Reviewerly/exHarmony huggingface dataset:
A few-shot segmentation method for prohibited item inspection

With the rapid development of deep learning, several neural network models have been proposed for automatic segmentation of prohibited items. These methods usually based on a substantial amount of labelled training data. However, for some prohibited items of rarely appearing, it is difficult to obtain enough labelled samples. Furthermore, the category of prohibited items varies in different scenarios and security levels, and new items may appear from time to time.In order to predict prohibited items with only a few annotated samples and inspect prohibited items of new categories without the requirement of retraining, we introduce an Attention-Based Graph Matching Network.This model applies a few-shot semantic segmentation network to address the issue of prohibited item inspection. First, a pair of graphs are modelled between a query image and several support images. Then, after the pair of graphs are entered into two Graph Attention Units with similarity weights and equal weights, the attentive matching results will be obtained. According to the matching results, the prohibited items can be segmented from the query image.Experiment results and comparison using the Xray-PI dataset and SIXray dataset show that our model outperforms several other state-of-the-art learning models.This study demonstrates that the similarity loss function and the space restriction module proposed by our model can effectively remove noise and supplement spatial information, which makes the segmentation of the prohibited items on X-ray images more accurate.
Investigating the Impact of Website Quality on User Satisfaction in IDN Times News Website

Abstract idntimes.com is an online media that focuses on providing the latest information on news articles and article writing activities in the IDN Times community. The problem from idntimes.com has had a significant difference the number of community website users at each level (member, community writer and verified writer). Furthermore, there are problems about the quality of the website, is that the bounce rate on the website is still relatively high, around 60 %, there are still more than 32 links are broken with a status of 404 code and the page load time is still quite long. So this research was conducted to determine the level of user satisfaction through the Webqual 4.0 method. As Huang said, if a company wants to build loyalty from its customers, it must pay attention to one aspect is the service quality of the website. This study used a sample of 350 respondents. The data obtained were analyzed using PLS analysis. The results of the research based on the hypothesis show that the t-value is smaller than the t-table value, so the variables used, namely usability, information quality, and service interaction quality were highly significant factors that directly affected user satisfaction according to the testing model. The amount of influence obtained based on the R-Square value is 88.2%
Bayesian strategies: probabilistic programs as generalised graphical models

Abstract We introduce Bayesian strategies , a new interpretation of probabilistic programs in game semantics. This interpretation can be seen as a refinement of Bayesian networks. Bayesian strategies are based on a new form of event structure , with two causal dependency relations respectively modelling control flow and data flow. This gives a graphical representation for probabilistic programs which resembles the concrete representations used in modern implementations of probabilistic programming. From a theoretical viewpoint, Bayesian strategies provide a rich setting for denotational semantics. To demonstrate this we give a model for a general higher-order programming language with recursion, conditional statements, and primitives for sampling from continuous distributions and trace re-weighting. This is significant because Bayesian networks do not easily support higher-order functions or conditionals.
Enhancing Teaching Approach with 3D Primitives in Virtual and Augmented Reality

Today, virtual reality (VR) and augmented reality (AR) are widely used in various fields, especially in computer systems, engineering and medicine. However, it remains one of the main barriers to education due to the high cost of conducting experimental research. This interactive virtual learning is very convenient and effective. Nowadays, it offers great opportunities to improve the quality of education by creating new methods and techniques. Traditionalist methods of substance introduction (fixed video, sound, contents) need personalization and communication. In addition to create a private virtual education environment which allows the students and researchers to get enough learning that is interactive and more visual, where students can learn through experimentation and interactions in the virtual world. There is a requirement for MOOC to grasp propelled learning strategies to give all the more captivating experience to its students. Finally, we create internal avatar model while using in virtual reality (VR) and augmented reality (AR) interfaces.
Adaptive Named Entity Recognition Using Distant Supervision for Contemporary Written Texts

Named entity recognition (NER) is the process of categorizing named entities in a given text that suffers from the lack of labeled corpora, which is a long-standing issue.Deep neural networks have been successfully applied to NER tasks.However, they require a large number of annotated data.Regardless of the number of data made available, annotation requires significant human effort, which is expensive and time-consuming.Moreover, collecting labeled data that reflect contemporary surrounding statuses requires exhaustive follow-up and incurs correspondingly higher costs.Current NERs typically focus on the supervised learning of hand-crafted data.The most well-known dataset for NER shared tasks, which was released at the 2003 Conference on Natural Language Learning, is used for basic training and evaluation.Although the data are qualified, the database has low coverage of timely material.In this paper, we illustrate methods for swiftly labeling up-to-date data via distant supervision.To tackle the difficulty of annotating contemporary written texts, we generate labeled data articles that reflect the latest issues.We evaluated the proposed methods with bidirectional long short-term memory conditional random-field architecture using static and contextualized embedding methods.Our proposed models perform higher than state-of-the-art methods with average F1-scores 3.09% better with weakly labeled Wikipedia data and 3.47% better with Cable News Network data.When using the NER model with Flair embedding, our method shows 1.50 and 3.26% higher F1-scores with weakly labeled Wikipedia and news data, respectively.Qualitatively, the proposed model also performs better when extracting contemporary keywords.
Visualizing Complex Ontologies Through Sub-Ontology Extraction

Ontologies represent the backbone of the semantic web since they represent knowledge in a machine-understandable way. The more complex the ontologies are, the greater is the need for visualization tools to ease the understanding of the large amount of knowledge represented by these ontologies. Visualizing large ontologies is a still under-investigated interesting challenge. In this paper, we present VisCOnto, a visualization tool of complex ontologies by extracting sub-ontologies that correspond to excerpts of a large ontology focusing on some concepts of interest. VisCOnto combined with WEBVOWL can create sub-ontologies to create expressive information visualizations by harnessing the semantic reasoning capabilities provided by ontologies. A proof of concept use case for applying VisCOnto to visualize an existing complex ontology of human behavior and character is presented.
The Evaluation Report of the Online Training for CUT and UJEP Students during Pandemic Period

This paper describes self-evaluation of online training. The online training was an unexpected change in IT Competence Network for Strengthening the Saxon-Czech Border Region (IKON) project due to the Covid-19 situation in countries of partner universities. IKON is a project to build up a cooperation network to strengthen the border region between Saxony and the Czech Republic. Main aim of the IKON project is to support students from partner universities by practice oriented training with cooperation of cross border industries. The partner university for Project IKON in Czech Republic is the Jan Evangelista Purkyně University (UJEP) in Ústí nad Labem. This work includes activities and changes in the project plan due to the Covid-19 pandemic and some report about self-assessment for online training.
Universities, Civility, and Repression in the Age of New Media

This chapter attends to surveillance performed under the guise of civility, with civility being deployed as an instrument for disciplining. The techniques of repression evident in the excerpt, perfected with the mix of surveillance and calibrated control under authoritarianism, embody the normative mode of governmentality of the neoliberal university. Civility serves the Empire by disciplining dissent while simultaneously deploying the White exceptionalism of academic freedom in asserting neocolonial power. The hegemonic US-based literature on new media and democratic processes point toward the democratizing roles of new media in the context of social movements as witnessed in the Arab Spring and other similar movements globally. In the absence of existing frameworks and channels for communication, new media infrastructures such as blogs, Facebook posts, and tweets often serve as sites for resisting the hegemonic ideologies. Incivility, performed as a communicative inversion, is the reference frame for silencing dissent on new media.
Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread

It is important to accurately segment anatomical brain barriers to cancer spread with multi-modal images, in order to assist definition of the clinical target volume (CTV). In this work, we explore a multi-modal segmentation method largely driven by domain knowledge. We apply 3D U-Net as the backbone model. In order to reduce the learning difficulty of deep convolutional neural networks, we employ a label merging strategy for the symmetrical structures which have both left and right labels, to highlight the structural information regardless of the locations. Moreover, considering the existence of visual preference for certain modality and mismatches in co-registration, we adopt a multi-modality ensemble strategy for multi-modal learning to enable the models better driven by domain knowledge of this task, which is different from fully data-driven methods, like early fusion strategy for multi-modal images. By contrast, multi-modality ensemble strategy yields better segmentation results. Our method achieved an average score of 0.895 on MICCAI 2020 Anatomical Brain Barriers to Cancer Spread Challenge’s final test dataset ( https://abcs.mgh.harvard.edu/ .). Detailed methodologies and results are described in this technical report (This work was done when X. Zou did remote internship with CUHK.).
Graph Extraction of Batik Image Using Region Adjacency Graph Representation

Abstract Image feature extraction done in image analysis based on the traditional pixel is not extracted effectively. This is due to the extraction that only represents the content. A very promising approach and the challenge are to extract a graph from an image that represents the content and their relationship. The results of graph extraction are obtained from the process of image segmentation. The selection of an appropriate segmentation method from many graph-based image segmentation methods is interesting to be reviewed. The method of super-pixel segmentation is one way to divide the image into regions. The regions obtained in segmentation are represented by vertices and edges represent connections between adjacent regions. This representation is called Region Adjacency Graph (RAG). In this paper, graph extraction of “batik” images has been successfully performed where its representation uses RAG and segmentation uses the Minimum Spanning Tree method. The results of graph extraction have been tested in a simple image retrieval process using the VF2 graph matching method.
LSOMP: Large Scale Ordinance Mining Portal

We propose a novel scalable Web portal called LSOMP (Large Scale Ordinance Mining Portal) to analyze ordinances and their tweets (of the order of thousands and millions). It entails commonsense knowledge (CSK) and natural language processing (NLP), disseminating ordinance-tweet mining results via interactive graphics and Question Answering (QA).
Innovative approach to organization and payment of work of teaching staff at higher school

The article deals with the problem of a sharp increase in the workload of scientific and pedagogical staff (SPS) of higher educational institutions, which has worsened under the COVID-19 pandemic. The analysis of the main reasons for the growing workload has been carried out (the need to develop e-learning courses and replace colleagues due to the increased morbidity), as well as the possible negative impact of this factor on the higher education system. The assessment of the increasing volume of educational and methodological work during the transition to a combination of traditional and distance education technologies is carried out. Based on the analysis of the standard procedure for rationing the work of scientific and pedagogical staff, which is widespread in the Russian universities, the proposals for creating the innovative system of organization and remuneration of teachers are developed. The system is based on the differentiation of the categories of SPS, depending on the main activities performed (educational, research, methodological, etc.) into three sub-categories: SPE of the specialized department, SPE of the academic department, and SPE of a professional organization. There is proposed a matrix distribution of official salaries, depending on the SPE subcategory and qualification level. The teacher's salary consists of a fixed official salary and a flexible system of allowances based on the use of weight coefficients that are changed in accordance with the priority directions of the educational organization's development strategy. The proposed system can be adapted to the changing requirements of the labor market and education legislation and motivate the teachers to self-development, to using innovative educational technologies and research activities, and at the same time can retain all state guarantees for remuneration.
Effective Tuning of Regression Models using an Evolutionary Approach: A Case Study

Hyperparameters enable machine learning algorithms to be customized for specific datasets. Choosing the right hyperparameters is a challenge often faced by machine learning practitioners. With this research, tuning of hyperparameters for regression models was explored. Models predicting house prices in King County were created using a detailed suite of regression algorithms. Traditional approaches, and evolutionary algorithms, for improving model accuracy were evaluated. A variety of feature selection methods and hyperparameter tuning using grid search, random search and pipeline optimization were also studied as part of the traditional approaches. Furthermore, evolutionary algorithms were applied to model optimization. In this paper, it is shown that an evolutionary approach, implemented with TPOT, achieves the highest accuracy for a regression model based on the King County dataset. Regarding metrics, combining the RMSE and metrics is shown to be an effective means of determining model accuracy. Finally, greedy feature selection performed best when a variety of feature selection methods are compared.
Cryptanalysis of Some Self-Synchronous Chaotic Stream Ciphers and Their Improved Schemes

In this paper, a cryptanalysis method that combines a chosen-ciphertext attack with a divide-and-conquer attack by traversing multiple nonzero component initial conditions (DCA-TMNCIC) is proposed. The method is used for security analysis of [Formula: see text]-D ([Formula: see text]) self-synchronous chaotic stream ciphers that employ a product of two chaotic variables and three chaotic variables ([Formula: see text]-D SCSC-2 and [Formula: see text]-D SCSC-3), taking 3-D SCSC-2 as a typical example for cryptanalysis. For resisting the combinational effect of the chosen-ciphertext attack and DCA-TMNCIC, several improved chaotic cipher schemes are designed, including 3-D SCSC based on a nonlinear nominal system (3-D SCSC-NNS) and [Formula: see text]-D SCSC based on sinusoidal modulation ([Formula: see text]-D SCSC-SM ([Formula: see text])). Theoretical security analysis validates the improved schemes.
Action Recognition Using Dynamic Mode Decomposition for Temporal Representation

This paper explores a new method for representing temporal information found in videos. Dynamic Mode Decomposition (DMD), a method commonly used to reduce the computational effort for other big-data tasks such as flow calculations, is used in this study to aggregate changes between multiple frames. This is applied to the challenge of human action recognition (HAR) tasks using a Two-Stream architecture. Such an architecture takes two convolutional neural networks (CNNs), one analyzing the spatial data and the other analyzing the temporal data as calculated using DMD. This method is compared against others using two common benchmarks, the UCF-101 dataset and the HMDB-51 dataset, achieving 46.0% accuracy on the UCF-101 and 30.8% on the HMDB-51.
Data-Driven Machine Learning Approach for Human Action Recognition Using Skeleton and Optical Flow

Human action recognition is a very challenging problem due to numerous variations in each body part. In this paper, we propose a method for extracting optical flow information from skeleton data to address the problem of body part movement variation in human action recognition. The additional arm part information was also analyzed how valuable it was. Then, different machine learning methods are applied such as k-Nearest Neighbors (KNNs) and deep learning to recognize human actions on the UTKinect-Action 3D dataset. We then design and train different KNNs models and Deep Convolutional Neural Networks (D-CNNs) on the obtained image and classify them into classes. Different numbers of features from histogram data collection are used to recognize 10 categories of human actions. The best accuracy we obtained is about 88%. The proposed method had improved accuracy to almost 97% with only 5 classes. These features are representative to describe the human action and recognition which does not rely on plenty of training data. Results of experiments show that using deep learning can lead to better classification accuracy.
Semantics of a Relational $$\lambda $$-Calculus

We extend the $$\lambda $$ -calculus with constructs suitable for relational and functional–logic programming: non-deterministic choice, fresh variable introduction, and unification of expressions. In order to be able to unify $$\lambda $$ -expressions and still obtain a confluent theory, we depart from related approaches, such as $$\lambda $$ Prolog, in that we do not attempt to solve higher-order unification. Instead, abstractions are decorated with a location, which intuitively may be understood as its memory address, and we impose a simple coherence invariant: abstractions in the same location must be equal. This allows us to formulate a confluent small-step operational semantics which only performs first-order unification and does not require strong evaluation (below lambdas). We study a simply typed version of the system. Moreover, a denotational semantics for the calculus is proposed and reduction is shown to be sound with respect to the denotational semantics.
New Proof Techniques Using the Properties of Circulant Matrices for XOR-based (&lt;i&gt;k&lt;/i&gt;, &lt;i&gt;n&lt;/i&gt;) Threshold Secret Sharing Schemes

Several secret sharing schemes with low computational costs have been proposed. XOR-based secret sharing schemes have been reported to be a part of such low-cost schemes. However, no discussion has been provided on the connection between them and the properties of circulant matrices. In this paper, we propose several theorems of circulant matrices to discuss the rank of a matrix and then show that we can discuss XOR-based secret sharing schemes using the properties of circulant matrices. We also present an evaluation of our software implementation.
Developing Individual Path of Teaching a Foreign Language to Ensure Sustainable Development of Educational System

The paper aims to develop an individual trajectory of teaching a foreign language to non-linguistic students. According to the author, relevance of this trajectory is conditioned by digitalization of learning environments and by the current economic situation in the context of the coronavirus pandemic. Scientific originality of the study lies in the fact that relying on pedagogical design methods and the data analysis method, the researcher constructs an individual trajectory of teaching a foreign language. The research findings are as follows: the author suggests an individual trajectory of teaching a foreign language, which promotes formation of innovative educational environment, increases students’ motivation for learning.
Your Password Is Music To My Ears: Cloud Based Authentication Using Sound

This paper details the research In progress into identifying and addressing the threats faced by voice assistants and audio based digital systems. The popularity of these systems continues to grow as does the number of applications and scenarios they are used in. Smart speakers, smart home devices, mobile phones, telephone banking and even vehicle controls all benefit from being able to be controlled to some extend by voice without diverting the attention of the user to a screen or having to use an input device such as a screen or keyboard. Whilst this removes barriers to use for those with accessibility challenges like visual impairment or motor skills issues and opens up a much more convenient user experience, a number of cyber security threats remain unanswered. This paper details a threat modelling exercise and suggests a model to address the key threats whilst retaining the usability associated with voice driven systems, by using an additional sound based authentication factor.
MLAI: An Integrated Automated Software Platform to Solve Machine Learning Problems

Artificial Intelligence has enhanced the capability of many organizations' decision-making at an extreme level. The demand for analyzing the structured and unstructured data that increases exponentially in recent days is really challenging task. To address this issue, we have developed MLAI software tool which could automate the process of typical data science problems. MLAI combines a number of efficient approaches to enhance the accuracy of several complex problems on predictive analysis for both structured and unstructured data. It creates a seamless interface for the user to interpret the data and also provides custom parameters for tuning the hyperparameters to address the specific business needs. The objective of the paper is to automate the classification problems through a software tool. It can intelligently analyze the whole dataset properly and also determines the features' importances and also reduces the dimensions of the dataset. It incorporates the advanced visualization techniques using Plotly Express graphs. MLAI presents a development environment that automate various supervised machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) on several datasets. The comparative analysis of results among aforesaid algorithms are presented and also observed that RF outperforms than rest of the classification algorithms.