Reviewerly/exHarmony huggingface dataset:
A Survey on Segmentation Techniques for Image Processing

The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.
LayoutReader: Pre-training of Text and Layout for Reading Order Detection

Reading order detection is the cornerstone to understanding visually-rich documents (e.g., receipts and forms).Unfortunately, no existing work took advantage of advanced deep learning models because it is too laborious to annotate a large enough dataset.We observe that the reading order of WORD documents is embedded in their XML metadata; meanwhile, it is easy to convert WORD documents to PDFs or images.Therefore, in an automated manner, we construct ReadingBank, a benchmark dataset that contains reading order, text, and layout information for 500,000 document images covering a wide spectrum of document types.This first-ever large-scale dataset unleashes the power of deep neural networks for reading order detection.Specifically, our proposed LayoutReader captures the text and layout information for reading order prediction using the seq2seq model.It performs almost perfectly in reading order detection and significantly improves both open-source and commercial OCR engines in ordering text lines in their results in our experiments.The dataset and models are publicly available at https: //aka.ms/layoutreader.
An Extensive Literature Review on Underwater Image Colour Correction

The topic of underwater (UW) image colour correction and restoration has gained significant scientific interest in the last couple of decades. There are a vast number of disciplines, from marine biology to archaeology, that can and need to utilise the true information of the UW environment. Based on that, a significant number of scientists have contributed to the topic of UW image colour correction and restoration. In this paper, we try to make an unbiased and extensive review of some of the most significant contributions from the last 15 years. After considering the optical properties of water, as well as light propagation and haze that is caused by it, the focus is on the different methods that exist in the literature. The criteria for which most of them were designed, as well as the quality evaluation used to measure their effectiveness, are underlined.
Development Viewboard As an Information Media at Official Site Asosiation

Along the progress of the era and technological developments, making the human lifestyle cannot be separated from its use. In digital era nowadays, technology is used to make human needs easily. A website is important for the progress of an Association. One of the ways that the Association's Website can grow is by maintaining the Website. The APTISI website is designed using the WordPress CMS (Content Management System). WordPress is an open system software and designed by the PHP programming language and MySQL database. This study is aim to develop a website by increasing the credibility of the association. In order to improve the statistics on website visits, it is necessary to have innovation and creativity in viewing and posting news on the website. The APTISI website make use of probability calculations and statistical data using a monitoring tool called statcounter. This tool functions to count the number of visitors based on unique visits which can be seen in the statistics column. In addition, the use of this statcounter is useful as a standard reference for website admins to update the viewboard on a better website in the future.standard for website admins to update the appearance of a better website in the future
On the Round Complexity of Secure Quantum Computation

We construct the first constant-round protocols for secure quantum computation in the two-party (2PQC) and multi-party (MPQC) settings with security against malicious adversaries. Our protocols are in the common random string (CRS) model. Finally, we perform a preliminary investigation into two-round secure quantum computation where each party must obtain output. On the negative side, we identify a broad class of simulation strategies that suffice for classical two-round secure computation that are unlikely to work in the quantum setting. Next, as a proof-of-concept, we show that two-round secure quantum computation exists with respect to a quantum oracle.
PVNAS: 3D Neural Architecture Search with Point-Voxel Convolution

3D neural networks are widely used in real-world applications (e.g., AR/VR headsets, self-driving cars). They are required to be fast and accurate; however, limited hardware resources on edge devices make these requirements rather challenging. Previous work processes 3D data using either voxel-based or point-based neural networks, but both types of 3D models are not hardware-efficient due to the large memory footprint and random memory access. In this paper, we study 3D deep learning from the efficiency perspective. We first systematically analyze the bottlenecks of previous 3D methods. We then combine the best from point-based and voxel-based models together and propose a novel hardware-efficient 3D primitive, Point-Voxel Convolution (PVConv). We further enhance this primitive with the sparse convolution to make it more effective in processing large (outdoor) scenes. Based on our designed 3D primitive, we introduce 3D Neural Architecture Search (3D-NAS) to explore the best 3D network architecture given a resource constraint. We evaluate our proposed method on six representative benchmark datasets, achieving state-of-the-art performance with 1.8-23.7× measured speedup. Furthermore, our method has been deployed to the autonomous racing vehicle of MIT Driverless, achieving larger detection range, higher accuracy and lower latency.
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models

The task of learning from only a few examples (called a few-shot setting) is of key importance and relevance to a real-world setting. For question answering (QA), the current state-of-the-art pre-trained models typically need fine-tuning on tens of thousands of examples to obtain good results. Their performance degrades significantly in a few-shot setting (< 100 examples). To address this, we propose a simple fine-tuning framework that leverages pre-trained text-to-text models and is directly aligned with their pre-training framework. Specifically, we construct the input as a concatenation of the question, a mask token representing the answer span and a context. Given this input, the model is fine-tuned using the same objective as that of its pre-training objective. Through experimental studies on various few-shot configurations, we show that this formulation leads to significant gains on multiple QA benchmarks (an absolute gain of 34.2 F1 points on average when there are only 16 training examples). The gains extend further when used with larger models (Eg:- 72.3 F1 on SQuAD using BART-large with only 32 examples) and translate well to a multilingual setting . On the multilingual TydiQA benchmark, our model outperforms the XLM-Roberta-large by an absolute margin of upto 40 F1 points and an average of 33 F1 points in a few-shot setting (<= 64 training examples). We conduct detailed ablation studies to analyze factors contributing to these gains.
ACP++: Action Co-Occurrence Priors for Human-Object Interaction Detection

A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples, resulting in training sets with a long-tailed distribution. The lack of positive labels can lead to low classification accuracy for these classes. Towards addressing this issue, we observe that there exist natural correlations and anti-correlations among human-object interactions. In this paper, we model the correlations as action co-occurrence matrices and present techniques to learn these priors and leverage them for more effective training, especially on rare classes. The efficacy of our approach is demonstrated experimentally, where the performance of our approach consistently improves over the state-of-the-art methods on both of the two leading HOI detection benchmark datasets, HICO-Det and V-COCO.
Research communities in cyber security: A comprehensive literature review

In order to provide a coherent overview of cyber security research, the Scopus academic abstract and citation database was mined to create a citation graph of 98,373 authors active in the field between 1949 and early 2020. The Louvain community detection algorithm was applied to the graph in order to identify existing research communities. The analysis discovered twelve top-level communities: access control, authentication, biometrics, cryptography (I & II), cyber–physical systems, information hiding, intrusion detection, malwares, quantum cryptography, sensor networks, and usable security. These top-level communities were in turn composed of a total of 80 sub-communities. The analysis results are presented for each community in descriptive text, sub-community graphs, and tables with, for example, the most-cited papers and authors. A comparison between the detected communities and topical areas defined by other related work, is also presented, demonstrating a greater researcher emphasis on cryptography, quantum cryptography, information hiding and biometrics, at the expense of laws and regulation, risk management and governance, and security software lifecycle.
DuRecDial 2.0: A Bilingual Parallel Corpus for Conversational Recommendation

In this paper, we provide a bilingual parallel human-to-human recommendation dialog dataset (DuRecDial 2.0) to enable researchers to explore a challenging task of multilingual and cross-lingual conversational recommendation.The difference between DuRecDial 2.0 and existing conversational recommendation datasets is that the data item (Profile, Goal, Knowledge, Context, Response) in DuRecDial 2.0 is annotated in two languages, both English and Chinese, while other datasets are built with the setting of a single language.We collect 8.2k dialogs aligned across English and Chinese languages (16.5k dialogs and 255k utterances in total) that are annotated by crowdsourced workers with strict quality control procedure.We then build monolingual, multilingual, and cross-lingual conversational recommendation baselines on DuRecDial 2.0.Experiment results show that the use of additional English data can bring performance improvement for Chinese conversational recommendation, indicating the benefits of DuRecDial 2.0.Finally, this dataset provides a challenging testbed for future studies of monolingual, multilingual, and cross-lingual conversational recommendation.
Sentiment Analysis of Review Text Based on BiGRU-Attention and Hybrid CNN

Convolutional neural networks (CNN), recurrent neural networks (RNN), attention, and their variants are extensively applied in the sentiment analysis, and the effect of fusion model is expected to be better. However, fusion model is confronted with some problems such as complicated structure, excessive trainable parameters, and long training time. The classification effect of traditional model with cross entropy loss as loss function is undesirable since sample category imbalance as well as ease and difficulty of sample classification is not taken into account. In order to solve these problems, the model BiGRU-Att-HCNN is proposed on the basis of bidirectional gated recurrent unit (BiGRU), attention, and hybrid convolutional neural networks. In this model, BiGRU and self-attention are combined to acquire global information, and key information weight is supplemented. Two parallel convolutions (dilated convolution and standard convolution) are used to obtain multi-scale characteristic information with relatively less parameters, and the standard convolution is replaced with depthwise separable convolution with two-step calculations. Traditional max-pooling and average-pooling are discarded, and global average pooling is applied to substitute the pooling layer and the fully-connected layer simultaneously, making it possible to substantially decrease the number of model parameters and reduce over-fitting. In our model, focal loss is used as the loss function to tackle the problems of unbalanced sample categories and hard samples. Experimental results illustrate that in terms of multiple indicators, our model outperforms the 15 benchmark models, even with intermediate number of trainable parameters.
The Applications of Blockchain in Artificial Intelligence

There has been increased interest in applying artificial intelligence (AI) in various settings to inform decision-making and facilitate predictive analytics. In recent times, there have also been attempts to utilize blockchain (a peer-to-peer distributed system) to facilitate AI applications, for example, in secure data sharing (for model training), preserving data privacy, and supporting trusted AI decision and decentralized AI. Hence, in this paper, we perform a comprehensive review of how blockchain can benefit AI from these four aspects. Our analysis of 27 English-language articles published between 2018 and 2021 identifies a number of research challenges and opportunities.
Cross-Domain Graph Anomaly Detection

Anomaly detection on attributed graphs has received increasing research attention lately due to the broad applications in various high-impact domains, such as cybersecurity, finance, and healthcare. Heretofore, most of the existing efforts are predominately performed in an unsupervised manner due to the expensive cost of acquiring anomaly labels, especially for newly formed domains. How to leverage the invaluable auxiliary information from a labeled attributed graph to facilitate the anomaly detection in the unlabeled attributed graph is seldom investigated. In this study, we aim to tackle the problem of cross-domain graph anomaly detection with domain adaptation. However, this task remains nontrivial mainly due to: 1) the data heterogeneity including both the topological structure and nodal attributes in an attributed graph and 2) the complexity of capturing both invariant and specific anomalies on the target domain graph. To tackle these challenges, we propose a novel framework COMMANDER for cross-domain anomaly detection on attributed graphs. Specifically, COMMANDER first compresses the two attributed graphs from different domains to low-dimensional space via a graph attentive encoder. In addition, we utilize a domain discriminator and an anomaly classifier to detect anomalies that appear across networks from different domains. In order to further detect the anomalies that merely appear in the target network, we develop an attribute decoder to provide additional signals for assessing node abnormality. Extensive experiments on various real-world cross-domain graph datasets demonstrate the efficacy of our approach.
Remote sensing satellite-based structural/alteration mapping for gold exploration in the Ketté goldfield, Eastern Cameroon

In-situ mineral prospecting studies in the tropics face challenging environmental conditions leading to paucity of data and structural mapping of new mineralizations. Here we present the case of the tropical mining goldfield of Ketté in Cameroon to demonstrate remote sensing techniques for mineral exploration purposes. In this investigation Visible Near Infra-Red (VNIR) and Short Wave Infra-Red (SWIR) bands of Landsat-8 (OLI) and Landsat-7 (ETM+) images were used with field data for lineaments and hydrothermal alterations mapping. Semi-automatic and automatic extraction methods were applied through sobel directional filters to detect the contours in the image. PCA, MNF transformation and a band ratio of 4/2, 6/5 and 6/7 were applied to map alteration minerals. The result revealed NE-SW to ENE-WSW main trends. Gold mineralization occurrences are spatially associated with WNW-ESE to NW-SE (110°–140°) ranging lineaments/faults and show a strong correlation with medium to high lineament density zones. Hydrothermal alteration minerals are spatially associated closely with gold occurrences and known mining sites that are structurally controlled by the NE-SW to ENE-WSW shear zone. The high-prospect zones for gold exploration are located along the Mama and Molé fault, Ngoubésseli, Boubara-Koumbé Tiko, Gwé, and Ndambi I-Tezoukpé.
On the Development of a Personalized Augmented Reality Spatial Ability Training Mobile Application

Augmented Reality has been integrated in educational settings in the field of engineering. Prior research has examined the learning outcomes and the pedagogical affordances of this technology. However, training undergraduate engineers, from diverse knowledge level, requires customized training approach, tailored to the individual learning pace. In this paper, we present PARSAT (Personalized Augmented Reality Spatial Ability Training), which is a mobile Augmented Reality application for the enhancement of students’ spatial visualization skills. The application takes into account the theoretical contents of engineering design, deployed through video tutorials, and student-computer interaction with 3D objects. Students interpret different views of a 3D object, which are represented on their mobile screen. PARSAT efficaciously strengthens students’ recognition of spatial structures and views, adjusted to the fulfillment of their personal needs. In terms of personalization, PARSAT consists of different levels, which do not follow a linear flow, as each student takes part in a different sequence of activities, according to their time spent in the 3D object manipulation, and their assessment scores at the end of each level. Furthermore, an agent is used to analyze students’ knowledge level, and send them feedback. The system reduces unnecessary cognitive load and, at the same time, improves students learning experience in learning engineering drawing.
Systematic Inequalities in Language Technology Performance across the World’s Languages

Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development.While the performance of NLP methods has grown enormously over the last decade, this progress has been restricted to a minuscule subset of the world's ≈6,500 languages.We introduce a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP.Our analyses involve the field at large, but also more in-depth studies on both user-facing technologies (machine translation, language understanding, question answering, text-to-speech synthesis) as well as foundational NLP tasks (dependency parsing, morphological inflection).In the process, we (1) quantify disparities in the current state of NLP research, (2) explore some of its associated societal and academic factors, and (3) produce tailored recommendations for evidencebased policy making aimed at promoting more global and equitable language technologies. 1
Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians

In spite of excellent performance of deep learning-based computer vision algorithms, they are not suitable for real-time surveillance to detect abnormal behavior because of very high computational complexity. In this paper, we propose a real-time surveillance system for abnormal behavior analysis in a closed-circuit television (CCTV) environment by constructing an algorithm and system optimized for a CCTV environment. The proposed method combines pedestrian detection and tracking to extract pedestrian information in real-time, and detects abnormal behaviors such as intrusion, loitering, fall-down, and violence. To analyze an abnormal behavior, it first determines intrusion/loitering through the coordinates of an object and then determines fall-down/violence based on the behavior pattern of the object. The performance of the proposed method is evaluated using an intelligent CCTV data set distributed by Korea Internet and Security Agency (KISA).
Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems considering the observation of thousands of pieces, which often present inconsistencies when specified by the product engineering team. In this investigation, we propose a solution for a real case study. We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoids, Fuzzy C-Means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Self-Organizing Maps (SOM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). We observed that the methods could automatically perform the grouping of parts considering physical characteristics present in the material master data, allowing anomaly detection and identification, which can consequently lead to cost reduction. The computational results indicate that the Hierarchical approach presented the best performance on 1 of 6 evaluation metrics and was the second place on four others indexes, considering the Borda count method. The K-Medoids win for most metrics, but it was the second best positioned due to its bad performance regarding SI-index. By the end, this proposal allowed identify mistakes in the specification and pricing of some items in the company.