Reviewerly/exHarmony huggingface dataset:
Usefulness of open domain model for identifying missing software requirements concepts

Summary Detecting missing requirements during software development is crucial to avoid unexpected consequences. However, this task is challenging due to limited domain knowledge of requirements analysts and the dynamic nature of software requirements. Previous studies have shown that requirement‐oriented domain models can help identify omissions in requirements, but they are often incomplete for many domains. Meanwhile, domain models constructed from other artifacts are available online. This raises the question: Can these domain models be useful in identifying missing functional information in requirement specifications? To address this question, we conducted a study to measure the overlap between entities in domain models and requirements. We analyzed the occurrence of overlapped entities, considering four distribution characteristics: the type of entities in the domain model, the distribution of mapped entities in the domain model, the family belonging of the mapped entities in the domain model, and the distribution of mapped entities in the requirements. Based on our findings, we proposed recommendations for missing requirements. Additionally, we performed experiments, including the use of the proposed metric “ancestors of the highest level with the most mapped entities” (AHME). The results showed significant improvements with gains of 146% and 223% in the two domains, highlighting the benefits of these distribution characteristics.
Performance Analysis of Various Sun-Tracking Systems in Terms of PV-Based Power Generation in the Hilly Region of Bangladesh

Despite the fact that there are numerous renewable energy sources available around the world, solar energy is regarded as the most indispensable renewable resource to this day. The energy converted by a solar cell is quite low due to its poor efficiency. Sun-tracking system was introduced to ameliorate the overall efficiency of PV panels by utilizing solar irradiance the most. Even though a sun-tracking system can notably enhance the electricity production of a PV-based plant, fixed-tilt PV systems are mostly seen in real practice. This research focuses on conducting a comparative analysis of different sun-tracking systems with a fixed tilt system in terms of PV power generation. This study concludes that for a 200 kW grid-independent PV-based power plant, a two-axis sun-tracking system can maximize the PV output by 28.75% compared to a fixed-tilt PV system. The PV system with continuous adjustment vertical axis sun tracking system generates 15.25% more electricity in comparison to a fixed-tilt system which is the second most. The PV generation from a continuous adjustment horizontal axis sun tracking system is moderately higher than a traditional fixed-tilt PV system which is approximately 6.85% more.
Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy

In recent years, pruning has emerged as a popular technique to reduce the computational complexity and memory footprint of Convolutional Neural Network (CNN) models. Mutual Information (MI) has been widely used as a criterion for identifying unimportant filters to prune. However, existing methods for MI computation suffer from high computational cost and sensitivity to noise, leading to suboptimal pruning performance. We propose a novel method to improve MI computation for CNN pruning, using the spatial aura entropy. The spatial aura entropy is useful for evaluating the heterogeneity in the distribution of the neural activations over a neighborhood, providing information about local features. Our method effectively improves the MI computation for CNN pruning, leading to more robust and efficient pruning. Experimental results on the CIFAR-10 benchmark dataset demonstrate the superiority of our approach in terms of pruning performance and computational efficiency.
Innovative Application of Internal Audit Digitization Based on Portrait Technology: A Case Study of Pumped Storage Enterprises

Under the context of dual-carbon and digital economy, audit digitization can not only achieve green and low-carbon goals, but also improve audit supervision level and audit efficiency. Therefore, to realize the digital innovation of auditing based on portrait technology, this paper constructs a three-dimensional labeling system for the basic situation of enterprises, audited cadres and audited projects, and designs a three-stage portrait model for group portrait, risk category portrait and individual portrait. Finally, take the accountability audit of pumped storage state-owned enterprises as an example, the TF-IDF algorithm and the K-means algorithm are used to conduct portrait modeling and visualization. The application path of label system, portrait design, visualization presentation constructed can effectively monitor and reveal the condition and the risk characteristics of the audited units and projects, which has positive theoretical value and practical significance for identifying risk issues, improving internal audit efficiency, and enhancing corporate governance capabilities.
Secure aggregation in federated learning: finding a more communication-computation efficient protocol

Secure aggregation provides privacy protection for devices in a federated learning network, but its high communication-computation overheads currently limit the scalability of federated learning to a higher number of users. This paper explores the state-of-the-art secure aggregation model and proposed communication-computation efficiency improvement techniques. An improved protocol, combining existing approaches using a sparse assignment graph to define device collaboration, and optimising the quantisation step of the protocol, is proposed, and found to be successful in part. Theoretical analysis and experimental results demonstrate a reduction in communication and computation overheads when heterogeneous quantisation is used in combination with a sparse assignment graph. The effectiveness of additionally using an autotune method to set the clipping range used in the quantisation step over multiple iterations is evaluated, but the results are unconvincing.
Image Restoration Via Frequency Selection

Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain by considering the large discrepancy between spectra of sharp/degraded image pairs. However, these algorithms commonly utilize transformation tools, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , wavelet transform, to split features into several frequency parts, which is not flexible enough to select the most informative frequency component to recover. In this paper, we exploit a multi-branch and content-aware module to decompose features into separate frequency subbands dynamically and locally, and then accentuate the useful ones via channel-wise attention weights. In addition, to handle large-scale degradation blurs, we propose an extremely simple decoupling and modulation module to enlarge the receptive field via global and window-based average pooling. Furthermore, we merge the paradigm of multi-stage networks into a single U-shaped network to pursue multi-scale receptive fields and improve efficiency. Finally, integrating the above designs into a convolutional backbone, the proposed Frequency Selection Network (FSNet) performs favorably against state-of-the-art algorithms on 20 different benchmark datasets for 6 representative image restoration tasks, including single-image defocus deblurring, image dehazing, image motion deblurring, image desnowing, image deraining, and image denoising.
Multitask Evolution Strategy With Knowledge-Guided External Sampling

Evolutionary multitask optimization employs similarities among tasks via evolutionary algorithms (EAs) with knowledge transfer techniques to address multiple optimization tasks simultaneously. Although existing knowledge transfer techniques achieved success on population-based EAs, they are inappropriate for evolution strategies (ESs) that employ probability distribution sampling. These techniques will face two difficulties when applied to ESs: 1) distribution adaptation errors and 2) convergence difficulties. This paper proposes a knowledge-guided external sampling (KGxS) method to provide effective knowledge transfer in multitask ESs (MTESs) for solving multitask optimization problems (MTOPs). KGxS guides the distribution evolution in the target task by transferring solutions from source tasks as external samples. Since these external samples are close to the target distribution, they can handle the difficulty of distribution adaptation errors. In addition, the convergence difficulty caused by negative knowledge transfer is also handled through a mitigation strategy, which adaptively controls the number of external samples. Besides, the external samples carry two kinds of knowledge: 1) domain knowledge which employs the similarity of the optimal domains among tasks, and 2) shape knowledge that utilizes the function shapes similarity among tasks. Furthermore, a general boundary constraint handling technique is proposed for ESs to adapt to unconstrained and constrained optimization environments. Empirical results show that KGxS can significantly enhance the positive transfer effect on different types of ES on MTOPs. Moreover, the proposed method obtained superior performance over 20 state-of-the-art algorithms on 38 benchmark problems and three types of real-world applications, including multitask, many-task, and constrained multitask optimization.
Redactable and sanitizable signature schemes: applications and limitations for use in decentralized digital identity systems

Redactable signature schemes and sanitizable signature schemes are methods that permit modification of a given digital message and retain a valid signature. This can be applied to decentralized identity systems for delegating identity issuance and redacting sensitive information for privacy-preserving verification of identity. We propose implementing these protocols on a digital credential and compare them against other privacy-enhancing techniques to assess their suitability.
Trustchain – trustworthy decentralised public key infrastructure for digital credentials

The sharing of public key information is central to the digital credential security model, but the existing Web PKI with its opaque Certification Authorities and synthetic attestations serves a very different purpose. We propose a new approach to decentralised public key infrastructure, designed for digital identity, in which connections between legal entities that are represented digitally correspond to genuine, pre-existing relationships between recognisable institutions. In this scenario, users can judge for themselves the level of trust they are willing to place in a given chain of attestations. Our proposal includes a novel mechanism for establishing a root of trust in a decentralised setting via independently-verifiable timestamping. We also present a reference implementation built on open networks, protocols and standards. The system has minimal setup costs and is freely available for any community to adopt as a digital public good.
Stealthy Frequency-Domain Backdoor Attacks: Fourier Decomposition and Fundamental Frequency Injection

The rising reliance on deep learning models that are black-box in nature is concerning stakeholders about their security in artificial intelligence (AI) applications. Backdoor attacks are a significant challenge due to their ability to remain undetectable. Currently, researchers are focusing on the injection of frequency-domain triggers to enhance the covert nature of these attacks. Nevertheless, this method can introduce uncertain frequency variations that reduce the effectiveness of the attacks. We propose a method for Frequency-Domain Backdoor Attacks in response. The method utilizes Fourier Decomposition and Fundamental Frequency Injection techniques. In our method, we employ Fourier decomposition to mask the fundamental frequency of unsuitable bands, thereby guaranteeing covert trigger injection. As a result, this technique enhances temporal and spectral camouflaging, considerably reducing the likelihood of discovery. Our research contributes to a deeper understanding of backdoor attacks and enhances the security of AI systems by examining this innovative approach. Our approach to AI security centres around exploiting the smooth characteristics of frequencies within the frequency domain. This approach forms the foundation of our work in the field of artificial intelligence security.
Deaf Helper Using Python

Abstract: The Deaf helper using machine learning project represents a pivotal endeavor aimed at bridging communication gaps and enhancing accessibility for the deaf and hard of hearing community. In a world where spoken language dominates, this project harnesses the power of machine learning to facilitate seamless communication for individuals who rely on sign language as their primary mode of expression. At its core, this project leverages state-of-the-art machine learning techniques, including computer vision and natural language processing, to recognize and translate sign language gestures into written or spoken language and vice versa. By fusing these technologies, the project endeavors to create an inclusive and accessible communication tool.
Mutation‐based data augmentation for software defect prediction

Abstract Software defect prediction (SDP) aims to distinguish between defective and nondefective instances, but the imbalance between these two classes often leads to reduced prediction performance. Conventional SDP approaches use oversampling techniques, such as synthetic oversampling, to tackle the problem of imbalanced data. However, these methods merely synthesize new instances based on traditional code features without considering actual defects at the code level. To address the issue of data imbalance while preserving semantic features of code samples, a mutation‐based data augmentation approach in SDP is proposed. The method utilizes the mutation operator to generate mutants that mutate nondefective instances and create new defective instances. Six projects from the PROMISE dataset are used to evaluate the approach, employing four traditional and two deep classifiers. The experimental results demonstrate the effectiveness of this method in improving defect prediction performance for both traditional and deep classifiers compared with other data augmentation methods.
Klasifikasi Citra Jenis Daun Berkhasiat Obat Menggunakan Algoritma Jaringan Syaraf Tiruan Extreme Learning Machine

Leaves are one part of a plant that has benefits for humans, especially for the health of the body. Leaves can be used as herbal medicine which can be an alternative that can help in increasing immunity and body resistance. However, not all leaves have medicinal properties, therefore knowledge of the types of medicinal leaves is important. The aim of this research is to develop a classification system for the image of medicinal leaves using the Extreme Learning Machine (ELM) artificial neural network model. To support the ELM algorithm, morphological feature extraction is used which can provide information about the shape characteristics of existing objects. Extreme Learning Machine (ELM) is also known as an artificial neural network approach that uses one hidden layer. At the classification stage, the Extreme Learning Machine (ELM) algorithm can determine the weight value between the input neurons and the hidden layer randomly so that the learning pattern becomes faster. Based on the results of the precision, recall and accuracy tests, the precision value is 90.67%, the recall value is 89.47% and accuracy of 90%. So, based on these results it can be said that the ELM model that was built can classify images of leaf types with medicinal properties well.
GroomGen: A High-Quality Generative Hair Model Using Hierarchical Latent Representations

Despite recent successes in hair acquisition that fits a high-dimensional hair model to a specific input subject, generative hair models, which establish general embedding spaces for encoding, editing, and sampling diverse hairstyles, are way less explored. In this paper, we present GroomGen , the first generative model designed for hair geometry composed of highly-detailed dense strands. Our approach is motivated by two key ideas. First, we construct hair latent spaces covering both individual strands and hairstyles. The latent spaces are compact, expressive, and well-constrained for high-quality and diverse sampling. Second, we adopt a hierarchical hair representation that parameterizes a complete hair model to three levels: single strands, sparse guide hairs, and complete dense hairs. This representation is critical to the compactness of latent spaces, the robustness of training, and the efficiency of inference. Based on this hierarchical latent representation, our proposed pipeline consists of a strand-VAE and a hairstyle-VAE that encode an individual strand and a set of guide hairs to their respective latent spaces, and a hybrid densification step that populates sparse guide hairs to a dense hair model. GroomGen not only enables novel hairstyle sampling and plausible hairstyle interpolation, but also supports interactive editing of complex hairstyles, or can serve as strong data-driven prior for hairstyle reconstruction from images. We demonstrate the superiority of our approach with qualitative examples of diverse sampled hairstyles and quantitative evaluation of generation quality regarding every single component and the entire pipeline.
Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning

The problem of algorithmic bias in machine learning has recently gained a lot of attention due to its potentially strong impact on our societies. In much the same manner, algorithmic biases can alter industrial and safety-critical machine learning applications, where high-dimensional inputs are used. This issue has, however, been mostly left out of the spotlight in the machine learning literature. Contrary to societal applications, where a set of potentially sensitive variables, such as gender or race, can be defined by common sense or by regulations to draw attention to potential risks, the sensitive variables are often unsuspected in industrial and safety-critical applications. In addition, these unsuspected sensitive variables may be indirectly represented as a latent feature of the input data. For instance, the predictions of an image classifier may be altered by reconstruction artefacts in a small subset of the training images. This raises serious and well-founded concerns about the commercial deployment of AI-based solutions, especially in a context where new regulations address bias issues in AI. The purpose of our paper is, then, to first give a large overview of recent advances in robust machine learning. Then, we propose a new procedure to detect and to treat such unknown biases. As far as we know, no equivalent procedure has been proposed in the literature so far. The procedure is also generic enough to be used in a wide variety of industrial contexts. Its relevance is demonstrated on a set of satellite images used to train a classifier. In this illustration, our technique detects that a subset of the training images has reconstruction faults, leading to systematic prediction errors that would have been unsuspected using conventional cross-validation techniques.
Human-Machine Interdependence Beyond Ai Development: the Case of Bitcoin

This paper aims to analyze Bitcoin as an identifiable system of human-machine interdependence. The authors start with a brief historical outline of the Bitcoin project and discuss questions that Bitcoin poses to social sciences, such as whether Bitcoin is money, how the Bitcoin project relates to economic theory, what determines the value of a Bitcoin, and what are the conditions for trust in Bitcoin? Finally, what happens when the Bitcoin project becomes a reality? In what follows, the authors correlate the existence of Bitcoin with the spread of artificial intelligence (AI) technologies as active intermediaries and participants in human interactions. After observing the similarities and differences between AI and the Bitcoin project, the idea of whether Bitcoin can act as “artificial money” for AI is discussed, and the reality of human-machine interdependence is exemplified. In conclusion, the authors define Bitcoin as a particular system of human-machine interdependence initially conceived as an alternative to money; however, in reality, it supplements the existing economic order.
Sustainable Energy Behaviors through Green Information Systems: An Exploratory Study

Green IS platforms provide new opportunities for educating users on how they can reduce energy consumption in the household and lessen their carbon footprint through targeted actions. However, our understanding of how to design green IS platforms for the promotion of sustainable energy behaviours over time and among user groups remains nascent. This challenge is amplified by the ‘invisible’ nature of users' patterns of interaction with energy systems and the environmental impact of unconscious habits in private households. Our study presents findings from a 36-month EU project which designed an e-learning platform to help users adopt energy-efficient behaviours. Building on semi-structured interviews and a two-hour focus group with 26 subject matter experts, we identify three user representations: energy unaware, living in denial, and energy aware and active. We conclude by developing a touchpoint model to discuss the digital interventions needed to promote sustainable energy behaviours across user-owned, social/external, market-owned and company-owned touchpoints.
Multi-agent Cooperative Computing Resource Scheduling Algorithm for Periodic Task Scenarios

The scheduling of large-scale service requests and jobs usually requires the service cluster to fully use node computing resources. However, due to the increasing number of server devices, the dependence between resource allocation and request, and the periodic external request received, the scheduling process of edge-oriented service requests is a complicated scientific problem. Existing studies do not take into account the periodic characteristics of service requests in different periods, leading to inaccurate scheduling decisions on external requests. This paper proposes a coordinated Multi-Agent recurrent Actor-Critic, based on a recursive network. CMARAC is used to solve the problem of computing resource allocation for periodic requests in edge computing scenarios. According to different resource information in the server cluster and the status of the task queue, the system state information and historical information are captured and maintained by integrating LSTM, and then the most appropriate service resources are selected by processing them in the Actor-Critic network. Tracking experiments using actual request data show that CMARAC can successfully learn the periodic state between external requests in the face of large-scale service requests. Compared with the baseline, the average throughput rate of the system implemented by CMARAC is improved by 2.1%, and the algorithm convergence rate is improved by 0.69 times. Finally, we optimized the parameters through experiments and determined the best parameter configuration of CMARAC.