Reviewerly/exHarmony huggingface dataset:
Solving String Constraints with Lengths by Stabilization

We present a new algorithm for solving string constraints. The algorithm builds upon a recent method for solving word equations and regular constraints that interprets string variables as languages rather than strings and, consequently, mitigates the combinatorial explosion that plagues other approaches. We extend the approach to handle linear integer arithmetic length constraints by combination with a known principle of equation alignment and splitting, and by extension to other common types of string constraints, yielding a fully-fledged string solver. The ability of the framework to handle unrestricted disequalities even extends one of the largest decidable classes of string constraints, the chain-free fragment. We integrate our algorithm into a DPLL-based SMT solver. The performance of our implementation is competitive and even significantly better than state-of-the-art string solvers on several established benchmarks obtained from applications in verification of string programs.
Blockchain Security-Efficiency Analysis based on DEA-SBM Model

It is estimated that by 2023 the security market will reach a value of $1.4 billion. This growth is primarily driven by the increasing use of technology in sectors like finance, healthcare and logistics. As more companies adopt technology there is a growing need to protect their data from hacking and other malicious activities. The security of the network plays a role in ensuring the implementation and adoption of technology. Given the rise in cyberattacks and data breaches it is expected that the importance of security will continue to grow in the coming years. In this study we will explore some companies that specialize in providing security solutions. Our analysis will be based on three factors and two desired outcomes. The selected companies include Hacken, Quantstamp, OpenZeppelin, Trail of Bits, ConsenSys, Certik, LeastAuthority, PWC Switzerland, Slowmist and Runtime Verification. The purpose of this research paper is to assess the effectiveness of the security industry for decision makers, experts and government entities. By gaining insights into this sector and enhancing network security measures for implementations, across industries.
A Comparison of Client Weighting Schemes in Federated Learning

Data is the new oil of the digital economy. Many business organizations are gathering and using their data to optimize their business performance. However, in some cases, an individual organization may not have a sufficient amount of data or data quality to build a well-performing model, especially in a dynamic environment. In some cases, the companies, which collect similar data, may be willing to exchange their knowledge, yet without sharing their data, e.g. due to privacy or legal issues. In such a case, federated learning is a solution. In horizontal federated learning, each client (organization) iteratively improves its model, so that it can be regularly aggregated and shared with all clients participating in the federation for further improvements. In the aggregation mechanism based on the weighted average, weights depend on the model's quality for each client. In this paper, we extend our previous approach to federated learning with missing information, and we investigate different weighting schemes, that depend on the effectiveness of the local models. The initial results are promising.
Boosting Knowledge Distillation via Intra-class Logit Distribution Smoothing

Previous arts built an intimate link between knowledge distillation (KD) and label smoothing (LS) that they both impose regularization on the model training. In this paper, we delve deeper into investigating the hidden reason rendering KD and LS to exert distinct effects on a model’s potential ability in sequential knowledge transferring. Specifically, we observe that the distilled model typically exhibits much higher intra-class variance than the regularized one, consequentially acting as the better teacher. Then we devise two exploratory experiments and identify that sufficient intra-class variance retained by a teacher model is an implicit distillation recipe for achieving competitive student performance. The observed properties allow us to further put forth a simple yet beneficial approach that promotes intra-class diversity at the optimizing process of the teacher models to accomplish the most promising performance of KD. Extensive experiments are conducted on various image classification tasks across three distillation paradigms, demonstrating our proposed method’s effectiveness and generalization. Additionally, we offer new interpretations to receive a more in-depth cognition of the gap issues, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i,e</i> ., better teacher, worse student, and the success of multi-generation self-distillation, respectively. Code will be made available at https://github.com/swift1988.
SISTEM PENJADWALAN MATA PELAJARAN SEKOLAH MENGGUNAKAN METODE HYBRID ARTIFICIAL BEE COLONY (HABC)

scheduling is very closely related to teaching and learning activities in a school that aims to support, facilitate, enhance the quality of education and improve discipline in the process of teaching and learning activities. The problem faced in making a schedule of subjects is time, the process of making a schedule takes quite a long time in the process of processing the schedule. Of the algorithms used in scheduling problems, algortima Artificial Bee Colony is an algorithm inspired by the behavior of honeybee colonies that work based on the way bees forage for food. Algortima Artificial Bee Colony is known to have advantages over other optimization algorithms that are very efficient in finding optimal solutions. But the Artificial Bee Colony algorithm has its drawbacks that, if the dimensions of the problem increase, the exchange of information is still limited to one dimension. The weakness of Artificial Bee Colony is what makes the opportunity to develop ABC, namely, Hybrid Artificial Bee Colony (HABC) by adding a crossover operator of genetic algorithms. Genetics algorithm's crossover operator inserted into ABC algorithm to improve exchange of information between bees. It can be concluded that the application of HABC methods can do the optimization process quite well in scheduling issues with fairly low schedule clashing results. Based on the results of trials conducted five times, the results that scheduling applications using the HABC Algorithm can produce a schedule of subjects by clashing to a minimum with an average accuracy percentage rate of 98.03%
Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems

High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm's performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges.
RTDOD: A large-scale RGB-thermal domain-incremental object detection dataset for UAVs

Recently, visual understanding using unmanned aerial vehicles (UAVs) has gained significant attention due to its wide range of applications, including delivery, security investigation and surveillance. However, most existing UAV-based datasets only capture color images under ideal illumination and weather conditions, typically sunny days. This limitation fails to account for the complexity of real-world scenarios, such as cloudy or foggy weather, and nighttime conditions. Deep learning methods trained on color images with good lighting and weather conditions struggle to adapt to the complex visual scenes in these scenarios. Moreover, color images may not provide sufficient visual information under the complex visual scenes. To bridge this gap and meet the demands of real-world applications, we propose a large-scale RGB-Thermal Domain-incremental Object Detection (RTDOD) dataset in this paper. Our dataset includes RGB and thermal videos synchronously captured using calibrated color thermal cameras mounted on UAVs. It covers various weather conditions, from sunny to foggy to rainy, and spans from day to night. We sample and obtain approximately 16,200 pairs of images, and manually label dense annotations, including object bounding boxes and object categories. With the proposed dataset, we introduce a challenging domain-incremental object detection task. We also present a baseline approach that uses task-related gates to filter features for knowledge distillation to reduce forgetting. Experimental results on the RTDOD dataset demonstrate the effectiveness of our proposed method in domain-incremental object detection. To facilitate future research and development in domain-incremental object detection tasks on aerial images, the RTDOD dataset and our baseline model are made available at https://github.com/fenght96/RTDOD. ARTICLE INFO.
Quantum Network Engineering in the NISQ Age: Principles, Missions, and Challenges

The quantum internet is envisaged to connect quantum systems at a global scale to provide diverse applications unimaginable with the classical internet. However, the technological shortcomings and practical challenges of noisy intermediate-scale quantum (NISQ) devices impose several constraints on the vision of a truly global quantum internet. Recent experimental developments for quantum networks are laying the pathway from small-scale to intermediate-scale noisy quantum networks dedicated to specific operational tasks. Although these networks are not at the global scale, they are capable of providing a clear quantum advantage in their dedicated tasks. We explore the cardinal features of these NISQ networks, discuss their design constraints, and explore the possible applications they provide. We also outline some technical challenges that need to be addressed for the practical implementation of these networks and eventually lead toward a truly global, fully functional quantum internet.
When ecological individual heterogeneity models and large data collide: An importance sampling approach

We consider the challenges that arise when fitting ecological individual heterogeneity models to “large” data sets. In particular, we focus on (continuous-valued) random effect models commonly used to describe individual heterogeneity present in ecological populations within the context of capture–recapture data, although the approach is more widely applicable to more general latent variable models. Within such models the associated likelihood is expressible only as an analytically intractable integral. Common techniques for fitting such models to data include, for example, the use of numerical approximations for the integral or a Bayesian data augmentation approach. However, as the size of the data set increases (i.e., the number of individuals increases), these computational tools may become computationally infeasible. We present an efficient Bayesian model-fitting approach, whereby we initially sample from the posterior distribution of a smaller subsample of the data, before correcting this sample to obtain estimates of the posterior distribution of the full data set using an importance sampling approach. We consider several practical issues, including the subsampling mechanism, computational efficiencies (including the ability to parallelise the algorithm) and combining subsampling estimates using multiple subsampled data sets. We initially demonstrate the feasibility (and accuracy) of the approach via simulated data before considering a challenging real data set of approximately 30,000 guillemots and, using the proposed algorithm, obtain posterior estimates of the model parameters in substantially reduced computational time, compared to the standard Bayesian model-fitting approach.
Recommender systems for sustainability: overview and research issues

Sustainability development goals (SDGs) are regarded as a universal call to action with the overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity for all people. In order to achieve these objectives, different AI technologies play a major role. Specifically, recommender systems can provide support for organizations and individuals to achieve the defined goals. Recommender systems integrate AI technologies such as machine learning, explainable AI (XAI), case-based reasoning, and constraint solving in order to find and explain user-relevant alternatives from a potentially large set of options. In this article, we summarize the state of the art in applying recommender systems to support the achievement of sustainability development goals. In this context, we discuss open issues for future research.
Knowledge, Integration and Scope of Deepfakes in Arts Education: The Development of Critical Thinking in Postgraduate Students in Primary Education and Master’s Degree in Secondary Education

If the appearance of digital images does not coincide with what they look like when they are presented as authentic photos or videos, we must reflect on concepts such as mimesis, truth, Artificial Intelligence and impersonation, something that is currently gaining prominence in the education of teachers and university professors. The current study presents the results of a research focused on the scope and knowledge of the so-called ‘deepfake images’ by the university student body of the Visual and Plastic Education subject, in the Graduate Certificate in Primary Education, and the student body of the Activity Design subject, in the master’s degree in Secondary Education—Specialty in Drawing, in the subject of Activity Design, University of Zaragoza. To do so, a quasi-experimental design with qualitative methodology was used, in which of 100 students participated. As a data collection strategy, reports were used, based on semi-structured questions, for the analysis of the development of critical thinking. The achieved results allow us to verify that deepfakes put forward an innovation in the study by university classrooms. The use of these deepfake representations is a novel issue that generates some moral and ethical controversy due to the different uses it can have, where the age of the interpreter is a differentiating factor for the knowledge and the possibilities that these imaginaries present. Therefore, it is a work area belonging to the field of artistic education on which little exploration and research has been conducted. Based on the obtained results, we conclude that there is currently a better awareness on the part of artistic education, and of education itself, in general terms, regarding the knowledge by the students, the relevance of its use, and the possibilities of these technological tools, both in their creative aspect and their misinformative ones.
Technological Advancement and Mechanization of the Hotel Industry

In this era of technological advancement, when everyone is utilizing technology in some manner, especially when the pandemic hit the world, everyone has now accepted technology in their vicinity. This chapter broadly classifies the robots into two different groups (i.e., industrial robots and service robots). The hospitality industry, being the focus of this chapter, is primarily known for its services to guests; thus, service robots remain the focus of this chapter as they are related to rendering services to people. This chapter tries to make the readers understand the basic metrics that are essential for developing a robot for the sole purpose of providing services to people.
A systematic approach for enhancement of homogeneous background images using structural information

Image enhancement is an indispensable pre-processing step for several image processing applications. Mainly, histogram equalization is one of the widespread techniques used by various researchers to improve the image quality by expanding the pixel values to fill the entire dynamic grayscale. It results in the visual artifact, structural information loss near edges due to the information loss (due to many-to-one mapping), and alteration in average luminance to a higher value. This paper proposes an enhancement algorithm based on structural information for homogeneous background images. The intensities are divided into two segments using the median value to preserve the average luminance. Unlike traditional techniques, this algorithm incorporates the spatial locations in the equalization process instead of the number of intensity values occurrences. The occurrences of each intensity concerning their spatial locations are combined using Rènyi entropy to enumerate a discrete function. An adaptive clipping limit is applied to the discrete function to control the enhancement rate. Then histogram equalization is performed on each segment separately, and the equalized segments are integrated to produce an enhanced image. The algorithm's effectiveness is validated by evaluating the proposed method on CEED, CSIQ, LOL, and TID2013 databases. Experimental results reveal that the proposed method improves the contrast while preserving structural information, detail information, and average luminance. They are quantified by the high value of contrast improvement index, structural similarity index, and discrete entropy, and low value of average mean brightness error values of the proposed method when compared with the methods available in the literature, including deep learning architectures.
Blockchain: A Comparative Study of Consensus Algorithms PoW, PoS, PoA, PoV

Since the inception of Blockchain, the computer database has been evolving into innovative technologies. Recent technologies emerge, the use of Blockchain is also flourishing. All the technologies from Blockchain use a mutual algorithm to operate. The consensus algorithm is the process that assures mutual agreements and stores information in the decentralized database of the network. Blockchain's biggest drawback is the exposure to scalability. However, using the correct consensus for the relevant work can ensure efficiency in data storage, transaction finality, and data integrity. In this paper, a comparison study has been made among the following consensus algorithms: Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Vote (PoV). This study aims to provide readers with elementary knowledge about blockchain, more specifically its consensus protocols. It covers their origins, how they operate, and their strengths and weaknesses. We have made a significant study of these consensus protocols and uncovered some of their advantages and disadvantages in relation to characteristics details such as security, energy efficiency, scalability, and IoT (Internet of Things) compatibility. This information will assist future researchers to understand the characteristics of our selected consensus algorithms.
Experimental certification of contextuality, coherence, and dimension in a programmable universal photonic processor

Quantum superposition of high-dimensional states enables both computational speed-up and security in cryptographic protocols. However, the exponential complexity of tomographic processes makes certification of these properties a challenging task. In this work, we experimentally certify coherence witnesses tailored for quantum systems of increasing dimension using pairwise overlap measurements enabled by a six-mode universal photonic processor fabricated with a femtosecond laser writing technology. In particular, we show the effectiveness of the proposed coherence and dimension witnesses for qudits of dimensions up to 5. We also demonstrate advantage in a quantum interrogation task and show it is fueled by quantum contextuality. Our experimental results testify to the efficiency of this approach for the certification of quantum properties in programmable integrated photonic platforms.
Radiology report generation with medical knowledge and multilevel image-report alignment: A new method and its verification

Medical report generation is an integral part of computer-aided diagnosis aimed at reducing the workload of radiologists and physicians and alerting them of misdiagnosis risks. In general, medical report generation is an image captioning task. Since medical reports have long sequences with data bias, the existing medical report generation models lack medical knowledge and ignore the interaction alignment between the two modalities of reports and images. The current paper attempts to mitigate these deficiencies by proposing an approach based on knowledge enhancement with multilevel alignment (MKMIA). To this end, it includes a knowledge enhancement (MKE) module and a multilevel alignment module (MIRA). Specifically, the MKE deals with general medical knowledge (MK) and historical knowledge (HK) obtained via data training. The general knowledge is embedded in the form of a dictionary with characteristic organs (referred to as Key) and organ aliases, disease symptoms, etc. (referred to as Value). It provides explicit exception candidates to mitigate data bias. Historical knowledge ensures the comparison of similar cases to provide a better diagnosis. MIRA furnishes coarse-to-fine multilevel alignment, reducing the gap between image and text features, improving the knowledge enhancement module's performance, and facilitating the generation of lengthy reports. Experimental results on two radiology report datasets (i.e., IU X-ray and MIMIC-CXR) proved the effectiveness of the proposed approach, achieving state-of-the-art performance.
Optimizing Heterogeneity in IoT Infra Using Federated Learning and Blockchain-based Security Strategies

The Internet of Things (IoT) and associated capabilities are becoming indispensable in the planning, operation, and administration of intricate systems of all sizes. High-end learning solutions that go beyond the boundaries of the problem are necessary for addressing the variety of communication concerns (compatibility, secure communication, etc.) in IoT settings. Building machine learning (ML) networks from disparate data sources is a cutting-edge practice known as Federated Learning (FL). In this article, we implement FL between edge-based servers and devices in a sparsely populated cloud to facilitate cohesive learning and the storage of critical information in smart IoT systems. FL enables collaborative training from a common model by aggregating smaller unit models via regulated edge network participants. Further, all the susceptible device’s information and sensitive message transactions are addressed via blockchain technology. Thus, a blockchain-based security mechanism is integrated to secure user privacy and facilitate widespread practical adoption. Finally, a comparison is made between the proposed model and the three best free, open-source Federated Learning models already in use (FedPD, FedProx, and FedAvg). In terms of statistical, and data heterogeneity (&gt;70% SDI, &gt;97% accuracy), the experimental findings suggest that the proposed model performs better than the existing techniques.