Reviewerly/exHarmony huggingface dataset:
VidAnomaly: LSTM-Autoencoder-Based Adversarial Learning for One-Class Video Classification With Multiple Dynamic Images

One-class video classification (anomalous video detection) serves an important role when abnormal videos are absent during training, poorly sampled or not well defined. However, one-class video classification is challenging. Due to the unavailability of abnormal samples, it is a cumbersome task to train an end-to-end deep supervised learning model. Meanwhile, video data representation is challenging because of the unstructured scheme of video contents. To represent video data with temporal and spatial information, we propose multiple dynamic images in our task because dynamic image encodes the temporal evolution of video frames and represents video contents at the level of the image pixels. Multiple dynamic images are viewed as the input sequence with temporal and spatial information and achieve dimension reduction of original video data. In this paper, we propose a LSTM-autoencoder-based adversarial learning model for one-class video classification ("VidAnomaly") without abnormal samples in the training stage. Our architecture is composed of three sub-networks. LSTM-autoencoder network (R) learns the temporal dependence of the input sequence and reconstructs the input sequence for the discriminator network (D) to achieve adversarial learning. The novelty of the proposed model is that we add an additional LSTM-encoder network (A) to obtain the latent representation of the reconstructed sequence. Minimizing the distance between the two latent representations from R and A benefits the model to further capture the training data distribution because it forces the LSTM-autoencoder network to yield an essential representation of training samples in latent space. In the inference stage, for a given abnormal sample as the input, the model poorly reconstructs the input abnormal sample and the reconstruction error would be high because the proposed model is trained merely on normal samples and its parameters are only suitable for reconstructing normal samples. Based on this, we detect abnormal samples. The experimental results show that VidAnomaly learns the target class distribution effectively and is superior to other methods.
Formal dialogue models for argumentation in education and linguistics

This paper offers a very short introduction to formal dialogue systems of the kind currently used in artificial intelligence and argumentation theory and applies them to some types of dialogue that are especially important in education. Persuasion dialogue is contrasted with deliberation dialogue. The fundamental problem of how to define relevance in such systems is briefly explained and discussed.
Strategies for building high quality bilingual lexicons from comparable corpora

This chapter outlines two strategies to automatically build bilingual dictionaries: One is based on the use of a pivot language and existing bilingual dictionaries, while the other relies on string similarity and cognate extraction. Both strategies have in common the use of translation equivalents extracted from comparable corpora to filter out odd bilingual pairs and validate the correct ones. The correctness of the entries validated with comparable corpora is very high, close to that achieved by using parallel corpora. The chapter reports several case studies describing how to build new high-quality bilingual lexicons, namely English-Galician, English-Portuguese, and Portuguese-Spanish dictionaries with more than 90% precision. This outperforms state-of-the-art systems on bilingual extraction from comparable corpora, whose best scores hardly reach 70 or 80%.
WordPrep: Word-based Preposition Prediction Tool

As big data heads towards big knowledge, data management and machine learning techniques work together to address several interesting problems. In this paper, we address a problem in natural language processing that involves learning by mining from large text databases. More specifically, we deal with the problem of preposition prediction, especially for ESL (English as a second language) learners. Prepositions are function words that typically show a relationship between a noun or a pronoun and other elements of a sentence. They play a key role in determining the meaning of a sentence. Accurate prediction of correct prepositions in a sentence is a challenging job since preposition usage is one of the most subtle aspects of the English grammar, making it difficult for non-native speakers. This paper proposes an approach for preposition prediction called WordPrep based on which we build a tool. WordPrep relies on mining based on the words themselves rather than on their lexical or syntactic connotations. This addresses the challenges of prepositions appearing in idiomatic phrases or in different semantic contexts, due to which the actual words are better than their grammatical positions. Our proposed solution entails a direct data-driven approach to predict the missing preposition in a sentence by learning from matching tokens consisting of ngrams with words before and after the preposition. Using various searches and pattern-matching methods against a large number of database records from big text corpora, this approach predicts the missing preposition(s). We describe our pilot approach, tool implementation and experiments in this paper. This work is particularly helpful for pedagogical applications.
Evaluation of Effective Cognition for the QGIS Processing Modeler

This article presents an evaluation of the QGIS Processing Modeler from the point of view of effective cognition. The QGIS Processing Modeler uses visual programming language for workflow design. The functionalities of the visual component and the visual vocabulary (set of symbols and line connectors) are both important. The form of symbols affects how workflow diagrams may be understood. The article discusses the results of assessing the Processing Modeler’s visual vocabulary in QGIS according to the Physics of Notations theory. The article evaluates visual vocabularies from the older QGIS 2.x and newer 3.x versions. The paper identifies serious design flaws in the Processing Modeler. Applying the Physics of Notations theory resulted in certain practical recommendations, such as changing the fill colour of symbols, increasing the size and variety of inner icons, removing functional icons, and using a straight connector line instead of a curved line. Another recommendation was to provide a supplemental preview window for the entire model in order to improve user navigation in huge models. Objective eye-tracking measurements validated some results of the evaluation using the Physics of Notations. The respondents read workflows to solve different tasks and their gazes were tracked. Evaluation of the eye-tracking metrics revealed the respondents’ reading patterns of the diagram. Evaluation using both Physics of Notation theory and eye-tracking measurements inspired recommendations for improving visual notation. A set of recommendations for users is also given, which can be applied easily in practice using a contemporary visual notation.
Digital services in academic libraries: present and future

With the development of digital technologies, academic libraries tend to prove a broad scale of digital services for faculty members and students. This column describes challenges for some common digital services in academic libraries and suggests potential collaborating strategies that might enhance the promotion of such services.
Some aspects of online education training and retraining of physical education and sports

This article actualizes the problem of training and retraining of personnel in the physical education and sports industry. Based on the results of sociological surveys of students, the most acceptable time for conducting online classes is provided, as well as criteria for evaluating the quality of online lectures (webinars) formed by the authors to concentrate the attention of students during the classes.
Study on Optimal Laminographic Tilt Angle: A Method for Analyzing Quantity Information Gained in Projections

In laminography imaging, the laminographic tilt angle is an important factor that affects the reconstruction result. However, the optimal tilt angle was mainly estimated from experience in the current research. In this article, an estimation approach for measuring the quantity information of projections has been proposed, which is based on the entropy of pixel grayscale distribution. The correlation of reconstructed image quality and tilt angle was analyzed quantitatively by the method. In this way, a solution has been proposed for seeking the optimal tilt angle, which aims to improve the image quality of reconstructed layers. Additionally, numerical simulation was designed to validate the method, in which the reconstructed layer images at different tilt angles were compared. The curves of grayscale MAE, PSNR and SSIM versus tilt angle verified the reliability of calculated optimal tilt angle, which has proved that the method is effective. Meanwhile, given a certain model of the scanned object, the optimal tilt angle is constant and computable.
Automatic Generation of Electronic Medical Record Based on GPT2 Model

Writing Electronic Medical Records (EMR) as one of daily major tasks of doctors, consumes a lot of time and effort from doctors. This paper reports our efforts to generate electronic medical records using the language model. Through the training of massive real-world EMR data, the CMedGPT2 model provided by us can achieve the ideal Chinese electronic medical record generation. The experimental results prove that the generated electronic medical record text can be applied to the auxiliary medical record work to reduce the burden on the compose and provide a fast and accurate reference for composing work.
Introduction to Clustering

In this chapter, we first define the commonly used tasks and terminologies in data analysis and show the importance of data clustering. Then we give a mathematical formulation of the clustering problem and explain the frequently used similarity measures. Finally, we provide a short survey on different types of existing clustering algorithms and describe some of the most popular applications of data clustering.
Protecting Privacy-Sensitive Locations in Trajectories with Correlated Positions

The location privacy issue has become a critical research topic recently. The existing solutions do not solve one typical problem in practice: people may only want to protect certain privacy-sensitive locations among a group of temporal and spatial correlated points in a trajectory. As an effort towards this issue, we analyze the impact of space-time relationship on location privacy preservation. In addition, we propose new privacy definitions to better evaluate the privacy level and prove that the target location's privacy can be enhanced by randomizing its time and space related points. Moreover, under the constraint of the total noise power, the problem of obfuscating a location in a temporal and spatial correlated trajectory is formulated as finding the best noise allocation vector which can achieve the highest privacy level. This problem is solved by our proposed location privacy preserving method which applies differential privacy scheme on a series of points with noise budget allocation. Lastly, the performance of the proposed scheme is evaluated by simulations.
Use of Teaching Learning Based Optimization for Data Clustering

In the literature, there are hundreds of population-based optimization algorithms, which are inspired by nature. These algorithms have been proposed to solve different problems of different research area. Among them, teaching-learning-based optimization (TLBO) algorithm is one, which is based on learner’s behavior in the classroom. For improvement of performance of this algorithm, number of variants of TLBO algorithms already have been proposed. This paper compares the performance of these variants of TLBO in terms of clustering of unlabeled data sets. Here, it has been shown how different variants of TLBO can be used to find the clusters of a user-specified cluster numbers. These algorithms are evaluated by using some real-life datasets and compared their performances by some statistical tests.
Reducing risk with end-to-end application security automation

No one wants to be ‘that guy’ – the one that got the company hacked. The one who made a simple but stupid mistake when developing a new software application, such as including a password in the code. No one wants to be ‘the person who got the company hacked’ – the one who made a simple mistake when developing software, such as including a password in the code. The best way to reduce this kind of risk is through automation, argues Cindy Blake of GitLab. By automating application security scanning, vulnerability remediation and monitoring the application's infrastructure, human error is removed and consistency is applied. And the best way to reduce the risk of non-compliance with regulatory controls is by automating them and having traceability along the way to capture who changed what, when and why.
Optimal Ensembles for Deep Learning Classification: Theory and Practice

Ensemble methods for classification problems construct a set of models, often called "learners", and then assign class labels to new data points by taking a combination of the predictions from these models. Ensemble methods are popular and used in a wide range of problem domains because of their good performance. However, a theoretical understanding of the optimality of ensembles is, in many instances, an open problem. In particular, improving the performance of an ensemble requires an understanding of the subtle interplay between the accuracy of the individual learners and the diversity of the learners in the ensemble. For example, if all of the learners in an ensemble were identical, then clearly the accuracy of the ensemble cannot be any better than the accuracy of the individual learning, no matter how many learners one were to use. Accordingly, here we develop a theory for understanding when ensembles are optimal, in an appropriate sense, by balancing individual accuracy against ensemble diversity, from the perspective of statistical correlations. The theory that we derive is applicable for many practical ensembles, and we provide a set of metrics for assessing the optimality of any given ensemble. Perhaps most interestingly, the metrics that we develop lead naturally to a set of novel loss functions that can be optimized using backpropagation giving rise to optimal deep neural network based ensembles. We demonstrate the effectiveness of these deep neural network based ensembles using standard benchmark data sets.
Dynamic Hybrid Graph Matching for Unsupervised Video-based Person Re-identification

Taking videos as nodes in a graph, graph matching is an effective technique for unsupervised video-based person re-identification (re-ID). However, most of existing methods are sensitive to noisy training data and mainly only focus on visual content relations between query and gallery videos, which may introduce large amount of false positives. To enhance the robustness to training data and alleviate the visual ambiguity, a Dynamic Hybrid Graph Matching (DHGM) method is proposed, which jointly considers both content and context information for person re-ID in an iterative manner. The content relations between video nodes are obtained by metric learning, based on which the context relation is acquired by encoding the bidirectional feature of each probe node relative to its graph neighbors. The model is iteratively updated during the process of graph construction for promoted distance measurement and further better matching performance. Experimental results on the PRID 2011 and iLIDS-VID datasets demonstrate the superiority of the DHGM.
Security validation testing environment in the cloud

Researchers are trying to find new ways of finding and pointing out Cybersecurity vulnerabilities by using innovative metrics. New theoretical proposals need to be tested in a real environment, using Cybersecurity tools applications that can validate the applicability of those in real life. This paper presents an experimental flexible environment, which can be used for the validation of several theoretical claims based on an “easy to use” architecture implemented in a cloud environment. The framework provides a much shorter time setup in the real world as well as a much better understanding based on log analysis provided by MS Azure. As a proof of concept, we have tested three different claims and provided proves of results such as screenshots and log samples.
Multi-layer Facial Representation Learning for Early Prediction of Septic Shock

Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) have become the state-of-the-art approaches for mining Electronic Health Records (EHRs). Generally speaking, RNN extracts the temporal dependency among features as a time series of hidden states, whereas CNN summarizes the local patterns among features as a set of feature maps. Many studies have leveraged their complementary effects by using a stack of neural network layers with CNN on the input, followed by RNN layers for the output. However, the feature representations learned by these two types of neural networks are often hard to be visualized and interpreted in a unified way. In this work, we propose a general framework which represents the extracted temporal relationships and local patterns in a unified and systematic way through facial representations that have evolving emotional expressions based on a patient's health conditions. This form of feature representation not only improves the potential to visualize EHRs, but also further benefits our downstream task on early prediction of septic shock. More specifically, we show that our proposed framework consistently out-performed all other baseline models including various deep learning models for sepsis shock early prediction.
Error checking of large land quality databases through data mining based on low frequency associations

Abstract The accuracy of databases on land quality, particularly on cultivated land quality, is a prerequisite for land quality assessment and land degradation evaluation. Error checking of land quality databases is an important step in ensuring the accuracy of these land quality databases. The existing methods do not consider the intrinsic relationships among data elements in error checking of land quality databases. This paper explores a new idea for error checking of land quality database through the use of intrinsic relationships that existed in the database. The main assumption behind this idea is that database errors tend to occur at low frequencies and exist as low‐frequency associations with other data items in a database. Thus, these errors can be located by analyzing the combinational relationships between the data items in the database. Based on this idea a new method, low‐frequency data associations (LFDA) through data mining was developed in this paper. The results from control experiments shows that LFDA is effective in locating errors introduced into a land quality database. The applied experiment using the Guangzhou land quality database further confirmed this finding. This research opens a new and significant way for error checking of land quality databases.
Regional Patch-Based Feature Interpolation Method for Effective Regularization

Deep Convolutional Neural Networks (CNNs) can be overly dependent on training data, causing a generalization problem in which trained models may not predict real-world datasets. To address this problem, various regularization methods such as image manipulation and feature map regularization have been proposed for their strong generalization ability. In this paper, we propose a regularization method that applies both image manipulation and feature map regularization based on patches. The method proposed in this paper has a regularization effect in two stages, which makes it possible to better generalize the model. Consequently, it improves the performance of the model. Moreover, our method adds features extracted from other images in the hidden state stage, which not only makes the model robust to noise but also captures the distribution of each label. Through experiments, we show that our method performs competently on models that generate a large number of parameter and multiple feature maps for the CIFAR and Tiny-ImageNet datasets.
A graph construction study for graph-based semi-supervised learning: Case study on unstructured text data

A study is presented on how the distance/similarity metrics impact the graph construction and the subsequent classification task in the cases of unstructured text data. Two unstructured text datasets, a E-com. and the 20 newsgroups data are considered and three distance/similarity metrics are evaluated. State-of-the-art metrics are observed to be more effective for graph construction metrics and prediction accuracy for the LP ZGL classification algorithm. Experimental results are promising given the scalable nature of graph-based SSL techniques, and scope of big data processing for text datasets.
An Emerging Approach to Intelligent Techniques—Soft Computing and Its Application

Over the past decade, artificial intelligence (AI) has appeared in high-tech labs, and people use it daily without feeling it. For example, current calculations of power technologies and flexible computing in other areas related to digital products, many applications that increase the contribution of artificial intelligence, are flexible computing technologies based on these problems, the medicine, biology, industry, manufacturing described as security, education, virtual environment. We was particularly encouraged. Support for the provision of new ideas and/or theoretical studies on soft computing technology, interdisciplinary research, real-world experimental application problems and flexible computer system design descriptions, diverse in development their new analytical structure Introduce the document Encourage a soft computing technology that is particularly practical. It provides an overview of the disciplines of Artificial Intelligence with respect to Soft Computing, and presents the benefits of soft computing over traditional hard computer technologies and their disadvantages.
An effective security protocol for GDS (group Data sharing) in Healthcare Cloud Environment

The data-sharing in cloud computing will enable many users to share the data. This helps in accessing the information easily and to share the data efficiently. The healthcare data will have to be shared amongst different healthcare centers and the medical practitioners. This involves security issues while sharing the healthcare data within a group. We provide the group model for sharing of data that is based on block based design where the group members will perform the key agreement to get the common group key that helps to shares the data securely. The fault tolerant property is provided by this design which can detect the malicious attackers during the group data sharing. The proposed group data-sharing is compared with the existing data sharing models w.r.t. the time cost of computation for group key generation and sharing of data.