Reviewerly/exHarmony huggingface dataset:
Learning English Writing Skills from Images

Learning from Demonstration (LfD) is a widely utilized technology within the realm of robotics, and the task of writing holds particular significance in this context. Typically, algorithms for learning alphabet writing necessitate a demonstrated trajectory to acquire the requisite skills, thereby relying on sensors to record these trajectories. However, this approach introduces complexities when dealing with the composition of English words or sentences, as it requires the manual specification of starting and ending points for each individual letter. This research introduces an innovative methodology aimed at resolving this predicament, effectively obviating the necessity for physical demonstrations and the explicit designation of starting and ending points for replication. Instead, the proposed method entails the generation of binary images, followed by the extraction of skeleton curves and graph nodes. Subsequently, an iterative process is employed to ensure the absence of intermediate nodes within the extracted trajectories, thereby adeptly encapsulating the writing skill intrinsic to each alphabet letter. When reproducing the writing, the same technique is applied to process newly generated images, designating the existing nodes as the sequential starting and ending points from left to right. Through the adoption of this approach, the aggregation of distinct skills can be seamlessly realized. The efficacy of the proposed algorithm is substantiated through a comprehensive validation process encompassing both simulations and real-world experiments. This robust validation underscores the algorithm's proficiency in addressing the complexities associated with skill acquisition and reproduction, offering a promising avenue for advancements in robotic writing capabilities.
Enabling Digital Transformation through Business Text Classification with Small Datasets

Digital transformation represents a complex process that entails the complete reconfiguration of an organization to harness the full potential of technology on a grand scale, aiming to deliver maximum value. To illustrate this transformative journey, we present a real-world case study centered around a company's ambitious pursuit to modernize its manual business text classification processes by embracing automated machine-based methods. During our endeavor, we encountered an imbalanced dataset provided by the business company, with certain categories containing only a few text examples while others had a substantial number of them. To rectify this imbalance, we balanced the dataset by removing excessive text examples, resulting in a more manageable and smaller dataset. To use it, we thoroughly evaluated four multilabel text classification methods: Binary Relevance, Classifier Chains, Label Powerset, and Generative Pre-trained Transformer (GPT) i.e., a large language model developed by OpenAI. The results of our investigation unveiled a success for the GPT-based approach, which significantly outperformed its counterparts across vital performance metrics, including Accuracy, F1-Score, Precision, and Recall. This outcome is particularly noteworthy because it showcases the remarkable effectiveness of GPT-based models in multi-label text classification tasks, even when dealing with relatively small datasets. In contrast, the other methods, Binary Relevance, Classifier Chains, and Label Powerset didn't demonstrate competence and exhibited comparatively less impressive performance levels. Notably, these traditional methods typically rely on larger datasets for training, making them less suitable for scenarios where data availability is limited. These findings underscore the power of GPT-based models in text classification tasks with small datasets, making them valuable assets for businesses in the complex landscape of digital transformation.
Text Summarization Generation Based on Improved Transformer Model

In the era of big data, the number of internet users is increasing yearly, and each user receives a massive amount of information every day. The low-value density of massive text data makes it difficult to utilize its information. In the face of this problem, text summarization generation technology that can extract the paper's leading content and condense the paper's main idea is a powerful solution. In text summarization generation, abstractive summarization techniques with high readability, clear expression, and closer proximity to human language habits are often favored. However, mainstream technologies still have issues like summarizations' detachment from focus. In this paper, keyword information is employed to join the text summarization generation task, and a keyword information module is added to the Transformer to better perform the summarization generation task. Finally, the ROUGE evaluation standard is used to compare the performance of the above models on the large-scale Chinese short text summarization (LCSTS) dataset. The experimental results show that the model generates higher-quality summarizations using keyword information, and the related methods have considerable potential. Future work can explore better model structures, more suitable methods for utilizing keyword information, and more accurate keyword extraction techniques to improve the quality of abstractive summarizations.
Towards Integrating Data Quality Assessments and Radiometer Uncertainty for Determining the Expanded Uncertainty of Three-Component Solar Radiation Measurements

Accurate solar irradiance data are fundamental for determining the design and performance characteristics of photovoltaic systems. The uncertainty of solar irradiance measurements depends on many factors including radiometer design, calibration, installation, maintenance, and operational environment. The key contributors to this uncertainty can be classified as the measurement uncertainty of a particular radiometer and the operational uncertainty determined for the time of measurement. Radiometer measurement uncertainty estimates (U <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</inf> ) can be based on well-established methods used as part of the radiometer calibration process. Estimates of operational uncertainties (U <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> ) require consideration of additional site-specific factors that affect data quality. A method is needed for establishing the accuracy of solar irradiance data by integrating an existing data quality process and measurement uncertainty estimates for specific radiometers. An algorithm has been developed to integrate data quality analyses and measurement uncertainty estimates for three-component solar irradiance data: global horizontal (total hemispheric) irradiance, direct normal (beam) irradiance, and diffuse horizontal (sky) irradiance collected at one- to 60-minute intervals. The algorithm has been tested using one-minute irradiance measurements. The goal of the project is to distribute a user-friendly software package based on the new algorithm.
Balanced Hashgraph Based on Dynamic MCMC: Efficient, Practical and Compatible

In a blockchain system based on the HashGraph consensus algorithm, picking a target server for block propagation is a key step. The traditional HashGraph blockchain system uses a completely random approach to pick the target server, which has the advantage of ensuring the security of the system and reducing the impact of attacks and single server failures received by the system. However, the random selection of target servers may lead to a situation where low performance servers are under high load but high performance servers are under load. In order to make each server process the block data with maximum performance, this paper proposes a balanced up-linking strategy, which calculates the server's reputation by evaluating the status of four dimensions, such as remaining performance, security, response time, and trustworthiness of the server, and then constructs a geometric probability model by Markov model and Monte Carlo method, so that the block is randomly propagated to each server with a specific probability according to the server status. In the process of running the blockchain system with this strategy, the probability of propagation to each server is dynamically adjusted according to the changing server status, so that the frequency distribution of all servers in the cluster receiving blocks obeys the target probability distribution. We evaluate the performance of our approach through extensive simulations and experiments, and the results demonstrate the feasibility and effectiveness of the proposed approach.
Neuromorphic Photonics for Digital Signal Processing

Neuromorphic photonic processors are novel devices that combine analog computing with the large bandwidth, low latency, and high power efficiency of optics. Here, we review the latest developments in neuromorphic photonics and discuss its applications for real-time digital signal processing in communications scenarios.
Plug and Play Measurement Device Independent quantum secure communication

We experimentally demonstrate a commercially viable plug and play Measurement Device Independent Quantum Key Distribution (MDI-QKD) prototype. MDI-QKD allows for sharing of critical components such as single photon detectors by multiple users, making it an ideal choice for star-type topology quantum networking.
Relational Prior Knowledge Graphs for Detection and Instance Segmentation

Humans have a remarkable ability to perceive and reason about the world around them by understanding the relationships between objects. In this paper, we investigate the effectiveness of using such relationships for object detection and instance segmentation. To this end, we propose a Relational Prior-based Feature Enhancement Model (RP-FEM), a graph transformer that enhances object proposal features using relational priors. The proposed architecture operates on top of scene graphs obtained from initial proposals and aims to concurrently learn relational context modeling for object detection and instance segmentation.Experimental evaluations on COCO show that the utilization of scene graphs, augmented with relational priors, offer benefits for object detection and instance segmentation. RP-FEM demonstrates its capacity to suppress improbable class predictions within the image while also preventing the model from generating duplicate predictions, leading to improvements over the baseline model on which it is built.
Counterexample Guided Inductive Synthesis Using Large Language Models and Satisfiability Solving

Generative large language models (LLMs) can follow human-provided instruction prompts and generate human-like responses. Apart from natural language responses, they have been found to be effective at generating formal artifacts such as code, plans, and logical specifications. Despite their remarkably improved accuracy, these models are still known to produce factually incorrect or contextually inappropriate results despite their syntactic coherence – a phenomenon often referred to as hallucinations. This limitation makes it difficult to use these models to synthesize formal artifacts used in safety-critical applications. Unlike tasks such as text summarization and question-answering, bugs in code, plan, and other formal artifacts produced by LLMs can be catastrophic. We posit that we can use the satisfiability modulo theory (SMT) solvers as deductive reasoning engines to analyze the generated solutions from the LLMs, produce counterexamples when the solutions are incorrect, and provide that feedback to the LLMs exploiting the dialog capability of LLMs. This interaction between inductive LLMs and deductive SMT solvers can iteratively steer the LLM to generate the correct response. In our experiments, we use planning over the domain of blocks as our synthesis task for evaluating our approach. We use GPT-4, GPT3.5 Turbo, Davinci, Curie, Babbage, and Ada as the LLMs and Z3 as the SMT solver. Our method allows the user to communicate the planning problem in natural language; even the formulation of queries to SMT solvers is automatically generated from natural language. Thus, the proposed technique can enable non-expert users to describe their problems in natural language, and the combination of LLMs and SMT solvers can produce provably correct solutions.
Margin Contrastive Learning with Learnable-Vector for Continual Learning

In continual learning, there is a serious problem "catastrophic forgetting", in which previously acquired knowledge is forgotten when a new task is learned. Various methods have been proposed to solve this problem. Among them, Replay methods, which store a portion of the past training data and regenerate it for later tasks, have shown excellent performance. In this paper, we propose a new online continuous learning method that adds a representative vector for each class and a margin for similarity computation to the conventional method, Supervised Contrastive Replay (SCR). Our method aims to mitigate the catastrophic forgetting caused by class imbalance by using learnable vectors of each class and adding a margin to the calculation of similarity. Experiments on multiple image classification datasets confirm that our method outperformed conventional methods.
SPARF: Large-Scale Learning of 3D Sparse Radiance Fields from Few Input Images

Recent advances in Neural Radiance Fields (NeRFs) treat the problem of novel view synthesis as Sparse Radiance Field (SRF) optimization using sparse voxels for efficient and fast rendering [14], [43]. In order to leverage machine learning and adoption of SRFs as a 3D representation, we present SPARF, a large-scale ShapeNet-based synthetic dataset for novel view synthesis consisting of ~17 million images rendered from nearly 40,000 shapes at high resolution (400 × 400 pixels). The dataset is orders of magnitude larger than existing synthetic datasets for novel view synthesis and includes more than one million 3D-optimized radiance fields with multiple voxel resolutions. Furthermore, we propose a novel pipeline (SuRFNet) that learns to generate sparse voxel radiance fields from only few views. This is done by using the densely collected SPARF dataset and 3D sparse convolutions. SuRFNet employs partial SRFs from few/one images and a specialized SRF loss to learn to generate high-quality sparse voxel radiance fields that can be rendered from novel views. Our approach achieves state-of-the-art results in the task of unconstrained novel view synthesis based on few views on ShapeNet as compared to recent baselines. The SPARF dataset is made public with the code and models on the project website abdullahamdi.com/sparf.
IDTransformer: Transformer for Intrinsic Image Decomposition

The aim of intrinsic image decomposition (IID) is to recover reflectance and the shading from a given image. As different combinations are possible, IID is an under constrained problem. Previous approaches try to constrain the search space using hand crafted priors. However, these priors are based on strong imaging assumptions and fall short when these do not hold. Deep learning based methods learn the problem end-to-end from the data. But these networks lack any explicit information about the image formation model.In this paper, an IID transformer approach (IDTransformer) is proposed by learning photometric invariant attention, derived from the image formation model, integrated in the transformer framework. The combination of invariant features in both a global and local setting allows the network to not only learn reflectance transitions, but also to group similar reflectance regions, irrespective of the spatial arrangement. Illumination and geometry invariant attention is exploited to generate the reflectance map, while illumination invariant and geometry variant attention is used to compute the shading map.Enabling physics-based explicit attention allows the network to be trained on a relatively small dataset. Ablation studies show that adding invariant attention improves the performance. Experiments on the Intrinsic In the Wild dataset shows competitive results with competing methods. The project page with the code is available at https://morpheus3000.github.io/IDTransformer.web/.
Temporally Consistent Semantic Segmentation using Spatially Aware Multi-view Semantic Fusion for Indoor RGB-D videos

The task of performing image semantic segmentation faces challenges in achieving consistent and robust results across a sequence of video frames. This problem becomes more prominent for indoor scenes where small camera movement can lead to drastic appearance changes, occlusions, and loss of global context information.To overcome these challenges, this paper proposes a novel approach that combines multi-view semantic fusion with spatial reasoning to produce view-invariant semantic features for temporally consistent semantic segmentation for indoor RGB-D videos.The experiments are conducted on the ScanNet dataset, showing that the proposed spatially aware multi-view fusion mechanism significantly improves the state-of-the-art image semantic segmentation methods Mask2Former and ViT-Adapter. In particular, the proposed pipeline offers improvements of 5%, 9.9%, and 14.4% in 2D mIoU, cross-view consistency, and temporal consistency, respectively, when compared to Mask2Former. Similarly, when compared to ViT-Adapter, the proposed mechanism offers enhancements of 4.8%, 8.9%, and 10.9% in the same metrics.
Learning Interpretable Forensic Representations via Local Window Modulation

The majority of existing image forgeries involve augmenting a specific region of the source image which leaves detectable artifacts and forensic traces. These distinguishing features are mostly found in and around the local neighborhood of the manipulated pixels. However, patch-based detection approaches quickly become intractable due to inefficient computation and low robustness. In this work, we investigate how to effectively learn these forensic representations using local window-based attention techniques. We propose Forensic Modulation Network (ForMoNet) that uses focal modulation and gated attention layers to automatically identify the long and short-range context for any query pixel. Furthermore, the network is more interpretable and computationally efficient than standard self-attention, which is critical for real-world applications. Our evaluation of various benchmarks shows that ForMoNet outperforms existing transformer-based forensic networks by 6% to 11% on different forgeries.
SCSC: Spatial Cross-scale Convolution Module to Strengthen both CNNs and Transformers

This paper presents a module, Spatial Cross-scale Convolution (SCSC), which is verified to be effective in improving both CNNs and Transformers. Nowadays, CNNs and Transformers have been successful in a variety of tasks. Especially for Transformers, increasing works achieve state-of-the-art performance in the computer vision community. Therefore, researchers start to explore the mechanism of those architectures. Large receptive fields, sparse connections, weight sharing, and dynamic weight have been considered keys to designing effective base models [39], [24], [63], [44]. However, there are still some issues to be addressed: large dense kernels and self-attention are inefficient, and large receptive fields make it hard to capture local features. Inspired by the above analyses and to solve the mentioned problems, in this paper, we design a general module taking in these design keys to enhance both CNNs and Transformers. SCSC introduces an efficient spatial cross-scale encoder and spatial embed module to capture assorted features in one layer. On the face recognition task, FaceResNet with SCSC can improve 2.7% with 68% fewer FLOPs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and 79% fewer parameters. On the ImageNet classification task, Swin Transformer with SCSC can achieve even better performance with 22% fewer FLOPs, and ResNet with CSCS can improve 5.3% with similar complexity. Furthermore, a traditional network (e.g., ResNet) embedded with SCSC can match Swin Transformer’s performance.
Flexible Power Control Method for AC-DC Hybrid Power Distribution System Using Multidimensional Data

In order to ensure the voltage stability of AC / DC hybrid distribution system and the coordination and interaction between AC subnet and DC, a flexible power control method for AC / DC hybrid distribution system based on multidimensional data is proposed. A multidimensional data analysis model is established to deeply mine the operation data of AC/DC hybrid distribution system. The power flow calculation model of flexible distribution unit is constructed to calculate the power flow results of AC port and DC port of flexible distribution unit. The control strategy of ac/dc converter and dc/dc converter of flexible distribution unit is adopted to realize the mutual coordination of AC and dc subnet power. The test results show that the multi-dimensional data analysis effect of this method is good, the operation data of AC / DC hybrid distribution system can be obtained in depth, and the application is good. After application, the load capacity is 606.81kb Above s <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> , the converter's current power is about 1.0pu, which can well complete the power interaction between the AC subnet and the DC subnet.
Benchmarking Image Classifiers for Physical Out-of-Distribution Examples Detection

The rising popularity of deep neural networks (DNNs) in computer vision has raised concerns about their robustness in the real world. Recent works in this field have well-demonstrated the vulnerability of these networks to carefully crafted adversarial attacks which yield out-of-distribution (OOD) samples. Interestingly, the majority of the existing literature focuses on adversarial attacks crafted for the digital domain only. Physical adversarial attacks are easier to deploy in the real world and yield higher attack success than digital perturbations. The prime limitation of such a dearth of studies handling physical out-of-distribution images is the lack of benchmark datasets. To overcome this limitation, this research proposes a novel out-of-distribution dataset using adversarial patches of different variations to advance the robustness of deep networks against such stealthy out-of-distribution images. We have also conducted extensive experiments both under seen and unseen patch settings and observed that unseen adversarial patches are hard to defend. By conducting this study and delving into the complexities of defending against patch attacks, we believe it will serve as inspiration for future researchers to incorporate physical OOD attacks into their defense strategies.
A Cross-Dataset Study on the Brazilian Sign Language Translation

Signed communication is an important form of natural language, often less studied, but still relevant. The main question we address in this paper is how to translate Brazilian Sign Language (LIBRAS) implementing Deep Learning networks with limited data availability. Previous studies often use a single dataset, in most cases collected by the authors themselves. We claim a cross-dataset approach would be more adequate to evaluate real-world scenarios. We investigate two methods based on spatial feature extraction. The first one uses pre-trained Convolutional Neural Networks (CNN) and the second one Body Landmark Estimation (skeleton information). A Long Short-Term Memory (LSTM) network is responsible for the sign classification. Our contribution encompasses data curation, alongside providing general guidelines for enhanced generalization.