Reviewerly/exHarmony huggingface dataset:
Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability

Authors:
raw_author_name: Md Sadman Sakib
raw_affiliation_string: Department of Computer Science, University of South Florida, Tampa, FL 33620, USA
raw_author_name: Yu Sun
raw_affiliation_string: Department of Computer Science, University of South Florida, Tampa, FL 33620, USA

Abstract: The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output. This paper presents a novel approach designed to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network. Leveraging GPT-4 further, the high-level task plan is converted into a low-level Planning Domain Definition Language (PDDL) plan executable by a robot. Evaluation results highlight the superior accuracy and efficiency of our approach compared to previous methodologies in the field of task planning.
Machine learning approach to optimal task scheduling in cloud communication

Authors:
raw_author_name: Faisal S. Alsubaei
raw_affiliation_string: Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
raw_author_name: Ahmed Y. Hamed
raw_affiliation_string: Department of Management Information Systems, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia; Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag 82524, Egypt
raw_author_name: M. Kh. Elnahary
raw_affiliation_string: Faculty of Computers and Artificial Intelligence, Department of Computer Science, Sohag University, Sohag 82524, Egypt

Abstract: Cloud communication is a combination of distributed computing and parallel computing. One of the biggest challenges in cloud communications is task scheduling, which is difficult due to the nondeterministic polynomial completeness (NP) of cloud systems. To solve this problem, various approximation techniques based on swarm intelligence have been developed. This study proposes a dual machine learning strategy using kmeans to optimize performance and aid in selecting cloud scheduling technologies. The first technique is called Efficient Kmeans (Ekmeans) and the second technique is called Kmeans HEFT (KmeanH), where HEFT stands for Heterogeneous Earliest End Time. Our main contribution is to reduce processing time and increase speed and efficiency for a given set of tasks. We evaluate the impact of both algorithms on different virtual machines (ranging from 2 to 32) and task sizes (ranging from 50 to 1000).
Transformation of a class of pure multipartite entangled states

Authors:
raw_author_name: Wendong Li
raw_affiliation_string: Department of Physics, Ocean University of China, Qingdao, 266100, People’s Republic of China
raw_author_name: Yongjian Gu
raw_affiliation_string: Department of Physics, Ocean University of China, Qingdao, 266100, People’s Republic of China

Abstract: Entanglement transformation can give insight into quantum entanglement and provide desired resources for quantum information processing. However, the complexity for analyzing entanglement transformation exponentially grows with the increased particle numbers. We find that a sort of multipartite entangled states can be described by the Valence-bond state (VBS) picture and can be decomposed into a series of cells. Thus the study of multipartite entanglement can be greatly simplified. We propose an inverse entanglement resource searching method by which one can find states that can be transformed into desired entangled resource states via local operations and classical communication (LOCC) and stochastic LOCC (SLOCC). In the case of bipartite entanglement, our method includes Nielsen’s majorization theorem as a special case. We apply the method to measurement based quantum computation (MBQC) and find some resource states for MBQC that can be transformed into Cluster states via LOCC or SLOCC. We also construct the Hamiltonians of two-body nearest neighbor interactions with the found resource states as their eigenstates.
Scale perception data compression method with blockchain technology for UAV communication

Authors:
raw_author_name: Xin Liu
raw_affiliation_string: School of Information Engineering, Xi'an Eurasia University, Xi’an 710000, China; School of Information and Navigation, Air Force Engineering University, Xi’an 710000, China
raw_author_name: Shanghong Zhao
raw_affiliation_string: School of Information and Navigation, Air Force Engineering University, Xi’an 710000, China
raw_author_name: Yanxia Liang
raw_affiliation_string: School of Communications & Information Engineering, Xi'an University of Posts & Telecommunications, Xi’an 710000, China

Abstract: The demand for high data throughput and security in Unmanned Aerial Vehicle (UAV) communication is rising. Efficient data transmission remains challenging due to limited wireless resources. This paper introduces a Scale Perception Compression Method that leverages compression and blockchain technology to optimize UAV communication. The method involves normalizing data into matrices, generating compression and decompression matrix codebook pairs, and selecting suitable pairs for small or large-scale matrices. Simulation results show that the error rate is associated with the distributions of the data and compression matrix. It remains relatively low when the data and compression matrix subjected to Uniform distribution compared to Gaussian distribution, regardless of the values of the number of rows m and columns r in compression matrix. Bubble method is more suitable to find out the proper matrix pair to compress and decompress transmitted data for small-scale codebook because it is stable, fast and results in lower difference between original and recovered data matrices. The same applies to Merge method for the large-scale codebook. The ER is always lower based on a small-scale codebook searching by Bubble method than that of on a large-scale codebook searching by Merge method, which indicates the former is sufficient for compression.
The completeness of reasoning algorithms for clause sets in description logic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg" display="inline" id="d1e76"><mml:mi mathvariant="script">ALC</mml:mi></mml:math>

Authors:
raw_author_name: Ken Kaneiwa
raw_affiliation_string: Department of Computer and Network Engineering, Graduate School of Informatics and Engineering, The University of Electro-Communications, Japan

Abstract: Metadata and ontologies are used to enable computers to read data on the Semantic Web. Description logics provide formal languages and reasoning algorithms for such metadata and ontologies. Tableau-based algorithms have been developed for deciding concept satisfiability in description logics but they lead to inefficient reasoning for complex logical expressions. The resolution principle and the Davis–Putnam–Logemann–Loveland (DPLL) solve the weakness by treating simplified clausal forms in description logics. However, resolution-based reasoning for description logics causes to compute various combinations of clause pairs to branch on. On the other hand, DPLL-based approaches have not presented a complete and decidable reasoning algorithm for directly deciding the satisfiability of clauses in description logics. In this paper, we propose a decidable and complete reasoning algorithm for clauses in description logic ALC. Using DPLL techniques, this algorithm can reduce reasoning steps for conjunctive normal form (CNF) concepts in ALC. We prove the termination, soundness, and completeness of the reasoning algorithm for the satisfiability of CNF concepts by introducing a restricted tableau for CNF concepts. We show some examples of reasoning steps for applying clauses in ALC to the algorithm.
Attack graph-based stochastic modeling approach for enabling cybersecure semiconductor wafer fabrication

Authors:
raw_author_name: Krystel K. Castillo-Villar
raw_affiliation_string: Department of Mechanical Engineering and Texas Sustainable Energy Research Institute. The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA

Abstract: The increasing interconnectivity between information technology (IT) and operational technology (OT) has brought numerous benefits to advanced manufacturing, including the semiconductor industry, by enhancing the quality and by reducing the costs of wafer and integrated circuits (IC) fabrication. However, the transition of the semiconductor industry to secure digital manufacturing has also increased the importance of safeguarding networked assets from cybercriminals. To this end, a flexible scheme is developed in this paper to model wafer fabrication as a directed attack graph. This graph representation is then used as the basis of a two-stage stochastic programming model that considers uncertainty in attack probabilities while minimizing expected cyber risk by employing optimal, budget-constrained countermeasures (i.e., arc interdiction strategies). A sample average approximation coupled with a progressive hedging algorithm or Bender's decomposition are proposed. Numerical experiments are performed to assess the viability of the proposed solution algorithms, the optimal arc interdiction strategies under various budget levels, and the effect of attack probability mappings. The results indicate that computing terminals nearest the manufacturing process have the highest defense priority and that the sample average approximation coupled with a progressive hedging algorithm is a suitable hybrid solution procedure for large instances when compared with sample average approximation coupled with Benders.
Image paragraph captioning with topic clustering and topic shift prediction

Authors:
raw_author_name: Ting Tang
raw_affiliation_string: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Jiansheng Chen
raw_affiliation_string: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Yiqing Huang
raw_affiliation_string: Machine Learning Platform Department, Tencent, Beijing 100086, China
raw_author_name: Huimin Ma
raw_affiliation_string: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Yudong Zhang
raw_affiliation_string: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
raw_author_name: Hongwei Yu
raw_affiliation_string: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

Abstract: Image paragraph captioning involves generating a semantically coherent paragraph describing an image's visual content. The selection and shifting of sentence topics are critical when a human describes an image. However, previous hierarchical image paragraph captioning methods have not fully explored or utilized sentence topics. In particular, the continuous and implicit modeling of topics in these methods makes it difficult to supervise the topic prediction process explicitly. We propose a new method called topic clustering and topic shift prediction (TCTSP) to solve this problem. Topic clustering (TC) in the sentence embedding space generates semantically explicit and discrete topic labels that can be directly used to supervise topic prediction. By introducing a topic shift probability matrix that characterizes human topic shift patterns, topic shift prediction (TSP) predicts subsequent topics that are both logical and consistent with human habits based on visual features and language context. TCTSP can be combined with various image paragraph captioning model structures to improve performance. Extensive experiments were conducted on the Stanford image paragraph dataset, and superior results were reported compared with previous state-of-the-art approaches. In particular, TCTSP improved the consensus-based image description evaluation (CIDEr) performance of image paragraph captioning to 41.67%. The codes are available at https://github.com/tt0059/TCTSP.
Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

Authors:
raw_author_name: Zakaria Abou El Houda
raw_affiliation_string: Institut National de la Recherche Scientifique, Centre Energie, INRS-EMT, Varennes (Quebec), J3X 1S2, Canada
raw_author_name: Bouziane Brik
raw_affiliation_string: Computer Science Department, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
raw_author_name: Adlen Ksentini
raw_affiliation_string: Communication Systems Department, EURECOM, France

Abstract: The advent of Industry 5.0, driven by cutting-edge 6G technologies such as immersive cloud eXtended Reality (XR), Autonomous vehicles, holographic communication, and Digital Twin (DT), is set to trigger a substantial upswing in the deployment of the Industrial Internet of Things (IIoT) devices interconnected within networks. This expansion of IIoT devices will create a wider attack surface and increase the risk of data breaches, privacy violations, and system disruptions. Therefore, it is essential to design innovative mechanisms to ensure the reliability and security of these advanced 6G applications, as well as the IIoT devices that support them. In this context, we propose a novel Software-defined Networking (SDN)-based Ensemble Learning (EL) Framework for Secure IIoT applications in 6G and beyond, called AdaptSDN. The proposed framework leverages SDN technology to dynamically allocate network resources and deploy security measures on demand. It also leverages EL techniques to improve the intrusion detection system's accuracy. By isolating IIoT devices into network slices, the framework limits the impact of attacks and reduces the potential for cascading failures. Digital twins are used for creating a virtual replica of the IIoT network, allowing for a real-time security threat detection. In particular, AdaptSDN includes three main modules: (1) A novel digital Twin (DT)-enabled data gathering and selection of informative features module to achieve two main objectives: reducing computational complexity and improving detection performance; (2) An SDN-based lightweight adaptive boosting module that uses an advanced boosting EL techniques to dynamically adjust weights and to effectively identify and respond to IIoT attacks in real-time; and (3) A zero-touch resources provisioning module that employs a non-cooperative game theory approach. This approach allows for automatically provisioning various resources in a network to efficiently mitigate network attacks; it enables SDN nodes to obtain the required virtual resources, i.e., storage, computing, and bandwidth, from the main orchestrator with respect to IIoT attack type. We carried out comprehensive experiments to assess the effectiveness of our proposed framework in detecting real-world IIoT attacks. The numerical results confirm that AdaptSDN has the potential to enable secure and reliable IIoT applications in 6G and beyond, meeting the stringent service requirements of the new emerging applications.
Using sky-classification to improve the short-term prediction of irradiance with sky images and convolutional neural networks

Authors:
raw_author_name: Miro Zeman
raw_affiliation_string: Photovoltaic Materials and Devices, Mekelweg 4, Delft, 2628CD, The Netherlands
raw_author_name: Olindo Isabella
raw_affiliation_string: Photovoltaic Materials and Devices, Mekelweg 4, Delft, 2628CD, The Netherlands
raw_author_name: Hesan Ziar
raw_affiliation_string: Photovoltaic Materials and Devices, Mekelweg 4, Delft, 2628CD, The Netherlands

Abstract: Clouds moving in front or away from the sun are the leading cause of irradiance variability. These variations have a repercussion on the electricity production of photovoltaic systems. Predicting such changes is essential for proper control of these systems and for maintaining grid stability. Images from the sky have proven to help with short-term solar irradiance forecasting, especially when combined with artificial intelligence. Nevertheless, these models tend to smooth the irradiance fluctuations. We propose a forecasting model to predict the clear-sky index in a forecast horizon of 20 min with a 1-minute resolution. Our model, based on a classifier to determine the sky conditions and, on an optical flow, applies an artificial intelligence model explicitly trained on each class of sky conditions. This strategy has an equivalent performance to an unclassified model and a forecast skill between 5 and 20% with respect to the smart persistence model for most classes of sky conditions while requiring considerably less training data. Although our model reduces the overall predicting error, it still has difficulties predicting irradiance changes and mainly overcast days. Our classifying strategy can be applied to other models targeting different objectives to predict sudden changes in either irradiance or power related to photovoltaic systems.
EESSO: Exploiting Extreme and Smooth Signals via Omni-frequency learning for Text-based Person Retrieval

Authors:
raw_author_name: Jingyi Xue
raw_affiliation_string: School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
raw_author_name: Zijie Wang
raw_affiliation_string: School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
raw_author_name: Guan-Nan Dong
raw_affiliation_string: School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
raw_author_name: Aichun Zhu
raw_affiliation_string: School of Computer Science and Technology, Nanjing Tech University, Nanjing, China

Abstract: Most of the existing methods manage to tackle the problem of text-based person retrieval from the spatial-wise perspective. In this paper, we manage to address the problem of this task from a novel perspective, namely, the frequency-wise perspective. To this end, we propose to Exploit Extreme and Smooth Signals via Omni-frequency learning (EESSO) through a jointly optimized multi-stream architecture. It consists of a Spatial Information Stream (SIS), an Extreme Signal Stream (ESS) and a Smooth Signal Stream (SSS). EESSO aims to excavate the complementary effect between spatial-wise and frequency-wise features, so as to achieve a superior performance. A novel Uncertainty-Guided Mutual Learning Mechanism (UG-MLM) is utilized during training, which not only enables the three streams to communicate with and learn from each other, but also models the data-related heteroscedastic uncertainty as a weight for knowledge transference, and hence enables each stream to adaptively allocate knowledge from the other two streams. A large number of experiments are carried out on the widely-used CUHK-PEDES, RSTPReid and ICFG-PEDES datasets to verify the effectiveness of EESSO. Through the experimental results, it may not be hard to find that EESSO has achieved the state-of-the-art performance in supervised, weakly supervised and cross-domain text-based person retrieval settings.
Variation-aware semantic image synthesis

Authors:
raw_author_name: Mingle Xu
raw_affiliation_string: Department of Electronics Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, South Korea
raw_author_name: Jaehwan Lee
raw_affiliation_string: Department of Electronics Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, South Korea
raw_author_name: Sook Yoon
raw_affiliation_string: Department of Computer Engineering, Mokpo National University, Mokpo, South Korea
raw_author_name: Hyongsuk Kim
raw_affiliation_string: Department of Electronics Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, South Korea
raw_author_name: Dong Sun Park
raw_affiliation_string: Department of Electronics Engineering, Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju, South Korea

Abstract: Semantic image synthesis (SIS) aims to produce photorealistic images aligning to given conditional semantic layout and has witnessed a significant improvement in recent years. Although the diversity in image-level has been discussed heavily, class-level mode collapse widely exists in current algorithms. Therefore, we declare a new requirement for SIS to achieve more photorealistic images, variation-aware, which consists of inter- and intra-class variation. The inter-class variation is the diversity between different semantic classes while the intra-class variation stresses the diversity inside one class. Through analysis, we find that current algorithms elusively embrace the inter-class variation but the intra-class variation is still not enough. Further, we introduce two simple methods to achieve variation-aware semantic image synthesis (VASIS) with a higher intra-class variation, semantic noise and position code. We combine our method with several state-of-the-art algorithms and the experimental result shows that our models generate more natural images and achieve slightly better FIDs and/or mIoUs than the counterparts. Our codes and models will be publicly available.
Anomaly detection for synthetic diamond grain using monocular depth estimation

Authors:
raw_author_name: Kazuki Fujiwara
raw_affiliation_string: Graduate School of Systems and Information Engineering, University of Tsukuba, Japan

Abstract: This paper studies the problem of estimating the maximum depth from a single two-dimensional monocular image of a grain of synthetic diamond to detect anomalies that would breach the quality criteria. To target this problem, we propose two models that employ different approaches to the depth map predicted by the zero-shot monocular depth estimation model, MiDaS, in combination with EfficientNet. The first model utilizes a normal map generated by differentiating the MiDaS-predicted depth map, taking an RGB image as input. The inclusion of the normal map provides valuable information about the pseudo-roughness of the diamond grain, aiding in the identification of anomalies. The second model incorporates a weighting scheme that enhances the MiDaS-predicted depth map through pointwise convolution with the RGB image. By assigning appropriate weights, the model highlights crucial features of the image, thus improving the overall performance. The main objective of both approaches is to improve the accuracy of the models by incorporating the per-pixel relative depth information extracted by MiDaS. Both of these proposed models outperformed existing state-of-the-art models in detecting anomalous synthetic diamond particles. Through this research, we aim to contribute to the advancement of anomaly detection in the field of synthetic diamond quality assessment, leveraging the potential of monocular depth estimation and innovative model architectures.