Reviewerly/exHarmony huggingface dataset:
Enhancing Visual Fidelity in Unpaired Night-to-Day Image Translation through a Perceptual Quality Focused Training Objective

Nighttime driving poses visibility challenges, but image translation methods can help by transforming night images into day-like scenes. The Cycle-GAN is a versatile unpaired image translation model which can easily be adept to night-to-day image translation tasks. However, it generates unnatural and unrealistic outcomes in these cases. This study is focused on addressing this with a novel training strategy for the Cycle-GAN, employing a tailored training objective that incorporates perceptual quality optimization. This training objective aims to boost the naturalness and perceptual quality of the generated images by preserving high-level image features. The optimization process involves minimizing Euclidean distances between synthesized and target image feature maps, which are derived from the pre-trained VGG19 network. Experimental findings attest to the effectiveness of this method, revealing noteworthy improvements of 8% in Inception Score, 2% in NIQE Score, and 13% in BRISQUE Score.
Improving Communication Systems Through Federated Learning: An Optimization of Decision-Making

This paper discusses the use of federated learning as a method for optimizing decision-making in communication systems. Federated learning is a machine learning technique that enables the training of models on decentralized data, allowing for the collection and analysis of data from multiple sources while maintaining the privacy and security of the data. This approach is particularly useful in communication systems, as it allows for the optimization of decision-making across a wide range of devices and networks. The paper examines the advantages of federated learning, including the ability to collect a large amount of data from a diverse range of devices, the protection of sensitive data, and the ability to adapt to changing conditions in real-time. The paper also provides specific examples of how federated learning can be used in the optimization of mobile networks and content delivery. The conclusion highlights the growing importance of federated learning in improving communication systems.
Quantifying OpenMP: Statistical Insights into Usage and Adoption

In high-performance computing (HPC), the demand for efficient parallel programming models has grown dramatically since the end of Dennard Scaling and the subsequent move to multi-core CPUs. OpenMP stands out as a popular choice due to its simplicity and portability, offering a directive-driven approach for shared-memory parallel programming. Despite its wide adoption, however, there is a lack of comprehensive data on the actual usage of OpenMP constructs, hindering unbiased insights into its popularity and evolution. This paper presents a statistical analysis of OpenMP usage and adoption trends based on a novel and extensive database, HPCORPUS, compiled from GitHub repositories containing C, C++, and Fortran code. The results reveal that OpenMP is the dominant parallel programming model, accounting for 45% of all analyzed parallel APIs. Furthermore, it has demonstrated steady and continuous growth in popularity over the past decade. Analyzing specific OpenMP constructs, the study provides in-depth insights into their usage patterns and preferences across the three languages. Notably, we found that while OpenMP has a strong “common core” of constructs in common usage (while the rest of the API is less used), there are new adoption trends as well, such as simd and target directives for accelerated computing and task for irregular parallelism. Overall, this study sheds light on OpenMP's significance in HPC applications and provides valuable data for researchers and practitioners. It showcases OpenMP's versatility, evolving adoption, and relevance in contemporary parallel programming, underlining its continued role in HPC applications and beyond. These statistical insights are essential for making informed decisions about parallelization strategies and provide a foundation for further advancements in parallel programming models and techniques. HPCORPUS, as well as the analysis scripts and raw results, are available at: https://github.com/Scientific-Computing-Lab-NRCN/HPCorpus
A Hypergraph Framework Based on Neighborhood and Category Association Relationships for Session-Based Recommendation

Session-based recommendation aims to generate personalized recommendations based on user behaviors within a browsing session. The traditional graph neural network (GNN) methods focus on modeling pair-wise relationships but ignore neighborhood and category relationships which is crucial for improving the accuracy of recommendations. This paper proposes a hypergraph framework based on neighborhood and category association relationships for session-based recommendation (HLNCA). In detail, we first construct a hypergraph that can acquire the neighborhood and category association relationships among items; then aggregate hyperedge representation to obtain the neighborhood and category relationships of nodes; and finally obtain the users' preference through the attention mechanism to calculate the predicted score of items in the candidate set according to the preference. The proposed HL-NCA model outperforms baseline models in terms of Recall@20 and MRR@20 evaluation metrics.
Unlocking Early-Exiting Semantic Segmentation with Branched Networks

Early-exit Deep Neural Networks (DNNs) and DNNs partitioning are valuable options to ease DNN implementation for image classification in resource-constrained devices and latency-sensitive applications. By inserting side branches into the usual DNN architecture, the resulting model can perform early classification attempts, using features extracted in layers that come before the model's output. DNN partitioning spreads DNN layers subsets across devices, the edge, and the cloud, bringing branches closer to the device. Offloading to the cloud is limited to hard-to-classify data. Given the success in image classification, it is natural to try to replicate these results with semantic segmentation. In this direction, we propose Branched-DeepLabV3 (BranDeepLabV3) as an early attempt to breach the gap between early-exit DNNs and semantic segmentation. Our results show that, by adding side branches to a semantic segmentation DNN, we can deliver adequate outputs without traversing the whole network. Moreover, our qualitative results show the usefulness of early-exits in semantic segmentation, as even coarse segmentations can be good enough to supply time-sensitive applications.
Comparative Evaluation between Accelerated RISC- V and ARM AI Inference Machines

Embedded AI development has been rapidly im-proving for the past few years and has had a great impact on edge AI networks. However, as neural networks become deeper and deeper it becomes more difficult to execute complicated tasks without sacrificing a good amount of power and performance. In this paper, we make a comparative evaluation between two AI acceleration devices. The first one features a RISC- V 64-bit processor while the other one is ARM powered. These devices are combined with AI co-processors, or ASICs, with computer vision capabilities. Our benchmark consists of a simple classification task split into multiple versions. The results showed that the RISC- V inference machine had 4 times lower consumption while the ARM machine was up to 15 times faster in our largest network. We discuss the results in great detail while keeping our focus on all aspects equally. Finally, we make recommendations based on their usage and application.
Utilization of a Blockchain-Based Reputation Management System for Energy Trading in Smart Grid 2.0

The futuristic energy grids comprise of predom-inantly renewable generation, to align with the sustainable development goals. This would require integration of renewable energy sources at different levels of the power system out of which, consumers turning into power producers, often referred to as prosumers is an important aspect. Prosumers who generate excess power beyond self-consumption are keen on selling it to the neighbourhood in a peer-to-peer manner, expecting a profit. On the other hand, consumers expect concessions for using more of the green energy. Further, with the high intermittency of these generation, Distribution System Operators (DSO) face the challenge of securing a reliable power supply. In the envisaged grid context, deciding the price for energy trading between prosumers and consumers as well as estimating the reliability of each DSO is considered to be crucial. Hence, this study proposes utilizing an end-to-end reputation management system to maintain the past record related to the performance of each stakeholder, which can be integrated in the price determination and selection decisions. To further ensure decentralized and secure operations of such reputation management systems, a blockchain-based service architecture and smart contracts have been incorporated. The impact of this to the performance of the proposed energy trading system is analysed through cost and latency indices.
Privacy-Preserved Collaborative Federated Learning Platform for Industrial Internet of Things

Federated learning (FL) is an intriguing approach to privacy-preserving collaborative learning. Decentralised FL is achieving increased favour for investigation due to the mitigation of vulnerability for a single point of failure and more controllability for end users over their models. However, many existing decentralised FL systems face limitations, such as privacy concerns, latency in aggregation, and real-world implementation challenges. To mitigate these issues, we introduce a novel FL protocol with a decentralised Peer-to-Peer (P2P) system using Differential Privacy (DP). It consists of a decentralised accuracy-based weighted averaging mechanism for both enhanced privacy and model aggregating accuracy. We implement our system in both virtual and real environments to evaluate the performance of the proposed mechanism. Moreover, we perform a comparative analysis of our proposal with both existing centralised and decentralised systems. To practically demonstrate the work, we consider a real-world use case of a recommendation system using smart carts. Experimental results show that our novel approach efficiently performs privacy-preserved aggregations over a decentralised peer network.
Binary Grating and Deep Learning for Phase Reconstruction: Enhancing Normal Camera Imaging

This work investigates the use of a binary grating integrated on a camera assisted by deep learning for phase imaging. Holographic images generated by overlapping 4 diffraction orders from the gratings were employed with a trained network for single image single-pass phase and amplitude imaging.
Low-Complexity Decoding Algorithm Utilizing Degeneracy for Quantum LDPC Codes

Quantum low-density parity-check (QLDPC) codes have been considered as a promising solution for the fault-tolerant quantum computing. However, the belief propagation (BP) decoding for QLDPC codes does not take into account the degeneracy, resulting in certain performance degradation. Recently, various post-processing algorithms have been proposed to address this issue but they in return require excessive additional complexity and/or long decoding latency. Motivated by this, in this paper, we propose an efficient decoding algorithm that takes reduced decoding complexity for the post-processing. In particular, the proposed algorithm performs the post-processing in such a way that it first selects a variable node based on a proposed metric, and the depolarizing channel model for the selected variable node is reinitialized according to the BP decoding results. Then, the BP decoding is performed for the depolarizing channel model in which the selected node follows the reinitialized channel characteristics. The process of selection, reinitialization and BP decoding, say a trial, is iterated until it reaches a predetermined value or all syndromes are met. The main ideas of this work lie in formulating the metric for selection and reinitialization to minimize the number of trials until the degeneracy is resolved. Finally, simulation results show that the proposed decoding algorithms can significantly reduce the decoding complexity with similar decoding performance to that of an existing algorithm.
Class-Incremental Learning using Diffusion Model for Distillation and Replay

Class-incremental learning aims to learn new classes in an incremental fashion without forgetting the previously learned ones. Several research works have shown how additional data can be used by incremental models to help mitigate catastrophic forgetting. In this work, following the recent breakthrough in text-to-image generative models and their wide distribution, we propose the use of a pretrained Stable Diffusion model as a source of additional data for class-incremental learning. Compared to competitive methods that rely on external, often unlabeled, datasets of real images, our approach can generate synthetic samples belonging to the same classes as the previously encountered images. This allows us to use those additional data samples not only in the distillation loss but also for replay in the classification loss. Experiments on the competitive benchmarks CIFAR100, ImageNet-Subset, and ImageNet demonstrate how this new approach can be used to further improve the performance of state-of-the-art methods for class-incremental learning on large scale datasets.
A Data-Driven Reliability Assessment Method for Composite Power Systems

A data driven approach for reliability assessment of composite power systems have been proposed in our paper. A Multi-Layer Extreme Learning Machine (MELM) is trained to graph the relations among system states versus minimum load curtailment. This approach is a computationally efficient alternative to the traditional optimal power flow (OPF) optimizations. Moreover, the pretraining process using denoising autoencoder (DAE) is utilized to improve the feature extraction and predictive performance. By conducting case studies on the RTS-79 system, the proposed methodology was evaluated. Results show that the proposed method is efficient and effective in enhancing the assessment of reliability of composite power systems.
From Scarcity to Understanding: Transfer Learning for the Extremely Low Resource Irish Sign Language

One of the most significant challenges to sign language recognition (SLR) today is the low resource nature of sign language datasets, with many datasets being extremely low resource. Transfer learning is therefore a promising, and likely indispensable, method of increasing recognition performance. The use of pose estimation models, which are typically trained on a large and diverse population, can also aid generalization for extremely low resource sign languages. However, research on transfer learning for pose estimation keypoints as inputs has been limited. In this work, we explore transfer learning as a means to improve SLR classification performance for the extremely low resource Irish Sign Language (ISL). We show that transfer learning on larger datasets containing secondary sign languages significantly improves performance on our target sign language, ISL. To understand these results and the attributes that make one dataset better than another for pre-training, we analyse the linguistic relationships between these datasets. We find that certain attributes of datasets are associated with better transfer learning performance. We hope that our findings will not only motivate further research into transfer learning for pose keypoint-based SLR but also act as a practical guide to researchers on choosing the most suitable datasets with which to pre-train models.
Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual Impairments

Grounded Situation Recognition (GSR) is capable of recognizing and interpreting visual scenes in a contextually intuitive way, yielding salient activities (verbs) and the involved entities (roles) depicted in images. In this work, we focus on the application of GSR in assisting people with visual impairments (PVI). However, precise localization information of detected objects is often required to navigate their surroundings confidently and make informed decisions. For the first time, we propose an Open Scene Understanding (OpenSU) system that aims to generate pixelwise dense segmentation masks of involved entities instead of bounding boxes. Specifically, we build our OpenSU system on top of GSR by additionally adopting an efficient Segment Anything Model (SAM). Furthermore, to enhance the feature extraction and interaction between the encoder-decoder structure, we construct our OpenSU system using a solid pure transformer backbone to improve the performance of GSR. In order to accelerate the convergence, we replace all the activation functions within the GSR decoders with GELU, thereby reducing the training duration. In quantitative analysis, our model achieves state-of-the-art performance on the SWiG dataset. Moreover, through field testing on dedicated assistive technology datasets and application demonstrations, the proposed OpenSU system can be used to enhance scene understanding and facilitate the independent mobility of people with visual impairments. Our code will be available at OpenSU.
On-device Real-time Custom Hand Gesture Recognition

Most existing hand gesture recognition (HGR) systems are limited to a predefined set of gestures. However, users and developers often want to recognize new, unseen gestures. This is challenging due to the vast diversity of all plausible hand shapes, e.g. it is impossible for developers to include all hand gestures in a predefined list.In this paper, we present a user-friendly framework that lets users easily customize and deploy their own gesture recognition pipeline. Our framework provides a pre-trained single-hand embedding model that can be fine-tuned for custom gesture recognition. Users can perform gestures in front of a webcam to collect a small amount of images per gesture. We also offer a low-code solution to train and deploy the custom gesture recognition model. This makes it easy for users with limited ML expertise to use our framework. We further provide a no-code web front-end for users without any ML expertise. This makes it even easier to build and test the end-to-end pipeline. The resulting custom HGR is then ready to be run on-device for real-time scenarios. This can be done by calling a simple function in our open-sourced model inference API, MediaPipe Tasks. This entire process only takes a few minutes.
ClipCrop: Conditioned Cropping Driven by Vision-Language Model

Image cropping has progressed tremendously under the data-driven paradigm. However, current approaches do not account for the intentions of the user, which is an issue especially when the composition of the input image is complex. Moreover, labeling of cropping data is costly and hence the amount of data is limited, leading to poor generalization performance of current algorithms in the wild. In this work, we take advantage of vision-language models as a foundation for creating robust and user-intentional cropping algorithms. By adapting a transformer decoder with a pre-trained CLIP-based detection model, OWL-ViT, we develop a method to perform cropping with a text or image query that reflects the user’s intention as guidance. In addition, our pipeline design allows the model to learn text-conditioned aesthetic cropping with a small cropping dataset, while inheriting the open-vocabulary ability acquired from millions of text-image pairs. We validate our model through extensive experiments on existing datasets as well as a new cropping test set we compiled that is characterized by content ambiguity.
Prototype Rectification with Region-Wise Foreground Enhancement for Few-Shot Classification

Few-Shot Classification is a challenging problem as it uses only very few labeled examples to assign labels for query samples. The Prototypical Network effectively addresses the issue by matching the nearest mean-based prototype for each query sample. However, the scarce labeled examples limit the ability of a model to represent the data distribution, which in turn biases the computed prototypes. In this paper, we propose a transductive bias diminishing method based on spatial similarity, which consists of a region-wise foreground enhancement (RFE) module and a prototype rectification (PR) module. RFE reconstructs the query sample’s features to emphasize the discriminative parts while preserving local region consistency. PR uses the difference in prediction scores between the nearest and second nearest prototypes of the enhanced query samples to rectify the prototypes by label propagation. We validate the effectiveness of our method with extensive experiments on four Few-Shot Classification datasets: miniImageNet, tieredImageNet, Stanford Dogs, and CUB. Our method achieves competitive performance across different settings and datasets.
Shared Nearest Neighbor Calibration for Few-Shot Classification

Few-shot classification aims to classify query samples using very few labeled examples. Most existing methods follow the Prototypical Network to classify query samples by matching them to the nearest centroid. However, scarce labeled examples tend to bias the centroids, which leads to query samples matching the wrong centroids. In this paper, we address the mismatching problem of centroids and query samples by optimizing the matching strategy. The idea is to combine the Shared Nearest Neighbor similarity with cosine similarity proportionally to calibrate the matching results of the latter. Furthermore, we also improve a bias-diminishing approach to increase the number of shared nearest neighbors between query samples and the centroid of their class. We validate the effectiveness of our method with extensive experiments on three few-shot classification datasets: miniImageNet, tieredImageNet, and CUB-200-2011 (CUB). Moreover, our method has achieved competitive performance across different settings and datasets.
Morphosyntactic Annotation in Universal Dependencies for Old Czech

Abstract We describe the first steps in preparation of a treebank of 14 th -century Czech in the framework of Universal Dependencies. The Dresden and Olomouc versions of the Gospel of Matthew have been selected for this pilot study, which also involves modification of the annotation guidelines for phenomena that occur in Old Czech but not in Modern Czech. We describe some of these modifications in the paper. In addition, we provide some interesting observations about applicability of a Modern Czech parser to the Old Czech data.