Reviewerly/exHarmony huggingface dataset:
<scp>MyrrorBot</scp> : A Digital Assistant Based on Holistic User Models for Personalized Access to Online Services

In this article, we present MyrrorBot , a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, and food recommendation s, in a personalized way, by exploiting a strategy for implicit user modeling called holistic user profiling ; (ii) query their own user models, to inspect the features encoded in their profiles and to increase their awareness of the personalization process. Basically, the system allows the users to formulate natural language requests related to their information needs. Such needs are roughly classified in two groups: quantified self-related needs (e.g., Did I sleep enough? Am I extrovert? ) and personalized access to online services (e.g., Play a song I like ). The intent recognition strategy implemented in the platform automatically identifies the intent expressed by the user and forwards the request to specific services and modules that generate an appropriate answer that fulfills the query. In the experimental evaluation, we evaluated both qualitative (users’ acceptance of the system, usability) as well as quantitative (time required to complete basic tasks, effectiveness of the personalization strategy) aspects of the system, and the results showed that MyrrorBot can improve the way people access online services and applications. This leads to a more effective interaction and paves the way for further development of our system.
LifeMon: A MongoDB-Based Lifelog Retrieval Prototype

We present LifeMon, a new lifelog retrieval prototype targeting LSC. LifeMon is based around the MongoDB document store, which is one of a host of scalable NoSQL systems developed over the last two decades, with a semi-structured data model that seems well matched with lifelog requirements. Preliminary results indicate that the system is efficient and that novice users can successfully use it to solve some LSC tasks.
Neural transition model for aspect-based sentiment triplet extraction with triplet memory

The aspect-based sentiment triplet extraction (ASTE), as a complete sentiment analysis task, aims to recognize the aspect term, the opinion expression, and the sentiment polarity in a sentence. Current state-of-the-art ASTE models employ a joint extracting scheme for better task improvements. However, how to better solve the triplet overlap issues in the task, and effectively model the mutual interactions between the triplet structures remain challenging. In this work, we explore a neural transition model for end-to-end ASTE. We model the triplet prediction as a graph structure, based on which we implement a transition system with neural design. We further propose a triplet memory mechanism to fully leverage the underlying interactions from the previously recognized triplets relevant to the current parse. We experiment on the benchmark datasets, and the results show that our model achieved state-of-the-art performances against current baselines, meanwhile being more efficient on decoding. Further analysis is conducted to verify the effectiveness of our transition framework.
Segmentation-Aware Text-Guided Image Manipulation

We propose a novel approach that improves text-guided image manipulation performance in this paper. Text-guided image manipulation aims at modifying some parts of an input image in accordance with the user’s text description by semantically associating the regions of the image with the text description. We tackle the conventional methods’ problem of modifying undesired parts caused by differences in representation ability between text descriptions and images. Humans tend to pay attention primarily to objects corresponding to the foreground of images, and text descriptions by humans mostly represent the foreground. Therefore, it is necessary to introduce not only a foreground-aware bias based on text descriptions but also a background-aware bias that the text descriptions do not represent. We introduce an image segmentation network into the generative adversarial network for image manipulation to solve the above problem. Comparative experiments with three state-of-the-art methods show the effectiveness of our method quantitatively and qualitatively.
Enhanced privacy preserving and truth discovery method for 5G and beyond vehicle crowd sensing systems

As 5G networks are being deployed the key constraints of 5G networks have been highlighted, which encourages the probing research of 5G and beyond networks as the next-generation solutions. Especially for vehicular networks which require a network topology with very low latency and high reliability. 5G Vehicular networks offer higher reliability, lower latency, and secure and efficient communication services but these advancements come at the cost of new security and privacy concerns. Especially in the case of a high number of connected vehicles which leads to the concept of Vehicle Crowd Sensing System (VCS) in 5G enabled Vehicular Ad hoc networks (VANETS). Hence, due to this, the number of connected vehicle nodes is also soaring drastically which is directly proportional to the expansion of the Vehicle Crowd Sensing System (VCS). These vehicles are equipped with the latest gadgetries and a large number of sensors, which works simultaneously for processing at end-user utility applications e.g. navigation, predictions, and traffic monitoring. Therefore, in this scenario, all vehicle nodes upload their sole respective data to the cloud for computing which arise the need for reliability and privacy. Thus, Privacy-Preserving Truth Discovery (PPTD) is one such scheme that distills reliable information with privacy from raw signals acquired from different sources. Currently, PPTD brings substantial overhead in terms of consumed data, computation at the Vehicle level and requires each vehicle to remain online till the completion of uploading at the cloud server. So, in this paper, we explain a newly proposed novel Efficient Privacy-Preserving Truth Discovery (EPTD) entailing double masking for VCS and allowing Vehicles to go offline anytime during upload processing while ensuring privacy and accuracy at the same time. Simulation results have shown that the proposed work has outperformed all the previously existing methods.
Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation

Lacking the ability to sense ambient environments effectively, blind and visually impaired people (BVIP) face difficulty in walking outdoors, especially in urban areas. Therefore, tools for assisting BVIP are of great importance. In this paper, we propose a novel "flying guide dog" prototype for BVIP assistance using drone and street view semantic segmentation. Based on the walkable areas extracted from the segmentation prediction, the drone can adjust its movement automatically and thus lead the user to walk along the walkable path. By recognizing the color of pedestrian traffic lights, our prototype can help the user to cross a street safely. Furthermore, we introduce a new dataset named Pedestrian and Vehicle Traffic Lights (PVTL), which is dedicated to traffic light recognition. The result of our user study in real-world scenarios shows that our prototype is effective and easy to use, providing new insight into BVIP assistance.
COVID19 Tweeter Dataset Sentiment Analysis

COVID19 (define as ‘CO’ stands for corona, ‘VI’ for virus, and ‘D’ for disease) is declared global pandemic by WHO. In starting of year 2020 it was limited with China but now More than 206 countries is affected due to this COVID-19 and more than 3.5 billion people infected on the globe and out of that more than 1 million people died due to this incurable disease. WHO did not approved any vaccine till current date. All people around the globe effected due to COVID19 and they wrote their view on social media mainly in Twitter. In span of last 9 month of time hundreds of billon text is written on twitter. Sentiment Analysis is natural language processing (NLP) application which is used to categories text sentiment as positive view, negative view or neutral. Different machine learning algorithms is used to extract sentiment from the text but those ML algorithms require text in specific. But that is major step in whole process of sentiment analysis because the data available at tweeter is available in raw form which required a lot of preprocessing and cleaning before using for sentiment analysis.In this article tweeter data related to COVID19 is discussed in detail like that what are different ways to use tweeter data for sentiment. What are different difficulties, what are different steps in tweeter data preprocessing, and finally ready form of dataset. Python is used as a programming language for sentiment analysis in this article. Same it is also used for data cleaning & preprocessing. Different python libraries which are used for data preprocessing also discussed.
A multi-level feature fusion method based on pooling and similarity for HRRS image retrieval

High-resolution remote sensing (HRRS) images contain complex visual contents and rich detailed information. This paper proposes a multi-level feature fusion method to improve the feature representation for HRRS image retrieval. Firstly, in order to obtain the multi-scale information of HRRS images, mid-level features and high-level features of VGG16 and GoogLeNet are extracted with different input sizes. Then a feature transformation method is proposed to adjust the size and the number of different feature maps, so that the distinct mid-level features and distinct high-level features can be fused separately using element-wise addition. There is a large amount of redundancy in the fusion features, thus small-region max-pooling method is adopted to aggregate the mid-level fusion feature, and global max-pooling method is used to aggregate the high-level fusion feature. Finally, an adaptive weight allocation method based on similarity is proposed to combine mid-level feature and high-level feature. Experimental results show that the multi-level feature fusion is an effective method to enhance the feature representation, thereby improving the retrieval performance.
PoRF: Proof-of-Reputation-based Consensus Scheme for Fair Transaction Ordering

Blockchain (BC)-driven applications ensure trust and transparency among multiple stakeholders through different consensus mechanisms. In consensus mechanisms, a dishonest node (or application), might collude with peer nodes, or miners, to prioritize its transactions over other node transactions, thereby reducing its latency and improving its quality-of-service (QoS). To address the above limitation, this paper proposes a consensus scheme, PoRF, based on a reputation-based consensus scheme that allows fair and random transaction selection. The scheme proposes that a sharded BC is considered, managed through shard managers (SM), to address latency and bandwidth issues. A reputation coefficient score is generated, and a reward-penalty setup is considered for nodes based on honest, or dishonest transaction proposals. Based on the reward-penalty outcome, the reputation score for nodes is modified. The scheme is compared with fixed transaction ordering scheme, Helix scheme, and practical byzantine fault tolerance (PBFT) consensus for transaction fairness, transaction time, and epochs required for consensus formations. The scheme outperforms the considered schemes for the considered parameters. For example, for 50% dishonest nodes, an improvement of 70 % is recorded against a fixed transaction scheme, and at 4000 nodes, the proposed scheme takes 130 milliseconds (ms) less for the execution of transactions. For consensus formation, at 250 nodes, PBFT takes 167 epochs for consensus, compared to 24 epochs in the proposed scheme, which indicates the scheme’s viability in real-world setups.
From Tables to Knowledge

A wealth of human knowledge is expressed in structured tables, across web pages, scientific articles, spreadsheets, and databases. This wealth of knowledge is mirrored by diversity in the vast number of layout structures, content types, formats, and surface forms used to express tables. Recent advances in representation learning and knowledge representation have made progress in exploiting structural regularities in tabular data to unlock this knowledge. In this tutorial, we provide a survey of these advances for a host of table understanding tasks, including table segmentation, semantic typing of cells, transforming tables to knowledge graphs, entity linking, and table retrieval tasks for question answering.
Light-Weight Mixed Stage Partial Network for Surveillance Object Detection with Background Data Augmentation

State-of-the-art (SoTA) models have improved object detection accuracy with a large margin via convolutional neural networks, however still with an inferior performance for small objects. Moreover, these models are trained mainly based on the COCO dataset, and its backgrounds are more complicated than road environments, and thus degrade the accuracy of small road object detection. Compared with the COCO dataset, the background of a surveillance video is relatively stable and can be used to enhance the accuracy of road object detection. This paper designs a computationally efficient mixed stage partial (MSP) network to detect road objects. Another novelty of this paper is to propose a mixed background data augmentation method to enhance the detection accuracy without adding new labelling efforts. During inference, only the input image is used to detect road objects without further using any subtraction information. Extensive experiments on KITTI and UA-DETRAC benchmarks show the proposed method achieves the SoTA results for highly-accurate and efficient road object detection.
Attack Trees vs. Fault Trees: Two Sides of the Same Coin from Different Currencies

This work compares formal approaches to define and operate with attack trees and fault trees. We start by investigating similarities between the syntactic structure, semantics, and qualitative analysis, of static attack trees and fault trees. Then we point out differences of the analysis methods and metrics between the two formalisms, providing a deeper insight for their dynamic variants. Finally, we overview several extensions and categorise them using the new concept of dimension, which allows us to compare these extensions and point out research gaps.
Printer source identification by feature modeling in the total variable printer space

Advances in the digital world have attracted the attention of many researchers in terms of developing digital forensics. Using machine learning methods for print source identification is one of the developing areas in the field of digital forensics. In this paper, a new method is presented for printer source identification by modeling the primary Local Binary Pattern (LBP) features in the total variable printer space and extracting the secondary features based on the joint factor analysis. Only one low-dimensional i-vector feature is employed for each document image without using any optical character recognition (OCR) algorithm or similar processes. This property eliminates the requirement for the majority voting algorithm and reduces the computational cost of the classification process. Furthermore, the proposed algorithm is not limited to a specific language or character set. The capabilities of the proposed method in extracting useful discriminant information from the sparse print shadow texture are revealed through simulation. The simulation results showed that the proposed algorithm obtained the accuracy of 98.48% by refining the basic features of LBP, which is comparable to the results of the state-of-the-art approaches in this field.
A Fast and Robust Seam Tracking Method for Spatial Circular Weld Based on Laser Visual Sensor

Spatial circular welding is an important application in industrial production. However, some feature points extraction methods for spatial circular welds have disadvantages such as large amounts of calculation and poor anti-interference ability. In addition, due to the welding thermal deformation, the spatial circular weld needs to be tracked. Therefore, a fast and robust seam tracking method for spatial circular weld based on laser visual sensors is proposed in this article. First of all, a self-updating template matching (SUTM) method is proposed to get weld feature points. Compared with the method based on geometric features, the proposed SUTM method has the advantages of rapidity and robustness. What is more, an algorithm for spatial circle center fitting based on random sampling consensus (RANSAC) is proposed, which overcomes the interference of outlier points and improves the robustness of circle center detection. At the same time, the tracking error model of the spatial circular weld is established and the welding deviation from the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> -, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> -, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> -directions is obtained. By correcting the deviation in real-time, the fast and robust real-time seam tracking of the spatial circular weld is realized. Through welding test, the proposed method can obtain the position of the spatial fillet circular weld quickly and robustly and complete the high-precision seam tracking.
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision

Computer vision tasks such as object detection and semantic/instance segmentation rely on the painstaking annotation of large training datasets. In this paper, we propose LocTex that takes advantage of the low-cost localized textual annotations (i.e., captions and synchronized mouse-over gestures) to reduce the annotation effort. We introduce a contrastive pre-training framework between images and captions, and propose to supervise the cross-modal attention map with rendered mouse traces to provide coarse localization signals. Our learned visual features capture rich semantics (from free-form captions) and accurate localization (from mouse traces), which are very effective when transferred to various downstream vision tasks. Compared with ImageNet supervised pre-training, LocTex can reduce the size of the pre-training dataset by 10× or the target dataset by 2× while achieving comparable or even improved performance on COCO instance segmentation. When provided with the same amount of annotations, LocTex achieves around 4% higher accuracy than the previous state-of-the-art "vision+language" pre-training approach on the task of PASCAL VOC image classification.
Toward a Smart and Sustainable Campus: Future Vision, Opportunities, and Challenges

The Internet of Things (IoT) is a buzzing technology nowadays. It is believed to have the potential for revolutionizing today's world. Several projects and publications addressed the use of IoT in various applications such as smart cities, smart homes, wearables, smart grids, connected vehicles, and health care. However, few works addressed the potential of using IoT in educational institutions. The technological revolution along with several changes through the past era, including the COVID-19 global pandemic, have made it indispensable to employ innovative learning methods, also, the students will be looking forward to existing in the environment of a smart campus. This study presents a framework to elucidate how IoT can be used to bring a smart and innovative university campus to life in order to improve the efficiency of delivering the daily educational activities. In the context of sustainable development, the social and environmental interactions should be considered to provide a sustainable campus. Also, challenges are investigated and research opportunities are highlighted. This framework can be put in practice in guiding universities to adopt novel visions to improve the performance of their educational activities and student life experience by properly adopting and deploying IoT technologies. These concepts are elaborated based on the case of the innovative campus of the Egypt-Japan University of Science and Technology, which is located in New Borg Elarab, Alexandria, Egypt.
2D-LCoLBP: A Learning Two-Dimensional Co-Occurrence Local Binary Pattern for Image Recognition

The rotation, scale and translation invariance of extracted features have a high significance in image recognition. Local binary pattern (LBP) and LBP-based descriptors have been widely used in image recognition due to feature discrimination and computational efficiency. However, most of the existing LBP-based descriptors have been designed to achieve rotation invariance while fail to achieve scale invariance. Moreover, it is usually difficult to achieve a good trade-off between the feature discrimination and the feature dimension. In this work, a learning 2D co-occurrence LBP termed 2D-LCoLBP is proposed to address these issues. Firstly, a weighted joint histogram is constructed in different neighborhoods and scales of an image to represent the multi-neighborhood and multi-scale LBP (2D-MLBP) and achieve the rotation invariance. A feature learning strategy is then designed to learn the compact and robust descriptor (2D-LCoLBP) from LBP pattern pairs across different scales in the extracted 2D-MLBP to characterize the most stable local structures and achieve the scale invariance, as well as decrease the feature dimension and improve the noise robustness. Finally, a linear SVM classifier is employed for recognition. We applied the proposed 2D-LCoLBP on four image recognition tasks—texture, object, face and food recognition with ten image databases. Experimental results show that 2D-LCoLBP has obviously low feature dimension but outperforms the state-of-the-art LBP-based descriptors in terms of recognition accuracy under noise-free, Gaussian noise and JPEG compression conditions.