insights into the learning process; this is particularly the case as it pertains to observing and understanding the effort that students are applying to their work.From the perspective of teachers, it is important for them to know which students are attending to and using computer-provided aid and which are taking advantage of the system to complete work without effectively learning the material.In this paper, we conduct a series of analyses based on response time decomposition (RTD) to explore student help-seeking behavior in the context of on-demand hints within a computer-based learning platform with particular focus on examining which students appear to be exhibiting effort to learn while engaging with the system.Our findings are then leveraged to examine how our measure of student effort correlates with later student performance measures.
Negotiating Visibility for Safe Autonomous Navigation in Occluding and Uncertain Environments

Navigation through an occluded environment is a challenging task for autonomous mobile robots (AMR), since they must balance both safety and speed in an attempt to fluidly steer around occlusions in uncertain environments. This is because real world environments have dynamic actors that may be occluded to the robot during motion, introducing uncertainty. One key element of eliminating this uncertainty is moving in such a way to maximize perception around these occlusions. This letter presents a novel control framework that combines both perception and safety constraints, resulting in motion that is quick and safe when occlusions are present. Perception is satisfied using a model predictive control (MPC)-based approach to provide inputs that increase visibility around occlusions while safety is promoted by modeling uncertainties as projected probabilities of occupancy derived from current observation and expected traffic motion. Improvements in visibility, safety, and speed are shown in simulations and are experimentally validated using an unmanned ground vehicle.
On Lattice Packings and Coverings of Asymmetric Limited-Magnitude Balls

We construct integer error-correcting codes and covering codes for the limited-magnitude error channel with more than one error. The codes are lattices that pack or cover the space with the appropriate error ball. Some of the constructions attain an asymptotic packing/covering density that is constant. The results are obtained via various methods, including the use of codes in the Hamming metric, modular B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> -sequences, 2-fold Sidon sets, and sets avoiding arithmetic progression.
Enhancing Public Key Cryptography using RSA, RSA-CRT and N-Prime RSA with Multiple Keys

Nowadays, the transmission of private statistics through the computer network needs protection. In a broad range of services, protection is equally troubling. In providing data protection against mischievous attacks, cryptographic algorithms play an important role. RS A has a set of rules widely used in renowned Public Key infrastructure implementations. An efficient RSA method with many public keys was implemented in this article. Two exceptional keys for cryptography, also referred to as Public Key cryptography, are used for asymmetrical key cryptography, also referred to as Public Key cryptography. One key is used to encrypt and another one is used to decrypt, where only the alternative equivalent key can be used. No other key, not even the initial key used for encryption, can decrypt the message now. Under this enhanced RSA Encryption Algorithm, several prosecutions have been carried out to make the use of four keys quicker and more efficient than the original encryption and decryption process. This article gives the implementation of the operation of continuous subtraction operation in the place of division operation. By applying this technique, a high computational speed can be achieved and decreased the complexities of mathematical steps.
Unsupervised Visual Representation Learning by Tracking Patches in Video

Inspired by the fact that human eyes continue to develop tracking ability in early and middle childhood, we propose to use tracking as a proxy task for a computer vision system to learn the visual representations. Modelled on the Catch game played by the children, we design a Catch-the-Patch (CtP) game for a 3D-CNN model to learn visual representations that would help with video-related tasks. In the proposed pretraining framework, we cut an image patch from a given video and let it scale and move according to a pre-set trajectory. The proxy task is to estimate the position and size of the image patch in a sequence of video frames, given only the target bounding box in the first frame. We discover that using multiple image patches simultaneously brings clear benefits. We further increase the difficulty of the game by randomly making patches invisible. Extensive experiments on mainstream benchmarks demonstrate the superior performance of CtP against other video pretraining methods. In addition, CtP-pretrained features are less sensitive to domain gaps than those trained by a supervised action recognition task. When both trained on Kinetics-400, we are pleasantly surprised to find that CtP-pretrained representation achieves much higher action classification accuracy than its fully supervised counterpart on Something-Something dataset.
Machine learning for video event recognition

In recent years, the spread of video sensor networks both in public and private areas has grown considerably. Smart algorithms for video semantic content understanding are increasingly developed to support human operators in monitoring different activities, by recognizing events that occur in the observed scene. With the term event, we refer to one or more actions performed by one or more subjects (e.g., people or vehicles) acting within the same observed area. When these actions are performed by subjects that do not interact with each other, the events are usually classified as simple. Instead, when any kind of interaction occurs among subjects, the involved events are typically classified as complex. This survey starts by providing the formal definitions of both scene and event, and the logical architecture for a generic event recognition system. Subsequently, it presents two taxonomies based on features and machine learning algorithms, respectively, which are used to describe the different approaches for the recognition of events within a video sequence. This paper also discusses key works of the current state-of-the-art of event recognition, providing the list of datasets used to evaluate the performance of reported methods for video content understanding.
An improved security assurance model for collaborating small material business processes

The goal of the work is to profit private company owners to comprehend their association security dangers levels and keep resources from digital assaults. During the most recent couple of decades, associations have re-built business forms on the rear of computerized information and PC systems. As of late, associations have started to understand that expanded openness and efficiency convey a concealed expense of making the information increasingly defenceless in security breaks based on the data sequence materials. It bodes well to join data security into key dynamic during business process. Significantly, organizations have regularly upgraded with least security into their business forms in light of security penetrates. The current examination presents a data security hazard investigation proactively directed at a globally well prestigious business association. The significant region of the work is to expect a proof security assertion model for group up business improvements. The business methods expend increasingly number of consecutive information to deal with touchy data either in organized or unstructured edge which might be streamed by security defects in business exchanges with incorporated data materials. A trial security model of a hazard based improved security for business forms is proposed with suitable measures and measurements during the reconciliation of the organized and unstructured data for business perseverance. This examination additionally depicts the dynamic enactment of proper protections; anticipation and recuperation from dangers because of both the organized and the unstructured data were thought of. The Anticipated Business Continuity (ABC) in business position and assault security list is arranged from the morals towards the security compliances. In arrangement with employable IT security and conventions, proactive administrative techniques, and unending evaluation of the organization's framework, the unimportant entrepreneur can withstand the business and shield it and counter to potential digital assaults on the association's system.