Reviewerly/exHarmony huggingface dataset:
Path Planning Collision Avoidance of a SCARA Manipulator using PRM with Fuzzy

Robotics was introduced in the world with the objective of automating industrial processes, that is, facilitating human work, and with this new algorithms have been emerging to increasingly improve the use of robots in an autonomous way. This paper compares the Probabilistic Road-map (PRM) and PRM-Fuzzy applied for a SCARA manipulator (Selective Compliance Assembly Robot Arm). The PRM-Fuzzy algorithm is used to optimize the path generated by the PRM, as it makes the path smoother and shorter. Are used in collision avoidance path planning and is compared by computational cost (processing time), multiple correlation coefficient, (R2), the mechanical energy consumption of the motors, and the shortest collision avoidance paths. The results show the trajectories generated by the algorithms in the Cartesian space and also the trajectories of each joint of the manipulator, calculated from the inverse kinematics. Velocities, accelerations, and torques for trajectories are also shown. Several scenarios with different obstacles were used and in all cases, the PRM-Fuzzy algorithm performed better than the conventional PRM for the comparisons performed.
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs

Large-scale graph machine learning is challenging as the complexity of learning models scales with the graph size. Subsampling the graph is a viable alternative, but sampling on graphs is nontrivial as graphs are non-Euclidean. Existing graph sampling techniques require not only computing the spectra of large matrices but also repeating these computations when the graph changes, e.g., grows. In this paper, we introduce a signal sampling theory for a type of graph limit -- the graphon. We prove a Poincar\'e inequality for graphon signals and show that complements of node subsets satisfying this inequality are unique sampling sets for Paley-Wiener spaces of graphon signals. Exploiting connections with spectral clustering and Gaussian elimination, we prove that such sampling sets are consistent in the sense that unique sampling sets on a convergent graph sequence converge to unique sampling sets on the graphon. We then propose a related graphon signal sampling algorithm for large graphs, and demonstrate its good empirical performance on graph machine learning tasks.
Convolutional Autoencoder-Based Models for Image Denoising: A Comparative Study

Images are often infected with noise for a variety of reasons, including camera sensor defects, transmission via noisy channels, and incorrect memory locations in the hardware. Effective denoising techniques are required to deal with such noisy images. This paper examines some prospective deep learning-enabled convolutional autoencoder networks for image denoising and presents a comparative view based on their quantitative performance on the same datasets in terms of standard evaluation measures such as PSNR. Such study will be useful in selecting appropriate image denoising model for different applications.
A Study on Altering the Latent Space of Pretrained Text to Speech Models for Improved Expressiveness

This report explores the challenge of enhancing expressiveness control in Text-to-Speech (TTS) models by augmenting a frozen pretrained model with a Diffusion Model that is conditioned on joint semantic audio/text embeddings. The paper identifies the challenges encountered when working with a VAE-based TTS model and evaluates different image-to-image methods for altering latent speech features. Our results offer valuable insights into the complexities of adding expressiveness control to TTS systems and open avenues for future research in this direction.
Actively Learning Numerical Relativity

Data analysis of gravitational waves detected by the Ligo-Virgo-Kagra collaboration and future observatories relies on precise modelling of the sources. In order to build, calibrate and validate current models, we resort to expensive simulations in Numerical Relativity (NR), the fully-fledged simulation of Einstein's Equations. Since simulation costs and the dimensionality of parameter space are prohibitive to perform a dense coverage, approximate models interpolate among the available simulation data. We put forward the technique of Gaussian Process Active Learning (GPAL), an adaptive, data-driven protocol, for parameter space exploration and training of gravitational wave approximants. We evaluate this proposal by studying a computationally inexpensive scenario, in which we calibrate the approximant TEOBResumS using the NR-informed model as a proxy for NR. In this case study, we find that GPAL reduces the computational cost of training by a factor of 4 with respect to uniform or randomly distributed simulations. Moreover, we consider a parallel implementation which reduces computational time, and hybrid strategies which improve pre-calibrated models. The Gaussian Process regression employed in this approach naturally endows the algorithm with notion of model uncertainty. We comment on the implications of this feature for data analysis.
HF-Detect A Hybrid Detector for Manipulated Face Detection*

The recent advancement of fake face creation and fake face generation motivates the development of an excellent fake face detection method that can effectively detect the difference between fake and real. Various fake detection methods are available with adequate performance, but the limitation of those available methods is they are not performing well with highly compressed images with degraded quality. Manipulation of face images is getting advanced, and becoming difficult to trust the content over the media, and generating and detection should go parallelly to balance society. Therefore we are proposing a novel approach to solve this problem which uses the hybrid model HF-Detect, which combines the advantage of the Xception network along with the F <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net.
H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognition

With the rapid advancement of machine learning models for NLP tasks, collecting high-fidelity labels from AI models is a realistic possibility. Firms now make AI available to customers via predictions as a service (PaaS). This includes PaaS products for healthcare. It is unclear whether these labels can be used for training a local model without expensive annotation checking by in-house experts. In this work, we propose a new framework for Human Correction of AI-Generated Labels (H-COAL). By ranking AI-generated outputs, one can selectively correct labels and approach gold standard performance (100% human labeling) with significantly less human effort. We show that correcting 5% of labels can close the AI-human performance gap by up to 64% relative improvement, and correcting 20% of labels can close the performance gap by up to 86% relative improvement.
Risk-averse Batch Active Inverse Reward Design

Designing a perfect reward function that depicts all the aspects of the intended behavior is almost impossible, especially generalizing it outside of the training environments. Active Inverse Reward Design (AIRD) proposed the use of a series of queries, comparing possible reward functions in a single training environment. This allows the human to give information to the agent about suboptimal behaviors, in order to compute a probability distribution over the intended reward function. However, it ignores the possibility of unknown features appearing in real-world environments, and the safety measures needed until the agent completely learns the reward function. I improved this method and created Risk-averse Batch Active Inverse Reward Design (RBAIRD), which constructs batches, sets of environments the agent encounters when being used in the real world, processes them sequentially, and, for a predetermined number of iterations, asks queries that the human needs to answer for each environment of the batch. After this process is completed in one batch, the probabilities have been improved and are transferred to the next batch. This makes it capable of adapting to real-world scenarios and learning how to treat unknown features it encounters for the first time. I also integrated a risk-averse planner, similar to that of Inverse Reward Design (IRD), which samples a set of reward functions from the probability distribution and computes a trajectory that takes the most certain rewards possible. This ensures safety while the agent is still learning the reward function, and enables the use of this approach in situations where cautiousness is vital. RBAIRD outperformed the previous approaches in terms of efficiency, accuracy, and action certainty, demonstrated quick adaptability to new, unknown features, and can be more widely used for the alignment of crucial, powerful AI models.
Blockchain In Air Pollution Monitoring

The detrimental effects of air pollution on human health have become significant issues in many parts of the world. The assessment and forecasting of the air quality near people have recently attracted more research attention. The Internet of Things (IoT), which connects several sensors in various areas to improve people’s quality of life, is widely used in many fields. This makes air quality monitoring easier than before. The traditional method of installing stationary sensors does not effectively give a full picture of polluted air in people’s surroundings because the closest monitors may be kilometers away. To promote pollution control, assessment and prosecution are used, which may entail actions like industry-wide shutdowns that are harmful to regional economic growth. A new economic era has begun with the application of blockchain technology, which is known as the foundation of Bitcoin. Recent years have seen the development of many blockchain-based programs and services. Blockchain applications are becoming more numerous and diverse across a range of sectors, such as the (IoT)Internet of Things, finance, and reliability. In this essay, we examine the application of blockchain technology to protect data in air quality monitoring systems.
Blockchain Scam Detection: State-of-the-Art, Challenges, and Future Directions

With the rapid development of blockchain platforms, such as Ethereum and Hyperledger Fabric, blockchain technology has been widely applied in various domains. However, various scams exist in the cryptocurrency transactions on the blockchain platforms, which has seriously obstructed the development of blockchain. Therefore, many researchers have studied the detection methods for blockchain scams. On the basis of introducing the mainstream types of scams, including Ponzi scheme, Phishing scam, Honeypot, and Pump and dump, this paper provides a thorough survey on the detection methods for these scams, in which 48 studies are investigated. The detection methods are categorized into the analysis-based methods and the machine learning-based methods in terms of the adopted techniques, and are summarized from multiple aspects, including the type of dataset, the extracted feature, the constructed model, etc. Finally, this paper discusses the challenges and potential future research directions in blockchain scam detection.
Overview

This comment was made in the context of Rendezvous [1], a dialog-based system Ted Codd envisioned back in 1974, aimed at allowing casual users to effectively communicate with structured databases. This project was after he introduced the relational model for database management, which earned him the Turing Award.
A Systematic Evaluation of Response Selection for Open Domain Dialogue

Recent progress on neural approaches for language processing has triggered a resurgence of interest on building intelligent open-domain chatbots. However, even the state-of-the-art neural chatbots cannot produce satisfying responses for every turn in a dialog. A practical solution is to generate multiple response candidates for the same context, and then perform response ranking/selection to determine which candidate is the best. Previous work in response selection typically trains response rankers using synthetic data that is formed from existing dialogs by using a ground truth response as the single appropriate response and constructing inappropriate responses via random selection or using adversarial methods. In this work, we curated a dataset where responses from multiple response generators produced for the same dialog context are manually annotated as appropriate (positive) and inappropriate (negative). We argue that such training data better matches the actual use case examples, enabling the models to learn to rank responses effectively. With this new dataset, we conduct a systematic evaluation of state-of-the-art methods for response selection, and demonstrate that both strategies of using multiple positive candidates and using manually verified hard negative candidates can bring in significant performance improvement in comparison to using the adversarial training data, e.g., increase of 3% and 13% in Recall@1 score, respectively.
A pessoa idosa e a mídia social: evolução do envelhecimento

In a context of pandemic and increased incidence of social isolation among the elderly, the study aimed to demonstrate how social networks, in their different forms of manifestation, can help with the inclusion and reintegration of this population group into society. This is documentary research with public domain data using the Social Network Analysis in Health Research methodology, with a descriptive and qualitative character. Among the total of 31 profiles analyzed, three study categories emerged and were grouped by similarity of meaning. It is concluded that the contemporary generation of elderly people demonstrated interest and ease in using digital technology, enabled their reintegration into society and managed to give new meaning to what it means to be elderly today, instigating a new version that elderly people can take for themselves, giving more meaning to this stage of life, contributing to active, conscious and integrative aging.
Food-Health-Chain: A Food Supply Chain for Internet of Health Things Using Blockchain

Foodborne disease is still a major public health concern in both advanced and emerging economies. In terms of adopting new technology, the health industry continues to lag behind all other sectors. India has traditionally relied on food farming to make money but which food item is good for whom we have no proper idea. Especially when it comes to healthcare sectors, the Internet of health things (IoHT) and blockchain can play an important role. It can enhance the quality of care for its users and reduce costs by allocating the correct food chain to the targeted users. We are currently employing blockchain in supply chain management to cluster the patients based on their health care type. For example, diabetic patients prefer a particular type of food. Segregating the food type within the supply chain can reduce a lot of effort and cost. Blockchain can provide a solution to the issues that arise in the healthcare-based supply chain system because of its decentralized and immutable nature. The service's execution has been depicted in pictures and accompanied by detailed descriptions. The ultimate result of this study was to demonstrate the immutability, availability, and transparency security of blockchain in the healthcare-based supply chain.
Reinforcement Learning from Diffusion Feedback: Q* for Image Search

Large vision-language models are steadily gaining personalization capabilities at the cost of fine-tuning or data augmentation. We present two models for image generation using model-agnostic learning that align semantic priors with generative capabilities. RLDF, or Reinforcement Learning from Diffusion Feedback, is a singular approach for visual imitation through prior-preserving reward function guidance. This employs Q-learning (with standard Q*) for generation and follows a semantic-rewarded trajectory for image search through finite encoding-tailored actions. The second proposed method, noisy diffusion gradient, is optimization driven. At the root of both methods is a special CFG encoding that we propose for continual semantic guidance. Using only a single input image and no text input, RLDF generates high-quality images over varied domains including retail, sports and agriculture showcasing class-consistency and strong visual diversity. Project website is available at https://infernolia.github.io/RLDF.
DUnE: Dataset for Unified Editing

Even the most advanced language models remain susceptible to errors necessitating to modify these models without initiating a comprehensive retraining process. Model editing refers to the modification of a model's knowledge or representations in a manner that produces the desired outcomes. Prior research primarily centered around editing factual data e.g. "Messi plays for Inter Miami" confining the definition of an edit to a knowledge triplet i.e. (subject, object, relation). However, as the applications of language models expand, so do the diverse ways in which we wish to edit and refine their outputs. In this study, we broaden the scope of the editing problem to include an array of editing cases such as debiasing and rectifying reasoning errors and define an edit as any natural language expression that solicits a change in the model's outputs. We are introducing DUnE-an editing benchmark where edits are natural language sentences and propose that DUnE presents a challenging yet relevant task. To substantiate this claim, we conduct an extensive series of experiments testing various editing approaches to address DUnE, demonstrating their respective strengths and weaknesses. We show that retrieval-augmented language modeling can outperform specialized editing techniques and neither set of approaches has fully solved the generalized editing problem covered by our benchmark.
The Roles of Machine Translation in Learning Among K-12 Students at the International Schools in the Great Bay Area, China

Proceedings of The 6th World Conference on Social Sciences Year: 2023 DOI: https://www.doi.org/10.33422/6th.worldcss.2023.08.120 [PDF] The Roles of Machine Translation in Learning Among K-12 Students at the International Schools in the Great Bay Area, China Zhitong Liu, Chen Ye, Tim Guo, Danyang Song, Aditya Chen Gupta, Xiuli Chen ABSTRACT: Intercultural foreign language learning among K -12 students at international schools involves the cross-cultural discovery of the changing habits of people whose mother tongue might be different instructive languages at schools. Bilingual education is essential in bridging the gap between the mother tongue and instructive languages. However, it needs effective strategies that combine in-classroom language learning with new technology. Several language learning and teaching trends concerning the digital environment and innovative technologies such as machine translation or digitalization are currently challenging K-12 schools and families. This research investigates the role of machine translation in learning among K-12 students at international schools in the cities of Shenzhen, Guangzhou, and Zhuhai. It aims to identify these gaps in existing research and propose innovative strategies for boosting learning outcomes to recognize the learning habits they developed as young learners in the 21st century and their cultural motivations. Bilingual surveys are being handed out to K-12 families. These surveys are written in both English and Chinese. In total, 221 of them were collected. The data was analyzed to examine how helpful machine translation is for academic learning in both languages. The strengths and weaknesses were summarized among K-12 students using machine translation, highlighting strategies used with it at international K-12 schools. keywords: Conceptual, English curricula, Intercultural, Technology, 21st Century
Mip-Splatting: Alias-free 3D Gaussian Splatting

Recently, 3D Gaussian Splatting has demonstrated impressive novel view synthesis results, reaching high fidelity and efficiency. However, strong artifacts can be observed when changing the sampling rate, \eg, by changing focal length or camera distance. We find that the source for this phenomenon can be attributed to the lack of 3D frequency constraints and the usage of a 2D dilation filter. To address this problem, we introduce a 3D smoothing filter which constrains the size of the 3D Gaussian primitives based on the maximal sampling frequency induced by the input views, eliminating high-frequency artifacts when zooming in. Moreover, replacing 2D dilation with a 2D Mip filter, which simulates a 2D box filter, effectively mitigates aliasing and dilation issues. Our evaluation, including scenarios such a training on single-scale images and testing on multiple scales, validates the effectiveness of our approach.
Monitor Placement for Fault Localization in Deep Neural Network Accelerators

Systolic arrays are a prominent choice for deep neural network (DNN) accelerators because they offer parallelism and efficient data reuse. Improving the reliability of DNN accelerators is crucial as hardware faults can degrade the accuracy of DNN inferencing. Systolic arrays make use of a large number of processing elements (PEs) for parallel processing, but when one PE is faulty, the error propagates and affects the outcomes of downstream PEs. Due to the large number of PEs, the cost associated with implementing hardware-based runtime monitoring of every single PE is infeasible. We present a solution to optimize the placement of hardware monitors within systolic arrays. We first prove that $2N-1$ monitors are needed to localize a single faulty PE and we also derive the monitor placement. We show that a second placement optimization problem, which minimizes the set of candidate faulty PEs for a given number of monitors, is NP-hard. Therefore, we propose a heuristic approach to balance the reliability and hardware resource utilization in DNN accelerators when number of monitors is limited. Experimental evaluation shows that to localize a single faulty PE, an area overhead of only 0.33% is incurred for a $256\times 256$ systolic array.
TransOpt: Transformer-based Representation Learning for Optimization Problem Classification

We propose a representation of optimization problem instances using a transformer-based neural network architecture trained for the task of problem classification of the 24 problem classes from the Black-box Optimization Benchmarking (BBOB) benchmark. We show that transformer-based methods can be trained to recognize problem classes with accuracies in the range of 70\%-80\% for different problem dimensions, suggesting the possible application of transformer architectures in acquiring representations for black-box optimization problems.