Reviewerly/exHarmony huggingface dataset:
ECBS-based Suboptimal Multi-robots Path Planning Algorithm

This paper focuses on the multi-robot path planning problem. Traditional algorithms add path constraints to robots by "exhaustive method" until all robots’ conflicts are eliminated, which has high completeness but low efficiency when dealing with large space and high complexity problems, and usually cannot find the target path within the specified time. To solve these problems, two ECBS-based suboptimal path planning algorithms for multiple robots are proposed in this paper. One classifies conflicts according to their characteristics, and different categories are equipped with different conflict resolution approaches, thus improving the search efficiency. The other one is based on the first scheme to establish a set of real-time updated dynamic priority assignment criteria to give priority to low-priority robots for path planning, thus giving way to high-priority robots and reducing the search time. Finally, it is experimentally verified that both optimization algorithms can retain the completeness of the traditional algorithm while reducing the size of the constraint tree. Only a small increase in path length boosts the experimental success rate increase and significantly reduces the computation time, improving the overall search efficiency. And as the number of robots increases, the optimization effect becomes more obvious.
Self-supervised Lunar Surface Image Feature Point Detection and Matching

Traditional visual odometry is difficult to extract features in the sparse texture environment of the moon surface. In this paper, a self-supervised learning convolutional neural network is proposed for feature extraction and a Sinkhorn-based matching approach for feature matching. We use homography and Gaussian blur to construct the self-supervised learning dataset to solve the difficulties of data labeling. Feature matching based on the score of interest points further improves the matching accuracy. Compared to other traditional feature points such as ORB, SHIF, our algorithm, trained on pybullet-built lunar surface simulation images, can achieve accurate matching with better robustness. Compared with other learning algorithms like SuperPoint, our algorithm has an advantage in speed and has less error in homography.
SP-Net: A Sparse Convolution and Point-Encoding Enhanced Network for 3D Object Detection in LiDAR Point Clouds

LiDAR 3D object detection for autonomous driving is an important issue. To address this issue, this paper provides a two-stage anchor-based solution. Firstly, voxel feature encoding and sparse convolution networks were employed for proposal generation. Secondly, we performed voxel feature aggregation and hierarchical feature learning in combination with the proposals, multi-level and multiscale voxel features, point encoding and voxel-wise ROI pooling. The aggregated object-wise voxel features were utilized to refine the proposals. Extensive experiments were conducted on three challenging datasets: KITTI, nuScenes and Waymo. Experimental results demonstrated that our SP-Net can effectively achieve multi-category 3D object detection in diverse scenarios offering satisfactory accuracy and speed (28.6 FPS).
Evaluation of Continual Learning Methods for Object Hardness Recognition

How to solve the problem of continual learning of robotic tactile perception in an open, dynamic environment is a pressing task. Although continual learning has been extensively studied in computer vision, the evaluation of those algorithms does not provide insights on whether their solutions may help continual learning in the context of tactile robotics. In this paper, we propose a continual learning scenario in the robotic tactile domain, and design experiments with tactile hardness dataset that allow us to compare the performance of many continual learning algorithms in the tactile domain. We examine the performance of regularization-based and replay methods in the tactile domain and discuss the effect of replay number on model performance. We show that regularization-based methods do not perform well on the tactile dataset. On the contrary, replay methods can maintain better anti-forgetting performance, but the number of model parameters will be larger, and the training time will be longer. We also found that the replay ratio should be higher than 0.2 to maintain the performance of the replay method on the tactile hardness dataset.
Spatial-Temporal-Rule-based Traffic Movement Status Interpretation at Intersections *

Traffic light is the most significant component of road transportation. Traffic lights control the traffic movement at intersections by allowing the movement of some lanes and forbidding that of the others to ensure safety and efficiency. It is important for intelligent vehicles to understand the meaning of traffic lights. Conventional research works on traffic light detection and tracking consider each light independently, so the occlusion of some lights may mislead the decision of the vehicle. Since all traffic lights at an intersection work together, it is more essential to understand the traffic movement status controlled by the traffic lights rather than the perception of a single light. Therefore, this paper proposes a spatial-temporal-rule-based framework to interpret the current traffic movement status and predict the next status. The spatial information, including position and class, is obtained by traffic light detection. The temporal information is perceived by the proposed topologic-feature-based tracking method. The rule information is recognized by combination of spatial and temporal information. A dual-view camera system consisting of a telephoto camera and a wide-angle camera is adopted to ensure long perception distance and wide coverage simultaneously. The successful tracking of traffic lights from 200 m to 10 m away provides enough data to recognize the phase-change rule, which leads to correct interpretation and prediction. A customized dataset was collected, and experiments were conducted to illustrate the capability of the proposed method.
Comparison of Transformer Models for Performance on Domain Specific Texts: A Systematic Evaluation of Intrinsic Model Performance

This paper presents a systematic evaluation of transformer models for Natural Language Processing (NLP) tasks on domain-specific text. The objective of the study is to understand if intrinsic performance statistics can help deter-mine how suitable a transformer model is for a given target corpus and NLP task, where directly measuring performance is not possible because of lacking labeled data. A number of pre-trained transformer models are considered, including BERT, BART, SciBERT, RoBERTa, and XLNET which are evaluated by measuring the intrinsic performance of such models, by examining the distribution of sentence embedding vectors in the sematic hyperspace, prior to applying any down-stream NLP task. We consider this intrinsic model performance for domain-specific texts (in our case on military do-main texts as well as texts on which the models were originally trained) in the case when that text deviates from the domain on which the model was originally trained. The results of the study provide insights into the characteristics and intrinsic performance of different transformer models on domain-specific text. Our results can be used to inform the selection of a model for downstream NLP tasks given an input dataset. Overall, the study contributes to the understanding of the trade-offs between different transformer models for NLP tasks on domain-specific texts, and how to make informed decisions about which model to use for a specific input dataset. This pa-per was originally presented at the NATO Science and Tech-nology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 RSY — the ICMCIS, held in Skopje, North Macedonia, 16-17 May 2023.
An Efficient 3D Registration Means for Dense Point Clouds Obtained by a Real-Time Structured Light System

With 3D reconstruction techniques being applied in a large number of fields and producing good results, 3D reconstruction based on structured light has gained wide attention. In this paper, based on the TSDF(Truncated Signed Distance Function) model, a method for fusing point clouds was proposed to efficiently register frames captured by a real-time structured light system. We present the mathematical model and related algorithms of our system, and experimentally evaluate the reconstruction effect of our method. The results show our system is able to perform dense 3D reconstruction with high accuracy, efficiency and geometric quality.
TM-DBSCAN: A Spatio-Temporal Memory Density Clustering for Battle Space Object Aggregation

The aggregation of Battle Space Objects (BSOs) within a situational picture is the combination of BSOs into units, i.e. higher organizational entities. Aggregation is part of situational assessment. As has been shown by [1], aggregation can be achieved by clustering, in particular Density-Based Spatial Clustering of Applications with Noise (DBSCAN). In this paper, we present a significant improvement of the DBSCAN approach, namely Temporal Memory (TM) DBSCAN: the situational picture is enhanced with a temporal memory (TM) so that previous situations can be taken into account for clustering the current situational picture. Thereby, it becomes possible to “remember” previous but still valid divisions of BSOs as well as to recognize outdated BSO representations that should be ignored. We describe the TM-DBSCAN algorithm in detail and explain its effects on aggregation. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 IST-200 RSY — the ICMCIS, held in Skopje, North Macedonia, 16–17 May 2023.
Multi-Document Summarization: A Comparative Evaluation

This paper is aimed at evaluating state-of-the-art models for Multi-document Summarization (MDS) on different types of datasets in various domains and investigating the limitations of existing models to determine future research directions. To address this gap, we conducted an extensive literature review to identify state-of-the-art models and datasets. We analyzed the performance of PRIMERA and PEGASUS models on BigSurvey-MDS and MS^2 datasets, which posed unique challenges due to their varied domains. Our findings show that the General-Purpose Pretrained Model LED outperforms PRIMERA and PEGASUS on the MS^2 dataset. We used the ROUGE score as a performance metric to evaluate the identified models on different datasets. Our study provides valuable insights into the models' strengths and weaknesses, as well as their applicability in different domains. This work serves as a reference for future MDS research and contributes to the development of accurate and robust models which can be utilized on demanding datasets with academically and/or scientifically complex data as well as generalized, relatively simple datasets.
Building a Digital Bridge Between Sri Lankan Farmers and Retailers: Conceptual Mobile Application Prototype

As an agricultural country, Sri Lanka has the potential for cultivation which uses 30% of the total labor force directly or indirectly. Hence there is a vacuum for an appropriate platform to integrate the farmers with other concerned parties also arming them with essential information to establish an effective and efficient process demanded by agriculture 4.0, also facilitating the novel distributed database or ledger technologies. This paper focuses on developing the “Agri Sri” mobile application that connects farmers with retailers, creating a marketplace to enable convenient and more efficient transactions, and providing essential information to the farmers. This approach aims to create an online market to minimize the effect of intermediaries while connecting farmers and retailers with a profitable price for their agricultural products allowing direct transactions between the two. With the proposed platform the farmers could find the right buyers at competitive open bids incorporating the blockchain distributed ledger technology. The proposed platform also allows for smarter synchronized product supply chains by leveraging the experiences of its users and finding innovative ways to improve the way they interact. The proposed platform also facilitates features like an online marketplace facilitating buying, selling, renting, database, prediction and analyzing, calling center, updates, and notifications. The “Agri Sri” also facilitates connecting and collaborating with retailers on market trends, pricing, and distribution methods with local retailers, providing real-time price comparison and location tracking features to help the rural economy. It is expected that the proposed platform will create a conducive environment with well-distributed facilities to support the seamless integration demanded by precision agriculture, the Internet of Things, and the utilization of big data to achieve a greater efficiency level demanded by the challenges of the rising population and climatic change issues.
SHADE: Semantic Hypernym Annotator for Domain-Specific Entities - Dungeons and Dragons Domain Use Case

Manual data annotation is an important NLP task but one that takes a considerable amount of resources and effort. In spite of the costs, labelling and categorizing entities are essential for NLP tasks such as semantic evaluation. Even though annotation can be done by non-experts in most cases, due to the fact that this requires human labour, the process is costly. Another major challenge encountered in data annotation is maintaining annotation consistency. Annotation efforts are typically carried out by teams of multiple annotators. The annotations need to maintain consistency in relation to both the domain truth and annotation format while reducing human errors. Annotating a specialized domain that deviates significantly from the general domain, such as fantasy literature, will see a significant amount of human error and annotator disagreement. So it is vital that proper guidelines and error reduction mechanisms are enforced. One way to enforce these constraints is by using a specialized application. Such an app can ensure that the notations are consistent, and the labels can be pre-defined or restricted reducing the room for errors. In this paper, we present SHADE, an annotation software that can be used to annotate entities in the high fantasy literature domain Dungeons and Dragons extracted from the Forgotten Realms Fandom Wiki.
BERT Layer Weighting Comparision with Short Text Classification

BERT [Bidirectional Encoder Representations from Transformers] was the first type of neural model which was used hugely with several downstream tasks specially with the generated embedding representation for text classification. But with the existing literature different layer combination was not tested with concatenation as well as mean averaging techniques to represent the features of a text record for short text classification performances. Moreover, it was not properly evaluated with different machine learning algorithms with short text. Therefore, this research work focused on using seven short text type datasets with BERT embedding to identify the best embedding representation and the best machine learning algorithm. Based on conducted experiment, usage of all layers with concatenation techniques was identified as the best embedding representation and bagging as well as support vector machine was identified as the best machine learning algorithm for short text classification. Performance improvements with bagging was more than 6–8% over the other algorithm and concatenation of all layers gave around 5–6 % performance improvements over the other techniques.
A Truly Decentralized Blockchain Consensus Protocol that Avoids Concentration of Power

A consensus protocol plays a vital role in any blockchain platform by ensuring all components agree on common data values and follow the same course of action, even in the presence of defective components. This research project has discovered a problem associated with contemporary blockchain consensus protocols, which has an impact on both users of blockchain technology and investors in cryptocurrencies. The question of whether current blockchain platforms are truly decentralized has been raised by the blockchain community over a period of time. The most popular blockchains use Proof of Work (PoW), Proof of Stake (PoS), or a combination of both, but both systems have limitations in terms of decentralization. Several alternative consensus protocols have emerged recently, but none have achieved total decentralization or welcomed any level user to be the next miner or forger. Therefore, true decentralization of a blockchain system is more challenging as most of the popular consensus protocols were invented to accommodate decentralization but eventually, driven by the centralization of power. This paper proposes a truly decentralized consensus protocol that is capable of avoiding the centralization of power even in the future regardless of the number of peers. The protocol also makes sure it avoids the use of specialized hardware resources, decreases the level of energy consumption, and encourages network activities. Additionally, the novel consensus protocol offers a more democratic and fair approach to blockchain consensus.
Deep Learning Based Low Light Enhancements for Advanced Driver-Assistance Systems at Night

In the modern automobile industry, full/partial autonomous driving has become a common phenomenon. These systems heavily rely on sensors, both visual and non-visual, such as cameras and LIDAR, to interpret their surroundings and aid the decision-making process. Recently, visual-only sensing devices, mainly cameras, have emerged as the primary sensor for obtaining an immersive feeling in autonomous driving. However, the performance of such systems is heavily constrained by fluctuating light intensity and shadows. The primary goal of the presented research is to develop an effective low-light image enhancement system that improves the perception and inter-pretability of images taken in low-light driving environments. The developed image enhancement architecture demonstrates remarkable capabilities, surpassing existing models in terms of real-time suitability, PSNR (Peak Signal-to-Noise Ratio), and SSIM (Structural Similarity Index) values. Additionally, the model has been specifically designed to have low resource usage, making it an ideal solution for deployment on low-end devices. With significantly lower inference time, the model is exceptionally well-suited for real-time operation in autonomous driving applications. Furthermore, this architecture provides an efficient and effective solution not only for autonomous driving but also for a range of applications in video/image processing and computer vision, such as surveillance systems, medical imaging, astronomy, underwater imaging, and the mining industry. Finally, the relatively short training time makes it an attractive option for researchers and developers. Overall, this architecture represents a significant advancement in low-light image enhancement and autonomous vehicles. A demo of the system and the source code of the proposed LLE_UNET can be found on https://github.com/pamudu123ILow_Light_Image_Enhancement.
Long-Run Real-Time Human Tracking at Low Light Condition

In this paper, we present a novel algorithm Long-Run Low-Light Tracker (LL_Tracker) for long-run, real-time object tracking in challenging environmental conditions such as low light and occlusion. The proposed LL_Tracker algorithm utilizes Retinex based color-enhancement along with Kalman filter and template matching-based object identification and tracking. Furthermore, the algorithm follows a multi-objective optimization approach to improve tracking accuracy. We compare the performance of LL_Tracker algorithm to several existing tracking algorithms including KCF, CSRT, GOTURN, MIL, and MOSSE in terms of tracking duration, accuracy, and robustness under occlusion and low light conditions. The results show that the LL_Tracker algorithm outperforms all other algorithms in each evaluation parameter. It tracks accurately for 100% of the video length achieving superior performance in challenging environmental conditions and real-time compatibility while others stop at 50% or below of their duration. A demo of the system and the source code can be found on https://github.com/Siva3551/LLT-for-long-run-low-light-tracking
Enhanced YOLOv4 for Facilitating Public Safety Management amidst Protests and Riots

Public safety management during protests is a critical issue requiring effective and efficient measures to minimise harm to law enforcement personnel and protesters. Our research introduces a faster variant of the You Only Look Once version 4 (YOLOv4) algorithm, specifically designed to identify objects frequently employed as weapons or for starting fires in public demonstrations and riots. We also developed a new dataset containing knives, swords, axes, stones, sticks, and objects commonly associated with arson. The proposed Accelerated YOLOv4 algorithm was trained and tested using the developed dataset. Its performance was compared to the standard YOLOv4 algorithm. The performance evaluation of the detection models showed that the Accelerated YOLOv4 model achieved higher precision scores than the YOLOv4 model for most objects. The mean average precision (mAP) of the Accelerated YOLOv4 model was 86%, which is higher than the mAP of the YOLOv4 model, which was 72%. These results indicate that the proposed Accelerated YOLOv4 algorithm can improve the effective management of public safety during protests.
A Systematic Approach for Object Detection in Unconstrained Environments

Object detection in unconstrained environments could be defined as identifying objects captured in challenging environments like smoky, wild, and cloudy environments. Object detection is widely used in computer vision applications. It also has many applications in security surveillance, where we often have to deal with unconstrained environments. Several algorithms and techniques have been developed for object detection over the past years. Overall, object detection approaches can be divided into two main types: conventional computer vision-based approaches and modern deep learning-based computer vision approaches. This study reviews different methods and algorithms developed in recent years for object detection in constrained environments. Most object detection methods in constrained environments produce good performance. However, object detection in unconstrained environments is still an open problem that requires unique methods. So far, only a handful of approaches have been proposed. This study also presents future development trends and research directions for object detection in unconstrained environments to produce automated intelligence security surveillance. This review's main conclusion is that object detection in unconstrained environments needs to be improved and well-established using unique computer vision methods based on modern machine learning and deep learning technologies.