Reviewerly/exHarmony huggingface dataset:
PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data.We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset.The PTMTorrent dataset (v1) is available at: https://app.globus.org/file-manager?origin_id=55e17a6e-9d8f-11ed-a2a2-8383522b48d9&origin_path=%2F%7E%2F.Our dataset generation tools are available on GitHub: https://doi.org/10.5281/zenodo.7570357
A Verified Optimizer for Quantum Circuits

We present voqc , the first verified optimizer for quantum circuits, written using the Coq proof assistant. Quantum circuits are expressed as programs in a simple, low-level language called sqir , a small quantum intermediate representation, which is deeply embedded in Coq. Optimizations and other transformations are expressed as Coq functions, which are proved correct with respect to a semantics of sqir programs. sqir programs denote complex-valued matrices, as is standard in quantum computation, but we treat matrices symbolically to reason about programs that use an arbitrary number of quantum bits. sqir ’s careful design and our provided automation make it possible to write and verify a broad range of optimizations in voqc , including full-circuit transformations from cutting-edge optimizers.
Face Recognition System Based on LBPH Algorithm

In this modern time, identifying a person using a face is a standard biometric approach to distinguishing an individual from others. So techniques are required to identify a face must be quick and sufficiently enough to work in real time. But there are many difficulties within the execution of face identification in low lighting condition. In this paper, we have proposed a system that is using Local Binary Patterns Histogram algorithm for identifying a face. It can recognize both front and side faces and upgrade the value of poor enlightened picture and also expands the recognition rate in real time.
Verifiable Machine Learning Models in Industrial IoT via Blockchain

The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model.
Interpreters for GNN-Based Vulnerability Detection: Are We There Yet?

Traditional vulnerability detection methods have limitations due to their need for extensive manual labor. Using automated means for vulnerability detection has attracted research interest, especially deep learning, which has achieved remarkable results. Since graphs can better convey the structural feature of code than text, graph neural network (GNN) based vulnerability detection is significantly better than text-based approaches. Therefore, GNN-based vulnerability detection approaches are becoming popular. However, GNN models are close to black boxes for security analysts, so the models cannot provide clear evidence to explain why a code sample is detected as vulnerable or secure. At this stage, many GNN interpreters have been proposed. However, the explanations provided by these interpretations for vulnerability detection models are highly inconsistent and unconvincing to security experts. To address the above issues, we propose principled guidelines to assess the quality of the interpretation approaches for GNN-based vulnerability detectors based on concerns in vulnerability detection, namely, stability, robustness, and effectiveness. We conduct extensive experiments to evaluate the interpretation performance of six famous interpreters (GNN-LRP, DeepLIFT, GradCAM, GNNExplainer, PGExplainer, and SubGraphX) on four vulnerability detectors (DeepWukong, Devign, IVDetect, and Reveal). The experimental results show that the target interpreters achieve poor performance in terms of effectiveness, stability, and robustness. For effectiveness, we find that the instance-independent methods outperform others due to their deep insight into the detection model. In terms of stability, the perturbation-based interpretation methods are more resilient to slight changes in model parameters as they are model-agnostic. For robustness, the instance-independent approaches provide more consistent interpretation results for similar vulnerabilities.
TextANIMAR: Text-based 3D animal fine-grained retrieval

3D object retrieval is an important yet challenging task that has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe this task can potentially drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from fully solved. As such, we provide insights into potential areas for future research and improvements. We believe we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.
Text Document Clustering Approach by Improved Sine Cosine Algorithm

Due to the vast amounts of textual data available in various forms such as online content, social media comments, corporate data, public e-services and media data, text clustering has been experiencing rapid development. Text clustering involves categorizing and grouping similar content. It is a process of identifying significant patterns from unstructured textual data. Algorithms are being developed globally to extract useful and relevant information from large amounts of text data. Measuring the significance of content in documents to partition the collection of text data is one of the most important obstacles in text clustering. This study suggests utilizing an improved metaheuristics algorithm to fine-tune the K-means approach for text clustering task. The suggested technique is evaluated using the first 30 unconstrained test functions from the CEC2017 test-suite and six standard criterion text datasets. The simulation results and comparison with existing techniques demonstrate the robustness and supremacy of the suggested method.
The Professionalization of Hackers: A Content Analysis of 30 Years of Hacker Communication

Underground hacking has evolved from its early countercultural roots to become a complex and varied phenomenon. By combining a historical review of the literature with a content analysis of 30 years of underground hacker communication, we show that hacking has evolved in three waves to embrace learning and creativity, intrusion and crime, as well as politics and cyberwarfare. We uncover a paradoxical relationship between hackers and society at large where underground hacking is considered a digital crime while at the same time inspiring and driving corporate innovation, cybersecurity, and even cyberwarfare. The outcome of our research provides a nuanced picture of the hacker underground by highlighting differences between competing discursive themes across time. Moreover, by translating these themes into a set of six contrasting personas of IS professionals, we discuss how knowledge, technologies, and creative practices of underground hackers are being professionalized. We use this discussion to provide implications and a research agenda for IS studies in cybersecurity, innovation, and cyberwarfare.
Structural and compositional techniques of infotainment in media texts of regional print media

The novelty of the study is due to the need to systematize the techniques of infotainment used in the work of print media. The empirical research material for the work was the corpus of journalistic materials of the Belgorod regional youth magazine “OnOnas” for the period from 2013 to 2021. The relevance of the study lies in the need to theorize valuable empirical material and classify non-verbal methods of infotainment in relation to the practice of print media. The relevance and novelty of the study are also due to the insufficient study of the phenomenon of infotainment in relation to the practice of regional print media, since modern researchers still maintain a stable association of infotainment with television practices to the detriment of other types of media. The methods used in the course of the study include a system analysis with an emphasis on a constructive and structural-functional approach, and the typology method is also used. The conclusions are drawn that the use of non-verbal structural and compositional techniques of infotainment makes it possible to increase the visual appeal of journalistic material, include elements of entertainment for the purpose of informational unloading of the text, visualize statistical information or the semantic part of the text, captivate the audience with new forms of visualization and representation of journalistic information.
Light Field Image Quality Assessment Using Natural Scene Statistics and Texture Degradation

Light field image (LFI) now is becoming increasingly popular in immersive media applications. Unlike traditional 2D and 3D images, images taken by light field cameras can capture both angular and spatial information. However, the spatial and angular information of LFI is highly inter-twined with varying disparities, which poses a higher challenge to the quality assessment of LFI. To address this issue, this paper proposes a full-reference light field image quality assessment (LFIQA) index that attempts to disentangle the coupling information from macro-pixel image (MacPI) to accurately evaluate the entire LFI quality. The proposed framework can be divided into three steps. Firstly, the LFIs are converted into the MacPIs, and then the spatial and angular feature maps are disentangled by using the spatial, angular and epipolar plane image (EPI) convolutions in the MacPI mode. Secondly, the structural similarity (SSIM) maps are calculated between the disentangled feature maps of the original and distorted LFIs. Furthermore, the quality-aware features of LFIs are extracted on the SSIM maps by utilized local binary patterns (LBP) and natural scene statistics (NSS). Finally, support vector regression (SVR) is utilized to predict the qualities of LFIs. Extensive experiments show that the proposed model outperforms multiple classical and state-of-the-art methods.
Moving From Narrative to Interactive Multi-Modal Sentiment Analysis: A Survey

A growing number of individuals are expressing their opinions and engaging in interactive communication with others through various modalities, including natural language (text), facial gestures (vision), acoustic behaviors (audio), and more. Within the realms of natural language processing (NLP) and artificial intelligence (AI), multi-modal sentiment analysis has consistently remained a fundamental research area. Building upon recent advancements, this survey aims to provide researchers with a comprehensive overview of the state-of-the-art techniques in multi-modal sentiment analysis, specifically focusing on various sentiment interaction tasks. It is worth noting that the existing literature on multi-modal sentiment analysis has rarely delved into the realm of sentiment interaction. This survey presents a novel perspective by outlining the progression of multi-modal sentiment analysis from narrative sentiment to interactive sentiment. Furthermore, it discusses the research background, problem definition, and various approaches in multi-modal sentiment analysis. Additionally, this survey provides insights into the development of multi-modal sarcasm recognition, emphasizing the shift from narrativity to interactivity. Lastly, we summarize the current scientific challenges related to interaction modeling and highlight future development trends in the field.
Optical Character Recognition Using Hybrid CRNN Based Lexicon-Free Approach with Grey Wolf Hyperparameter Optimization

Optical Character Recognition (OCR) is a technique for transforming typed images, text written by hand, or text that has been printed into machine-readable text from scanned documents, screenshots of transcripts, scene photos of signs, or text that has been placed on an image. OCR is the challenging task due to existence of various writing styles and different shape and position of the words. In this paper, a three-stage approach using CNN, RNN and transcription layer is designed to detect hand-written. CNN extract the image feature and these input vector sequences are simulated using the RNN component to estimate label patterns, which are then translated into the transcription layer using the Deep Convolutional Classification algorithm. The efficiency of proposed deep learning model is improved by optimizing the hyper parameters of CNN and RNN using grey wolf optimization algorithm. The obtained results are evaluated using various performance parameters like loss function, accuracy, precision, recall and Fscore. The obtained results being compared with conventional CNN and RNN approaches highlights that the proposed hybrid CRNN model outperforms the existing algorithms in terms of diverse performance parameters.
Quantum Computing from Hype to Game Changer

This paper describes the current state of quantum computer development in the world (including the hype) from three perspectives and introduces our research and development of quantum computers. The first is the complex market structure. While investments in quantum computers are increasing, these investments do not always immediately bring value to consumers, and this delay results in hype. CMOS annealing technology to fill this delay is described. The second is the accountability of the impact produced by a quantum computer that does not yet exist. This paper discusses why so many people continue to question the value of quantum computers despite their clear applications, and our struggles. The third is technology development, which includes many research aspects. This paper introduces our top-down approach that takes full advantage of semiconductor technologies. Some of our activities including “Shuttling qubit” for the next milestone in the development of silicon quantum computers, which is qubit operation in a scalable qubit array structure, are also described.
Chatbots: A Framework for Improving Information Security Behaviours using ChatGPT

This paper proposes a framework for improving information security behaviours using ChatGPT, a natural language processing chatbot. The framework leverages the Theory of Planned Behaviour (TPB) and the Persuasion Theory to promote secure behaviours through targeted interventions such as education, training, gamification, security tips, reminders, and nudges. ChatGPT can provide personalized and interactive training on information security best practices, deliver relevant threat alerts and tips, and assist with security assessments. Gamification can be used to increase engagement and retention of information security knowledge. The use of nudges and reminders can help sustain secure behaviours over time. Overall, the ChatGPT framework offers a promising approach for organizations looking to enhance their cybersecurity posture by promoting a culture of information security and empowering individuals with the knowledge and tools to protect sensitive information. The framework was then validated by prompting ChatGPT to raise awareness. The responses were then validated by experts to verify if the content from ChatGPT was good.
HDS-YOLOv5: An improved safety harness hook detection algorithm based on YOLOv5s

&lt;abstract&gt; &lt;p&gt;Improperly using safety harness hooks is a major factor of safety hazards during power maintenance operation. The machine vision-based traditional detection methods have low accuracy and limited real-time effectiveness. In order to quickly discern the status of hooks and reduce safety incidents in the complicated operation environments, three improvements are incorporated in YOLOv5s to construct the novel HDS-YOLOv5 network. First, HOOK-SPPF (spatial pyramid pooling fast) feature extraction module replaces the SPPF backbone network. It can enhance the network's feature extraction capability with less feature loss and extract more distinctive hook features from complex backgrounds. Second, a decoupled head module modified with confidence and regression frames is implemented to reduce negative conflicts between classification and regression, resulting in increased recognition accuracy and accelerated convergence. Lastly, the Scylla intersection over union (SIoU) is employed to optimize the loss function by utilizing the vector angle between the real and predicted frames, thereby improving the model's convergence. Experimental results demonstrate that the HDS-YOLOv5 algorithm achieves a 3% increase in mAP@0.5, reaching 91.2%. Additionally, the algorithm achieves a detection rate of 24.0 FPS (frames per second), demonstrating its superior performance compared to other models.&lt;/p&gt; &lt;/abstract&gt;
Indonesian national assessment support: Can RE-STEM Android app improve students’ scientific literacy skills?

The Indonesian National Assessment set by the government is in line with the Program for International Student Assessment (PISA) assessment which includes students’ scientific literacy skills for a 21st century education succeeds. This study determined the increase in students’ scientific literacy skills through ethnoscience, science, technology, engineering, mathematics or ethno-STEM learning assisted by RE-STEM App. A quasi-experimental design with the type of one-group pretest-posttest design was used to obtain data from the treatment. There were 102 students involved by purposive sampling technique from a 338 population of grade 8 students in a rural secondary school in Central Java, Indonesia. RE-STEM App involves four themes of Indonesian cultures as learning resources, namely &lt;em&gt;tarutu&lt;/em&gt;, tin-telephone, &lt;em&gt;calung&lt;/em&gt;, and bird whistle. This study revealed that students’ scientific literacy skills increased by 61.33% in the medium category in general, also in each aspect. Research data indicate that the RE-STEM App has supported teachers to improve students’ scientific literacy skills. By applying ethno-STEM through RE-STEM App, teachers can help students to investigate the science concepts in ethnoscience. This strategy proved that ethnoscience aspect can be an important factor in developing students’ scientific literacy skills.
JOSS’s Commitment to the Principles of Open Scholarly Infrastructure

The Journal of Open Source Software (JOSS) is committed to the Principles of Open Scholarly Infrastructure, and here we summarize our status in doing so, followed by a more detailed discussion of how we do so, as well as explaining some when we do not, and some work in progress.
Assessing Super-Resolution GANs For Randomly-Seeded Particle Fields

In this work, we demonstrate and assess the performances of a novel super-resolution generative adversarial network (GAN) framework. The algorithm, recently introduced by the authors, leverages random spatial sampling in particle images to provide incomplete views of the high-resolution underlying fields. The main novelty is that the architecture, named Randomly Seeded GAN (RaSeedGAN), does not need full high-resolution training samples. The training is performed directly using the sparse sensors (e.g. particles) available in each snapshot, reduced on a regular grid by spatial averaging in bins. Bins without vectors are simply skipped during training. Provided that the particles randomly sample the space within the dataset, it is possible to reconstruct the mapping from low to high-resolution input with such incomplete ``gappy'' views. The proposed technique is tested on several synthetic datasets based on simulations, ocean surface temperature distribution measurements, and particle image velocimetry data of a zero-pressure-gradient turbulent boundary layer flow. While the applications in this work target fluid mechanics examples, the proposed method can be applied to more general frameworks where mapping is performed by moving sensors and/or with random on-off status. The results show increased accuracy compared to standard processing and direct cubic interpolation of the scattered velocity vectors. An analysis of the turbulent flow features, turbulence statistics, and spectra demonstrates an increase in spatial resolution of at least a factor of 3 in terms of cut-off frequency, with physically consistent estimated fields.
Path Planning for Multiple Tethered Robots Using Topological Braids

Path planning for multiple tethered robots is a challenging problem due to the complex interactions among the cables and the possibility of severe entanglements.Previous works on this problem either consider idealistic cable models or provide no guarantee for entanglement-free paths.In this work, we present a new approach to address this problem using the theory of braids.By establishing a topological equivalence between the physical cables and the space-time trajectories of the robots, and identifying particular braid patterns that emerge from the entangled trajectories, we obtain the key finding that all complex entanglements stem from a finite number of interaction patterns between 2 or 3 robots.Hence, non-entanglement can be guaranteed by avoiding these interaction patterns in the trajectories of the robots.Based on this finding, we present a graph search algorithm using the permutation grid to efficiently search for a feasible topology of paths and reject braid patterns that result in an entanglement.We demonstrate that the proposed algorithm can achieve 100% goal-reaching capability without entanglement for up to 10 drones with a slack cable model in a high-fidelity simulation platform.The practicality of the proposed approach is verified using three small tethered UAVs in indoor flight experiments.