Reviewerly/exHarmony huggingface dataset:
MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation

Large language models have demonstrated the ability to generate both natural language and programming language text. Although contemporary code generation models are trained on corpora with several programming languages, they are tested using benchmarks that are typically monolingual. The most widely used code generation benchmarks only target Python, so there is little quantitative evidence of how code generation models perform on other programming languages. We propose MultiPL-E, a system for translating unit test-driven code generation benchmarks to new languages. We create the first massively multilingual code generation benchmark by using MultiPL-E to translate two popular Python code generation benchmarks to 18 additional programming languages. We use MultiPL-E to extend the HumanEval benchmark [1] and MBPP benchmark [2] to 18 languages that encompass a range of programming paradigms and popularity. Using these new parallel benchmarks, we evaluate the multi-language performance of three state-of-the-art code generation models: Codex [1], CodeGen [3]and InCoder [4]. We find that Codex matches or even exceeds its performance on Python for several other languages. The range of programming languages represented in MultiPL-E allow us to explore the impact of language frequency and language features on model performance. Finally, the MultiPL-E approach of compiling code generation benchmarks to new programming languages is both scalable and extensible, making it straightforward to evaluate new models, benchmarks, and languages.
A Survey of Federated Learning From Data Perspective in the Healthcare Domain: Challenges, Methods, and Future Directions

Recent advances in deep learning (DL) have shown that data-driven insights can be used in smart healthcare applications to improve the quality of life for patients.DL needs more data and diversity to build a more accurate system.To satisfy these requirements, more data need to be pooled at the centralized server to train the model deeply, but the process of pooling faces privacy and regulatory challenges.To settle them, the concept of sharing model learning rather than sharing data through federated learning (FL) is proposed.FL creates a more reliable system without transferring data to the server, resulting in the right system with stronger security and access rights to data that protect privacy.This research aims to (1) provide a literature review and an in-depth study on the roles of FL in the fields of healthcare; (2) highlight the effectiveness of current challenges facing standardized FL, including statistical data heterogeneity, privacy and security concerns, expensive communications, limited resources, and efficiency; and (3) present lists of open research challenges and recommendations for future FL for the academic and industrial sectors in telemedicine and remote healthcare applications.An extensive review of the literature on FL from a data-centric perspective was conducted.We searched the Science Direct, IEEE Xplore, and PubMed databases for publications published between January 2018 and February 2023.A new crossover matching between the approaches that solve or mitigate all types of skewed data has been proposed to open up opportunities to other researchers.In addition, a list of various applications was organized by learning application task types such as prediction, diagnosis, and classification.We think that this study can serve as a helpful manual for academics and industry professionals, giving them guidance and important directions for future studies.
Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. Aiming to reduce the influence of non-complete ground threat information on UAH path planning, a ground threat prediction-based path planning method is proposed based on artificial bee colony (ABC) algorithm by collaborative thinking strategy. Firstly, a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats. The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time. Then, a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats. By adding the collision warning mechanism to the path planning model, the flight path could be dynamically adjusted according to changing no-fly zones. Furthermore, a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy. The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution, and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy, which makes the optimization performance of ABC algorithm more controllable and efficient. Finally, simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.
Understanding the encoding of object locations in small-scale spaces during free exploration using eye tracking

Navigation is instrumental to daily life and is often used to encode and locate objects, such as keys in one's house. Yet, little is known about how navigation works in more ecologically valid situations such as finding objects within a room. Specifically, it is not clear how vision vs. body movements contribute differentially to spatial memory in such small-scale spaces. In the current study, participants encoded object locations by viewing them while standing (stationary condition) or by additionally being guided by the experimenter while blindfolded (walking condition) after viewing the objects. They then retrieved the objects from the same or different viewpoint, creating a 2 × 2 within subject design. We simultaneously recorded participant eye movements throughout the experiment using mobile eye tracking. The results showed no statistically significant differences among our four conditions (stationary, same viewpoint as encoding; stationary, different viewpoint; walking, same viewpoint; walking, different viewpoint), suggesting that in a small real-world space, vision may be sufficient to remember object locations. Eye tracking analyses revealed that object locations were better remembered next to landmarks and that participants encoded items on one wall together, suggesting the use of local wall coordinates rather than global room coordinates. A multivariate regression analysis revealed that the only significant predictor of object placement accuracy was average looking time. These results suggest that vision may be sufficient for encoding object locations in a small-scale environment and that such memories may be formed largely locally rather than globally.
A novel approach to generate distractors for Multiple Choice Questions

Multiple Choice Questions (MCQs) have been predominantly used as an assessment tool in the educational domain. The MCQ comprises a question called ‘Stem’, one correct answer called ‘Key’, and the incorrect options called ‘Distractors’. Identifying distractors is an essential step in MCQ construction because distractors need to be misleading and plausibly incorrect. Therefore the manual construction of MCQ is error-prone, and requires cumbersome efforts. Hence existing works have focused on automatic generation of MCQs but primarily towards vocabulary assessment. However, very few works inclined towards the technical domain have failed to analyze the plausibility of distractors. In this context, the proposed research DIstractor GENeration (DIGEN) is targeted to generate distractors for the MCQ in the technical domain. Hence, the novel contribution here is DIGEN takes unstructured text as well as multiple-choice questions with key as mandatory source along with ontology which may be an optional source to generate distractors automatically in the technical domain. Distractors generated have been evaluated based on Item Response Theory, which shows promising results.
ML-KnockoffGAN: Deep online feature selection for multi-label learning

Many online platforms now generate data in a streaming manner, resulting in the continuous production of new features. Multi-label data generation has also surged in recent years, making feature selection for online multi-label data essential. However, existing feature selection methods are mainly based on single-label data or offline selection approaches. Only a few methods exist for multi-label data in an online framework, and most of these methods use classical or evolutionary-based techniques, paying little attention to deep learning. In this study, we propose a novel deep-learning feature selection technique that utilizes generative adversarial nets (GANs). We develop a framework, called ML-KnockoffGAN, which generates knockoff features in a multi-label setting, and then features are selected by considering both the generated knockoff features and real features together. As the features arrive online in a continuous fashion, our proposed method incorporates online features and selects them in a group-wise manner. We tested our method on various multi-label data sets from different domains, including text, biology, and audio, and our results show that our approach outperforms existing methods, with an average improvement of 7.1−16.3% for all evaluation metrics. Our method also illustrates the benefits of deep learning techniques in utilizing existing trained parameters to train new windows of features, requiring fewer epochs.
GFL: Federated Learning on Non-IID Data via Privacy-Preserving Synthetic Data

Federated learning (FL) enables large amounts of participants to construct a global learning model, while storing training data privately at local client devices. A fundamental issue in FL systems is the susceptibility to the highly skewed distributed data. A series of methods have been proposed to mitigate the Non-IID problem by limiting the distances between local models and the global model, but they cannot address the root cause of skewed data distribution eventually. Some methods share extra samples from the server to clients, which requires comprehensive data collection by the server and may raise potential privacy risks. In this work, we propose an efficient and adaptive framework, named Generative Federated Learning (GFL), to solve the skewed data problem in FL systems in a privacy-friendly way. We introduce Generative Adversarial Networks (GAN) into FL to generate synthetic data, which can be used by the server to balance data distributions. To keep the distribution and membership of clients' data private, the synthetic samples are generated with random distributions and protected by a differential privacy mechanism. The results show that GFL significantly outperforms existing approaches in terms of achieving more accurate global models (e.g., 17% - 50% higher accuracy) as well as building global models with faster convergence speed without increasing much computation or communication costs.
A Discriminative DeepLab Model (DDLM) for Surface Anomaly Detection and Localization

Recent reconstructive model-based visual anomaly detection (AD) methods have been shown to reconstruct the normal regions of the image but often struggle to detect the required anomalous deviations. These approaches are typically trained on images without anomalies and consequently require manual post-processing techniques to localize the anomalies. In this paper, we proposed a discriminative deeplab model that is trained discriminatively end-to-end on out-of-distribution simulated anomalies. Our proposed framework directly localizes anomalies without the need for complex post-processing via learning a combined representation of anomalous images and a decision boundary between anomalous and normal samples. The effectiveness of our DDLM is evaluated extensively on the MVTec AD dataset. Our results show that the DDLM framework outperformed state-of-the-art frameworks between 1.5% and 18.8% in AUROC on the anomaly detection challenge and between 20.2% and 22.9% with regards to the AP on the anomaly localization challenge.
Machine Learning in Embedded Systems: Limitations, Solutions and Future Challenges

Machine learning has attracted a lot of interest in the last few years as a solution to a variety of difficult challenges in many disciplines. An emerging area is that of embedded devices, where machine learning is deployed to efficiently carry out tasks like data analysis, prediction, and decision-making in real-time applications. Challenges such as the necessity for fast and effective algorithms and the restricted resources available in embedded systems to cover the computational and storage demands need to be confronted to successfully integrate machine learning models into embedded systems. This work aims to provide an overview of the use of machine learning in embedded systems, including past and current solutions, and to present the challenges that need to be addressed. Future directions for the use of machine learning in embedded systems are also discussed.
A Survey of Advanced Methods for Efficient Text Summarization

Text Summarization is the process of extracting essential parts from a source text. The generated summary will aid in decreasing the time and effort necessary to read the complete text. We will look into the use case of text summarization of meeting transcripts in this paper. We look at how to find words and phrases that effectively reflect or contribute to the semantic essence of decisions made in multi-party human-to-human talks. Before evaluating alternative ways for getting information from these interactions, we will first provide a quick overview of our approach to modelling decision debates in meetings. We consider the difficulty of compressing a series of related sentences into a single short sentence, often known as multi-sentence compression. We will also consider the necessity to recognise and distinguish between decisions and conversation, as well as argument mining.
Canny Edge Detection on GPU using CUDA

Edge detection is a crucial step in many of today's computer vision applications. Canny edge detection in particular involves several steps to achieve real-time results. Many systems currently deployed leverage the compute capability that a graphics processing unit (GPU) can achieve. This paper covers the implementation and testing of a Canny edge detection algorithm using CUDA C. The results cover a comparison of the naive implementation in sequential C, a parallelized implementation using OneAPI Threading Building Blocks (TBB), and a tiled, shared memory approach using CUDA C. A comparison between the NVIDIA GTX 1060 and NVIDIA RTX 3090 are also performed. The CUDA C implementation shows an improvement of up to 100 times that over the naive sequential implementation for an RGB image at 4k resolution, and an improvement of 10 times when compared to the TBB approach. Additionally, the RTX 3090 showed roughly a speed up of 1.5 times that of the GTX 1060, demonstrating the advances made between the generations of GPUs. These results overall show the benefits of using a GPU accelerated approach to edge detection, with further improvements left to achieve.
Evaluating Question generation models using QA systems and Semantic Textual Similarity

Question generation based on conversational context is a difficult problem to solve. A widely used technique for generating quality questions using fine-tuned models relies on a suitable answer and the context, usually the passage. But when it comes to conversational settings, the questions generated are not of the highest quality as they lack the contextual element in the question, especially due to the lack of co-reference resolution of the entity. Furthermore, in most of the evaluation techniques for generating questions, there seems to be a lack of utilizing powerful question-answering systems to judge the answerability of the questions generated. The most prevalent metric used for judging machine-generated text against the human gold standard, BLUE, unfortunately doesn't factor in whether a question answering system would be able to answer the question, but instead focuses mostly on the number of substrings that match against each other. Various question generation models following a generalized encoder-decoder architecture were evaluated using semantic textual similarity for both the generated questions and the generated answers. Although higher parameters in a model usually lend to better performance, our experiment displayed that such is not always the case, at least when there is a massive amount of context missing.
A Comparison Study on AI Language Detector

This paper presents a comparison between different AI Language detectors. Currently, language models are gaining popularity due to its ability to generate text, or code. However, there is a growing concern over the misuse of language model in schools and universities for assignments or essays. This leads to a rise of AI language detector to ensure the text generated by AI language model are traceable. Through this review, we have tried to explore the different types of existing model that detects whether a text is generate by AI. These types of computational intelligent models can prevent language model misuse, including plagiarism detection, and language model fine-tuning. The review also examines the limitations of these models.
Gazed Pottery: an Archaeometric-Cognitive Approach to Material Culture Visuality

This paper presents a study of visual perception through the application of eye-tracking of prehistoric ceramics. The study is a feasible methodology to understand the agency of material culture through quantitative techniques, which allows for the analyses of possible relationships between visual-perceptual behaviour, material culture and social complexity. In particular, the horizontality of gaze is shown to be associated with pottery from early periods and its verticality increases in pottery materials from later, more complex societies. These, and other results, confirm that differential patterns of visual response by observers are determined by the material characteristics of each ceramic style. Implications for improved interpretation of archaeological phenomena are discussed including the possibilities of new applications for heritage management. Therefore, eye-tracking analysis appears to be a powerful and profitable archaeometric technique.
Cooperative towing for double unmanned surface vehicles connected with a floating rope via vertical formation and adaptive moment control

The surface round-up and towing operations are essential for surface garbage removal, oil spill recovery and shipwrecked ship rescue. All of which are related to the national economy and the people's livelihood, and it is urgent to improve operation efficiency. This work considers connecting double unmanned surface vehicles (DUSVs) with floating ropes to achieve efficiently autonomous round-up and towing. The core of this research is how to realize autonomous cooperative towing control of DUSV under the interference of floating rope hydrodynamic moment and wind, wave and current. Therefore, a vertical formation control algorithm was proposed to improve the control performance of the DUSV. The virtual leader–follower was treated as the guidance target, and the relative distance between the DUSV and the guidance target should be maintained properly. Next, to reduce the probability of drag separation of DUSV, on the basis of the sensors equipped on the floating rope, an adaptive moment control algorithm was designed via the control of the heading angle error and the adjustment of the moment correction. The results of the simulation and water experiments verified the excellent performance of our algorithms.
Remix: Towards the transferability of adversarial examples

Deep neural networks (DNNs) are susceptible to adversarial examples, which are crafted by deliberately adding some human-imperceptible perturbations on original images. To explore the vulnerability of models of DNNs, transfer-based black-box attacks are attracting increasing attention of researchers credited to their high practicality. The transfer-based approaches can launch attacks against models easily in the black-box setting by resultant adversarial examples, whereas the success rates are not satisfactory. To boost the adversarial transferability, we propose a Remix method with multiple input transformations, which could achieve multiple data augmentation by utilizing gradients from previous iterations and images from other categories in the same iteration. Extensive experiments on the NeurIPS 2017 adversarial dataset and the ILSVRC 2012 validation dataset demonstrate that the proposed approach could drastically enhance the adversarial transferability and maintain similar success rates of white-box attacks on both undefended models and defended models. Furthermore, extended experiments based on LPIPS show that our method could maintain a similar perceived distance compared to other baselines.
Graphical Retrieval and Analysis of Temporal Information Systems (GRATIS): An Integrative Mixed Methodology and Open-Access Software to Analyze the (Non-)Linear Chronological Evolution of Information Embedded in Textual/Qualitative Data

Like a video that reveals much more than a single photo, the incorporation of time to the analysis of qualitative evidence promotes contextualized understandings and allows research participants and readers to interactively review the processes and rationale that researchers followed to craft their findings and conclusions. However, mixed methods and qualitative methodologies available today forfeit the nuances gained by analyzing the chronological/temporal evolution of processes. We contribute to mixed methods research by introducing graphical retrieval and analysis of temporal information systems (GRATIS), a methodology (and open-access software) designed to visualize and analyze the time-based richness embedded in all qualitative/textual data. GRATIS employs dynamic network visualizations and data science mining/retrieval tools to combat the assumption that longitudinal studies require large timespans. We showcase how all qualitatively- or machine-learning-coded textual data may be analyzed with no extra feature engineering (i.e., data cleaning or preparation), rendering fully integrative/interactive outputs that strengthen the transparency of our findings and conclusions and open the “analytic black box” that characterizes most of mixed methods and qualitative studies to date. GRATIS contributes to democratizing data science by removing financial and computer programming barriers to benefit from data science applications. All data and software to replicate the analyses are provided with this submission.