Reviewerly/exHarmony huggingface dataset:
Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning

Large language models trained on massive code corpora can generalize to new tasks without the need for task-specific fine-tuning. In few-shot learning, these models take as input a prompt, composed of natural language instructions, a few instances of task demonstration, and a query and generate an output. However, the creation of an effective prompt for code-related tasks in few-shot learning has received little attention. We present a technique for prompt creation that automatically retrieves code demonstrations similar to the developer task, based on embedding or frequency analysis. We apply our approach, Cedar, to two different programming languages, statically and dynamically typed, and two different tasks, namely, test assertion generation and program repair. For each task, we compare Cedar with state-of-the-art task-specific and fine-tuned models. The empirical results show that, with only a few relevant code demonstrations, our prompt creation technique is effective in both tasks with an accuracy of 76% and 52% for exact matches in test assertion generation and program repair tasks, respectively. For assertion generation, Cedar outperforms existing task-specific and fine-tuned models by 333% and 11%, respectively. For program repair, Cedar yields 189% better accuracy than task-specific models and is competitive with recent fine-tuned models. These findings have practical implications for practitioners, as Cedar could potentially be applied to multilingual and multitask settings without task or language-specific training with minimal examples and effort.
How personalized and effective is immersive virtual reality in education? A systematic literature review for the last decade

Abstract During the last decade, there has been a substantial increase of interest in studies related to Virtual Reality (VR) as a learning tool. This paper presents a systematic literature review of personalization strategies utilized in immersive VR for educational objectives in the classroom. For the purposes of this review, 69 studies between 2012 and 2022 were analyzed in terms of their benefits, limitations and development features. The novelty of the study mainly arises from the in-depth analysis and reporting of personalization strategies as well as gamification techniques used in VR applications. The significance of this research lies in the observation that earlier studies’ applications did not sufficiently incorporate adaptive learning content, indicating the necessity for more research in this field and revealing a research gap. In conclusion, as it encourages future research of this field, this study may be a beneficial reference for those interested in researching the implementation of Virtual Reality in education, including academics, students, and professionals.
Dynamic RNA Coding Color Image Cipher Based on Chain Feedback Structure

This paper proposes a dynamic RNA-encoded color image encryption scheme based on a chain feedback structure. Firstly, the color pure image is decomposed into red, green, and blue components, and then a chaotic sequence based on plaintext association is introduced to encrypt the red component. Secondly, the intermediate ciphertext is obtained by diffusion after encryption by bit-level permutation, RNA dynamic encoding, RNA dynamic operation rules, and RNA dynamic decoding. Finally, to enhance the security of the image cryptosystem, the green and blue components of the image are repeatedly encrypted using the chain encryption mechanism associated with the intermediate ciphertext to obtain the color cryptographic image. In this paper, a 2D-SFHM chaotic system is used to provide pseudo-random chaotic sequences, and its initial key is calculated by combining the hash function and external parameters of the image, and the one-time ciphertext encryption strategy causes the proposed encryption to effectively resist cryptographic attacks. Experimental results and security analysis show that our encryption algorithm has excellent encryption effects and security performance against various typical attacks.
Electrically programmable phase-change photonic memory for optical neural networks with nanoseconds in situ training capability

Optical neural networks (ONNs), enabling low latency and high parallel data processing without electromagnetic interference, have become a viable player for fast and energy-efficient processing and calculation to meet the increasing demand for hash rate. Photonic memories employing nonvolatile phase-change materials could achieve zero static power consumption, low thermal cross talk, large-scale, and high-energy-efficient photonic neural networks. Nevertheless, the switching speed and dynamic energy consumption of phase-change material-based photonic memories make them inapplicable for in situ training. Here, by integrating a patch of phase change thin film with a PIN-diode-embedded microring resonator, a bifunctional photonic memory enabling both 5-bit storage and nanoseconds volatile modulation was demonstrated. For the first time, a concept is presented for electrically programmable phase-change material-driven photonic memory integrated with nanosecond modulation to allow fast in situ training and zero static power consumption data processing in ONNs. ONNs with an optical convolution kernel constructed by our photonic memory theoretically achieved an accuracy of predictions higher than 95% when tested by the MNIST handwritten digit database. This provides a feasible solution to constructing large-scale nonvolatile ONNs with high-speed in situ training capability.
Evidence of Kardar-Parisi-Zhang scaling on a digital quantum simulator

Understanding how hydrodynamic behaviour emerges from the unitary evolution of the many-particle Schr\"odinger equation is a central goal of non-equilibrium statistical mechanics. In this work we implement a digital simulation of the discrete time quantum dynamics of a spin-$\frac{1}{2}$ XXZ spin chain on a noisy near-term quantum device, and we extract the high temperature transport exponent at the isotropic point. We simulate the temporal decay of the relevant spin correlation function at high temperature using a pseudo-random state generated by a random circuit that is specifically tailored to the ibmq-montreal $27$ qubit device. The resulting output is a spin excitation on a highly inhomogeneous background. From the subsequent discrete time dynamics on the device we are able to extract an anomalous super-diffusive exponent consistent with the conjectured Kardar-Parisi-Zhang (KPZ) scaling at the isotropic point. Furthermore we simulate the restoration of spin diffusion with the application of an integrability breaking potential.
Computational advantage of quantum random sampling

Quantum random sampling is the leading proposal for demonstrating a computational advantage of quantum computers over classical computers. Recently, first large-scale implementations of quantum random sampling have arguably surpassed the boundary of what can be simulated on existing classical hardware. In this article, we comprehensively review the theoretical underpinning of quantum random sampling in terms of computational complexity and verifiability, as well as the practical aspects of its experimental implementation using superconducting and photonic devices and its classical simulation. We discuss in detail open questions in the field and provide perspectives for the road ahead, including potential applications of quantum random sampling.
A robust hybrid machine learning model for Bengali cyber bullying detection in social media

Social networking platforms give users countless opportunities to share information, collaborate, and communicate positively. The same platform can be extended to a fabricated and poisonous atmosphere that gives an impersonal, harmful platform for online misuse and assault. Cyberstalking is when someone uses an internet system to ridicule, torment, insult, criticize, slander, and discredit a victim while never seeing them. With the growth of social networks, Facebook has become the online arena for bullying. Since the effects could result in a widespread contagion, it’s vital to have models and mechanisms in place for the automatic identification and removal of internet cyberbullying data. This paper presents a robust hybrid ML model for cyberbullying detection in the Bengali language on social media. The Bengalibullying proposal involves an effective text preprocessing to make the Bengali text data into a useful text format, feature extraction using the TfidfVectorizer (TFID) to get the beneficial information of text data and resampling by Instance Hardness Threshold (IHT) procedure to balance the dataset to avoid overfitting or underfitting problems. In our experiment, we used the publicly available Bangla text dataset (44,001 comments) and got the highest performance ever published works on it. The model achieved the most elevated accuracy rate of 98.57% and 98.82% in binary and multilabel classification to detect cyberbullying on social media in the Bengali language. Our best performance findings are more effective than any previous effort in identifying and categorizing bullying in the Bengali language. As a result, we might use our model to correctly classify Bengali bullying in online bullying detection systems, protecting people from being the targets of social bullying.
Communication accommodation theory: Past accomplishments, current trends, and future prospects

This Special Issue commemorates the 50th anniversary of communication accommodation theory (CAT) in 2023, formerly known as speech accommodation theory. This article reflects on the diversity of CAT research as seen in recent studies (2021–2023) as well as the empirical papers to follow in this Special Issue. It provides an overview of CAT's history by reference to previously suggested stages in the evolution of research, with many updates to stages therein, demonstrating the theory's cross-disciplinary influences across many applied social contexts, diverse social groups, languages and cultures, and other communicative features. Next, we overview burgeoning recent research in computer-mediated and human-machine interactions that leads to the identification of a seventh stage of CAT and an agenda of research questions for future work suggested by it. In conclusion, we propose a refined and expanded set of Principles of Accommodation.
Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.
Exploiting Dynamic Vector-Level Operations and a 2D-Enhanced Logistic Modular Map for Efficient Chaotic Image Encryption

Over the past few years, chaotic image encryption has gained extensive attention. Nevertheless, the current studies on chaotic image encryption still possess certain constraints. To break these constraints, we initially created a two-dimensional enhanced logistic modular map (2D-ELMM) and subsequently devised a chaotic image encryption scheme based on vector-level operations and 2D-ELMM (CIES-DVEM). In contrast to some recent schemes, CIES-DVEM features remarkable advantages in several aspects. Firstly, 2D-ELMM is not only simpler in structure, but its chaotic performance is also significantly better than that of some newly reported chaotic maps. Secondly, the key stream generation process of CIES-DVEM is more practical, and there is no need to replace the secret key or recreate the chaotic sequence when handling different images. Thirdly, the encryption process of CIES-DVEM is dynamic and closely related to plaintext images, enabling it to withstand various attacks more effectively. Finally, CIES-DVEM incorporates lots of vector-level operations, resulting in a highly efficient encryption process. Numerous experiments and analyses indicate that CIES-DVEM not only boasts highly significant advantages in terms of encryption efficiency, but it also surpasses many recent encryption schemes in practicality and security.
Efficient Assamese Word Recognition for Societal Empowerment: A Comparative Feature-Based Analysis

The preservation and digitization of historical data are crucial for ensuring the continuity and accessibility of information over successive generations. The present study investigates the utilization of machine learning methodologies in the identification of Assamese words, focusing specifically on their distinctive visual characteristics. The main aim of this project is to improve word recognition technologies in Indic languages, specifically focusing on Assamese, in order to preserve and provide access to Assamese literature for future generations. The classification procedure entails the examination of 19 shape-related attributes through a range of machine learning algorithms, such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machine (SVM) with various kernels, K Nearest Neighbors, and Gradient Boosting. The assessment of the model involves the utilization of various metrics such as Accuracy, Precision, Kappa, F1-score, Model Build Time, and Model Run Time to evaluate the computational efficiency. Additionally, the metrics of Area under the Curve (AUC) and Receiver Operating Characteristic (ROC) are also considered in the evaluation process. Out of the four datasets analyzed, Dataset 3 exhibits the highest level of performance. It is worth noting that Gradient Boosting demonstrates the highest level of accuracy, reaching 96.03% for conventional machine learning appraoches. Logistic Regression and SVM with RBF kernel closely trail behind, achieving accuracies of 95.64% and 95.60% respectively. Furthermore, the research conducted in this study also employs multiple layers of Convolutional Neural Networks (CNN), resulting in a remarkable recognition accuracy of 97.3%. This finding demonstrates that the CNN model and the proposed feature-set are in close proximity to one another in terms of the evaluation metrics.
Zero-shot Image-to-Image Translation

Large-scale text-to-image generative models have shown their remarkable ability to synthesize diverse, high-quality images. However, directly applying these models for real image editing remains challenging for two reasons. First, it is hard for users to craft a perfect text prompt depicting every visual detail in the input image. Second, while existing models can introduce desirable changes in certain regions, they often dramatically alter the input content and introduce unexpected changes in unwanted regions. In this work, we introduce pix2pix-zero, an image-to-image translation method that can preserve the original image's content without manual prompting. We first automatically discover editing directions that reflect desired edits in the text embedding space. To preserve the content structure, we propose cross-attention guidance, which aims to retain the cross-attention maps of the input image throughout the diffusion process. Finally, to enable interactive editing, we distill the diffusion model into a fast conditional GAN. We conduct extensive experiments and show that our method outperforms existing and concurrent works for both real and synthetic image editing. In addition, our method does not need additional training for these edits and can directly use the existing pre-trained text-to-image diffusion model.
Study of the effect of an additional layer of insulation on the rate of heat loss in the cabin of an electric vehicle in conditions of extremely low arctic temperatures

This article examines the reduction of speedand heat loss in the passenger compartment of an electric vehicle (EV) when installing an additional thermal insulation gasket on the doors. The study was conducted in conditions of extremely low outdoor temperatures that occur in the winter season in the northern and arctic regions. To conduct a series of observations, the doors of the passenger compartment of an electric car were equipped with an additional (second) heat-insulating rubber gasket. The results were recorded for two groups of measurements, at an outdoor temperature of -25 ... -30 0C and at outdoor temperature -35 ... -40 0C.
A systematic review of federated learning: Challenges, aggregation methods, and development tools

Since its inception in 2016, federated learning has evolved into a highly promising decentral-ized machine learning approach, facilitating collaborative model training across numerous devices while ensuring data privacy. This survey paper offers an exhaustive and systematic review of federated learning, emphasizing its categories, challenges, aggregation techniques, and associated development tools. To start, we outline our research strategy used for this survey and evaluate other existing reviews related to federated learning. We initiate the discussion, about federated learning concepts, with a detailed examination of the primary challenges inherent in federated learning, including communication overhead, device and data heterogeneity, and data privacy issues. Subsequently, we scrutinize and classify various aggregation techniques designed to mitigate these challenges, such as federated averaging, secure aggregation, and strategies leveraging clustering and optimization methodologies. Furthermore, we delve into the exploration of cutting-edge development tools and frameworks that expedite efficient implementations of federated learning. Through our review, we aspire to provide a holistic understanding of the federated learning landscape, thereby setting the stage for future investigations, advancements, and practical implementations in this prosperous field.
To see further: Knowledge graph-aware deep graph convolutional network for recommender systems

Applying a graph convolutional network (GCN) or its variants to user-item interaction graphs is one of the most commonly used approaches for learning the representation of users and items in modern recommender systems. However, these models perform poorly in recommending high-order items due to the over-smoothing when the GCN deepens and the lack of connectivity between the cold-start items and the target user. To improve the ability to recommend higher-order items, we propose our Knowledge graph-aware Deep Graph Convolutional Network with Initial Residual Connection (KDGCN-IC) and Knowledge graph-aware Deep Graph Convolutional Network with Dense Connection (KDGCN-DC) methods. First, we introduce an item knowledge graph to rebuild the connectivity between nodes in the user-item graph, especially the users and the cold-start items. Then, we present our methods for improving the information flow by reusing features to alleviate the over-smoothing issue to deepen the GCN to capture the higher-order collaborative signal. More specifically, KDGCN-IC reuses the initial feature information, and KDGCN-DC reuses the feature information of each layer. Extensive experiments demonstrated that our models were able to achieve substantial improvement over state-of-the-art models in so far as their recommendation accuracy and higher-order recommendations.
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models

This work aims to improve the applicability of diffusion models in realistic image restoration. Specifically, we enhance the diffusion model in several aspects such as network architecture, noise level, denoising steps, training image size, and optimizer/scheduler. We show that tuning these hyperparameters allows us to achieve better performance on both distortion and perceptual scores. We also propose a U-Net based latent diffusion model which performs diffusion in a low-resolution latent space while preserving high-resolution information from the original input for the decoding process. Compared to the previous latent-diffusion model which trains a VAE-GAN to compress the image, our proposed U-Net compression strategy is significantly more stable and can recover highly accurate images without relying on adversarial optimization. Importantly, these modifications allow us to apply diffusion models to various image restoration tasks, including real-world shadow removal, HR non-homogeneous dehazing, stereo super-resolution, and bokeh effect transformation. By simply replacing the datasets and slightly changing the noise network, our model, named Refusion, is able to deal with large-size images (e.g., 6000 × 4000 × 3 in HR dehazing) and produces good results on all the above restoration problems. Our Refusion achieves the best perceptual performance in the NTIRE 2023 Image Shadow Removal Challenge and wins 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> place overall.
Security Vulnerability Analysis using Penetration Testing Execution Standard (PTES): Case Study of Government's Website

The rapid development of technology has impacted various aspects of life, including the way individuals, organizations, and governments deliver accurate, effective, and efficient information. XYZ local government, which is responsible for serving the community in the trade field, manages its information through the Communication and Information Agency (Diskominfo) of the XYZ region. Diskominfo employs technological advancements to provide the people of the XYZ region with direct access to accurate, precise, and reliable data through their website. However, the security of the website has become a crucial aspect to prevent attacks from malicious individuals that can cause damage to the system and harm the website owner. To analyze the website's security loopholes and vulnerabilities, the author performed a simulation of an attacker. The analysis aimed to evaluate the level of risk and confidence in the website. The results showed 42 alerts categorized into four risk levels: 9 vulnerabilities with a high-risk level, 13 vulnerabilities with a medium-risk level, 11 vulnerabilities with a low-risk level, and 9 vulnerabilities with an informational-risk level.