Reviewerly/exHarmony huggingface dataset:
MFCFSiam: A Correlation-Filter-Guided Siamese Network with Multifeature for Visual Tracking

With the development of deep learning, trackers based on convolutional neural networks (CNNs) have made significant achievements in visual tracking over the years. The fully connected Siamese network (SiamFC) is a typical representation of those trackers. SiamFC designs a two-branch architecture of a CNN and models’ visual tracking as a general similarity-learning problem. However, the feature maps it uses for visual tracking are only from the last layer of the CNN. Those features contain high-level semantic information but lack sufficiently detailed texture information. This means that the SiamFC tracker tends to drift when there are other same-category objects or when the contrast between the target and the background is very low. Focusing on addressing this problem, we design a novel tracking algorithm that combines a correlation filter tracker and the SiamFC tracker into one framework. In this framework, the correlation filter tracker can use the Histograms of Oriented Gradients (HOG) and color name (CN) features to guide the SiamFC tracker. This framework also contains an evaluation criterion which we design to evaluate the tracking result of the two trackers. If this criterion finds the SiamFC tracker fails in some cases, our framework will use the tracking result from the correlation filter tracker to correct the SiamFC. In this way, the defects of SiamFC’s high-level semantic features are remedied by the HOG and CN features. So, our algorithm provides a framework which combines two trackers together and makes them complement each other in visual tracking. And to the best of our knowledge, our algorithm is also the first one which designs an evaluation criterion using correlation filter and zero padding to evaluate the tracking result. Comprehensive experiments are conducted on the Online Tracking Benchmark (OTB), Temple Color (TC128), Benchmark for UAV Tracking (UAV-123), and Visual Object Tracking (VOT) Benchmark. The results show that our algorithm achieves quite a competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.
Team_Swift at SemEval-2020 Task 9: Tiny Data Specialists through Domain-Specific Pre-training on Code-Mixed Data

Code-mixing is an interesting phenomenon where the speaker switches between two or more languages in the same text.In this paper, we describe an unconventional approach to tackling the SentiMix Hindi-English challenge (uid: aditya_malte).Instead of directly fine-tuning large contemporary Transformer models, we train our own domain-specific embeddings and use them for downstream tasks.We also discuss how this technique provides comparable performance while making for a much more deployable and lightweight model.It should be noted that we have achieved the stated results without using any ensembling techniques, thus respecting a paradigm of efficient and production-ready NLP.
The ADAPT Centre’s Participation in WAT 2020 English-to-Odia Translation Task

This paper describes the ADAPT Centre sub-missions to WAT 2020 for the English-to-Odia translation task. We present the approaches that we followed to try to build competitive machine translation (MT) systems for English-to-Odia. Our approaches include monolingual data selection for creating synthetic data and identifying optimal sets of hyperparameters for the Transformer in a low-resource scenario. Our best MT system produces 4.96BLEU points on the evaluation test set in the English-to-Odia translation task.
Introduction of 5G as a Next-generation Mobile Network

5G aren't just about significantly improving network connectivity. It's a next-generation mobile network that promises to be a game changer in the way we live. The true breakthrough of 5G is the capacity of up to 1,000 5G connected devices per person. It covers all 7 billion people worldwide. One of the great expectations for the future is that not only will all humans be connected to the Internet, but most items of our lives will also be connected. With 5G, coverage will be improved, capacity will be increased, latency will be reduced, and data speed will significantly improve. Future 5G solutions will outperform current 4G mobile networks in several ways. Significant improvements in device density, transfer speeds and latencies, and a 90% reduction in power consumption are just a few of the 5G goals.&#x0D; On the other hand, the harmful effects of frequency radiation have already been proven. Even before 5G was proposed, dozens of petitions and appeals by international scientists, including the Flyberger appeal signed by more than 3,000 doctors, stopped the expansion of wireless technology and made new base stations. Requested a moratorium. Negative microbiological effects have also been recorded. Government regulators will consider deploying 5G, especially with the additional infrastructure needed to expand their networks. 5G deployments need to address both standard and advanced cybersecurity threats. It is the responsibility of the carrier and network consortium to provide customers with digital safety nets, except that customer complacency can be an issue as well.
Seeing Both the Forest and the Trees: Multi-head Attention for Joint Classification on Different Compositional Levels

In natural languages, words are used in association to construct sentences.It is not words in isolation, but the appropriate combination of hierarchical structures that conveys the meaning of the whole sentence.Neural networks can capture expressive language features; however, insights into the link between words and sentences are difficult to acquire automatically.In this work, we design a deep neural network architecture that explicitly wires lower and higher linguistic components; we then evaluate its ability to perform the same task at different hierarchical levels.Settling on broad text classification tasks, we show that our model, MHAL, learns to simultaneously solve them at different levels of granularity by fluidly transferring knowledge between hierarchies.Using a multi-head attention mechanism to tie the representations between single words and full sentences, MHAL systematically outperforms equivalent models that are not incentivized towards developing compositional representations.Moreover, we demonstrate that, with the proposed architecture, the sentence information flows naturally to individual words, allowing the model to behave like a sequence labeller (which is a lower, word-level task) even without any word supervision, in a zero-shot fashion.
Cyclic Codes via the General Two-Prime Generalized Cyclotomic Sequence of Order Two

Suppose that <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>q</mi> </math> are two distinct odd prime numbers with <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>n</mi> <mo>=</mo> <mi>p</mi> <mi>q</mi> </math> . In this paper, the uniform representation of general two-prime generalized cyclotomy with order two over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <msub> <mrow> <mi>ℤ</mi> </mrow> <mrow> <mi>n</mi> </mrow> </msub> </math> was demonstrated. Based on this general generalized cyclotomy, a type of binary sequences defined over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msub> <mrow> <mi mathvariant="double-struck">F</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> </math> was presented and their minimal polynomials and linear complexities were derived, where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>l</mi> <mo>=</mo> <msup> <mrow> <mi>r</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msup> </math> with a prime number <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>r</mi> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi mathvariant="normal">gcd</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>l</mi> <mo>,</mo> <mi>n</mi> </mrow> </mfenced> <mo>=</mo> <mn>1</mn> </math> . The results have indicated that the linear complexities of these sequences are high without any special requirements on the prime numbers. Furthermore, we employed these sequences to obtain a few cyclic codes over <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <msub> <mrow> <mi mathvariant="double-struck">F</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> </math> with length <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mi>n</mi> </math> and developed the lower bounds of the minimum distances of many cyclic codes. It is important to stress that some cyclic codes in this paper are optimal.
Delexicalized Paraphrase Generation

We present a neural model for paraphrasing and train it to generate delexicalized sentences. We achieve this by creating training data in which each input is paired with a number of reference paraphrases. These sets of reference paraphrases represent a weak type of semantic equivalence based on annotated slots and intents. To understand semantics from different types of slots, other than anonymizing slots, we apply convolutional neural networks (CNN) prior to pooling on slot values and use pointers to locate slots in the output. We show empirically that the generated paraphrases are of high quality, leading to an additional 1.29% exact match on live utterances. We also show that natural language understanding (NLU) tasks, such as intent classification and named entity recognition, can benefit from data augmentation using automatically generated paraphrases.
Design Patterns and Electric Vehicle Charging Software

The development and maintenance of complex software systems, with ever-changing requirements has benefited from the implementation of design patterns, which ensure a higher degree of maintainability. The present article illustrates the essential role of design patterns in sketching the software architecture for an electric vehicle charging management platform. We have integrated a series of design patterns to create a solid base structure for the API (Application Programming Interface). Furthermore, we have explored cloud design patterns in the deployment process to ensure a proper multi-tenant cloud application with the best possible tenant isolation for the cost. The aim of this paper is to offer readers an introduction in the case study theme, to describe a complex platform development through design patterns. With proper examples from real projects debated in the industry. The paper demonstrates the design patterns applicability from software level to cloud resources plan and advocates for a high-quality solution in every segment of project development. In conclusion, the results are promising, and the functionality of the established methods on this type of platforms will be evaluated during the implementation period. Overall, design patterns have proven to be essential in the development process, ensuring effective team communication and the delivery of qualitative software solutions.
Chain Code-Based Occupancy Map Coding for Video-Based Point Cloud Compression

In video-based point cloud compression (V-PCC), occupancy map video is utilized to indicate whether a 2-D pixel corresponds to a valid 3-D point or not. In the current design of V-PCC, the occupancy map video is directly compressed losslessly with High Efficiency Video Coding (HEVC). However, the coding tools in HEVC are specifically designed for natural images, thus unsuitable for the occupancy map. In this paper, we present a novel quadtree-based scheme for lossless occupancy map coding. In this scheme, the occupancy map is firstly divided into several coding tree units (CTUs). Then, the CTU is divided into coding units (CUs) recursively using a quadtree. The quadtree partition is terminated when one of the three conditions is satisfied. Firstly, all the pixels have the same value. Secondly, the pixels in the CU only have two kinds of values and they can be separated by a continuous edge whose endpoints lie on the side of the CU. The continuous edge is then coded using chain code. Thirdly, the CU reaches the minimum size. This scheme simplifies the design of block partitioning in HEVC and designs simpler yet more effective coding tools. Experimental results show significant reduction of bit-rate and complexity compared with the occupancy map coding scheme in V-PCC. In addition, this scheme is also very efficient to compress the semantic map.
Sumterms, Summands, Sumtuples, and Sums and the Meta-Arithmetic of Summation

Sumterms are introduced as syntactic entities, and sumtuples are introduced as semantic entities. Equipped with these concepts a new description is obtained of the notion of a sum as (the name for) a role which can be played by a number. Sumterm splitting operators are introduced and it is argued that without further precautions the presence of these operators gives rise to an instance of the so-called sum splitting paradox. A survey of solutions to the sum splitting paradox is given.
Study on Student-centered artificial intelligence online teaching + home learning model during the COVID-19 epidemic

With the rapid development of Internet technology, traditional online learning can no longer meet the adaptive learning needs of students, and smart education concepts, such as mushrooms, use data generated by traditional platforms to use machine learning and depth. Artificial intelligence technology with learning as a means has gradually become a new research hotspot through re-analysis technology. How to further use these big data resources for adaptive learning and push to improve the quality of student training has become an important issue in the current research field. For the protection of students' learning during the COVID-19 epidemic prevention and control, national universities, primary schools and secondary schools solved the problem of "Classes Suspended but Learning Continue" through online teaching. Students learn online at home and the family plays a vital role as a special classroom. Based on the analysis of the factors affecting home study, this article compares the live broadcast platforms and constructs a student-centered network broadcast + home learning model under the epidemic situation. After the implementation effect investigation, the evaluation effect is good. It is hoped that this model can provide a reference for teachers and students in the new situation and solve some problems currently facing online teaching at home.
Strain and Displacement Measurements in Reinforced Self-Compacting Concrete Beams with Openings Using Digital Image Correlation Technique

In structural engineering, strain and displacement are critical parameters, and measuring these parameters outside of the laboratory is a challenge for concrete researchers. Recent advances have enabled digital image correlation (DIC) to calculate the concrete surface displacements of chosen targets in a series of images recorded during an experiment. This paper presents a comparison between traditional and optical measurements to evaluate the efficiency of the DIC technique in recording the deformations of reinforced concrete beams. Tests were perfomed on two small-scale reinforced self-compacting concrete beams with small circular openings in 3-point bending.
CS-NET at SemEval-2020 Task 4: Siamese BERT for ComVE

In this paper, we describe our system for Task 4 of SemEval 2020, which involves differentiating between natural language statements that confirm to common sense and those that do not.The organizers propose three subtasks -first, selecting between two sentences, the one which is against common sense.Second, identifying the most crucial reason why a statement does not make sense.Third, generating novel reasons for explaining the against common sense statement.Out of the three subtasks, this paper reports the system description of subtask A and subtask B. This paper proposes a model based on transformer neural network architecture for addressing the subtasks.The novelty in work lies in the architecture design, which handles the logical implication of contradicting statements and simultaneous information extraction from both sentences.We use a parallel instance of transformers, which is responsible for a boost in the performance.We achieved an accuracy of 94.8% in subtask A and 89% in subtask B on the test set.
Joint Transformer/RNN Architecture for Gesture Typing in Indic Languages

Gesture typing is a method of typing words on a touch-based keyboard by creating a continuous trace passing through the relevant keys.This work is aimed at developing a keyboard that supports gesture typing in Indic languages.We begin by noting that when dealing with Indic languages, one needs to cater to two different sets of users: (i) users who prefer to type in the native Indic script (Devanagari, Bengali, etc.) and (ii) users who prefer to type in the English script but want the transliterated output in the native script.In both cases, we need a model that takes a trace as input and maps it to the intended word.To enable the development of these models, we create and release two datasets.First, we create a dataset containing keyboard traces for 193,658 words from 7 Indic languages.Second, we curate 104,412 English-Indic transliteration pairs from Wikidata across these languages.Using these datasets we build a model that performs path decoding, transliteration and transliteration correction.Unlike prior approaches, our proposed model does not make co-character independence assumptions during decoding.The overall accuracy of our model across the 7 languages varies from 70-95%.
Image Classification of Tourist Attractions with K-Nearest Neighbor, Logistic Regression, Random Forest, and Support Vector Machine

K-Nearest Neighbor (KNN), Logistic regression (LR), Random Forest (RF), and Support Vector Machine (SVM) are four methods of identification. The methods are widely used in various research in data mining, especially classifications in recent years. We have used the four classification methods in the study to classify images of five natural attractions, namely Danau Toba (North Sumatra), Nusa Penida (Bali), Raja Ampat (West Papua), Tanah Lot (Bali), and Wakatobi (Southeast Sulawesi). Our research results have concluded that the Logistic Regression method's performance has the best performance in classifying natural images as done in this research. The LR method can classify images that other methods such as kNN, SVM, and RF cannot be correctly classified. However, SVM also shows good performance by only making one error in the classification results; it can even be corrected using the Linear Kernel. In general, it is shown that the LR method has the highest precision value of 100%, followed by the method of kNN and SVM with a precision of 91.9% and RF with a precision of 81.9%. Variations of the variables used in the experiment also determine each method's precision. Chebyshev Metric has the highest precision value in the kNN method, and Ridge Regularization has the highest precision value in the LR method. The number of best on the RF method is 11, and Linear Kernel is the Kernel that gets the best precision value on the SVM method.
Improved Named Entity Recognition in Turkish News via Word Lookup Methods

Named Entity Recognition (NER) is a natural language processing task for extracting and classifying important entities such as person, location, and organization. In this paper, we propose a model including methods to lookup word vectors in word embeddings for the Turkish language. We study the proposed methods comprehensively through extensive experiments and out-of-vocabulary analysis. The proposed model only uses word embeddings and character representation via Conditional Random Fields followed by Bidirectional Long Short Memory to tag the sequence of words with the named entity tags. The proposed model achieves 94.05% F1 score on the most commonly used news dataset for the Turkish NER, which is the state of the art in the literature.
Shape Analysis of Segmentation Variability

Patient-specific cardiac simulation rely on accurate geometric models extracted from medical images.Segmentation of cardiac images is a key, yet possibly error-prone part of patient-specific simulations, e.g., heart propagation models, ECG forward simulation, and ECG Imaging.In this study, we performed shape analysis on multiple segmentations of the same patient to quantify variability.We found that segmentation shape varied most in the basal region of the ventricles and the right ventricular outflow tract in all three structures, which could have significant impact on pipelines that depend on geometric models.The statistical shape-model generated using ShapeWorks provides a pathway to subsequently quantify the impact of the segmentation variability on modeling pipelines with uncertainty quantification.
Teaching in Library and Information Studies: Creating videos to provide educational content

COVID-19 has forced several changes in the teaching of Library and Information Studies. In this article we explain our experience in creating and publishing videos as educational content at the University of Barcelona. Students having to learn by distance teaching have different needs in regard to the provision of educational materials. Similarly, professors need to give the best possible experience to distance students when face-to-face courses are not possible. This leads to the question of the roles that information literacy and digital literacy play, especially for professors.
Developing Japanese Ikebana as a Digital Painting Tool via AI

In this research, we have carried out various experiments to perform mutual transformation between a domain of Ikebana (Japanese traditional flower arrangement) photos and other domains of images (landscapes, animals, portraits) to create new artworks via CycleGAN, a variation of GANs (Generative Adversarial Networks) - new AI technology that can perform deep learning with less training data. With the capability of achieving transformation between two image sets using CycleGAN, we obtained several interesting results in which Ikebana plays the role of a digital painting tool due to the flexibility and minimality of the Japanese culture form. Our experiments show that Ikebana can be developed as a painting tool in digital art with the help of CycleGAN and opens a new way to create digital artworks of high-abstracted level by applying AI techniques to elements from traditional culture.
Powerlifting score prediction using a machine learning method

This research discusses an interesting topic, using artificial intelligence methods to predict the score of powerlifters. We collected the characteristics of powerlifters, and then used the reservoir computing extreme learning machine to build a predictive model. In order to further improve the prediction results, we propose a method to optimize the reservoir computing extreme learning machine using the whale optimization algorithm. Experimental results show that our proposed method can effectively predict the score of powerlifters with the coefficient of determination value is 0.7958 and root-mean-square error of prediction value is 16.73. This provides a reliable basis for experts to judge the results before the competition.