Reviewerly/exHarmony huggingface dataset:
Feature Fusion and Center Aggregation for Visible-Infrared Person Re-Identification

The visible-infrared pedestrian re- identification (VI Re-ID) task aims to match cross-modality pedestrian images with the same labels. Most current methods focus on mitigating the modality discrepancy by adopting a two-stream network and identity supervision. Based on current methods, we propose a novel feature fusion and center aggregation learning network (<inline-formula> <tex-math notation="LaTeX">$F^{2}$ </tex-math></inline-formula>CALNet) for cross-modality pedestrian re- identification. <inline-formula> <tex-math notation="LaTeX">$F^{2}$ </tex-math></inline-formula>CALNet focuses on learning modality-irrelevant features by simultaneously reducing inter-modality discrepancies and increasing the inter-identity variations in a single framework. Specifically, we first adopt a two-stream backbone network to extract modality-independent and modality-shared information. Then, we embed modality mitigation modules in a two-stream network to learn feature maps that are stripped of the modality information. Finally, we devise a feature fusion and center aggregation learning module, which first merges two different granularity features to learn distinguishing features, then, we organize two kinds of center-based loss functions to reduce the intra-identity inter- and intra-modality differences and increase inter-identity variations by simultaneously pulling the features of the same identity close to their centers and pushing far away the centers of different identities. Extensive experiments on two public cross-modality datasets (SYSU-MM01 and RegDB) show that <inline-formula> <tex-math notation="LaTeX">$F^{2}$ </tex-math></inline-formula>CALNet is superior to the state-of-the-art approaches. Furthermore, on the SYSU-MM01 datasets, our model outperforms the baseline by 5.52&#x0025; and 4.25&#x0025; for the accuracy of rank1 and mAP, respectively.
Community Impact on a Cryptocurrency: Twitter Comparison Example Between Dogecoin and Litecoin

Context: The third generation of cryptocurrencies gathers cryptocurrencies that are as diverse as the market is big (e.g., Dogecoin or Litecoin). While Dogecoin is seen as a memecoin, the other gathers a very different category of investors. To our knowledge, no study has independently assessed the crypto community’s economical impact on these cryptocurrencies. Furthermore, various methodological possibilities exist to forecast cryptocurrency price—mainly coming from online communities. Method: Our study has retrospectively studied (from 01/01/2015 to 03/11/2021)—using open access data—the association strength (using normalized mutual information) and the linear correlation (using Pearson’s correlation) between Twitter activity and cryptocurrency economical attributes. In addition, we have computed different models (ADF, ARIMA, and Interpretable MultiVvariable Long Short-Term Memory recurrent neural network) that forecast past price values and assessed their precision. Findings and conclusions: While the average Dogecoin transaction value is impacted by tweets, tweets are impacted by Litecoin transactions number and average Litecoin transaction value. Tweet number is impacted by Dogecoin whale behavior, but no significant relationship was found between Litecoin whales and tweets. The forecasting error resulting from our ARIMA (0,0,0) models was 0.08% (with Litecoin) and 0.22% (with Dogecoin). Therefore, those are just the beginning of scientific findings that may lead to building a trading robot based on these results. However, in itself, this study is only for academic discussion, and conclusions need to be drawn by further research. The authors cannot be liable if any financial investment is made based on its conclusions.
Text pre-processing of multilingual for sentiment analysis based on social network data

&lt;span&gt;Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.&lt;/span&gt;
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark

Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulated in cloud APIs to serve over half a billion people worldwide and process over one hundred billion word generations per day. Thus, NLG APIs have already become essential profitable services in many commercial companies. Due to the substantial financial and intellectual investments, service providers adopt a pay-as-you-use policy to promote sustainable market growth. However, recent works have shown that cloud platforms suffer from financial losses imposed by model extraction attacks, which aim to imitate the functionality and utility of the victim services, thus violating the intellectual property (IP) of cloud APIs. This work targets at protecting IP of NLG APIs by identifying the attackers who have utilized watermarked responses from the victim NLG APIs. However, most existing watermarking techniques are not directly amenable for IP protection of NLG APIs. To bridge this gap, we first present a novel watermarking method for text generation APIs by conducting lexical modification to the original outputs. Compared with the competitive baselines, our watermark approach achieves better identifiable performance in terms of p-value, with fewer semantic losses. In addition, our watermarks are more understandable and intuitive to humans than the baselines. Finally, the empirical studies show our approach is also applicable to queries from different domains, and is effective on the attacker trained on a mixture of the corpus which includes less than 10% watermarked samples.
LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection

As of today, the best accuracy in line segment detection (LSD) is achieved by algorithms based on convolutional neural networks -CNNs.Unfortunately, these methods utilize deep, heavy networks and are slower than traditional model-based detectors.In this paper we build an accurate yet fast CNNbased detector, LSDNet, by incorporating a lightweight CNN into a classical LSD detector.Specifically, we replace the first step of the original LSD algorithm -construction of line segments heatmap and tangent field from raw image gradients -with a lightweight CNN, which is able to calculate more complex and rich features.The second part of the LSD algorithm is used with only minor modifications.Compared with several modern line segment detectors on standard Wireframe dataset, the proposed LSDNet provides the highest speed (among CNN-based detectors) of 214 FPS with a competitive accuracy of 78 F H .Although the best-reported accuracy is 83 F H at 33 FPS, we speculate that the observed accuracy gap is caused by errors in annotations and the actual gap is significantly lower.We point out systematic inconsistencies in the annotations of popular line detection benchmarks -Wireframe and York Urban, carefully reannotate a subset of images and show that (i) existing detectors have improved quality on updated annotations without retraining, suggesting that new annotations correlate better with the notion of correct line segment detection; (ii) the gap between accuracies of our detector and others diminishes to negligible 0.2 F H , with our method being the fastest.
An Approach for Clustering of Seismic Events using Unsupervised Machine Learning

New and effective approaches for the analysis of seismic data make it possible to identify the distribution of earthquakes helping further to assess frequency of occurrence any associated risks.This paper proposes an effective approach for detecting areas with increased spatial density of seismic events and zoning territories on the map based on the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN algorithm).The validity of the choice of this clustering algorithm is explained by the fact that the DBSCAN algorithm can detect clusters of complex shapes including geographical coordinates.This study uses seismic data from the seismic catalog of the Republic of Kazakhstan from 2011 to 2021 inclusive.Finally, the clusters detected over a certain period of time allowed for the presentation of a spatial model of the distribution of earthquakes and the detection of areas with increased spatial density on the map.In general, the results of the study were also compared and well associated with the general map of the seismic zoning of the Republic of Kazakhstan showing reliable results of clustering based on density.In addition, the architecture of intelligent information and the analytical system for analyzing seismic data is based on the proposed approach.
Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Networks

Image segmentation is a challenging problem in medical applications.Medical imaging has become an integral part of machine learning research, as it enables inspecting interior human body with no surgical intervention.Much research has been conducted to study brain segmentation.However, prior studies usually employ one-stage models to segment brain tissues, which could lead to a significant information loss.In this paper, we propose a multi-stage Generative Adversarial Network (GAN ) model to resolve existing issues of one-stage models.To do this, we apply a coarse-to-fine method to improve brain segmentation using a multi-stage GAN .In the first stage, our model generates a coarse outline for both the background and brain tissues.Then, in the second stage, the model generates a refine outline for the white matter (WM ), gray matter (GM ), and cerebrospinal fluid (CSF).We perform a fusion of the coarse and refine outlines to achieve high results.Despite using very limited data, we obtain an improved Dice Coefficient (DC) accuracy of up to 5% compared to one-stage models.We conclude that our model is more efficient and accurate in practice for brain segmentation of both infants and adults.In addition, we observe that our multi-stage model is 2.69-13.93minutes faster than prior models.Moreover, our multi-stage model achieves higher performance with only a few-shot learning, in which only limited labeled data is available.Therefore, for medical images, our solution is applicable to a wide range of image segmentation applications for which convolution neural networks and one-stage methods have failed.This helps to advance the process of analyzing brain images, thus providing many advantages to the healthcare system, especially in critical health situations where urgent intervention is needed.
HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing

Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention.Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advantage of both since of the mismatch between natural language and logicform SQL.In this work, we propose a History Information Enhanced text-to-SQL model (HIE-SQL) to exploit context-dependence information from both history utterances and the last predicted SQL query.In view of the mismatch, we treat natural language and SQL as two modalities and propose a bimodal pretrained model to bridge the gap between them.Besides, we design a schema-linking graph to enhance connections from utterances and the SQL query to the database schema.We show our history information enhanced methods improve the performance of HIE-SQL by a significant margin, which achieves new state-of-theart results on the two context-dependent textto-SQL benchmarks, the SparC and CoSQL datasets, at the writing time.
Hyperbolic Relevance Matching for Neural Keyphrase Extraction

Keyphrase extraction is a fundamental task in natural language processing that aims to extract a set of phrases with important information from a source document.Identifying important keyphrases is the central component of keyphrase extraction, and its main challenge is learning to represent information comprehensively and discriminate importance accurately.In this paper, to address the above issues, we design a new hyperbolic matching model (Hy-perMatch) to explore keyphrase extraction in hyperbolic space.Concretely, to represent information comprehensively, HyperMatch first takes advantage of the hidden representations in the middle layers of RoBERTa and integrates them as the word embeddings via an adaptive mixing layer to capture the hierarchical syntactic and semantic structures.Then, considering the latent structure information hidden in natural languages, HyperMatch embeds candidate phrases and documents in the same hyperbolic space via a hyperbolic phrase encoder and a hyperbolic document encoder.To discriminate importance accurately, HyperMatch estimates the importance of each candidate phrase by explicitly modeling the phrase-document relevance via the Poincaré distance and optimizes the whole model by minimizing the hyperbolic margin-based triplet loss.Extensive experiments are conducted on six benchmark datasets and demonstrate that HyperMatch outperforms the recent state-of-the-art baselines.
RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm

Answer selection (AS) is a critical subtask of the open-domain question answering (QA) problem. The present paper proposes a method called RLAS-BIABC for AS, which is established on attention mechanism-based long short-term memory (LSTM) and the bidirectional encoder representations from transformers (BERT) word embedding, enriched by an improved artificial bee colony (ABC) algorithm for pretraining and a reinforcement learning-based algorithm for training backpropagation (BP) algorithm. BERT can be comprised in downstream work and fine-tuned as a united task-specific architecture, and the pretrained BERT model can grab different linguistic effects. Existing algorithms typically train the AS model with positive-negative pairs for a two-class classifier. A positive pair contains a question and a genuine answer, while a negative one includes a question and a fake answer. The output should be one for positive and zero for negative pairs. Typically, negative pairs are more than positive, leading to an imbalanced classification that drastically reduces system performance. To deal with it, we define classification as a sequential decision-making process in which the agent takes a sample at each step and classifies it. For each classification operation, the agent receives a reward, in which the prize of the majority class is less than the reward of the minority class. Ultimately, the agent finds the optimal value for the policy weights. We initialize the policy weights with the improved ABC algorithm. The initial value technique can prevent problems such as getting stuck in the local optimum. Although ABC serves well in most tasks, there is still a weakness in the ABC algorithm that disregards the fitness of related pairs of individuals in discovering a neighboring food source position. Therefore, this paper also proposes a mutual learning technique that modifies the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. We tested our model on three datasets, LegalQA, TrecQA, and WikiQA, and the results show that RLAS-BIABC can be recognized as a state-of-the-art method.
OpenIaC: open infrastructure as code - the network is my computer

Abstract Modern information systems are built fron a complex composition of networks, infrastructure, devices, services, and applications, interconnected by data flows that are often private and financially sensitive. The 5G networks, which can create hyperlocalized services, have highlighted many of the deficiencies of current practices in use today to create and operate information systems. Emerging cloud computing techniques, such as Infrastructure-as-Code (IaC) and elastic computing, offer a path for a future re-imagining of how we create, deploy, secure, operate, and retire information systems. In this paper, we articulate the position that a comprehensive new approach is needed for all OSI layers from layer 2 up to applications that are built on underlying principles that include reproducibility, continuous integration/continuous delivery, auditability, and versioning. There are obvious needs to redesign and optimize the protocols from the network layer to the application layer. Our vision seeks to augment existing Cloud Computing and Networking solutions with support for multiple cloud infrastructures and seamless integration of cloud-based microservices. To address these issues, we propose an approach named Open Infrastructure as Code (OpenIaC), which is an attempt to provide a common open forum to integrate and build on advances in cloud computing and blockchain to address the needs of modern information architectures. The main mission of our OpenIaC approach is to provide services based on the principles of Zero Trust Architecture (ZTA) among the federation of connected resources based on Decentralized Identity (DID). Our objectives include the creation of an open-source hub with fine-grained access control for an open and connected infrastructure of shared resources (sensing, storage, computing, 3D printing, etc.) managed by blockchains and federations. Our proposed approach has the potential to provide a path for developing new platforms, business models, and a modernized information ecosystem necessary for 5G networks.
Multidimensional Analysis and Evaluation of College English Teaching Quality Based on an Artificial Intelligence Model

In view of the lack of teaching resources and the impossibility of real-time sharing and application of teaching resources in English teaching, this paper proposes a multidimensional analysis and application of English teaching quality based on an artificial intelligence model. This paper analyzes the basic framework and application of the Internet of Things technology and puts forward the corresponding hierarchical classification and teaching quality monitoring mode. Secondly, the monitoring framework of the Internet of Things to achieve the quality of English teaching and then the basic theory of the Internet of Things is analyzed. Finally, the experiment shows that the average score of English has been greatly improved by comparing the traditional teaching mode and the new classroom quality monitoring mode under the Internet of Things technology. The main dimensions of teaching quality, such as teachers’ quality, teaching attitude, teaching content, and teaching methods, are analyzed in depth, and the coefficient of most sample data is more than 0.7, which has a good application effect. Whether running on the test set or the mixed test set, the accuracy of the new classroom quality monitoring model proposed in this paper is the highest among the three models. The correct rate on the test set can reach 99.71%, the correct rate on the mixed test set can reach 98.01%, and the correct rate can reach 98.67%, which shows the superiority of the performance of the new classroom quality monitoring model.
Performance Evaluation of Several Machine Learning Techniques Used in the Diagnosis of Mammograms

Throughout the world breast cancer has become a common disease among the women and it is also a life threatening diseases. Machine learning(ML) approach has been widely used for the diagnosis of benign and malignant masses in the mammogram. In this manuscript, I have represented the theoretical research and practical advances on various machine learning techniques the diagnosis of benign and malignant masses in the mammogram. The objective of this manuscript is to analyze the performance of distinct machine learning techniques used in the diagnosis of the Digital Mammography Image Analysis Society (MIAS) database. In this work I have compared performance of four machine learning approaches i.e. Support Vector, Naive Bayes, K-Nearest Neighbours and Multilayer Perceptron. The above four types of machine learning algorithm are used to categorize mammograms image. The achievements of these four techniques were recognized to discover the most acceptable classifier. On the end of the examine, derived outcomes indicates that support vector is a successful approach compares to other approach.
Dataset for file fragment classification of image file formats

File fragment classification of image file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with image formats. Therewith, there is no public dataset for file fragments of image file formats. So, a big research challenge in file fragment classification of image file formats is to compare the performance of the developed methods over the same datasets.In this study, we present a dataset that contains file fragments of ten image file formats: Bitmap, Better Portable Graphics, Free Lossless Image Format, Graphics Interchange Format, Joint Photographic Experts Group, Joint Photographic Experts Group 2000, Joint Photographic Experts Group Extended Range, Portable Network Graphic, Tagged Image File Format, and Web Picture. Corresponding to each format, the dataset contains the file fragments of image files with different compression settings. For each pair of file format and compression setting, 800 file fragments are provided. Totally, the dataset contains 25,600 file fragments.
A Faster Half Subtractor Circuit Using Reversible Quantum Gates

Quantum computers base their operations on optimized circuit designs.These quantum circuits, unlike classic circuits, follow the set of rules determined by quantum mechanics.Currently, one of the main problems to solve in Quantum Computation is Shor's algorithm, which consists in factoring large numbers.It is based on arithmetic operations, therefore to optimize such operations is hence relevant.In this work, a novel half subtractor circuit is presented; it is referred to as F GE * and it has demonstrated to be a 25% faster than the state-of-the-arts reversible circuits.It is based on reversible quantum gates and it does not have any garbage output.Moreover, a robust metric is considered for comparing, in terms of resources and speed, the proposed circuit with other circuits in the literature.