Reviewerly/exHarmony huggingface dataset:
Generic Negation of Pair Encodings

Attribute-based encryption (ABE) is a cryptographic primitive which supports fine-grained access control on encrypted data, making it an appealing building block for many applications. Pair encodings (Attrapadung, EUROCRYPT 2014) are simple primitives that can be used for constructing fully secure ABE schemes associated to a predicate relative to the encoding. We propose a generic transformation that takes any pair encoding scheme (PES) for a predicate \(P\) and produces a PES for its negated predicate \(\bar{P}\). This construction finally solves a problem that was open since 2015. Our techniques bring new insight to the expressivity and generality of PES and can be of independent interest. We also provide, to the best of our knowledge, the first pair encoding scheme for negated doubly spatial encryption (obtained with our transformation) and explore several other consequences of our results.
Pairwise-Covariance Multi-view Discriminant Analysis for Robust Cross-View Human Action Recognition

Human action recognition (HAR) under different camera viewpoints is the most critical requirement for practical deployment. In this paper, we propose a novel method that leverages successful deep learning-based features for action representation and multi-view analysis to accomplish robust HAR under viewpoint changes. Specifically, we investigate various deep learning techniques, from 2D CNNs to 3D CNNs to capture spatial and temporal characteristics of actions at each separated camera view. A common feature space is then constructed to keep view-invariant features among extracted streams. This is carried out by learning a set of linear transformations that project private features into the common space in which the classes are well distinguished from each other. To this end, we first adopt Multi-view Discriminant Analysis (MvDA). The original MvDA suffers from odd situations in which the most class-discrepant common space could not be found because its objective is overly concentrated on pushing classes from the global mean but unaware of the distance between specific pairs of adjoining classes. We then introduce a pairwise-covariance maximizing extension that takes pairwise distances between classes into account, namely pc-MvDA. The novel method also differs in the way that could be more favorably applied for large high-dimensional multi-view datasets. Extensive experimental results on four datasets (IXMAS, MuHAVi, MICAGes, NTU RGB+D) show that pc-MvDA achieves consistent performance gain, especially for harder classes. The code is publicly available for research purpose at https://github.com/inspiros/pcmvda.
Uniaxial 3D Measurement with Auto-Synchronous Phase-Shifting and Defocusing Based on a Tilted Grating

Conventional uniaxial techniques generally require shifting objects or projection grating with the assistance of a high-precision mechanical moving component. To overcome this limitation, we propose a novel uniaxial 3-D shape measurement system with auto-synchronous phase-shifting and defocusing by using a tilted and fixed projection grating. The tilted focused image plane (FIP), which is reflected by a mirror at about 90 degrees, could be shifted across the measured surface by slightly rotating the mirror within a small angle range. This procedure will simultaneously introduce the change in defocusing and phase-shifting of the fringe. The modulation curve of each point can be deciphered by Fourier fringe analysis after a sequence of fringe intensities is acquired. Since both the measured object and projection grating are fixed, the proposed method could make the measurement system more compact and flexible. Both computer simulation and experiments are carried out to demonstrate the validity of this proposed system.
Implementing Eye-Tracking for Persona Analytics

Investigating users' engagement with interactive persona systems can yield crucial insights for the design of such systems. Using eye-tracking, researchers can address the scarcity of behavioral user studies, even during times when physical user studies are difficult or impossible to carry out. In this research, we implement a webcam-based eye-tracking module into an interactive persona system, facilitating remote user studies. Findings from the implementation can show what information users pay attention to in persona profiles.
3D CNN for Human Action Recognition

Recognizing different human actions from still images or videos is an important research area in the computer vision and artificial intelligence domains. It represents a key step for a wide range of applications including: human-computer interaction, ambient assisted living, intelligent driving and video surveillance. However, unless the many research works being involved, there are still many challenges ahead including: the high changes in human body shapes, clothing and viewpoint changes and the conditions of system acquisition (illumination variations, occlusions, etc.). With the emergence of new deep learning techniques, many approaches are recently proposed for Human Action Recognition (HAR). Compared with conventional machine learning methods, deep learning techniques have more powerful learning ability. The most wide-spread deep learning approach is the Convolutional Neural Network (CNN/ConvNets). It has shown remarkable achievements due to its precision and robustness. As a branch of neural network, 3D CNN is a relatively new technique in the field of deep learning. In this paper, we propose a HAR approach based on a 3D CNN model. We apply the developed model to recognize human actions of KTH and J-HMDB datasets, and we achieve state of the art performance in comparison to baseline methods.
A Systematic Literature Review: Blockchain Technology and Organizational Theories in the Perspective of Supply Chain Management

Abstract Due to the numerous benefits of blockchain technology (BCT), firms are eager to adopt blockchain technologies in their businesses for greater information sharing, increasing efficiency, and reducing the cost. But in the field of supply chain management, blockchain technology literature is in its early phase for lacking theoretical foundation. The primary purpose of this research is to determine the organization theory used in the BCT literature from the perspective of operations and supply chain related fields. The findings reveal that six organizational methods used in BCT literature in supply chain-related fields, including resource-based view, network theory, institutional theory, agency theory, information theory, and transaction cost analysis. This article provides a foundation for future researchers who want to use organizational theory and deepen research on BTC in the field of supply chain management, thereby contributing to the BCT discussion.
Automatic Arabic Short Answers Scoring Using Longest Common Subsequence and Arabic WordNet

The manual process of scoring short answers of Arabic essay questions is exhaustive, susceptible to error and consumes instructor's time and resources. This paper explores longest common subsequence (LCS) algorithm as a string-based text similarity measure for effectively scoring short answers of Arabic essay questions. To achieve this effectiveness, the longest common subsequence is modified by developing weight-based measurement techniques and implemented along with using Arabic WordNet for scoring Arabic short answers. The experiments conducted on a dataset of 330 students' answers reported Root Mean Square Error (RMSE) value of 0.81 and Pearson correlation r value of 0.94. Findings based on experiments have shown improvements in the accuracy of performance of the proposed approach compared to similar studies. Moreover, the statistical analysis has shown that the proposed method scores students' answers similar to that of human estimator.
<scp>SemRec</scp> – An efficient ensemble recommender with sentiment based clustering for social media text corpus

Summary The frequent user interactions happening in the form of textual contents like reviews, ratings, tags, blogs, testimonials, and so forth transformed the social media platform into a contextualized and personalized data warehouse focusing its users' unique likes and dislikes. The huge volume of social media content makes it difficult for the end users to consume relevant information by themselves. The need of a tool to deal with such scenario leads to the development of recommendation systems. This work proposes an ensemble multi‐stage recommender system with sentiment based clustering to deal with social media text corpus where each stage performing unique functionalities of information retrieval, natural language processing, user segmentation, prediction, and recommendation generation. The proposed system leverages a hybrid approach of content‐based, collaborative, and demographic filtering techniques to predict and recommend contents, products, or services according to user interests. The experimental results gathered using standard datasets are promising and found more efficient than the traditional approaches.
A Noisy-sample-removed Under-sampling Scheme for Imbalanced Classification of Public Datasets

Classification technology plays an important role in machine learning. In the process of classification, the presence of noisy samples in datasets tends to reduce the performance of a classifier. This work proposes a clustering-based Noisy-sample-Removed Under-sampling Scheme (NUS) for imbalanced classification. First, the samples in the minority class are clustered. For each cluster, its center is taken as a spherical center, and the distance of the minority class samples farthest from the cluster center is taken as the radius to form a hypersphere. The Euclidean distance from the center of the cluster to every of the majority samples is calculated to decide if they are in the hypersphere. Then, we propose a NUS-based policy to decide if a majority sample in the hypersphere is a noisy sample. Similarly, the noises samples of the minority class are found. Second, We remove noisy-samples from the majority and minority classes and propose NUS. Finally, logistics regression, Decision Tree, and Random Forest are used in NUS as the base classifiers, respectively and compare with Random Under-Sampling (RUS), EasyEnsemble (EE), and Inverse Random Under-Sampling (IRUS) on 13 public datasets. Results show that our method can improve the classification performance in comparison with its state-of-the art peers.
An Improved Dictionary Based Genre Classification Based on Title and Abstract of E-book Using Machine Learning Algorithms

The amount of digital books or e-books is increasing day by day. Book Assortment is the job of assigning a category or set of appropriate genres to a book. The goal of this research paper is to classify books with related genres. Many existing approaches, like Support Vector Machine (SVM), Neural Text Categorizer (NTC), etc. are available for text mining. We applied existing machine learning algorithms with different datasets and implemented existing feature selection methods to select features. In our proposed dictionary-based approach, we classified books by its attributes like title, description, genre, and author using text mining. In the learning part, we created a dictionary of keywords from the book's description and title and then assigned genres to the keywords. In the classification part, we attributed genres to a book. For classifying the books, we extracted a dataset from web pages using web scraping. Our proposed approach outperforms traditional approaches to reduce the time of training when massive data is considered.
Unsupervised Anomaly Detection In Digital Pathology Using GANs

Machine learning (ML) algorithms are optimized for the distribution represented by the training data. For outlier data, they often deliver predictions with equal confidence, even though these should not be trusted. In order to deploy ML-based digital pathology solutions in clinical practice, effective methods for detecting anomalous data are crucial to avoid incorrect decisions in the outlier scenario. We propose a new unsupervised learning approach for anomaly detection in histopathology data based on generative adversarial networks (GANs). Compared to the existing GAN-based methods that have been used in medical imaging, the proposed approach improves significantly on performance for pathology data. Our results indicate that histopathology imagery is substantially more complex than the data targeted by the previous methods. This complexity requires not only a more advanced GAN architecture but also an appropriate anomaly metric to capture the quality of the reconstructed images.
Second-Order Semantic Role Labeling With Global Structural Refinement

Semantic role labeling (SRL) is one of the important tasks in natural language processing. Current end-to-end SRL, compared with traditional pipeline SRL, has achieved competitive performance via graph-based neural models. However, these are all first-order methods, where decisions for detecting predicate-argument pairs are made in isolation with local short-term features, thus being error-prone. Besides, they may potentially suffer from long-distance dependency, and perform poorly on the data-scarce scenario. In this paper, we explore a second-order end-to-end SRL model that considers simultaneously two pairs of predicate-argument when making scoring. We next propose a structural refinement mechanism to further model higher-order interactions at a global scope. After iterations of refinement, the underlying token representations are refined to be more informative. Experimental results show that our model significantly outperforms the state-of-the-art baselines on multiple benchmarks (i.e., CoNLL08, CoNLL09 and CoNLL12). Further analysis demonstrates that our framework is able to learn latent SRL constraints, being more robust on data-scarce settings, meanwhile relieving the long-range dependency issue.
A TOPSIS-ELM framework for stock index price movement prediction

In recent years Extreme Learning Machine (ELM) has gained the interest of various researchers due to its superior generalization and approximation capability. The network architecture and type of activation functions are the two important factors that influence the performance of an ELM. Hence in this study, a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) oriented multi-criteria decision making (MCDM) framework is suggested for analyzing various ELM models developed with distinct activation functions with respect to sixteen evaluation criteria. Evaluating the performance of the ELM with respect to multiple criteria instead of single criterion can help in designing a more robust network. The proposed framework is used as a binary classification system for pursuing the problem of stock index price movement prediction. The model is empirically evaluated by using historical data of three stock indices such as BSE SENSEX, S&amp;P 500 and NIFTY 50. The empirical study has disclosed promising results by evaluating ELM with different activation functions as well as multiple criteria.
IMPLEMENTASI MEDIA DIGITAL UNTUK PERKEMBANGAN BAHASA INGGRIS ANAK USIA DINI

&lt;p align="center"&gt;&lt;strong&gt;&lt;em&gt;Abstract &lt;/em&gt;&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;This study aimed to investigate the phenomenon about implementation of digital media suporting the development of English Language in early childhood students at Tunas Mekar Kindergarten Tegaljadi Village. This study find out perceptions of teachers, children, and parents regarding the how was the implementation process of digital media suporting English developmet in early childhood students in Tunas Mekar Tegaljadi Village, and to determine the perception of teacher and parents how efective digital media supporting English development in early childhood students at Tunas Mekar Tegaljadi Village. This research is a qualitative research using descriptive method&lt;/em&gt;&lt;em&gt; with &lt;/em&gt;&lt;em&gt;observation sheets and interview guides to teachers, children, and parents. As for the results, first, &lt;/em&gt;&lt;em&gt;In general, it runs smoothly through three processes, namely the preparation process, activities, and evaluation. At each stage this has been carried out optimally by teachers and students who are accompanied by their parents. Second, learning English through digital media has also been able to improve the development of early childhood English in TK Tunas Mekar II. They have been able to pronounce simple vocabulary in English. Able to say greetings and be able to follow orders through verbs in English. Besides that, the children did this activity with great enthusiasm and fun&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;
Using a linked data-based knowledge navigation system to improve teaching effectiveness

A linked data approach provides new opportunities for annotating, interlinking, sharing and enriching massive open online educational resources. However, it can be difficult for non-expert users to build and utilize the educational linked data in educational settings. Thus, flexible and user-friendly ways to represent, interlink, visualize and utilize the educational linked data become increasingly important. This paper proposes a linked data-based knowledge navigation system (LDKNS) for improving the teaching effectiveness. In the system, learning resource ontology was implemented to represent learning contents, and linked data visualization technologies were used in a formal curriculum structure. It presents a use case of building the educational linked data and creating interactive knowledge visualizations to support teachers and students in art design education. A contrast experiment was conducted to evaluate the effectiveness of the proposed system. The experiment involved 115 second year undergraduate students divided into experimental and control groups. It was found that there was a significant increase in the motivation of students who used the LDKNS. It was also found that students’ achievement in the experimental group performs better. Furthermore, the results of a survey on cognitive load revealed that using the system can decrease their cognitive load. Thus, we believe that art design education supplemented with the LDKNS yields a significant learning advantage for students by improving learning performance.
A Frequency-Based Approach to Extract Aspect for Aspect-Based Sentiment Analysis

Data is king nowadays, and users worldwide express their views on different platforms to aggregate this data and analyze it. Sentiment analysis becomes a major tool for analysts. Sentiment analysis can be done on different levels. This will be discussing a more granular level of sentiment analysis using aspect-based sentiment analysis, which aims to predict the sentiment polarity of text for a specific target. The majority of work done in this field focuses on the extraction of aspect or feature and then finding their sentiments polarity and aggregating them to find the whole text's final polarity. Aspect extraction is the key to this process; our work will be focusing on aspect extraction. In this paper, we will address the issue of aspect extraction and then propose our approach to deal with it and show how it is better than these existing approaches.
Recommendations for item set completion: on the semantics of item co-occurrence with data sparsity, input size, and input modalities

Abstract We address the problem of recommending relevant items to a user in order to “complete” a partial set of already-known items. We consider the two scenarios of citation and subject label recommendation, which resemble different semantics of item co-occurrence: relatedness for co-citations and diversity for subject labels. We assess the influence of the completeness of an already known partial item set on the recommender’s performance. We also investigate data sparsity by imposing a pruning threshold on minimum item occurrence and the influence of using additional metadata. As models, we focus on different autoencoders, which are particularly suited for reconstructing missing items in a set. We extend autoencoders to exploit a multi-modal input of text and structured data. Our experiments on six real-world datasets show that supplying the partial item set as input is usually helpful when item co-occurrence resembles relatedness, while metadata are effective when co-occurrence implies diversity. The simple item co-occurrence model is a strong baseline for citation recommendation but can provide good results also for subject labels. Autoencoders have the capability to exploit additional metadata besides the partial item set as input, and achieve comparable or better performance. For the subject label recommendation task, the title is the most important attribute. Adding more input modalities sometimes even harms the results. In conclusion, it is crucial to consider the semantics of the item co-occurrence for the choice of an appropriate model and carefully decide which metadata to exploit.
Subject Classification of Texts in Polish - from TF-IDF to Transformers

The paper concerns the issue of documents in Polish classification according to the subject category. We compare five approaches, starting from classical TF-IDF one, through word2vec methods (fastText in the supervised mode and doc2vec method) up to contemporary transformer-based BERT models (i.e., two models pre-trained on Polish corpora are used). These five approaches are evaluated using five corpora with subject categories (ranging from 5 to 36 labels), different sizes, and lengths of texts. Results suggest that BERT methods outperform other approaches in almost all tests. Due to the size of the model and therefore required time for tuning, the Polbert model is recommended as a solution for Polish text subject classification. The paper includes detailed analysis of recognition and time performance of analyzed methods.
Joint Segmentation Of Multiple Sclerosis Lesions And Brain Anatomy In MRI Scans Of Any Contrast And Resolution With CNNs

We present the first deep learning method to segment Multiple Sclerosis lesions and brain structures from MRI scans of any (possibly multimodal) contrast and resolution.Our method only requires segmentations to be trained (no images), as it leverages the generative model of Bayesian segmentation to generate synthetic scans with simulated lesions, which are then used to train a CNN.Our method can be retrained to segment at any resolution by adjusting the amount of synthesised partial volume.By construction, the synthetic scans are perfectly aligned with their labels, which enables training with noisy labels obtained with automatic methods.The training data are generated on the fly, and aggressive augmentation (including artefacts) is applied for improved generalisation.We demonstrate our method on two public datasets, comparing it with a state-of-the-art Bayesian approach implemented in FreeSurfer, and dataset specific CNNs trained on real data.The code is available at https://github.com/BBillot/SynthSeg.