Reviewerly/exHarmony huggingface dataset:
Exploiting Fusion Opportunities in Linear Algebraic Graph Query Engines

Queries in a graph database are often converted into a sequence of graph operations by a graph query engine. In recent years, it has been recognized that the query engine benefits from using high-performance graph libraries via the GraphBLAS interface to implement time-consuming operations such as graph traversal. However, using GraphBLAS requires explicitly casting data into linear algebra objects and decomposing the query into multiple operations, some of which are expressible by the GraphBLAS. The combination of these two requirements translates into increased memory footprints and additional execution times. In this paper, we show that fusing different stages of the query engines into GraphBLAS calls can reduce the size of the intermediate data generated during the query. Furthermore, by relaxing the semi-ring constraints imposed by GraphBLAS, more aggressive fusions of the stages can be performed. We show a speedup of up to 1235.89x (8.82x on geometric average) relative to an open-source graph query engine using GraphBLAS (i.e. RedisGraph) for processing undirected subgraph enumeration queries.
Uncertainty Considerations in Bifacial Photovoltaic Systems with High Albedo Seasonality

Bifacial photovoltaic systems are experiencing an increased deployment with the promise of lower generation costs. The energy gain of these systems highly depends on the albedo, which is uncertain and changes over time. This study examines the deviations in the energy yield predictions as a function of albedo at different temporal resolution from local, nearby and satellite datasets. DNV's SolarFarmer is used to model a three-year public dataset of a bifacial horizontal single-axis tracking PV system and validate the plane-of-array irradiance and DC energy yield modelling. The results showed that sub-hourly calculation timesteps can provide more accurate predictions. Similarly, high temporal resolution of albedo can benefit intra-daily energy yield predictions, but daily and monthly albedo can be sufficient for accumulated energy yield over longer periods of time. Accuracy differences were found between albedo source data, where satellite-derived albedo reported up to 2.1 % error increase compared to energy calculations using local site or nearby albedo data. The findings of the paper provide insights about the spatiotemporal resolution of albedo and its effects for energy assessment.
Python Open-Source Code Traceability Model Based on Graph Neural Networks

When programmers write project code, they may copy or reference some open-source code, which may include defective code, causing vulnerabilities in the project. This causes a potential threat to the project and threatens the security of the software supply chain. Therefore, to protect the code security, the Python open-source code traceability model based on graph neural networks is proposed to calculate the similarity between the programmers' Python code and the Python open-source code. Firstly, each function in Python code is parsed into one Type Abstract Syntax Tree. Secondly, graph neural networks are used to calculate the function similarity between the two Type Abstract Syntax Trees of the original code and open-source code. Thirdly, the overall similarity of a Python project that consists of many functions is calculated based on the function similarity following the maximum retention principle. The experiment was conducted on three datasets: StudentWork, GitDown, and Obfuscated-GitDown. The experiment shows that the results calculated by our model are more reasonable, which places more emphasis on similarity in code structure than on code text. Taking the Pyobfuscate obfuscation scenario as an example, our model considering code structure gets similarity 18.12%~39.54% higher than other methods that calculate similarity based on the code text.
PV Plant Performance Review Methodology: Key Performance Indicators (KPI) Estimation

The detailed procedure to estimate two key performance indicators (KPIs) of Solar PV power plant i.e., Performance Ratio (PR) & Capacity Utilization Factor (CUF) using statistical methods has been presented. Calculation of PR and CUF by simply using the standard formulas results in wide inaccuracies in the results. Comprehensive methodology to deal with large data sets has been set down to estimate these key performance indicators with more accuracy.
A Comparative Analysis of Image Steganography Techniques in Spatial and Transform Domains

Image steganography is the study and practice of concealing information within images with the purpose of deceiving the viewer as if there is no information hidden within the images. Transferring the embedded information to the destination securely without being detected is considered the biggest challenge. In this paper, a comparative analysis of image steganography in both the spatial and a transform domains, particularly the Least Significant Bit (LSB) and Discrete Cosine Transform (DCT) was conducted. Two experiments were carried out to evaluate the performance of LSB and DCT based steganography techniques with respect to the quality of the generated stego images and their robustness against some known attacks. ALASKA2 and BOSSBase datasets were utilized in this study. The results show that the DCT based stenography technique outperforms the LSB counterpart as it generates stego images with higher quality and higher (Peak to Signal Ratio) PSNR values. It was also shown that DCT steganography is more robust against steganalysis.
Robust Object Detection Based on a Comparative Learning Perspective

With the advent of adversarial samples, the safety of Artificial Intelligence is of particular concern, as only a small amount of perturbation can be added to mislead a model's judgment. Therefore there is an urgent need for research on models that can resist adversarial perturbations. To alleviate this problem, we first analyze the vulnerability of adversarial samples and propose a uniform perspective robust model for object detection that accurately identifies both clean and ad-versarial samples. We propose a robust object detection based on the contrastive learning perspective (RCP), which can learn features from both clean and adversarial samples from a fine-grained perspective, and can recognize adversarial samples more accurately. Extensive experiments on PASCAL VOC and MS COCO show that our proposed method only degrades the clean sample detection performance by a small amount in exchange for a large robustness improvement against adversarial attacks, achieving state of the art results.
Generalizability of Neural Network-based Identification of PV in Aerial Images

Identification of PV panels from aerial imagery is a potential strategy for building comprehensive behind-the-meter PV datasets. Several previous studies have utilized Convolutional Neural Networks with the goal of producing tools that can perform these identification tasks. Neural Network approaches rely on labelled data for training, with several aerial imagery datasets with labelled PV already available. This study aims to investigate generalizability of models trained on one set of labelled PV data to other datasets, to further understanding of how these models can be applied. Six different PV datasets were utilized, and test data results were compared. Overall, we find that generalizability suffers when models are presented with different data than they were trained on. We describe some dataset features that led to particularly poor generalization. This study highlights the need for further research to investigate strategies for improving generalizability of trained Neural Network models.
Effects of Period of Record Extension, Model Diversification, and DHI Measurements on Measure-Correlate-Predict Analyses for On-site Solar Resource Assessments

For solar PV projects, on-site irradiance measurements are used with historical satellite models to develop long-term solar resource estimates. Current industry practices rely on one year of measurements correlated to a single satellite model and are commonly performed only on GHI. This study will explore three areas in which current industry practices may want to add further consideration for Measure-Correlate-Predict (MCP) analyses: (1) extending the period of record of the measurement campaign (2) including a diverse set of modeled long-term datasets, and (3) including DHI measurements in the campaign. Industry-standard collection periods for a typical solar resource campaign are a minimum of one year. More extended periods of record, however, could lower the uncertainty of MCP analyses for prospective projects. Other common industry practices include the use of only one long-term satellite dataset for correlation. Reliance on a single long-term satellite dataset for correlation may result in increased sensitivity and uncertainty, as satellite models across the industry exhibit varying estimations of interannual variability. Lastly, while GHI measurements have historically been the primary focus of solar measurement campaigns, Diffuse Horizontal Irradiance (DHI) measurements are an integral component in the calculation of Plane of Array (POA), the main driver in energy simulations. Using over 10 test cases across various regions of the US, this study evaluates the potential uncertainty benefits of using a 1+ year campaign, a diverse set of satellite models, and DHI measurements thereby mitigating the financial risk of preconstruction projects.
Assessing How Large Language Models Can Be Integrated with or Used for Blockchain Technology: Overview and Illustrative Case Study

While Large Language Models (LLMs) are turning the field of natural language processing on its head with their remarkable ability to understand context and generate creative content, blockchain is currently an excellent option for building decentralized systems that are both secure and transparent. While these two technologies are already powerful tools individually, combining them can only be promising. In this article, we will explore the opportunities offered by integrating LLMs with Blockchain. By reviewing existing work and research and discussing use cases, this article evaluates the benefits and analyzes the challenges of LLMs in Blockchain-based applications. We also propose two possible architectures and outline our approach for integrating LLMs into decentralized applications as a tool capable of understanding the sometimes imprecise and ambiguous intentions of decentralized application users and translating them into clear and executable instructions for optimal transactions. This approach aims to improve and optimize the interaction between users and the blockchain by better understanding their intentions, which could pave the way for massive use and adoption of decentralized systems. This work therefore contributes to Blockchain by proposing state-of-the-art LLM integration perspectives while presenting a precise approach to this integration.
Predicting Site-Specific Adjustments to P50 Energy Production Estimates from Sub-Hourly Irradiance Data

Energy production models developed as part of the technical due diligence required for financing of photovoltaic solar projects are customarily based on hourly weather data, which—when compared to higher-frequency (“sub-hourly”) data—have been shown by recent studies to overestimate energy production, particularly for systems with higher DC/AC ratios. Since long-term weather data at suitable sub-hourly resolutions are not widely available, techniques must be developed to adjust estimates based on long-term hourly data with insights derived from short-term sub-hourly data. This paper presents a multifaceted machine-learning approach to tailor such adjustments to a project site based on its corresponding hourly attributes. The method enables seamless integration of calculated factors into the energy production model and is suitable for use with both satellite-based and ground-measured sources of sub-hourly data.
FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data

This study investigates the challenging task of training visual models with very few available data, further complicated by the distribution being imbalanced and scattered across nodes. To address this diverse availability of training data in different federated settings, a customized self-supervised learning approach tailored specifically for each scenario is being proposed. In particular, a hybrid approach combining self-supervised and supervised learning techniques under a federated umbrella has been utilized at both the global and local level, harnessing the potential of unlabeled data. Extensive experiments provide a detailed analysis of the problem at hand and demonstrate the particular characteristics of the proposed learning schemes in distributed scenarios. The overall proposed approach achieves superior recognition performance in the currently broadest public dataset, surpassing all baselines by a substantial margin. The proposed solution can operate efficiently at a local level without prior knowledge of the characteristics or distribution of data across nodes.
Constant Scaling Asymptotics of Communication Bounds in Covert Channels Against Selective Adversary

In the existing works on asymptotics of throughput of covert communications, an adversary is typically assumed to run a single binary hypothesis testing to determine the presence of active transmission, which is equivalent to distinguish between an induced mixture distribution by all codewords and the distribution with an inactive transmitter. In this paper, we show that both of the first and second order asymptotics are O(1) when the adversary can afford running parallel simple binary hypothesis testing against detection of each single codeword. The covert constraint in this case is given as an upper-bound δ on the total variation distance (TVD) between the simple distribution induced by each codeword and the one of pure noise. The result is drastically different in a negative way than the commonly known Square Root Law for covert communication over AWGN channels with a more benign adversary.
Interaction-Aware Prompting for Zero-Shot Spatio-Temporal Action Detection

The goal of spatial-temporal action detection is to determine the time and place where each person’s action occurs in a video and classify the corresponding action category. Most of the existing methods adopt fully-supervised learning, which requires a large amount of training data, making it very difficult to achieve zero-shot learning. In this paper, we propose to utilize a pre-trained visual-language model to extract the representative image and text features, and model the relationship between these features through different interaction modules to obtain the interaction feature. In addition, we use this feature to prompt each label to obtain more appropriate text feature. Finally, we calculate the similarity between the interaction feature and the text feature for each label to determine the action category. Our experiments on J-HMDB and UCF101-24 datasets demonstrate that the proposed interaction module and prompting make the visual-language features better aligned, thus achieving excellent accuracy for zero-shot spatio-temporal action detection. The code will be available at https://github.com/webber2933/iCLIP.
Study of MobileNets Model in Federated Learning

The challenges of handling decentralised data lead to the demand for research on secure gathering, efficient processing, and analysing of the data. In decentralised systems, each node (device) can make independent decisions, reducing the complexity and challenges of dealing with extensive data. Privacy has become a significant concern for our society due to the rise in the number of Edge/IoT devices, the lack of presence of a centralised system, etc. To solve this conundrum, federated learning was proposed. Federated learning works on the sharing of parameter values rather than the data. Worldwide, 10.2 Billion non-IoT and 19.8 billion IoT devices will be active in 2023. These devices lack security when it comes to using traditional machine learning. However, federated learning models solve this problem using techniques such as Secure Aggregation and Differential Privacy, which provide security for the devices and efficient communication between them. The challenges arise from heterogeneous devices, leading to the client selection problem, unbalanced data, and many more problems. The Proposed work focuses on using the MobileNets series of model architecture for federated learning using the FedAvg Strategy. MobileNets architecture has always been robust and reliable when it come to devices with resource constraints. An older generation system is used to show that federated learning is a viable technique for decentralized machine learning.
On the Effectiveness of LayerNorm Tuning for Continual Learning in Vision Transformers

State-of-the-art rehearsal-free continual learning methods exploit the peculiarities of Vision Transformers to learn task-specific prompts, drastically reducing catastrophic forgetting. However, there is a tradeoff between the number of learned parameters and the performance, making such models computationally expensive. In this work, we aim to reduce this cost while maintaining competitive performance. We achieve this by revisiting and extending a simple transfer learning idea: learning task-specific normalization layers. Specifically, we tune the scale and bias parameters of LayerNorm for each continual learning task, selecting them at inference time based on the similarity between task-specific keys and the output of the pre-trained model. To make the classifier robust to incorrect selection of parameters during inference, we introduce a two-stage training procedure, where we first optimize the task-specific parameters and then train the classifier with the same selection procedure of the inference time. Experiments on ImageNet-R and CIFAR-100 show that our method achieves results that are either superior or on par with the state of the art while being computationally cheaper. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices

Image restoration aims to restore high-quality images from degraded counterparts and has seen significant advancements through deep learning techniques. The technique has been widely applied to mobile devices for tasks such as mobile photography. Given the resource limitations on mobile devices, such as memory constraints and runtime requirements, the efficiency of models during deployment becomes paramount. Nevertheless, most previous works have primarily concentrated on analyzing the efficiency of single modules and improving them individually. This paper examines the efficiency across different layers. We propose a roadmap that can be applied to further accelerate image restoration models prior to deployment while simultaneously increasing PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index). The roadmap first increases the model capacity by adding more parameters to partial convolutions on FLOPs nonsensitive layers. Then, it applies partial depthwise convolution coupled with decoupling upsampling/downsampling layers to accelerate the model speed. Extensive experiments demonstrate that our approach decreases runtime by up to 13% and reduces the number of parameters by up to 23%, while increasing PSNR and SSIM on several image restoration datasets. Source Code of our method is available at https://github.com/xiangyu8/MOFA.
Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models

In temporal action localization, given an input video, the goal is to predict which actions it contains, where they begin, and where they end. Training and testing current state-of-the-art deep learning models requires access to large amounts of data and computational power. However, gathering such data is challenging and computational resources might be limited. This work explores and measures how current deep temporal action localization models perform in settings constrained by the amount of data or computational power. We measure data efficiency by training each model on a subset of the training set. We find that TemporalMaxer outperforms other models in data-limited settings. Furthermore, we recommend TriDet when training time is limited. To test the efficiency of the models during inference, we pass videos of different lengths through each model. We find that TemporalMaxer requires the least computational resources, likely due to its simple architecture.
Evaluating the practical range of harmonic radar to detect smart electronics

Prior research has found that harmonic radar systems are able to detect the presence of electronic devices, even if the devices are powered off. These systems could be a powerful tool to help mitigate privacy invasions. For example, in a rental property devices such as cameras or microphones may be surreptitiously placed by a landlord to monitor renters without their knowledge or consent. A mobile harmonic radar system may be able to quickly scan the property and locate all electronic devices. The effective range of these systems for detecting consumer-grade electronics, however, has not been quantified. We address that shortcoming in this paper and evaluate a prototype harmonic radar system. We find the system, a variation of what has been proposed in the literature, is able to reliably detect some devices at a range of about two meters. We discuss the effect of hardware on the range of detection and propose an algorithm for automated detection.
Multimodal Error Correction with Natural Language and Pointing Gestures

Error correction is crucial in human-computer interaction, as it can provide supervision for incrementally learning artificial intelligence. If a system maps entities like objects or persons with unknown class to inappropriate existing classes, or misrecognizes entities from known classes when there is too high train-test discrepancy, error correction is a natural way for a user to improve the system. Provided an agent with visual perception, if such entity is in the view of the system, pointing gestures can dramatically simplify the error correction. Therefore, we propose a modularized system for multimodal error correction using natural language and pointing gestures. First, pointing line generation and region proposal detects whether there is a pointing gesture, and if yes, which candidate objects (i.e. RoIs) are on the pointing line. Second, these RoIs (if any) and the user’s utterances are fed into a VL-T5 network to extract and link both the class name and the corresponding RoI of the referred entity, or to output that there is no error correction. In the latter case, the utterances can be passed to a downstream component for Natural Language Understanding. We use additional, challenging annotations for an existing real-world pointing gesture dataset to evaluate our proposed system. Furthermore, we demonstrate our approach by integrating it on a real-world steerable laser pointer robot, enabling interactive multimodal error correction and thus incremental learning of new objects.