Reviewerly/exHarmony huggingface dataset:
Semantic Segmentation of High-Resolution Remote Sensing Images Based on Sparse Self-Attention and Feature Alignment

Semantic segmentation of high-resolution remote sensing images (HRSI) is significant, yet challenging. Recently, several research works have utilized the self-attention operation to capture global dependencies. HRSI have complex scenes and rich details, and the implementation of self-attention on a whole image will introduce redundant information and interfere with semantic segmentation. The detail recovery of HRSI is another challenging aspect of semantic segmentation. Several networks use up-sampling, skip-connections, parallel structure, and enhanced edge features to obtain more precise results. However, the above methods ignore the misalignment of features with different resolutions, which affects the accuracy of the segmentation results. To resolve these problems, this paper proposes a semantic segmentation network based on sparse self-attention and feature alignment (SAANet). Specifically, the sparse position self-attention module (SPAM) divides, rearranges, and resorts the feature maps in the position dimension and performs position attention operations (PAM) in rearranged and restored sub-regions, respectively. Meanwhile, the proposed sparse channel self-attention module (SCAM) groups, rearranges, and resorts the feature maps in the channel dimension and performs channel attention operations (CAM) in the rearranged and restored sub-channels, respectively. SPAM and SCAM effectively model long-range context information and interdependencies between channels, while reducing the introduction of redundant information. Finally, the feature alignment module (FAM) utilizes convolutions to obtain a learnable offset map and aligns feature maps with different resolutions, helping to recover details and refine feature representations. Extensive experiments conducted on the ISPRS Vaihingen, Potsdam, and LoveDA datasets demonstrate that the proposed method precedes general semantic segmentation- and self-attention-based networks.
Industry 4.0 and Industrial Robots: A Study from the Perspective of Manufacturing Company Employees

Background: Human–robot collaboration is essential for efficient manufacturing and logistics as robots are increasingly used. Using industrial robots as part of an automation system results in many competitive benefits, including improved quality, efficiency, productivity, and reduced waste and errors. When robots are used in production, human coworkers’ psychological factors can disrupt operations. This study aims to examine the effect of employees’ negative attitudes toward robots on their acceptance of robot technology in manufacturing workplaces. Methods: A survey was conducted with employees in manufacturing companies to collect data on their attitudes towards robots and their willingness to work with them. Data was collected from 499 factory workers in Istanbul using a convenience sampling method, which allowed for the measurement of variables and the analysis of their effects on each other. To analyze the data, structural equation modeling was used. Results: The results indicate that negative attitudes towards robots have a significant negative effect on the acceptance of robot technology in manufacturing workplaces. However, trust in robots was found to be a positive predictor of acceptance. Conclusions: These findings have important implications for manufacturing companies seeking to integrate robot technology into their operations. Addressing employees’ negative attitudes towards robots and building trust in robot technology can increase the acceptance of robots in manufacturing workplaces, leading to improved efficiency and productivity.
Adaptive graph regularization and self-expression for noise-aware feature selection

Many traditional unsupervised feature selection algorithms utilize manifold information to mine the local structure of the data. However, the noise existing in the raw data reduces the accuracy of the manifold information of data, which affects the learning effect of the entire algorithm. In order to solve the above problems and more fully find the internal structure inside the data, this paper proposes an adaptive graph regularization and self-expression for noise-aware feature selection (ASNFS). Firstly, the algorithm adopts non-negative matrix factorization to decompose the raw data matrix, and utilizes the low-dimensional matrix generated after decomposition to replace the raw high-dimensional data matrix. This allows the algorithm to reveal some internal structural information of the raw data while reducing the dimensionality of the data. ASNFS also introduces the orthogonal basis clustering with excellent clustering effect, and the interpretability of the algorithm is enhanced. Secondly, in addition to preserving the manifold information in the low-dimensional projection subspace, the algorithm also preserves the manifold information in the non-negative matrix factorization subspace. Meanwhile, the adaptive graph regularization term added to the objective function enables the algorithm to continuously update the similarity matrix. It can effectively remove the noise inside the raw data, and prevent the occurrence of over-fitting phenomenon of the experimental results caused by the fixed similarity matrix. Finally, the similarity matrix retains the local structure information of the data with each iteration, and the results of the feature selection are reused for the construction of the similarity matrix. ASNFS adopts an alternate iterative method to optimize the objective function, which is simple and effective. Then, the algorithm complexity and convergence are analyzed. ASNFS is compared with seven feature selection algorithms on nine datasets, and the experimental results reflect the effectiveness of ASNFS in feature selection.
SCEOMOO: A novel Subspace Clustering approach using Evolutionary algorithm, Off-spring generation and Multi-Objective Optimization

Subspace clustering is an extension of conventional clustering techniques that seeks to find clusters in different subspaces within a data set with higher dimensions. But, recent research results indicate that there are few challenges to be addressed efficiently, such as finding number of subspace clusters automatically and getting optimal subspaces by satisfying multiple objective criteria. This paper focused on these two challenges and proposed a framework called Subspace Clustering using Evolutionary algorithm, Off-Spring generation and Multi-Objective Optimization (SCEOMOO) to find the optimal subspace clusters. The proposed SCEOMOO model is tested on six standard data sets with different validity indices and results are compared with well known existing subspace clustering methods. The comparative performance analysis reveals that the proposed SCEOMOO gives significant improvements in the performance with respect to some of the well known existing models.
Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation

Path reasoning is a notable recommendation approach that models high-order user-product relations, based on a Knowledge Graph (KG). This approach can extract reasoning paths between recommended products and already experienced products and, then, turn such paths into textual explanations for the user. Unfortunately, evaluation protocols in this field appear heterogeneous and limited, making it hard to contextualize the impact of the existing methods. In this paper, we replicated three state-of-the-art relevant path reasoning recommendation methods proposed in top-tier conferences. Under a common evaluation protocol, based on two public data sets and in comparison with other knowledge-aware methods, we then studied the extent to which they meet recommendation utility and beyond objectives, explanation quality, and consumer and provider fairness. Our study provides a picture of the progress in this field, highlighting open issues and future directions. Source code: https://github.com/giacoballoccu/rep-path-reasoning-recsys .
From Baseline to Top Performer: A Reproducibility Study of Approaches at the TREC 2021 Conversational Assistance Track

This paper reports on an effort of reproducing the organizers' baseline as well as the top performing participant submission at the 2021 edition of the TREC Conversational Assistance track. TREC systems are commonly regarded as reference points for effectiveness comparison. Yet, the papers accompanying them have less strict requirements than peer-reviewed publications, which can make reproducibility challenging. Our results indicate that key practical information is indeed missing. While the results can be reproduced within a 19% relative margin with respect to the main evaluation measure, the relative difference between the baseline and the top performing approach shrinks from the reported 18% to 5%. Additionally, we report on a new set of experiments aimed at understanding the impact of various pipeline components. We show that end-to-end system performance can indeed benefit from advanced retrieval techniques in either stage of a two-stage retrieval pipeline. We also measure the impact of the dataset used for fine-tuning the query rewriter and find that employing different query rewriting methods in different stages of the retrieval pipeline might be beneficial. Moreover, these results are shown to generalize across the 2020 and 2021 editions of the track. We conclude our study with a list of lessons learned and practical suggestions.
Overview of Touché 2023: Argument and Causal Retrieval

The goal of Touché is to foster and support the development of technologies for argument and causal retrieval and analysis. For the fourth time, we organize the Touché lab featuring four shared tasks: (a) argument retrieval for controversial topics, where participants retrieve web documents that contain high-quality argumentation and detect the argument stance, (b) causal retrieval, where participants retrieve documents that contain causal statements from a generic web crawl and detect the causal stance, (c) image retrieval for arguments, where participants retrieve images showing support or opposition to some stance from a focused web crawl, and (d) intra-multilingual multi-target stance classification, where participants detect the stance of comments on proposals from the multilingual participatory democracy platform CoFE. In this paper, we briefly summarize the results of Touché 2022 and describe the planned setup for the fourth lab edition at CLEF 2023.
The 6th International Workshop on Narrative Extraction from Texts: Text2Story 2023

Over these past five years, significant breakthroughs, led by Transformers and large language models, have been made in understanding natural language text. However, the ability to capture contextual nuances in longer texts is still an elusive goal, let alone the understanding of consistent fine-grained narrative structures in text. These unsolved challenges and the interest in the community are at the basis of the sixth edition of Text2Story workshop to be held in Dublin on April 2nd, 2023 in conjunction with the 45th European Conference on Information Retrieval (ECIR'23). In its sixth edition, we aim to bring to the forefront the challenges involved in understanding the structure of narratives and in incorporating their representation in well-established models, as well as in modern architectures (e.g., transformers) which are now common and form the backbone of almost every IR and NLP application. It is hoped that the workshop will provide a common forum to consolidate the multi-disciplinary efforts and foster discussions to identify the wide-ranging issues related to the narrative extraction and generation task. Text2Story includes sessions devoted to full research papers, work-in-progress, demos and dissemination papers, keynote talks and space for an informal discussion of the methods, of the challenges and of the future of this research area.
A General Regression Neural Network based Blurred Image Restoration

Image distortion may result from a variety of factors, such as changes in electronic imaging equipment that create noise. The goal of blur image alignment is to determine which images are blurred and to restore them. Due to this, a unique blurred image restoration approach is developed, which consists of a point spread function, canny edge detection, GLCM extraction, and general regression neural network for identifying the type of blurred images, and a wiener filter for restoring the image. This technique uses a combination of the General Regression Neural Network (GRNN) and Deep Neural Network (DNN) to detect the kind of a blur and determine the calibration efficiency of the DNN and the degradation efficiency of the GRNN. The mean square error (MSE), the covariance factor, and the peak signal-to-noise ratio (PSNR) are used to assess the effectiveness of the proposed method. In comparison to conventional procedures, the proposed method yields superior outcomes.
An Examination on Implementation of Deep Fake in Images Through Deep Learning

Deep learning has been successfully used to address a variety of challenging problems, including large-scale information analysis, PC vision, and human-level control. However, deep learning engines have also been used to create software that may create risk protection, a majority rules system, and public safety. Deepfake is one of those recently developed deep learning-controlled apps. Deeply false calculations can produce phoney images and recordings that are difficult for viewers to distinguish from real ones. Deepfake is a tool used in manufactured media to produce phoney visual and auditory content based on a person's current media consumption. To make the deepfake appear plausible, fake media is used in place of a person's voice and face. Deepfakes have been used to spread misinformation as a result of this. Deepfakes are videos or pictures in which the face of one person is replaced with the face of another, usually a more famous person. With the most recent technological developments, even someone with little mechanical experience can create these recordings. This gives them the ability to mimic a prominent public figure, such as a president or VIP, increasing the potential that deepfakes will be used for other despicable ends.
Identification of Fake accounts in social media using machine learning

Fake profiles are used in advanced persistent threats and are also used in other nefarious activities. As we all know, Globally, billions of individuals utilize Social networking sites like Facebook, Twitter, LinkedIn, Instagram, etc. to establish connections. A new era of networking has been ushered in by social networks simplicity and accessibility. At the same time, various types of scammers are drawn to these social media platforms. These scammers make fake profiles to spread their content and carry out scams. In this project, we used Deep Neural Networking and Machine Learning algorithms namely Artificial Neural Networks(ANN), Random Forest and Support vector machine(SVM) algorithms to assess the likelihood that Facebook account information is accurate or not. The dataset used in this paper is taken from GitHub which is a Facebook profile Dataset to identify faux and genuine profiles, also we have described the associated classes and libraries. Here we are going to predict the faux and real profiles using the best accurate model after comparing the outcomes of the three techniques employed.
Towards faster settlement in HTLC-based Cross-Chain Atomic Swaps

Hashed Timelock (HTLC)-based atomic swap protocols enable the exchange of coins between two or more parties without relying on a trusted entity. This protocol is like the American call option without premium. It allows the finalization of a deal within a certain period. This puts the swap initiator at liberty to delay before deciding to proceed with the deal. If she finds the deal unprofitable, she just waits for the time-period of the contract to elapse. However, the counterparty is at a loss since his assets remain locked in the contract. The best he can do is to predict the initiator’s behavior based on the asset’s price fluctuation in the future. But it is difficult to predict as cryptocurrencies are quite volatile, and their price fluctuates abruptly. We perform a game theoretic analysis of HTLC-based atomic cross-chain swap to predict whether a swap will succeed or not. From the strategic behavior of the players, we infer that this model lacks fairness. We propose Quick Swap, a two-party protocol based on hashlock and timelock that fosters faster settlement of the swap. The parties are required to lock griefing-premium along with the principal amount. If the party griefs, he ends up paying the griefing-premium. If a party finds a deal unfavorable, he has the provision to cancel the swap. We prove that Quick Swap is more participant-friendly than HTLC-based atomic swap. Our work is the first to propose a protocol to ensure fairness of atomic-swap in a cyclic multi-party setting.
Natural language processing using text augmentation for chatbot

Chatbot is an application that helps in virtual interactions. Chatbot simulates the interaction in a more effective way by natural language processing. An artificial neural network makes better understanding of the user intent and entity. There are two broad classifications for chatbot i.e rule based and conversational AI chatbot. In the case of rule based chatbot, for all user inquiries automated responses are generated based on set of rules. Whereas in conversational AI chatbot, it requires better natural language processing for understanding user inquiries. Text augmentation provides the platform for the chatbot system to train more on the limited amount of the dataset. This paper focuses more on the text augmentation techniques for creating the robust chatbot system using the text augmentation open source library NLPAug. The recent studies outline that machine learning models are more vulnerable to even for small adversarial attack. The result shows that NLPAug perform the text augmentation in three levels those are character level, word level and sentence level to avoid the adversarial attacks. Text augmentation enables the chatbot to provide accurate result for the user query. Multimedia files can be augmented in the future work.
Text Summarization using Transformer Model

The increased availability of online feedback or review tools, and the enormous amount of information on these platforms, have made text summarization a vital research area in natural language processing. Instead of potential consumers going through thousands of reviews to get needed information, summarization will enable them to see a concise form of a chunk of reviews with relevant information. News and scientific articles have been used in text summarization models. This study proposes a text summarization method based on the Text-to- Text Transfer Transformer (T5) model. We use the University of California, Irvine's (UCI) drug reviews dataset. We manually created human summaries for the ten most useful reviews of a particular drug for 500 different drugs from the dataset. We fine-tune the Text-to- Text Transfer Transformer (T5) model to perform abstractive text summarization. The model's effectiveness was evaluated using the ROUGE metrics, and our model achieved an average of ROUGE1, ROUGE2, and ROUGEL scores of 45.62, 25.58, and 36.53, respectively. We also fine-tuned this model on a standard dataset(BBC News Dataset) previously used for text summarization and got average ROUGE1, ROUGE2, and ROUGEL scores of 69.05, 59.70, and 52.97, respectively.
Locomotion-Based UAV Control Towards the Internet of Senses

The emergence of 5th-generation networks and the introduction of the ultra-low latency Internet, namely, tactile internet, by the International Telecommunication Union (ITU), has opened up a wide range of applications. xr, holoportation, and remote control of machines are among the ones that would revolutionize the future of factories, smart cities, and digital healthcare. vr technology provides users with highly realistic visual and auditory experiences, enabling a high sense of immersion and embodiment in virtual environments. In the real world, however, we use more senses than only vision and hearing to perceive our surroundings. Particularly, tactile sensation is the only bidirectional modality that enables us to perceive and interact with the objects and surfaces around us. This paper introduces a real-life testbed uav-based system that leverages different technologies, including vr, 360∘ video streaming over 4G/5G, and edge computing to enable 6dof view with haptic feedback for a maximized immersion. The demonstration videos of the testbed are made publicly available on the following links: https://www.youtube.com/watch?v=WTUvWkDJFIQ and https://www.youtube.com/watch?v=Bmc9ddydjYY.
Path Planning for Autonomous Underwater Vehicles Based on an Improved Artificial Jellyfish Search Algorithm in Multi-Obstacle Ocean Current Environment

Path planning for autonomous underwater vehicles (AUVs) is a key research focus in the marine domain, requiring consideration of the underwater environment’s complexity and the efficiency of the planning algorithms. Firstly, this paper uses the grid method to construct a realistic ocean terrain for the underwater environment and incorporates obstacles and ocean currents. Also, the AUV’s movement strategy and posture are illustrated and constrained by a visual search strategy. Secondly, an improved artificial jellyfish search (IJS) algorithm is to overcome the drawbacks of low convergence accuracy and adaptability of the basic algorithm. The IJS algorithm determines the optimal path point by optimizing the objective function. This objective function not only takes into account factors such as path navigation time and path safety but also designs an ocean current disturbance model, which facilitates the AUV to successfully reach the target point while avoiding obstacles and strong side currents. Finally, the optimal smoothed path is output using a cubic spline midpoint interpolation method. Multiple simulation experiments are carried out on the constructed environment model. The comparison results show that the IJS algorithm with a short running time has the optimal time cost and ocean current penalty cost for the planned path. In addition, the IJS algorithm is also shown to be adaptable in the field of multi-AUV movements.