Reviewerly/exHarmony huggingface dataset:
Aspect-Based Sentiment Quantification

In the current literature, many methods have been devised for sentiment quantification. In this work, we propose AspEntQuaNet, one of the first methods for aspect-based sentiment quantification. It extends the state-of-the-art QuaNet deep learning method for sentiment quantification in two ways. First, it considers aspects and ternary sentiment quantification concerning these aspects instead of binary sentiment quantification. Second, it improves on the results of QuaNet with an entropy-based sorting procedure instead of multisorting. Other sentiment quantification methods have also been adapted for ternary sentiment quantification instead of binary sentiment quantification. Using the modified version of the SemEval 2016 dataset for aspect-based sentiment quantification, we show that AspEntQuaNet is superior to all other considered existing methods based on obtained results for various aspect categories. In particular, AspEntQuaNet outperforms QuaNet often by a factor of 2 on all considered evaluation measures.
Dual-Stream Feature Fusion Network for Detection and ReID in Multi-object Tracking

Multi-object Tracking (MOT) focuses on associating detection boxes with previous results from the same detector using motion and appearance features. Recently, the Joint detection and embedding (JDE) paradigm is showing its efficiency to explore the potentially shared information between the tasks of detection and re-identification (ReID). However, formulating the tasks of detection and ReID into a shared feature fusion network may bring inevitable competition during training. To tackle this problem, we propose a novel dual-stream feature fusion network (DSFFN) to alleviate the competition between the two tasks and obtain better task-dependent feature expression. It forms the detection and ReID feature fusion tasks into a parallel network, and they only share the low-level feature information. Further, to solve the inconsistency across different scales of ReID embeddings and improve the awareness of important information, we propose a multi-scale cross-connected attention network (MSCCAN) for ReID feature fusion. At last, we modify the prediction head to be decoupled design to tackle the conflicts between multi-task. Our method obtains 78.8% MOTA and 74.3% IDF1 on MOT16 test sets, which outperforms the previous works and achieves state-of-the-art performance.
Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations

Surgical scene segmentation is fundamentally crucial for prompting cognitive assistance in robotic surgery. However, pixel-wise annotating surgical video in a frame-by-frame manner is expensive and time consuming. To greatly reduce the labeling burden, in this work, we study semi-supervised scene segmentation from robotic surgical video, which is practically essential yet rarely explored before. We consider a clinically suitable annotation situation under the equidistant sampling. We then propose PGV-CL, a novel pseudo-label guided cross-video contrast learning method to boost scene segmentation. It effectively leverages unlabeled data for a trusty and global model regularization that produces more discriminative feature representation. Concretely, for trusty representation learning, we propose to incorporate pseudo labels to instruct the pair selection, obtaining more reliable representation pairs for pixel contrast. Moreover, we expand the representation learning space from previous image-level to cross-video, which can capture the global semantics to benefit the learning process. We extensively evaluate our method on a public robotic surgery dataset EndoVis18 and a public cataract dataset CaDIS. Experimental results demonstrate the effectiveness of our method, consistently outperforming the state-of-the-art semi-supervised methods under different labeling ratios, and even surpassing fully supervised training on EndoVis18 with 10.1% labeling. Our code is available at https://github.com/yangyu-cuhk/PGV-CL.
Exploring Feature Coupling for Multiple Operations Type and Order Detection

To assess the authenticity and integrity of digital image history, forensic analysts not only need to identify the types of tampering operations that multimedia has undergone but also distinguish their topological combination order. However, due to the interaction among tampering operations, a forensic method proposed for the operation identification yields unacceptably low performance when the content of a given image was forged by operations in a multi-operator chain. In this paper, we propose an efficient detection framework for determining the type and order of the tampering operations. Specifically, we first divide the operation detection problem into two aspects: operation type identification and their combination order detection. Then, by exploring the feature coupling relationships among different features with Pearson's correlation coefficient, optimal coupled features are designed for tampering operations detection. Finally, the detection of multiple operations applied to the previously JPEG-compression images is examined, and the effectiveness of our proposed framework has been demonstrated by simulation results.
Data Ownership in Food-Related Information

This Chapter reconstructs and examines the state of the current debate in data ownership while framing it to the domain of food safety information. Crucially, it identifies a certain degree of convergence towards a new paradigm of ownership that is based on trust. To do so, it examines the traditional concepts of ownership under different perspectives—legal, technical, ethical—and contextualises it to the domain under discussion. New interpretative proposals, based on a new concept of data ownership drawn from the current debate, are then made, together with some possible concrete implementations
Security Challenges Prospective Measures In The Current Status of Internet of Things (IoT)

This paper provides a high-level review and evaluation of the present situation regarding IoT Securities. An Internet of Things design seeks to connect everyone with anything they want, whenever they want it. The perception layer, network layer, the application layer is the most important three layers which mainly make the Internet of Things. To achieve a stable IoT reality, a range of safety precautions must be imposed at each tier. The only way to assure the future of the IoT infrastructure is to address and resolve the security issues that it has. Many researchers have attempted to address the security concerns specific to IoT layers and devices by applying suitable measures. This paper provides a top level view of safety ideas, technology and security problems, possible remedies, and the IoT's future directions for securing and also in this paper, a depth evaluation of the safety associated demanding situations and raw sources of danger with inside the IoT programs have been showcased. Latter discussion on the safety issue, diverse rising and present technology centered for attaining an excessive diploma of agree with inside the IoT programs are discussed.
Multiple Bangla Sentence Classification using Machine Learning and Deep Learning Algorithms

Text classification is an essential and the most well-known topic of Artificial Intelligence as a discipline of Natural Language Processing (NLP). Because of the abundance of textual documents in Bangla, text classification has become a crucial subject. Natural Language Processing (NLP) in Bangla, at the same time, is not as developed as it is in English, and little study has been done in the context of the Bengali dialect, which is among the most widely used languages in the world. As a consequence, it's past time to address this issue in order to effective information management and data structure. The following is an example of a Bangla phrase from a narrative: assertive, interrogative, imperative, optative, or exclamatory text document. Numerous machine learning (ML) and deep learning (DL) algorithms are applied to categorize the sentence in the text document using the dataset. Our dataset is unique in that it was created by hand while keeping Bangla's sentence structure and origin in perspective. Within all the machine learning (ML) techniques, there are two that stands out: RN and DT provides the supreme exactness at 89.42%. As a deep learning strategy, between LSTM and RNN, LSTM exhibit superlative accuracy, with having an accuracy of 88.2 percent. Our experiment also offers a benefit in NLP for detecting the expression of textual data in the future execution, and hybrid approaches will be performed by increasing our dataset for improving the interaction between Bangla and the Natural language processing (NLP) field.
Real-Time Infrared Target Tracking on Embedded Platform Based on Improved Staple Tracker

Infrared target tracking aims to continuously obtain the position and size of specific infrared target in image sequences or videos. Existing tracking methods based on correlation filter are limited to the spatial texture of infrared target, which cannot tackle completely insufficiency of feature information in tracking process. To solve the problem, we propose a real-time infrared target tracking method in this paper, which combines the probability map of edge features and Staple tracking algorithm. Specifically, the proposed method enhances the edge details between infrared target and background and learns the deformation of infrared target. To cope with occlusion during infrared target tracking, we incorporate a template update module with average peak correlation energy to detect the occlusion of infrared target and selectively update the tracking template model. The experimental results show that the improved method achieves satisfactory improvement compared with the original Staple algorithm in the tracking precision and success rate. Furthermore, we also achieve real-time running on the embedded platform.
Human Activity Recognition Based on Convolutional Neural Network via Smart-phone Sensors

To resolve the problem of insufficient accuracy in human activity recognition based on a single accelerometer sensor and traditional machine learning, this paper collects data on human activities using a smartphone embedded with multisensors, and then develop a framework based on a convolution neural network to classify human activities. When building a four- layer neural network, maximum pooling is utilized for every two layers, and the dropout technique is employed in case of overfitting. A full connection layer is then built using average pooling, and the Softmax approach is used for multi- classification. Experiments reveal that the suggested framework of convolution neural network enhances the accuracy of human activity recognition significantly.
A novel approach for image restoration using convolution network-based image denoising technique

When imaging a target in space, atmospheric turbulence might change the direction of the path of light. This might be caused by the random motion of the turbulent medium leading to drastic distortion of the image. In order to correct the temporally varying blur, reduction spatially, and geometric distortion, a blind deblurring atmospheric turbulence based on the convolutional network is proposed. This includes image reconstruction subnetwork, asymmetric U-net, and feature extraction noise suppression block (FENSB). In the U-net, instead of the traditional convolution layer that is normally used, a deblurring noise suppression block (DNSB) is used. The basic idea behind this methodology is to reduce the noise before deblurring occurs. Hence the aspect of noise suppression before deblurring is carried out by the DNSB block along with FENSB. At the time of convolutional encoding, DNSB and FENSB are used to identify and extract the rich features of the maps and further suppress noise. In order to fuse high-level and low-level features, the subnetwork is skip connected with FENSB thereby improving the integrity of the system during the process of reconstruction. This mechanism of increasing the training difficulty of the network in a step-by-step manner is used to slowly converge into a complex model facilitating better quality of images despite the turbulence. The results of simulation data, as well as that of real data, indicate that it is possible to suppress the noise and restore the image with high efficiency.
Evaluation and Analysis of the NLP Model Zoo for Ukrainian Text Classification

One of the crucial problems of natural language processing for languages such as Ukrainian is lack of datasets both unlabeled (for pretraining of word embeddings or large deep learning models) and labeled (for benchmarking existing approaches). In this paper we describe a framework for simple classification dataset creation with minimal labeling effort. We create a dataset for Ukrainian news classification and compare several pretrained models for Ukrainian language in different training settings. We show that ukr-RoBERTa, ukr-ELECTRA and XLM-R large tend to show the highest performance, although XLM-R large and ukr-ELECTRA tend to perform better on longer texts, while ukr-RoBERTa substantially outperforms other models on shorter sequences. To analyze these results deeper, we use SHAP to gain insights into the patterns these models learnt. We publish this dataset on Kaggle ( https://www.kaggle.com/c/ukrainian-news-classification/ ) and suggest to use it for further comparison of approaches for Ukrainian text classification.
3D Head-Position Prediction in First-Person View by Considering Head Pose for Human-Robot Eye Contact

For a humanoid robot to make eye contact and initiate communication with a person, it is necessary to estimate the person's head position. However, eye contact becomes difficult due to the mechanical delay of the robot when the person is moving. Owing to these issues, it is important to conduct a head-position prediction to mitigate the effect of the delay in the robot motion. Based on the fact that humans turn their heads before changing direction while walking, we hypothesized that the accuracy of three-dimensional (3D) head-position prediction from a first-person view can be improved by considering the head pose. We compared our method with a conventional Kalman filter-based approach, and found our method to be more accurate. The experiment results show that considering the head pose helps improve the accuracy of 3D head-position prediction.
Implementation of the Problem Based Learning Model With E-Learning Media Through the Google Classroom Application in Introduction to Information Technology Courses

Good education can be realized by providing effective support in the teaching and learning process. One of the supports in the learning process is the use of models and media, including the Problem Based Learning model with E-learning media through the Classroom application. This study aims to determine the improvement of student learning outcomes of Class A semester 1 Uniba Madura and to find out how students respond after applying the Problem Based Learning model with E-learning media through the Classroom application. The method used in this research is Pre-Experimental Design type One Group Pretest-Posttest Design. The analysis of learning outcomes obtained shows that the t-count value in class A semester 1 is 2.097 and class B semester 1 is 2.100 with a t table of 2.056. This shows that after applying the Probem Based Learning learning model with E-learning media using Classroom, learning outcomes have increased. While the N-Gain value obtained results of 0.6 and 0.5 which indicate moderate criteria. This means that students' understanding of mathematical concepts increases. In addition, the results of the analysis of student responses showed scores in the range of 60%-80%. This states that student responses show good satisfaction with the given model.
How Your Cultural Dataset is Connected to the Rest Linked Open Data?

More and more publishers tend to create and upload their data as digital open data, and this is also the case for the Cultural Heritage (CH) domain. For facilitating their Data Interchange, Integration, Preservation and Management, publishers tend to create their data as Linked Open Data (LOD) and connect them with existing LOD datasets that belong to the popular LOD Cloud, which contains over 1,300 datasets (including more than 150 datasets of CH domain). Due to the high amount of available LOD datasets, it is not trivial to find all the datasets having commonalities (e.g., common entities) with a given dataset at real time. However, it can be of primary importance for several tasks to connect these datasets, for being able to answer more queries and in a more complete manner (e.g., for better understanding our history), for enriching the information of a given entity (e.g., for a book, a historical person, an event), for estimating the veracity of data, etc. For this reason, we present a research prototype, called ConnectionChecker, which receives as input a LOD Dataset, computes and shows the connections to hundreds of LOD Cloud datasets through LODsyndesis knowledge graph, and offers several measurements, visualizations and metadata for the given dataset. We describe how one can exploit ConnectionChecker for their own dataset, and we provide use cases for the CH domain, by using two real linked CH datasets: a) a dataset from the National Library of Netherlands, and b) a dataset for World War I from the Universities of Aalto and Helsinki.
Comprehensive Formal Modeling and Automatic Vulnerability Detection for a Bitcoin-Compatible Mixing Protocol

Blindcoin applies a Bitcoin-compatible mixing protocol with a blind signature scheme to improve the design of the popular Mixcoin. Given the openness of Bitcoin and the decentralization of the P2P network, it is imperative to formally analyze whether the malicious can break the security goals of the Blindcoin protocol. This work proposes a symbolic model for Blindcoin and conducts comprehensive formal verification. Fine-grained security goals of Blindcoin are formalized and subsequently encoded as model lemmas. However, it is challenging to verify the Blindcoin in a formal and automatic way. To tackle the challenges, we propose a tool-friendly symbolic model that can capture the semantics of multi-layers of Bitcoin and the features of Blindcoin. Our formal verification covers real-world security scenarios and discovers the Blindcoin vulnerabilities without human interaction. Furthermore, we offer several suggestions to fix the detected Blindcoin vulnerabilities and discuss the generalization of the proposed model.
Estimation of the probabilistic description of quantum physical systems

Finding the analytic solution of the Liouville-von Neumann equation is mathematically very challenging. Thus, in this paper, we propose an alternative method that estimates and predicts the dynamics of quantum systems through the estimation of the probability distribution describing the vectorisation of the associated density operator involved in the Liouville-von Neumann equation. In particular, we show that the dynamics of the density operator and measurement states of a quantum system can be characterised by complex Gaussian probability density functions. Unlike conventional methods that provide a deterministic estimate of the density operator, our probabilistic framework provide a general estimation method that also characterises the uncertainty and imperfections in quantum systems due to noises and other sources. Comparison of exact and estimated solutions for a real molecule interacting with an external electric field demonstrates the effectiveness of the proposed estimation method.
Web Extension for Phishing URL Identification

Phishing attacks have been the most consistent and emerging cyber-attack among other social engineering attacks. In spite of several strict mechanisms and policies to identify and avoid phishing websites, new techniques and tools are used by the attackers to exploit the security measures. This paper proposes a system that uses machine learning algorithm to identify phishing URL based on web parameters. Among several machine learning algorithms, Random Forest algorithm is chosen as it provides more accuracy and performance for the identification process. This system is implemented as a web extension in order to increase the performance of the identification process and to decrease the overhead of the system caused when using a standalone software.
Persons with Disabilities in COVID 19: Bangladesh Perspective

United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) Persons with disabilities (PWD) remain vulnerable and susceptible to discrimination in crisis, whereas they are entitled to inclusive protection. During the COVID-19 pandemic, global health and other public services nearly collapsed, putting the disability needs on the backfoot. Hence, the World Health Organization (WHO) and the United Nations (UN) provided comprehensive and disability-inclusive disaster management guidelines for the member states to secure PWD. Even though Bangladesh adopted response plans in compliance, disability integration failed to reach the level of expectation. The cardinal disability rights instrument, the United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) 2006, obliges the state parties to target and mainstream disability rights during risks and emergencies. Though Bangladesh has the Rights and Protection of Person with Disability Act (RPPDA) 2013 in compliance, the scope of the legislation during crises remains uncertain. This qualitative research investigates whether the current safeguard measures are adequate in protecting PWD of Bangladesh amid the COVID-19 pandemic. The paper first critically examines the existing national disability safeguard measures and then explores the extent of the protection during the pandemic in compliance with international guidance. Relevant primary sources like national and international legislation, guidelines, and policies are analyzed. Secondary sources like authoritative journal articles, books, and newspaper reports are explored to investigate the disability situation. The pandemic management strategies in Bangladesh fall short in disability protection without comprehensive planning and adequate enforcement mechanism. Disability-inclusive disaster regulatory measures and proper monitoring are necessary to yield better protection in future crises.
Embodied Learning

Embodied learning method provides interaction with a real or virtual world when we start a learning process by using this method. For example, physical movement plays a significant role when we learn a new sport. The purpose of embodied learning will make the mind and body work together to enhance the learning process with physical feedback and actions. This technology collects the physical, biological, personal data, visual systems that keep track of movement with wearable sensors, and mobile devices respond to behavior such as tilting and supporting the motion. This chapter introduces Embodied learning, its principles on bringing it to the classroom, and how it can help the students.