Reviewerly/exHarmony huggingface dataset:
Scale-Aware Frequency Attention network for super-resolution

Scale-arbitrary super-resolution (SASR) has recently gained widespread attention in the community, which aims to super-resolve an image to arbitrary desired resolutions. Existing SASR methods always use a fixed convolution neural network (CNN) as the backbone to share the same image features for different resolutions, leading to limited reconstruction performance due to the lack of distinctive feature extraction for individual resolutions. Meanwhile, these CNN-based methods always tend to overfit low-frequency components while losing high-frequency ones. This frequency gap leads to blurred structures in the reconstructed image. To address these issues, we propose a Scale Aware Frequency Attention network (SAFANet), which consists of Scale-Aware Frequency Attention (SAFA) modules and a Scale-Guided Continuous Reconstruction (SGCR) module. Additionally, frequency-domain attention learning is introduced to optimize the scale-adapted features to explicitly focus on high-frequency components, thereby overcoming the frequency restoration gap. Finally, the SGCR module combines the input scale as guidance with the optimized features to reconstruct images with arbitrary resolutions by using an implicit reconstruction function, thus adaptively achieving continuous image restoration. Our proposed SAFANet can be easily incorporated into existing CNN-based SR networks to achieve interactive SR at arbitrary scales. Extensive experiments demonstrate that SAFANet achieves superior performance, which is competitive with or even better than the state-of-the-art methods across a vast scale range (×1∼×30) via a single model.
evosax: JAX-Based Evolution Strategies

The deep learning revolution has greatly been accelerated by the 'hardware lottery': Recent advances in modern hardware accelerators, compilers and the availability of open-source software paved the way for large-scale batch gradient optimization. Evolutionary optimization, on the other hand, has mainly relied on CPU-parallelism, e.g. using Dask scheduling and distributed multi-host infrastructure. Here we argue that also modern evolutionary computation can significantly benefit from the massive computational throughput provided by GPUs and TPUs. In order to better harness these resources and to enable the next generation of black-box optimization algorithms, we release evosax : A JAX-based library of evolution strategies which allows researchers to leverage powerful function transformations such as just-in-time compilation, automatic vectorization and hardware parallelization.1 evosax implements 30 evolutionary optimization algorithms including finite-difference-based, estimation-of-distribution evolution strategies and various genetic algorithms. Every single algorithm can directly be executed on hardware accelerators and automatically vectorized or parallelized across devices using a single line of code. It is designed in a modular fashion and allows for flexible usage via a simple ask-evaluate-tell API. We thereby hope to facilitate a new wave of scalable evolutionary optimization algorithms.
SThy-Net: a feature fusion-enhanced dense-branched modules network for small thyroid nodule classification from ultrasound images

Deep learning studies of thyroid nodule classification from ultrasound (US) images have focused mainly on nodules with diameters > 1 cm. However, small thyroid nodules measuring $$\le 1$$ cm, especially nodules with high-risk stratification, are prevalent in the population but without enough focus, including papillary thyroid microcarcinoma (PTMC) as their common malignant type. Additionally, small nodules with high-risk stratification are difficult for physicians to diagnose from US images due to their atypical features. In this work, we propose a small thyroid nodule classification network (SThy-Net) to classify benign and PTMC small thyroid nodules with high-risk stratification from US images. We design two main components, a dense-branched module and a Gaussian-enhanced feature fusion module, to help recognize small thyroid nodules. To our knowledge, this work is the first to address the challenging task of classifying small thyroid nodules using US images. Our SThy-Net achieves as high accuracy as $$87.4\%$$ compared to five state-of-the-art thyroid nodule diagnosis studies, several state-of-the-art deep learning models, and three radiologists. From visual explainability, our network shows an intuitive feature extraction method and consistency with US image analysis of radiologists. The results suggest that our network has the potential to be an affordable tool for radiologists to diagnose small nodules with high-risk stratification in clinical practice.
A Tutorial on the Implementation of a Hyperledger Fabric-based Security Architecture for IoMT

Internet of Medical Things (IoMT) have improved individuals' quality of life by enabling IoMT-based healthcare monitoring systems to grow dramatically in recent years. Therefore, cutting-edge security techniques are needed to address the security risks of IoMT networks effectively and in a timely manner. On the other hand, blockchain technology has the potential to play a significant role in both securing IoMT devices and preventing unauthorized access during data transmission and it has been anticipated by the industry and the research community to be a disruptive technology that can be incorporated into novel security solutions for IoMT networks. In this regard, the goal of this research work is to demonstrate the integration of blockchain technology into novel security solutions for IoMT networks and to deploy a Hyperledger Fabric-based blockchain security architecture for IoMT-based healthcare monitoring systems by utilizing the features of the Hyperledger Fabric Platform, its utilities, and its lightweight consensus nature in order to: i) improve security in IoMT-based healthcare monitoring systems, ii) provide secure data storage in a decentralized way, and iii) eliminate single point of failure.
VulDefend: A Novel Technique based on Pattern-exploiting Training for Detecting Software Vulnerabilities Using Language Models

The detection of vulnerabilities in source code is a critical task in software assurance. In this work, we propose a semi-supervised learning approach that leverages pattern-exploiting training and cloze-style questions. Our approach involves training a language model on the SARD and Devign datasets of code snippets with vulnerabilities, where the input is generated by masking parts of the code and asking the model to predict the masked tokens. Experimental results demonstrate that our approach can effectively detect vulnerabilities in source code, while leveraging the pattern information learned from the code snippets. This work highlights the feasibility of using pattern-exploiting training and cloze-style questions for improved performance in the detection of vulnerabilities in source code.
ChatGPT and the Chinese Room Argument: An Eloquent AI Conversationalist Lacking True Understanding and Consciousness

This paper explores the cognitive implications of recent advancements in large language models (LLMs), with a specific focus on ChatGPT. We contribute to the ongoing debate about the cognitive significance of current LLMs by drawing an analogy to the Chinese Room Argument, a thought experiment that questions the genuine understanding of language in machines (computer programs). Our argument posits that current LLMs, including ChatGPT, generate text resembling human-like responses, akin to the process depicted in the Chinese Room Argument. In both cases, the responses are provided without a deep understanding of the language, thus lacking true signs of consciousness.
A WSN-Based, Intelligent Medical IoT Systems Integrating Computer Vision Platform

As wireless connectivity technology advanced, wireless sensor technologies also became more frequently used in many societal sectors. This research introduces wireless sensors to the world of health care systems, and we present a sensor network-based medical imaging. Through the communication system, it may send the participant’s physiological data as well as pictures to the healthcare monitoring ground station, allowing for continuous pathologic growth tracking and prompt treatment diagnosis. We discussed the use of remote sensing techniques in the remote health care surveillance system and expanded on the ideas for its implementation on the theory of sensor networks, present circumstances, and trends in China’s medical care. The design of a network infrastructure, the OS with the interposes communication, as well as the software are the challenges of a wireless sensor network. Our suggested framework is capable of handling the issues in an acceptable manner. To evaluate the overall effectiveness of the model, the suggested approach is contrasted with other recent methods.
A Smart Contract Vulnerability Detection Mechanism Based on Deep Learning and Expert Rules

Traditional techniques for smart contract vulnerability detection rely on fixed expert criteria to discover vulnerabilities, which are less generalizable, scalable, and accurate. Deep learning algorithms help to address these issues, but most fail to encode true expert knowledge and remain interpretable. In this paper, we present a smart contract vulnerability detection mechanism that operates in phases with graph neural networks and expert patterns in deep learning to mutually address the deficiencies of the two detection approaches and improve smart contract vulnerability detection capabilities. Experiments show that our vulnerability detection mechanism outperforms the original deep learning model by an average of 6 points in detecting vulnerabilities and that the second stage of the checking mechanism can also block contract transactions containing dangerous actions at the Ethernet Virtual Machine (EVM) level and generate error reports for submission. This strategy helps to construct more stable smart contracts and to create a secure environment for smart contracts.
Using PBL and Agile to Teach Artificial Intelligence to Undergraduate Computing Students

Project-based learning (PBL) is an active learning methodology focused on developing both soft and hard skills by solving real-world problems. In PBL, teachers act as facilitators while students take charge of their own learning. While the practice of this methodology in computing education has been growing in recent years, it still poses some challenges that need to be addressed. Integrating Scrum and Agile methodologies into PBL can be a valuable addition when teaching computing subjects to students. Therefore, this paper presents a case study of a successful implementation of PBL with Scrum, applying Agile values and principles to teach Artificial Intelligence to undergraduate students. This study contributes to the limited research on Scrum in education, as well as helps bridge the research gap in AI teaching and learning. The case study involved 30 students from an undergraduate computing program, divided into five groups, who successfully developed five different Machine Learning (ML) models to tackle the challenging real-life problem of breast cancer prediction for the Cancer Institute of the State of São Paulo. The findings of the study indicate that the students effectively utilized Scrum and Agile methodologies throughout the process and expressed satisfaction with the approach. Additionally, they developed problem-solving abilities, critical thinking and communication skills, teamwork capabilities, and gained experience in working with real-life situations and problems. The study also demonstrates that the proposed technique aligns with the foundations of the PBL approach in computing education discussed in previous literature. It serves as a valuable resource for future research on PBL implementation, by comparing similarities and differences with existing literature and discussing the strategies employed to address implementation challenges.
A Comprehensive Framework for Cyber Behavioral Analysis Based on a Systematic Review of Cyber Profiling Literature

Cybercrime presents a significant threat to global society. With the number of cybercrimes increasing year after year and the financial losses escalating, law enforcement must advance its capacity to identify cybercriminals, collect probative evidence, and bring cybercriminals before the courts. Arguably to date, the approach to combatting cybercrime has been technologically centric (e.g., anti-virus, anti-spyware). Cybercrimes, however, are the result of human activities based on human motives. It is, therefore, important that any comprehensive law enforcement strategy for combatting cybercrime includes a deeper understanding of the hackers that sit behind the keyboards. The purpose of this systematic review was to examine the state of the literature relating to the application of a human-centric investigative tool (i.e., profiling) to cybercrime by conducting a qualitative meta-synthesis. Adhering to the PRISMA 2020 guidelines, this systematic review focuses specifically on cybercrime where a computer is the target (e.g., hacking, DDoS, distribution of malware). Using a comprehensive search strategy, this review used the following search terms: “cybercrime”, “computer crime”, “internet crime”, “cybercriminal”, “hacker”, “black hat”, “profiling”, “criminal profiling”, “psychological profiling”, “offender profiling”, “criminal investigative analysis”, “behavioral profiling”, “behavioral analysis”, “personality profiling”, “investigative psychology”, and “behavioral evidence analysis” in all combinations to identify the relevant literature in the ACM Digital Library, EBSCOhost databases, IEEE Xplore, ProQuest, Scopus, PsychInfo, and Google Scholar. After applying the inclusion/exclusion criteria, a total of 72 articles were included in the review. This article utilizes a systematic review of the current literature on cyber profiling as a foundation for the development of a comprehensive framework for applying profiling techniques to cybercrime—described as cyber behavioral analysis (CBA). Despite decades of research, our understanding of cybercriminals remains limited. A lack of dedicated researchers, the paucity of research regarding human behavior mediated by technology, and limited access to datasets have hindered progress. The aim of this article was to advance the knowledge base in cyber behavioral sciences, and in doing so, inform future empirical research relating to the traits and characteristics of cybercriminals along with the application of profiling techniques and methodologies to cybercrime.
Application of Analytic Hierarchy Process in Mineral Prospecting Prediction Based on an Integrated Geology-Aerogeophysics-Geochemistry Model

Determining mineral prospecting targets is crucial for mineral prediction and evaluation. In this study, an evaluation index system for solid mineral exploration and metallogenic target assessment was established using the Analytic Hierarchy Process (AHP) for the Naoniushan area (China). Furthermore, an integrated model combining geology–aerogeophysics–geochemistry was developed for copper, lead, zinc, silver, and other polymetallic deposits. The information content of each index in the model was reasonably assigned, and the mineral prospecting targets in the central and southern parts of the Daxinganling were recommended. By focusing on the copper polymetallic mineral prospecting target in the Naoniushan area, this paper demonstrates that the AHP method can comprehensively consider various influencing factors and their interactions, realize a reasonable division of the optimal mineral prospecting target, and reflect the key factors affecting the mineral prospecting target to a certain extent. Importantly, this approach reduces the influence of human subjective factors, and the optimization results are objective and scientifically grounded.
The Deep Sleep Optimizer: A Human-Based Metaheuristic Approach

Owing to the no free lunch theorem, no single optimisation algorithm can solve all optimisation problems accurately, so new optimisation techniques are required. In this paper, a novel metaheuristic called the deep sleep optimiser (DSO) is proposed. The deep sleep optimiser mimics the sleeping patterns of humans to solve optimisation problems. The DSO is modelled on the rise and fall of homeostatic pressure during the human sleep process. Human sleep is often modelled on the four sleep stages and the deep sleep stage is employed in this work. The mathematical model of sleep homeostatic pressure is employed to simulate and determine the deep sleep state. The performance of DSO is demonstrated by employing 23 traditional functions (i.e., unimodal, multimodal, and fixed multi-modal functions), six composite functions, three engineering design problems, two knapsack problems, and six widely known travelling salesman’s problems. Additionally, the performance is evaluated in terms of accuracy, computational running time, the Wilcoxon rank sum, and the Friedman test. Lastly, the DSO is compared with 11 other metaheuristics, including GA, PSO, TLBO, and GWO. The DSO fares comparably well and, in most instances, it outperforms other metaheuristics.
Fast Genetic Algorithm for feature selection — A qualitative approximation approach

We propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. The proposed approach involves constructing a lightweight qualitative meta-model by sub-sampling data instances and then using this meta-model to carry out the feature selection task.
Future Trends of Blockchain Technology in the Technological Fields

The digital mechanism is rapidly transforming the future, which is expected to be beneficial for all stakeholders of society. The role of blockchain in the current environment, the feasibility of its impact, and the in-practice values of blockchain will define its future role. Therefore, this research proposes to study the implemented applications of blockchain in various domains to explore the core roles of blockchain. By elaborating on the scope of blockchain, the future aspects and usability patterns are being debated, which will yield the future trend of blockchain. Such discussion will be the subsequent initiative towards the smart world integrated with blockchain technology, where decentralization, security, immutability, efficiency, and transparency are guaranteed towards personal, social, and professional lives. Such sustainable technological integration would be effective towards attaining the various significant goals of the defense industry, where country sovereignty, war preparedness, the efficiency of intelligent agencies, and effective operations of the state-supported organization are materialized. The research would be assistive for the policymakers towards decisive policy implementation for blockchain integration.
A Way to Mark Attentance using Face Recognition using PL

The computer system that is used to take attendance online is going to be upgraded as part of this project. This attendance tracking system is able to hold the technology known as facial recognition, which is a useful feature. This device helps students take attendance by utilizing both a biometric technology and artificial intelligence algorithms in one convenient package. When pupils enter the classroom, the face recognition software can identify them, making it useful for educational institutions of any kind. The existing system has a number of flaws that make it difficult to accurately record the attendance of both students and teachers. The approach makes it simpler to mark attendance and lowers the number of mistakes that might occur when doing so.Both students and teachers will gain a significant amount by utilizing these techniques. The ability to recognize people based on their faces is becoming increasingly significant in practically every industry. Face recognition techniques will be used to understand the faces. An attendance record is compared to the record that corresponds to it that was previously saved in the database. A student's photograph is used as a check point for this comparison.
An Introduction to Bayesian Knowledge Tracing with pyBKT

This study aims to introduce Bayesian Knowledge Tracing (BKT), a probabilistic model used in educational data mining to estimate learners’ knowledge states over time. It also provides a practical guide to estimating BKT models using the pyBKT library available in Python. The first section presents an overview of BKT by explaining its theoretical foundations and advantages in modeling individual learning processes. In the second section, we describe different variants of the standard BKT model based on item response theory (IRT). Next, we demonstrate the estimation of BKT with the pyBKT library in Python, outlining data pre-processing steps, parameter estimation, and model evaluation. Different cases of knowledge tracing tasks illustrate how BKT estimates learners’ knowledge states and evaluates prediction accuracy. The results highlight the utility of BKT in capturing learners’ knowledge states dynamically. We also show that the model parameters of BKT resemble the parameters from logistic IRT models.
GASP: a genetic algorithm for state preparation on quantum computers

The efficient preparation of quantum states is an important step in the execution of many quantum algorithms. In the noisy intermediate-scale quantum (NISQ) computing era, this is a significant challenge given quantum resources are scarce and typically only low-depth quantum circuits can be implemented on physical devices. We present a genetic algorithm for state preparation (GASP) which generates relatively low-depth quantum circuits for initialising a quantum computer in a specified quantum state. The method uses a basis set of [Formula: see text], [Formula: see text], [Formula: see text], and CNOT gates and a genetic algorithm to systematically generate circuits to synthesize the target state to the required fidelity. GASP can produce more efficient circuits of a given accuracy with lower depth and gate counts than other methods. This variability of the required accuracy facilitates overall higher accuracy on implementation, as error accumulation in high-depth circuits can be avoided. We directly compare the method to the state initialisation technique based on an exact synthesis technique by implemented in IBM Qiskit simulated with noise and implemented on physical IBM Quantum devices. Results achieved by GASP outperform Qiskit's exact general circuit synthesis method on a variety of states such as Gaussian states and W-states, and consistently show the method reduces the number of gates required for the quantum circuits to generate these quantum states to the required accuracy.