Reviewerly/exHarmony huggingface dataset:
Multiple Fisheye Camera Tracking via Real-Time Feature Clustering

Recently, Multi-Target Multi-Camera Tracking (MTMC) makes a breakthrough due to the release of DukeMTMC and show the feasibility of related applications. However, most of the existing MTMC methods focus on the batch methods which attempt to find the global optimal solution from the entire image sequence and thus are not suitable for the real-time applications, e.g., customer tracking in unmanned stores. In this paper, we propose a low-cost online tracking algorithm, namely, Deep Multi-Fisheye-Camera Tracking (DeepMFCT) to identify the customers and locate the corresponding positions from multiple overlapping fisheye cameras. Based on any single camera tracking algorithm (e.g., Deep SORT), our proposed algorithm establishes the correlation between different single camera tracks. Owing to the lack of well-annotated multiple overlapping fisheye cameras dataset, the main challenge of this issue is to efficiently overcome the domain gap problem between normal cameras and fisheye cameras based on existed deep learning based model. To address this challenge, we integrate a single camera tracking algorithm with cross camera clustering including location information that achieves great performance on the unmanned store dataset and Hall dataset. Experimental results show that the proposed algorithm improves the baselines by at least 7% in terms of MOTA on the Hall dataset.
Training Classifiers to Identify TCP Signatures in Scientific Workflows

Identifying network anomalies is an important measure to ensure reliability and quality of data transfers among facilities. Scientific workflows in particular heavily rely on good network performance to ensure their smooth executions. In this paper, we present a lightweight classifier system that is able to recognize anomalous TCP transfers. Using random forest trees and labeled data sets, we evaluate the classifier with real workflow transfers for ground truth data. Our studies reveal that various TCP congestion algorithms behave differently in anomalous conditions. We show that training classifiers on these separately can aid detection in network performance deterioration. Results reveal that our classifiers are able to better predict anomalous flows for TCP Reno and Hamilton compared to Cubic and BBR, due to the manner how their congestion control algorithms handle the anomalies.
Recursive Polynomial Minimum Mean-Square Error Estimation with Applications to Orbit Determination

This paper presents a systematic generalization of the linear update structure associated with the extended Kalman filter for high-order polynomial estimation of nonlinear dynamical systems. The minimum mean-square error criterion is used as the cost function to determine the optimal polynomial update during the estimation process. The high-order series representation is implemented effectively using differential algebra techniques. Numerical examples show that the proposed algorithm, named the high-order differential algebra Kalman filter, provides superior robustness and/or mean-square error performance as compared to linear estimators under the condition considered.
Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm

The digital retina excels at providing enhanced visual sensing and analysis capability for city brain in smart cities, and can feasibly convert the visual data from visual sensors into semantic features. With the deployment of deep learning or handcrafted models, these features are extracted on front-end devices, then delivered to back-end servers for advanced analysis. In this scenario, we propose a model generation, utilization and communication paradigm, aiming at strong front-end sensing capabilities for establishing better artificial visual systems in smart cities. In particular, we propose an integrated multiple deep learning models reuse and prediction strategy, which dramatically increases the feasibility of the digital retina in large-scale visual data analysis in smart cities. The proposed multi-model reuse scheme aims to reuse the knowledge from models cached and transmitted in digital retina to obtain more discriminative capability. To efficiently deliver these newly generated models, a model prediction scheme is further proposed by encoding and reconstructing model differences. Extensive experiments have been conducted to demonstrate the effectiveness of proposed model-centric paradigm.
SVM-Enabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems

In recent years, there are several cost-effective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body structures of the body. Further, such intelligent sensing systems such as ultrasound systems have been deployed by medical doctors around the globe for efficient detection of several diseases and disorders in the human body. Even though the ultrasound sensing system is a useful tool for obtaining the imagery of various body parts, there is always a possibility of inconsistencies in these images due to the variation in the settings of the system parameters. Therefore, in order to overcome such issues, this research devises an SVM-enabled intelligent genetic algorithmic model for choosing the universal features with four distinct settings of the parameters. Subsequently, the distinguishing characteristics of these features are assessed utilizing the Sorensen-Dice coefficient, T-test, and Pearson's R measure. It is apparent from the results of the SVM-enabled intelligent genetic algorithmic model that this approach aids in the effectual selection of universal features for the breast cyst images. In addition, this approach also accomplishes superior accuracy in the classification of the ultrasound image for four distinct settings of the parameters.
Sequential Pattern Learning of Visual Features and Operation Cycles for Vision-Based Action Recognition of Earthmoving Excavators

Excavator action recognition is a major task for cycle-time and productivity analysis. Many researchers have developed vision-based methods and showed promising results for automated action recognition. However, previous methods did not fully incorporate sequential working patterns of excavators although it is crucial to explain their natural operation procedure. To overcome the limitations, this paper proposes a vision-based excavator action recognition framework that considers the sequential patterns of visual features and operation cycles. The framework was validated with 1,340 images collected from actual earthmoving sites, and the average precisions and recall rates were 90.9% and 89.2% respectively. This research performed additional experiments with the other CNN model, and the precision and recall rate were decreased to 77.5% and 71.6% respectively. The experimental results showed the applicability of the developed framework and the positive impacts of sequential pattern learning. This study can contribute to more reliable automated cycle-time analysis and productivity measurement.
Artificial Vision on Edge IoT Devices: A Practical Case for 3D Data Classification

Nowadays, with the huge advance of sensor technology and the increase of the amount of data generated by them, techniques have to be developed to be able to process all this amount of information in real-time applications on edge devices, close to where data is being generated. If all that information has to be sent to the cloud to be processed, it has certain disadvantages in terms of latency, bandwidth, privacy and reliability, compared to locally processing it on the edge. In this paper, the implementation of deep learning algorithms in low power and limited resources devices in an Internet of Things scenario is studied. In order to work in real-time applications, the influence of different low power consumption deep learning hardware accelerators is studied. Finally, a practical case for smart farming is shown with comparative results in terms of power consumption and performance when running the same artificial vision algorithm on different devices.
Dataset for comparable evaluation of machine translation between 11 South African languages

This data article describes the Autshumato machine translation evaluation set. The evaluation set contains data that can be used to evaluate machine translation systems between any of the 11 official South African languages. The dataset is parallel with four reference translations available for each of the following languages: Afrikaans, English, isiNdebele, isiXhosa, isiZulu, Sepedi, Sesotho, Setswana, Siswati, Tshivenḓa and Xitsonga.
A Simple Scale-Invariant Two-Sample Test for High-dimensional Data

A new scale-invariant test for two-sample problems for high-dimensional data is proposed and studied. Under some regularity conditions and the null hypothesis, the proposed test statistic and a chi-square-type mixture are shown to have the same limiting distribution after they are normalized. The limiting distribution can be normal or non-normal, depending on the underlying covariance structure of the high-dimensional data. To approximate the null distribution of the proposed test, the well-known Welch-Satterthwaite chi-square approximation is applied. The resulting test is shown to be adaptive to the shape of the underlying null distribution in the sense that when the test statistic is asymptotically normally distributed under the null hypothesis, so is the approximation distribution, and when the approximation distribution is asymptotically non-normally distributed, so is the underlying null distribution of the test statistic. The asymptotic powers of the proposed test under some local alternatives are derived. Simulation studies and a real data application are used to demonstrate the good performance of the proposed test compared with several existing competitors in the literature.
Analysis and Evaluation of Integrated Cyber Crime Offences

Cyber Crime is an illegal activity in which offender makes use of the smart devices such as computers and other network devices as the primary source in order to gain some profit from the victim by violating the rules. Cyber attacks are persistently rising, detection of cyber crimes and providing preventive measures by manual investigation are often failed to control the cyber attacks. Therefore, machine learning plays a vital role in detecting those cybercrimes. It has the ability to detect and analyze the cyber attack and provides the preventive measures in order to reduce the incarnation of the cyber crimes. Therefore, incorporating machine learning techniques such as classification and clustering into our framework can help to build a cyber crime detection system and prediction of cyber attacks annually. Existing literature in the area of cybercrime offences by feature extraction focuses on several techniques. In this a novel framework for cybercrime offences by feature extractions is proposed. In this proposed framework one can upload any unstructured cyber crime report to generate the structure data through TFID technique. Later this framework can give a report on categorization and resolution of the cyber crime offences (especially ID theft, Hacking and Copyright attacks) by its severity and occurrence. It is achieved by extracting the feature description using text mining algorithms and by using the performance measurements and prediction analysis of cyber crime.
Foreign Language Education in the Era of Artificial Intelligence

With the development of big data, cloud computing and supercomputers, artificial intelligence is widely used in various fields. The deep integration of artificial intelligence technology and education has changed the mode of modern teaching and learning, which also brought opportunities and challenges to foreign language education. Artificial intelligence has a profound impact on foreign language teachers, learners, teaching mode and methods, the ways of evaluation, etc. However, the application of artificial intelligence technology in foreign language education is far from satisfaction. The improvement of current situation needs the efforts from whom it may concern. The college should take some measures to motivate the application of artificial intelligence and provide financial and technical support; foreign language teachers must change their concepts first, improve their abilities of applying new information technology into teaching process, adapt to the goal of future foreign language education, and keep up with the pace of education reform in the Era of artificial intelligence so as to promote the development of foreign language education.
Language industry views on the profile of the post-editor

Abstract The more language service companies (LSCs) include machine translation post-editing (MTPE) in their workflows, the more important it is to know how the PE task is performed, who the post-editors are, and what skills they should have. This research is designed to address such questions. It aims to deepen our knowledge of current practices to later create new training content and adapt existing training methodologies to different types of audiences. Based on the results of a survey of LSCs and other companies who currently use MTPE, we present a picture of evolving practices in the contemporary European MTPE market, and opinions held about this emerging métier . Our research finds that a high level of expertise in MTPE may not necessarily be indicative of the industry, and that the post-editor of MT has a multi- and transdisciplinary profile.
Memeify

Interest in the research areas related to meme propagation and generation has been increasing rapidly in the last couple of years. Meme datasets available online are either specific to a context or contain no class information. Here, we prepare a large-scale dataset of memes with captions and class labels. The dataset consists of 1.1 million meme captions from 128 classes. We also provide a reasoning for the existence of broad categories, called 'themes' across the meme dataset; each theme consists of multiple meme classes. Our generation system uses a trained state-of-the-art transformer based model for caption generation by employing an encoderdecoder architecture. We develop a web interface, called Memeify for users to generate memes of their choice, and explain in detail, the working of individual components of the system. We also perform qualitative evaluation of the generated memes by conducting a user study. A link to the demonstration of the Memeify system is https://youtu.be/P_Tfs0X-czs.
Toward human-centric deep video understanding

Abstract People are the very heart of our daily work and life. As we strive to leverage artificial intelligence to empower every person on the planet to achieve more, we need to understand people far better than we can today. Human–computer interaction plays a significant role in human-machine hybrid intelligence, and human understanding becomes a critical step in addressing the tremendous challenges of video understanding. In this paper, we share our views on why and how to use a human centric approach to address the challenging video understanding problems. We discuss human-centric vision tasks and their status, highlighting the challenges and how our understanding of human brain functions can be leveraged to effectively address some of the challenges. We show that semantic models, view-invariant models, and spatial-temporal visual attention mechanisms are important building blocks. We also discuss the future perspectives of video understanding.
A Timing Attack on the HQC Encryption Scheme

The HQC public-key encryption scheme is a promising code-based submission to NIST’s post-quantum cryptography standardization process. The scheme is based on the decisional decoding problem for random quasi-cyclic codes. One problem of the HQC’s reference implementation submitted to NIST in the first round of the standardization process is that the decryption operation is not constant-time. In particular, the decryption time depends on the number of errors decoded by a BCH decoder. We use this to present the first timing attack against HQC. The attack is practical, requiring the attacker to record the decryption time of around 400 million ciphertexts for a set of HQC parameters corresponding to 128 bits of security. This makes the use of constant-time decoders mandatory for the scheme to be considered secure.
The Digital and Physical Footprint of Dark Net Markets

Over the past decade, the world has been contending with a growing set of challenges related to illicit traffic as advancements in technology, communications, and global integration facilitate the operation of black markets and greater organization of criminal activity. In this study, the dark web and associated dark net markets are introduced as an important context for scholars interested in international marketing. Furthermore, the scale, scope, and structure of the real-world drug trade is empirically analyzed as an example of the work possible within this dark world. The study concludes by highlighting key themes from the literature in international marketing scholarship and focuses on how they might be co-opted to contribute toward the understanding and countermarketing of illicit systems of exchange.
Deep Learning-Based Frameworks for Aspect-Based Sentiment Analysis

Opinions are key influencers of almost all human practices. One can easily find a number of opinions about any product or services in the form of product reviews. These product reviews are available in a tremendous amount. It is not feasible or even impossible to go through each review and make a concise decision about any product. Aspect-based sentiment analysis (ABSA) comes as a solution to this problem. It gives an approach to examine online reviews and provides a summary based on these reviews. Machine learning techniques have been broadly utilized for ABSA. Recently with the evolution of processing power of computers and digitization of the society, deep learning is taking off. Deep learning methods produced state-of-the-art results in various NLP tasks without intensive feature engineering. In this chapter, we present an introduction about ABSA following a comprehensive overview of various deep learning models used in the field of ABSA.
Dynamic Autonomous Surface Vehicle Control and Applications in Environmental Monitoring

This paper addresses the problem of robotic operations in the presence of adversarial forces. We presents a complete framework for survey operations: waypoint generation, modelling of forces and tuning the control. In many applications of environmental monitoring, search and exploration, and bathymetric mapping, the vehicle has to traverse in straight lines parallel to each other, ensuring there are no gaps and no redundant coverage. During operations with an Autonomous Surface Vehicle (ASV) however, the presence of wind and/or currents produces external forces acting on the vehicle which quite often divert it from its intended path. Similar issues have been encountered during aerial or underwater operations. By measuring these phenomena, wind and current, and modelling their impact on the vessel, actions can be taken to alleviate their effect and ensure the correct trajectory is followed.
Introduction to Email, Web, and Message Forensics

With the advent of information and communication technologies, e-mail is one of the prime tools for communication. Almost everyone uses emails for business communications as well as for sharing personal information. E-mail is vulnerable against cybercrimes. This chapter focuses on the basic concepts of web forensics and then provides an insight about the e-mailing policies, email architecture, and existing investigation techniques used by forensic investigators. Most of the organizations implement some standard e-mailing policies; however, it is not sufficient to handle and prevent the digital crimes. We need to monitor and investigate emails for proper check. This chapter discusses some prominent tools and techniques through which forensic experts can gather and analyze data about suspected email accounts for investigation and produce evidence in the court of law.
A Novel Hadith Processing Approach Based on Genetic Algorithms

Quran and Hadith are the main religious sources in Islam.Hadith is mainly the saying of Prophet Mohammad, and it consists of two parts: first is ''Isnad''; which is the series of persons (narrators) who report (narrate) Hadith, and the second is ''Matn''; which is the saying or the narration itself.Isnad can be divided into phrases called ''Isnad-Phrases'', which could be tagged according to proposed Part-Of-Isnads (POIs) that represent the entity types of Isnad, such as Narrator Name, Prophet Name and Received Method.One of the main objectives of Hadith sciences is to ascertain the validity of Hadith and determine if it is accepted or rejected; this is known as Hadith judgment.Hadith scholars put many rules to judge Hadiths; some of these rules are related to the chain of narrators.Therefore, to judge Hadith, first, the narrators' names in Isnad should be extracted then judgment rules can be applied.Many researches proposed different methods to extract narrators' names from Isnad.To the best of the author's knowledge, there is no research process Isnad using Genetic Algorithms (GA).In this research, a novel approach is introduced for Isnad processing based on GA.This approach aims to predict the narrators' names and the other POIs for Isnad.The experiments, which conducted on all Hadiths narrated in ''Sahih Muslim'' book, show that the proposed approach achieved 81.44% accuracy.For further research in this genre, investigation of utilizing other approaches for Isnad processing, such as deep learning, is recommended.
Power Prediction for Heterogeneous Ground Robots Through Spatial Mapping and Sharing of Terrain Data

Ground robot power consumption often varies significantly throughout an off-road environment, depending on the terrain. Previous traversals over the environment can inform future predictions through spatial mapping of collected power data. However, such predictions are particular to a single robot. We develop a framework based on multi-task Gaussian process regression (MTGP) to share terrain information across multiple robots. The framework allows for the efficient selection of hyperparameters and the prediction of power consumption along paths through nearest neighbor data selection. The scaling of this approach is demonstrated in simulation. Furthermore, experimental testing with two robots shows a significant reduction in power prediction error when an environment, mapped by one robot, informs the power predictions of another robot.
Offline Signature Verification using Structural Dynamic Time Warping

In recent years, different approaches for handwriting recognition that are based on graph representations have been proposed (e.g. graph-based keyword spotting or signature verification). This trend is mostly due to the availability of novel fast graph matching algorithms, as well as the inherent flexibility and expressivity of graph data structures when compared to vectorial representations. That is, graphs are able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. However, the vast majority of the proposed approaches match the graphs from a global perspective only. In the present paper, we propose to match the underlying graphs from different local perspectives and combine the resulting assignments by means of Dynamic Time Warping. Moreover, we show that the proposed approach can be readily combined with global matchings. In an experimental evaluation, we employ the novel method in a signature verification scenario on two widely used benchmark datasets. On both datasets, we empirically confirm that the proposed approach outperforms state-of-the-art methods with respect to both accuracy and runtime.