Reviewerly/exHarmony huggingface dataset:
HAFFseg: RGB-Thermal semantic segmentation network with hybrid adaptive feature fusion strategy

RGB-Thermal (RGB-T) semantic segmentation provides the pixel-level prediction of surrounding environments for autonomous vehicles and mobile robots in harsh conditions such as insufficient illumination and severe weather. Existing RGB-Thermal semantic segmentation networks with two modalities feature extraction branches and insufficient feature fusion strategy limit their segmentation performance. To this end, this study proposed a novel RGB-Thermal semantic segmentation network with hybrid adaptive feature fusion strategy (HAFFseg). The proposed HAFFseg contains three feature extraction branches to achieve outstanding feature extraction capacity. The frequency domain feature enhanced module is invented and inserted in the first feature extraction stage to enhance the low-level extracted features. A hybrid adaptive feature fusion strategy which consists of hybrid feature fusion architecture and feature enhanced adaptive fusion modules (FEAFM) to obtain excellent feature fusion performance is designed for the proposed network. The full-scale fusion connection decoder is developed to perform multi-stage feature integration and further boost segmentation accuracy. Extensive ablation studies and comparison experiments have been conducted on MF dataset and RoadScene-seg dataset. Experimental results proved the effectiveness of the proposed HAFFseg structure and demonstrated the superiority of HAFFseg over other state-of-the-art RGB-T semantic segmentation networks.
THE USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES BY FUTURE TEACHERS IN THE EDUCATIONAL PROCESS OF A HIGHER EDUCATION INSTITUTION

It has been found that the use of information and communication technologies in the educational process of higher education institutions is predetermined by the urgent need of training future teachers to be able to perform their professional functions in the modern society. As information is increasingly becoming a socially significant resource, it requires highly qualified specialists who have a good command of ICT and effectively use them in their professional activities. It is worth noting that in educational activities, it is often necessary to use laboratory equipment, stands and complexes in order to increase the visibility of various phenomena and processes, so information and communication technologies are part of such complex that should perform various functions.&#x0D; It has been established that the large-scale introduction of information and communication technologies into the educational process leads to the fact that future teachers and pedagogues can be separated in the educational process and in space and time, but at the same time there is an alleged face-to-face didactic interaction between them. The peculiarities of the use of information and communication technologies by future teachers of professional training in the educational process of a higher education institution are revealed. The process of using information and communication technologies by teachers of professional education in higher educational institutions is determined and substantiated, and the peculiarities of students’ mental development before the use of information and communication technologies are characterized.&#x0D; It is established that while mastering the educational material, the formation of the motivational sphere of students is carried out through the awareness of the target setting, drawing the attention of future teachers not only to the importance of this educational component, but also to the further professional activities. The effectiveness of learning is determined not only by the content, but also by the way of learning, or the operational side of the activity. The identification of the operational side of learning activities and the process of forming it in future teachers is a central psychological problem of learning. It is proved that in the course of training, future teachers have the opportunity to analyse the received computer model and make quantitative measurements, solve the problem of choosing optimal parameters. As a part of this work, they perform the role of researchers who conduct an experiment and interpret its results. The program provides the possibility of reusing the material and choosing an individual pace of work in the classroom.
ANALISIS KESULITAN MEMBACA PERMULAAN SISWA DI KELAS III SEKOLAH DASAR

The research method that the author uses in this research is a qualitative method with the approach in this research is a case study approach to produce descriptive data in the form of written or spoken words from subjects/actors and behaviors that can be observed with the aim of knowing in depth and trying to find the underlying meaning regarding students' initial reading difficulties. There were 7 informants in the interview where 2 people became key informants, namely students who had difficulty reading at the beginning, 2 parents of students, 1 homeroom teacher of grade III students and 2 friends from grade III became supporting informants. To find out students' difficulties in beginning reading, researchers made direct observations and conducted interviews. After conducting interviews, the researcher obtained information that of the 32 third grade students at SD Negeri 047166 Sukadame that there are 12 students who already have good reading skills and 20 students who have difficulty in reading. Factors that cause students to have difficulty in beginning reading are physiological, intellectual, environmental and psychological factors.
SGA Model for Prediction in Cloud Environment

With virtual information, cloud computing has made applications available to users everywhere. Efficient asset workload forecasting could help the cloud achieve maximum resource utilisation. The effective utilization of resources and the reduction of datacentres power both depend heavily on load forecasting. The allocation of resources and task scheduling issues in clouds and virtualized systems are significantly impacted by CPU utilisation forecast. A resource manager uses utilisation projection to distribute workload between physical nodes, improving resource consumption effectiveness. When performing a virtual machine distribution job, a good estimation of CPU utilization enables the migration of one or more virtual servers, preventing the overflow of the real machineries. In a cloud system, scalability and flexibility are crucial characteristics. Predicting workload and demands would aid in optimal resource utilisation in a cloud setting. To improve allocation of resources and the effectiveness of the cloud service, workload assessment and future workload forecasting could be performed. The creation of an appropriate statistical method has begun. In this study, a simulation approach and a genetic algorithm were used to forecast workloads. In comparison to the earlier techniques, it is anticipated to produce results that are superior by having a lower error rate and higher forecasting reliability. The suggested method is examined utilizing statistics from the Bit brains datacentres. The study then analyses, summarises, and suggests future study paths in cloud environments.
Understanding the Role of Images on Stack Overflow

Images are increasingly being shared by software developers in diverse channels including question-and-answer forums like Stack Overflow. Although prior work has pointed out that these images are meaningful and provide complementary information compared to their associated text, how images are used to support questions is empirically unknown. To address this knowledge gap, in this paper we specifically conduct an empirical study to investigate (I) the characteristics of images, (II) the extent to which images are used in different question types, and (III) the role of images on receiving answers. Our results first show that user interface is the most common image content and undesired output is the most frequent purpose for sharing images. Moreover, these images essentially facilitate the understanding of 68% of sampled questions. Second, we find that discrepancy questions are more relatively frequent compared to those without images, but there are no significant differences observed in description length in all types of questions. Third, the quantitative results statistically validate that questions with images are more likely to receive accepted answers, but do not speed up the time to receive answers. Our work demonstrates the crucial role that images play by approaching the topic from a new angle and lays the foundation for future opportunities to use images to assist in tasks like generating questions and identifying question-relatedness.
BAILIF: A Blockchain Agnostic Interoperability Framework

A Decentralised Energy Trading Ecosystem ferent blockchain networks and platforms, resulting in data, assets, and information silos.This lack of interoperability causes inefficiencies in the energy trading process, impeding access to vital information about renewable energy production, distribution, and consumption, ultimately hindering transparency and accountability in the renewable energy supply chain.To address these challenges, there is a need to develop standards and protocols for blockchain interoperability, enabling greater collaboration and innovation in the renewable energy sector.To illustrate, consider the hypothetical DET ecosystem depicted in Figure 1.Here, the blockchain frameworks HARB, PlexiChain, and RECChain operate independently without interoperability.HARB is a P2P energy trading solution [2], PlexiChain enables flexibility trading [4], and RECChain tracks and trades renewable energy certificates [5].However, the lack of integration between these platforms creates a web of data, assets, and information silos.For instance, if HARB and PlexiChain are not connected, grid utilities may not have real-time information on the availability of renewable energy, leading to suboptimal energy management.Likewise, if RECChain is not integrated with HARB, verifiers may face challenges in efficiently verifying the REC's origin, thereby limiting transparency and accountability in the renewable energy supply chain.To facilitate data-sharing, asset exchange, and asset transfer in the emerging blockchain interoperability landscape, various Abstract-Blockchain technology has the potential to revolutionize the energy sector by enabling peer-to-peer energy trading, demand-side flexibility t rading, a nd renewable e nergy certificate trading, among other decentralised energy trading use cases.However, the lack of interoperability between blockchain networks and platforms is a significant c hallenge t hat l eads t o data and information silos.To address this challenge, a Blockchain Agnostic Interoperability Framework (BAILIF) is proposed, which provides a decentralized notary service and a cross-chain attestation and verification protocol.BAILIF adheres to the core principles of blockchain, such as decentralization, transparency, and trust, and can be adopted in other decentralized ecosystems where blockchain interoperability is required.A proof of concept for a distributed energy trading application demonstrates the solution's feasibility.The results showed that BAILIF could achieve a throughput of up to 666 transactions per second, indicating its potential to enable seamless data sharing across blockchain platforms and promote the adoption of renewable energy sources.
Variation Encoded Large-Scale Swarm Optimizers for Path Planning of Unmanned Aerial Vehicle

Different from existing studies where low-dimensional optimizers are utilized to optimize the path of an unmanned aerial vehicle (UAV), this paper attempts to employ large-scale swarm optimizers to solve the path planning problem of UAV, such that the path can be subtler and smoother. To this end, a variation encoding scheme is devised to encode particles. Specifically, each dimension of a particle is encoded by a triad consisting of the relative movements of UAV along the three coordinate axes. With this encoding scheme, a large number of anchor points can be optimized to form the path and repetitive anchor points can be avoided. Subsequently, this paper embeds this encoding scheme into four representative and well-performed large-scale swarm optimizers, namely the stochastic dominant learning swarm optimizer (SDLSO), the level-based learning swarm optimizer (LLSO), the competitive swarm optimizer (CSO), and the social learning particle swarm optimizer (SL-PSO), to optimize the path of UAV. Experiments have been conducted on 16 scenes with 4 different numbers of peaks in the landscapes. Experimental results have demonstrated that the devised encoding scheme is effective to cooperate with the four large-scale swarm optimizers to solve the path planning problem of UAV and SDLSO achieves the best performance.
Diversity Optimization for the Detection and Concealment of Spatially Defined Communication Networks

In recent years, computing diverse sets of high quality solutions for an optimization problem has become an important topic. The goal of computing diverse sets of high quality solutions is to provide a variety of options to decision makers, allowing them to choose the best solution for their particular problem. We consider the problem of constructing a wireless communication network for a given set of entities. Our goal is to minimize the area covered by the senders' transmissions while also avoiding adversaries that may observe the communication. We provide evolutionary diversity optimization (EDO) algorithms for this problem. We provide a formulation based on minimum spanning forests that are used as a representation and show how this formulation can be turned into a wireless communication network that avoids a given set of adversaries. We evaluate our EDO approach based on a number of benchmark instances and compare the diversity of the obtained populations in respect to the quality criterion of the given solutions as well as the chosen algorithm parameters. Our results demonstrate the effectiveness of our EDO approaches for the detection and concealment of communication networks both in terms of the quality and the diversity of the obtained solutions.
Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems

We study a Stackelberg game between one attacker and one defender in a configurable environment. The defender picks a specific environment configuration. The attacker observes the configuration and attacks via Reinforcement Learning (RL trained against the observed environment). The defender's goal is to find the environment with minimum achievable reward for the attacker. We apply Evolutionary Diversity Optimization (EDO) to generate diverse population of environments for training. Environments with clearly high rewards are killed off and replaced by new offsprings to avoid wasting training time. Diversity not only improves training quality but also fits well with our RL scenario: RL agents tend to improve gradually, so a slightly worse environment earlier on may become better later. We demonstrate the effectiveness of our approach by focusing on a specific application, Active Directory (AD). AD is the default security management system for Windows domain networks. AD environment describes an attack graph, where nodes represent computers/accounts/etc., and edges represent accesses. The attacker aims to find the best attack path to reach the highest-privilege node. The defender can change the graph by removing a limited number of edges (revoke accesses). Our approach generates better defensive plans than the existing approach and scales better.
Performance Analysis of Time-series Network for Pattern Classification of Dynamic Radar Cross Section

In this paper, we propose a method to conduct dynamic radar cross section (DRCS) measurements. This method uses an electromagnetic simulator for the target classification of moving objects and a time-series network with excellent classification performance. The DRCS method yields measurements based on theoretical calculations after the generation of the static RCS. To propose a reliable, time-series network with excellent classification performance, we consider two cases. The first uses only a ballistic trajectory, and the second uses a maneuver and ballistic trajectory. In addition, to compensate for the disadvantage of the RCS, which is vulnerable to noise, we simulated a noisy situation. We confirmed that the classification performance was lower in the first compared with the second case. However, a bidirectional long short-term memory (BiLSTM) yielded the best classification performances in both cases, even in noisy conditions. Furthermore, we verified that the performance of LSTM was significantly improved compared with that of the gated recurrent unit when the bidirectional theory was applied. Hence, we suggest the use of an optimized BiLSTM as a DRCS classification network.
Password Processing Scheme using Enhanced Visual Cryptography and OCR in Hybrid Cloud Environment

to authenticate the user using the password can be achieved by simply converting the password into the values is called hash. Even though there are many websites which can unlock the hash values by using some cracking tools called as cyber attacks. This cyber attacks are very much common by the way of hacking the passwords by hackers. Hackers can undeniably figure out the plain text using such software's it gives n-number of examples of plaintext samples which the hackers can execute and crack open or penetrate into personal information in the Hybrid Cloud Environment. To overcome this type of trouble or mishap our system is recommended. The proposal is that our system not only converts the plaintext into hash values but it will be stored as an image, the user will only be having an ID and login password (at the time of user creation) . The user when he wants to login he will receive an email in which a share-1 image will be present which is encrypted completely even he himself can't be able to recognize what it is, the server will ask him to download the share-2 image then he has to enter his user ID for the server to recognize his User-ID. Since these images are encrypted both share images 1&amp;2 are encrypted by VC (Visual Cryptography) then the user has to merge these two images, if it matches then only the user can login. This merge method undergoes OCR (0ptical cryptography recognition). Our aim is to prevent hackers from gaining access to personal information of the users in Hybrid Cloud Computing Environment.
A threat‐intelligence driven methodology to incorporate uncertainty in cyber risk analysis and enhance decision‐making

Abstract The challenge of decision‐making under uncertainty in information security has become increasingly important, given the unpredictable probabilities and effects of events in the ever‐changing cyber threat landscape. Cyber threat intelligence provides decision‐makers with the necessary information and context to understand and anticipate potential threats, reducing uncertainty, and improving the accuracy of risk analysis. The latter is a principal element of evidence‐based decision‐making, and it is essential to recognize that addressing uncertainty requires a new, threat‐intelligence (TI) driven methodology, and risk analysis approach. We propose a solution to this challenge by introducing a TI‐based security assessment methodology and a decision‐making strategy that considers both known unknowns and unknown unknowns. The proposed methodology aims to enhance the quality of decision‐making by utilizing causal graphs, which offer an alternative to conventional methodologies that rely on attack trees, resulting in a reduction of uncertainty. Furthermore, we consider tactics, techniques, and procedures that are possible, probable, and plausible, improving the predictability of adversary behavior. Our proposed solution provides practical guidance for information security leaders to make informed decisions in uncertain situations. This paper offers a new perspective on addressing the challenge of decision‐making under uncertainty in information security by introducing a methodology that can help decision‐makers navigate the intricacies of the dynamic and continuously evolving landscape of cyber threats.
Qubit Condensation for Assessing Efficacy of Molecular Simulation on Quantum Computers

Quantum computers may demonstrate significant advantages over classical devices, as they are able to exploit a purely quantum-mechanical phenomenon known as entanglement in which a single quantum state simultaneously populates two-or-more classical configurations. However, due to environmental noise and device errors, elaborate quantum entanglement can be difficult to prepare on modern quantum computers. In this paper, we introduce a metric based on the condensation of qubits to assess the ability of a quantum device to simulate many-electron systems. Qubit condensation occurs when the qubits on a quantum computer condense into a single, highly correlated particle-hole state. While conventional metrics like gate errors and quantum volume do not directly target entanglement, the qubit-condensation metric measures the quantum computer's ability to generate an entangled state that achieves nonclassical long-range order across the device. To demonstrate, we prepare qubit condensations on various quantum devices and probe the degree to which qubit condensation is realized via postmeasurement analysis. We show that the predicted ranking of the quantum devices is consistent with the errors obtained from molecular simulations of H2 using a contracted quantum eigensolver.
Some False Laws of Logic

&#x0D; &#x0D; &#x0D; This paper argues that some widely used laws of implication are false, and arguments based upon them invalid. These laws are Exportation, Commutation, (as well as various restricted forms of these), Exported Syllogism and Disjunctive Syllogism. All these laws are false for the same reason – that they license the suppression or replacement in some position of some class of propositions which cannot legitimately be suppressed or replaced. These laws fail to preserve the property of sufficiency of premiss set for conclusion. They are false, and can be seen to be false, independently of their respon- sibility for the paradoxes. Hence the main ‘independent’ argument for the paradoxes – that they follow from an allegedly immaculate set of laws – is undermined. Counterexamples to all these laws are produced. &#x0D; &#x0D; &#x0D;
Artificial Intelligence and Novel Services: Exploring Opportunities in the Marketing Landscape

Customer loyalty and trust are critical factors for businesses aiming to establish long-term relationships with their customers. With the advancement of artificial intelligence (AI) technologies, there is a growing interest in leveraging AI to measure and enhance customer loyalty and trust. This research aims to explore the role of AI in measuring and enhancing customer loyalty and trust by examining the current literature and conducting empirical studies. The research methodology involves a systematic review of relevant studies and the development of a conceptual framework. The research results highlight the significant impact of AI on customer loyalty and trust, demonstrating how AI-powered solutions can effectively collect and analyze customer data to gain insights and personalize customer experiences. The discussion encompasses the implications of AI in measuring and enhancing customer loyalty and trust, including the importance of data privacy and ethics. The study concludes with recommendations for businesses on implementing AI strategies to build and maintain strong customer relationships based on loyalty and trust. Overall, this research contributes to the understanding of how AI can revolutionize customer relationship management and provides valuable insights for practitioners and researchers in the field of marketing and AI.