Reviewerly/exHarmony huggingface dataset:
Graph laplacian matrix learning from smooth time-vertex signal

In this paper, we focus on inferring graph Laplacian matrix from the spatiotemporal signal which is defined as "time-vertex signal". To realize this, we first represent the signals on a joint graph which is the Cartesian product graph of the time- and vertex-graphs. By assuming the signals follow a Gaussian prior distribution on the joint graph, a meaningful representation that promotes the smoothness property of the joint graph signal is derived. Furthermore, by decoupling the joint graph, the graph learning framework is formulated as a joint optimization problem which includes signal denoising, time- and vertex-graphs learning together. Specifically, two algorithms are proposed to solve the optimization problem, where the discrete second-order difference operator with reversed sign (DSODO) in the time domain is used as the time-graph Laplacian operator to recover the signal and infer a vertex-graph in the first algorithm, and the time-graph, as well as the vertex-graph, is estimated by the other algorithm. Experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively infer meaningful time- and vertex-graphs from noisy and incomplete data.
WITHDRAWN: Deep convolutional neural network for real time object detection using tensor flow

Object detection is blooming area in computer vision that is maturing very quickly. Consistently, new algorithms/ models keep on outperforming the previous one. Most of the computer vision system will first collect the data regarding the object to be detected and next the collects the data regarding the scene. After detecting the object its likely accessible to get the other data like precise instance, trace the thing over an image sequence and to retrieve the data about the object which is detected. As technology is developing day by day, we are in a circumstance that it is hard to recognize the particular objects, which are available in adjacent world. On the off chance that we distinguish the item additionally, we are not ready to recognize its highlights. So, to handle this issue, the proposed system is developed in android application based on Deep Convolutional Neural Network (CNN) algorithm using Tensor Flow, which distinguishes the item, and its highlights with high accuracy.
VCFShark: how to squeeze a VCF file

Variant Call Format (VCF) files with results of sequencing projects take a lot of space. We propose the VCFShark, which is able to compress VCF files up to an order of magnitude better than the de facto standards (gzipped VCF and BCF). The advantage over competitors is the greatest when compressing VCF files containing large amounts of genotype data. The processing speeds up to 100 MB/s and main memory requirements lower than 30 GB allow to use our tool at typical workstations even for large datasets.https://github.com/refresh-bio/vcfshark.Supplementary data are available at Bioinformatics online.
Semantic intelligence: An overview

The web is ubiquitously influencing all aspects of commerce and society, as well as information processing and dissemination. With the emergence of the semantic web, it is moving toward application and knowledge deployment. Richer, meaningful representations offer more insight, powerful reasoning capabilities, and enhanced security. Web Semantics strengthen the description of web resources for exploiting them better and making them more meaningful for both human and machine.
Feature-Enhanced Occlusion Perception Object Detection for Smart Cities

Object detection is used widely in smart cities including safety monitoring, traffic control, and car driving. However, in the smart city scenario, many objects will have occlusion problems. Moreover, most popular object detectors are often sensitive to various real-world occlusions. This paper proposes a feature-enhanced occlusion perception object detector by simultaneously detecting occluded objects and fully utilizing spatial information. To generate hard examples with occlusions, a mask generator localizes and masks discriminated regions with weakly supervised methods. To obtain enriched feature representation, we design a multiscale representation fusion module to combine hierarchical feature maps. Moreover, this method exploits contextual information by heaping up representations from different regions in feature maps. The model is trained end-to-end learning by minimizing the multitask loss. Our model obtains superior performance compared to previous object detectors, 77.4% mAP and 74.3% mAP on PASCAL VOC 2007 and PASCAL VOC 2012, respectively. It also achieves 24.6% mAP on MS COCO. Experiments demonstrate that the proposed method is useful to improve the effectiveness of object detection, making it highly suitable for smart cities application that need to discover key objects with occlusions.
Development of an Automatic Process for Recommending Well Adapted Educational Resources in an E-learning Environment

Learners in e-learning environments often try to go beyond what can be provided to them by teachers in terms of educational resources, trying to find other external resources on the web related to their preferences and needs. Based on this observation, and with the goal of personalizing the learning process by recommending the most appropriate teaching resources to learners, we propose a model for analyzing learners' searches to determine the relevant words that reflect their interests, with the aim of enriching their profiles. Our approach is to collect the descriptions of the links returned by the search engine, which will constitute a corpus on which we will apply the TF-IDF (term frequency-inverse document frequency) method to determine the relevant words. We will then use the Word2vec technique to determine words similar to these relevant words in the description of internal educational resources, so that we can recommend those that best correspond to the learner's needs. We developed a platform and launched an experiment with a group of real students. The data generated will be analyzed and the results will allow us to evaluate our approach and also to see areas for improvement.
Ideal structure of Zq + uZq and Zq + uZq-cyclic codes

In this paper, we study cyclic codes of length n over R = Zq + uZq, u2 = 0, where q is a power of a prime p and (n; p) = 1. We have determined the complete ideal structure of R. Using this, we have obtained the structure of cyclic codes and that of their duals through the factorization of xn-1 over R. We have also computed total number of cyclic codes of length n over R. A necessary and sufficient condition for a cyclic code over R to be self-dual is presented. We have presented a formula for the total number of self-dual cyclic codes of length n over R. A new Gray map from R to Z2rp is defined. Using Magma, some good cyclic codes of length 4 over Z9 + uZ9 are obtained.
A Multiple-Integration Encoder for Multi-Turn Text-to-SQL Semantic Parsing

This paper studies multi-turn text-to-SQL generation, which is a new but important task in semantic parsing. In order to deal with its two challenges, i.e., multi-turn interaction and cross-domain evaluation, this paper proposes a multiple-integration encoder, which derives the vector representations of user utterances and database schemas using three custom-designed modules for information integration. First, an utterance representation enhancing module is built to integrate the information of history utterances into the representation of each token in current utterance by attentive selection. Second, a schema discrepancy enhancing module is designed to integrate previous predicted SQL query into the representation of schema items. Third, a latent schema linking module is employed to integrate schema information into utterance representations for better dealing with unseen database schemas. These three modules are all implemented based on a lightweight multi-head attention mechanism, which reduces the number of parameters in conventional multi-head attention. Experimental results on the SParC dataset show that our method achieved better accuracy of multi-turn text-to-SQL generation than the most advanced benchmarks. Further ablations studies and analysis also demonstrate the effectiveness of the three modules designed for information integration in the encoder.
Learning Representational Invariances for Data-Efficient Action Recognition

Data augmentation is a ubiquitous technique for improving image classification when labeled data is scarce. Constraining the model predictions to be invariant to diverse data augmentations effectively injects the desired representational invariances to the model (e.g., invariance to photometric variations) and helps improve accuracy. Compared to image data, the appearance variations in videos are far more complex due to the additional temporal dimension. Yet, data augmentation methods for videos remain under-explored. This paper investigates various data augmentation strategies that capture different video invariances, including photometric, geometric, temporal, and actor/scene augmentations. When integrated with existing semi-supervised learning frameworks, we show that our data augmentation strategy leads to promising performance on the Kinetics-100/400, Mini-Something-v2, UCF-101, and HMDB-51 datasets in the low-label regime. We also validate our data augmentation strategy in the fully supervised setting and demonstrate improved performance.
Automatically Classifying Non-functional Requirements with Feature Extraction and Supervised Machine Learning Techniques: A Research Preview

Context and Motivation: In large projects, extracting the relevant NFR-information as per the stakeholder’s responsibility and needs can be time-consuming and challenging. Question/Problem: Classification of NFRs is one way to mitigate this problem. However, because of the size and complexity of the SRS, the manual classification of NFRs is considered time-consuming, labour-intensive, and error-prone. An automated solution is needed that provides a reliable and efficient classification of NFRs. Principal ideas/results: Using natural language processing and supervised machine learning (SML) algorithms, we investigate feature extraction techniques (i.e., POS-tagging based, BoW, and TF-IDF) to assess their efficacy in automated classification, in conjunction with the SML algorithms (such as: SVM, SGD SVM, LR, DT, Bagging DT, Extra Tree, RF, GNB, MNB, and BNB). Contribution: The proposed combinations: (i) SVM with TF-IDF, (ii) LR with POS and BoW, and (iii) MNB with BoW, all achieve precision and recall values greater than 0.85, and process execution time of less than 0.1 s. Comparison with related work is favourable as is preliminary validation using an industry dataset.
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks

The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems. However, graphs alone cannot capture the multi-level interactions present in many complex systems and the expressive power of such schemes was proven to be limited. To overcome these limitations, we propose Message Passing Simplicial Networks (MPSNs), a class of models that perform message passing on simplicial complexes (SCs). To theoretically analyse the expressivity of our model we introduce a Simplicial Weisfeiler-Lehman (SWL) colouring procedure for distinguishing non-isomorphic SCs. We relate the power of SWL to the problem of distinguishing non-isomorphic graphs and show that SWL and MPSNs are strictly more powerful than the WL test and not less powerful than the 3-WL test. We deepen the analysis by comparing our model with traditional graph neural networks (GNNs) with ReLU activations in terms of the number of linear regions of the functions they can represent. We empirically support our theoretical claims by showing that MPSNs can distinguish challenging strongly regular graphs for which GNNs fail and, when equipped with orientation equivariant layers, they can improve classification accuracy in oriented SCs compared to a GNN baseline.
SQL-Middleware: Enabling the Blockchain with SQL

With the development of blockchain, blockchain has a broad prospect as a new type of data management system. However, limited to the data modeling method of blockchain, the usability of blockchain is restricted; In addition, every blockchain system has its own native but naive interfaces, when developing based on the different blockchain systems, which will leads to low development efficiency and high development costs. In this study, we construct a SQL-Middleware for blockchain system to solve these problems. The SQL-Middleware first performs relational modeling of blockchain data, mapping the blockchain data into a relational table; On the basis of modeling the blockchain data, SQL-Middleware encapsulates a set of SQL interfaces for blockchain system, thus realizing the unification of interface access methods of different blockchain systems. At last, we implement the SQL-Middleware based on the open source blockchain system CITA. Demonstration shows that the SQL-Middleware greatly improves the data management capabilities of blockchain and simplifies the blockchain access steps.
Imaginative Networks: Tracing Connec­tions Among Early Modern Book Dedi­cations

This essay uses network metrics (centrality, density, clustering coefficients) to account for shifts in dedicatory practice resulting from political crises, religious turmoil, and changes in book production practices. It constructs a network from the names that appear in dedications of EEBO-TCP texts; names are detected using the linguistic markup from the EarlyPrint project. The essay argues that we learn more about early modern book history by constructing networks of all the names that appear in dedications, not just those of authors, printers, and patrons. The network includes a mixture of religious and political figures, literary personalities, fictional characters, and bookmaking professionals, because this is the full range of names that dedicatory practice covers in the period. By proceeding in this way, network metrics can account for a range of dedicatory phenomena, including Queen Elizabeth’s popularity on both sides of the political aisle long after her death and, especially, consolidation around non-contemporary names in dedicatory practice as a result of both the Civil War and the Restoration. The imaginative networks revealed by early modern dedications are organized mainly around untimely figures from the recent and distant past, but despite this the networks are sensitive to historical change, especially at moments of political and social crisis.
Compressed Wavelet Tensor Attention Capsule Network

Texture classification plays an important role for various computer vision tasks. Depending upon the powerful feature extraction capability, convolutional neural network (CNN)-based texture classification methods have attracted extensive attention. However, there still exist many challenges, such as the extraction of multilevel texture features and the exploration of multidirectional relationships. To address the problem, this paper proposes the compressed wavelet tensor attention capsule network (CWTACapsNet), which integrates multiscale wavelet decomposition, tensor attention blocks, and quantization techniques into the framework of capsule neural network. Specifically, the multilevel wavelet decomposition is in charge of extracting multiscale spectral features in frequency domain; in addition, the tensor attention blocks explore the multidimensional dependencies of convolutional feature channels, and the quantization techniques make the computational storage complexities be suitable for edge computing requirements. The proposed CWTACapsNet provides an efficient way to explore spatial domain features, frequency domain features, and their dependencies which are useful for most texture classification tasks. Furthermore, CWTACapsNet benefits from quantization techniques and is suitable for edge computing applications. Experimental results on several texture datasets show that the proposed CWTACapsNet outperforms the state-of-the-art texture classification methods not only in accuracy but also in robustness.
DESIGNING LEARNING MEDIA FOR BATAKNESE CUISINE USING MULTIMEDIA DEVELOPMENT LIFE CYCLE (MDLC) METHOD

Learning media are generally defined as tools, methods, and techniques that are used sequentially to channel messages and learning information so that communication and interaction between lecturers and students can be more effective in the process of education and teaching. With the advancement of technology, learning media are no longer limited in form and number and can be easily used in the teaching process. Thus, learning media is an important part of the education process, and is one aspect that must be mastered by each teacher to be able to carry out its professional functions. The purpose of this research is to design a learning media for Bataknese Cuisine using Multimedia Development Life Cycle (MDLC) method. The result of this study is Bataknese Cuisine learning application developed and built using Unity software. This application design is expected to provide information and teach the user about Bataknese Cuisine.
Hybrid Artificially Intelligent Multi-Layer Blockchain and Bitcoin Cryptology (AI-MLBBC)

This article presents an integrated secured framework of blockchain and bitcoin cryptology with the artificial intelligence of neural networks and machine learning. Recently blockchain has received special attention and been used as a new platform for digital information and to store encrypted data and process secure digital transactions. Furthermore, data on blockchain and bitcoin platforms are assumed to be highly encrypted and secured. Although blockchain and bitcoin databases are encrypted using private keys, many cryptocurrency bitcoin wallets have been reported hacked, which has resulted in losing millions of dollars. The artificial intelligent neural networks (AINN) algorithms possess the capability features of processing and operating encrypted data and will lead to minimal risk as a part of the blockchain to protect personal data and information as third wall defense against hacking. Simulation results and analysis demonstrates the effectiveness of this technique for protecting personal data and financial information.
Classification of Textures Using Pixel Range Calculation Method

A good texture classification algorithm can easily detect and classify different types of objects. Several texture classification techniques are there to match the similar objects and distinguish different types of objects or surfaces. In this article an improvised version of texture classification technique called Pixel Range Calculation (PRC) technique has been presented. The PRC method provides better classification accuracy and takes less time for computation as compared to other discussed methods. In this article the proposed method has been compared with two state of art methods called Multi Fractal Spectrum (MFS) and Gliding Box Method (GBM). The textural dataset of Brodatz, CUReT (Columbia-Utrecht Reflectance and Texture Database) and Describable Textures Dataset (DTD) have been taken for experiment. From experimental result it has been observed that, the PRC method outperforms other two methods by its matching accuracy of 98.54%, 97.44% and 97.19% for Brodatz, CUReT and DTD dataset respectively. From confusion matrix it is observed that the PRC method is most accurate one for classification.
Multimedia Design Principles for Microlearning

According to Mayer (2019), “meaningful learning occurs when the learner engages in appropriate cognitive processing during learning” (p. 464). Mayer’s (2009) Principles of Multimedia Learning are based on the view that the most effective learning materials are those that manage limited cognitive resources by emphasizing generative processing, handling cognitive processing, and limiting extraneous processing. The question then arises, how can this be achieved with microlearning? This chapter provides microlearning design tips based on the research-based Principles of Multimedia Learning and the Multimedia Design Principles for Effective Instructional Videos (Mayer, Fiorella, &amp; Stull, 2020). By applying the principles and keeping in mind key tenets of Cognitive Load Theory, upon which the principles are based, microlearning objects can be created to maximize learning through web-based multimedia.
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games

Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.
FEEL-IT: Emotion and Sentiment Classification for the Italian Language

While sentiment analysis is a popular task to understand people’s reactions online, we often need more nuanced information: is the post negative because the user is angry or sad? An abundance of approaches have been introduced for tackling these tasks, also for Italian, but they all treat only one of the tasks. We introduce FEEL-IT, a novel benchmark corpus of Italian Twitter posts annotated with four basic emotions: anger, fear, joy, sadness. By collapsing them, we can also do sentiment analysis. We evaluate our corpus on benchmark datasets for both emotion and sentiment classification, obtaining competitive results. We release an open-source Python library, so researchers can use a model trained on FEEL-IT for inferring both sentiments and emotions from Italian text.
Metrical Tagging in the Wild: Building and Annotating Poetry Corpora with Rhythmic Features

A prerequisite for the computational study of literature is the availability of properly digitized texts, ideally with reliable meta-data and ground-truth annotation.Poetry corpora do exist for a number of languages, but larger collections lack consistency and are encoded in various standards, while annotated corpora are typically constrained to a particular genre and/or were designed for the analysis of certain linguistic features (like rhyme).In this work, we provide large poetry corpora for English and German, and annotate prosodic features in smaller corpora to train corpus driven neural models that enable robust large scale analysis.We show that BiLSTM-CRF models with syllable embeddings outperform a CRF baseline and different BERT-based approaches.In a multi-task setup, particular beneficial task relations illustrate the inter-dependence of poetic features.A model learns foot boundaries better when jointly predicting syllable stress, aesthetic emotions and verse measures benefit from each other, and we find that caesuras are quite dependent on syntax and also integral to shaping the overall measure of the line.
Mode Effects’ Challenge to Authorship Attribution

The success of authorship attribution relies on the presence of linguistic features specific to individual authors. There is, however, limited research assessing to what extent authorial style remains constant when individuals switch from one writing modality to another. We measure the effect of writing mode on writing style in the context of authorship attribution research using a corpus of documents composed online (in a web browser) and documents composed offline using a traditional word processor. The results confirm the existence of a “mode effect” on authorial style. Online writing differs systematically from offline writing in terms of sentence length, word use, readability, and certain part-of-speech ratios. These findings have implications for research design and feature engineering in authorship attribution studies.