Miracle-dz/UniFAR-FTUs huggingface dataset:
WebInf: Accelerating WebGPU-based In-browser DNN Inference via Adaptive Model Partitioning

Artificial intelligence (AI) model inference performance in browsers is constrained, and transmitting data to the server consumes substantial transfer time by cloud computing. In this paper, we investigate the status quo of cloud and browser processing and explore model computation partitioning methods. Our study is rooted in WebGPU and employs the Tensorflow.js framework, encompassing seven AI models spanning computer vision, natural language processing, and automatic speech recognition domains. Leveraging the characteristics of neural network layers, we find a significant performance boost through a method that partitions AI models at layer granularity. We design a system called WebInf to partition AI models at layer granularity between the browser and server for faster inferencing-based adaptive model partitioning. WebInf supports diverse hardware, wireless networks, neural network structures, servers, and adaptive partitioning models for optimal inference performance. We evaluate WebInf on two laptops and servers, demonstrating that WebInf yields inference time improvements of 30% and 52%, respectively, when compared to separate inference execution in servers and browsers. The improvements can even peak at 33% and 69% respectively.
Differentially Private Federated Learning via Alternating Direction Method of Multipliers

Federated Learning (FL) has emerged as a promising approach for collaborative machine learning across decentralized data sources. However, FL faces substantial challenges when confronted with heterogeneous data distributions and privacy concerns. In this paper, we introduce a novel method called DP-FedADMM, which aims to address these challenges by leveraging differential privacy (DP) and the alternating direction method of multipliers (ADMM). We find that introducing dual variables can effectively mitigates the impact of data heterogeneity. Furthermore, the incorporation of artificial noise into the update messages ensures client-level differential privacy during the federated learning process. Additionally, our research uncovers an interesting insight: the well-known DP-FedAvg can be viewed as a specific instance of DP-FedADMM. Our experiments demonstrate that DP-FedADMM exhibits superior convergence and a better trade-off between utility and privacy on heterogeneous data compared to DP-FedAvg.
Unbalanced Effects of Dermatan Sulfates with Different Sulfation Patterns on Coagulation, Thrombosis and Bleeding

Summary We compared the anticoagulant, antithrombotic and bleeding effects of highly sulfated dermatan sulfates from invertebrates and their mammalian counterpart. An invertebrate dermatan sulfate containing 2-O-sulfated α-L-iduronic acid and 4-O-sulfated N-acetyl-β-D-galactosamine residues is a potent anticoagulant due to a high heparin cofactor II activity. It inhibits thrombin due to the formation of a covalent complex with heparin cofactor II, as in the case of mammalian dermatan sulfate, but the effect occurs at lower concentrations for the invertebrate polysaccharide. Surprisingly, the invertebrate dermatan sulfate has a lower potency to prevent thrombus formation on an experimental model and a lower bleeding effect in rats than the mammalian dermatan sulfate. In contrast, another invertebrate dermatan sulfate, also enriched in 2-O-sulfated α-L-iduronic acid, but in this case sulfated at O-6 position of the N-acetyl-β-D-galactosamine units, has no in vitro or in vivo anticoagulant activity, does not prevent thrombus formation but shows a bleeding effect similar to the mammalian glycosaminoglycan. Overall, these results demonstrate unbalanced effects of dermatan sulfates with different sulfation patterns on coagulation, thrombosis and bleeding, and raise interesting questions concerning the relationship among these three biological actions of sulfated polysaccharides.
Energy efficient transmission strategies for Body Sensor Networks with energy harvesting

This paper addresses the problem of developing energy efficient transmission strategies for Body Sensor Networks (BSNs) with energy harvesting capabilities. It is assumed that two transmission modes that allow a tradeoff between the energy consumption and packet error probability are available to the sensors. Decision policies are developed to determine the transmission mode to use at a given instant of time in order to maximize the quality of coverage. The problem is formulated in a Markov Decision Process (MDP) framework and an upper bound on the performance of arbitrary policies is determined. Our results show that the quality of coverage associated with the MDP formulation outperforms the other policies.
Multiagent Charging of Electric Vehicles Respecting Distribution Transformer Loading and Voltage Limits

In this paper, a market based multiagent control mechanism that incorporates distribution transformer and voltage constraints for the charging of a fleet of electric vehicles (EVs) is presented. The algorithm solves a utility maximization problem in a distributed way, assigning most charging power to the EVs with the highest need for energy. The algorithm does not rely on an iterative exchange of messages, but finds the optimal solution after the exchange of just one single message. A substation agent is responsible for guaranteeing a safe network operation. It uses the remaining capacity of each of the EV chargers for reactive voltage control. The performance of the algorithm is evaluated on an existing three-phase four-wire distribution grid. Simulation results show that the fleet of EVs can be charged at a minimal increase of costs, without jeopardizing the network.
UHRF1 Links the Histone Code and DNA Methylation to Ensure Faithful Epigenetic Memory Inheritance

Epigenetics is the study of the transmission of cell memory through mitosis or meiosis that is not based on the DNA sequence. At the molecular level the epigenetic memory of a cell is embedded in DNA methylation, histone post-translational modifications, RNA interference and histone isoform variation. There is a tight link between histone post-translational modifications (the histone code) and DNA methylation, as modifications of histones contribute to the establishment of DNA methylation patterns and vice versa. Interestingly, proteins have recently been identified that can simultaneously read both methylated DNA and the histone code. UHRF1 fulfills these requirements by having unique structural domains that allow concurrent recognition of histone modifications and methylated DNA. Herein, we review our current knowledge of UHRF1 and discuss how this protein ensures the link between histone marks and DNA methylation. Understanding the molecular functions of this protein may reveal the physiological relevance of the linkage between these layers of epigenetic marks.
Variational Graph Autoencoder Based on High-Order Attention Mechanism

Network embedding aims to learn low-dimensional representations of nodes by integrating the graph structure and node attribute information. In recent years, high-order information preservation network embedding has attracted increasing attention and many works have explored high-order structural proximity by combining random walks with shallow models. However, real-world networks are often complex, and shallow models cannot capture deep nonlinear information in the network, resulting in unreliable embeddings. Therefore, we propose a variational graph autoencoder based on a high-order attention mechanism (VGAEHA) to capture complex graph structure and node attribute information. VGAEHA obtains the high-order structural and attribute proximity of the graph through random surfing and integrates high-order information into each layer’s attention calculation, which broadens the receptive field of each layer of the deep learning model and mitigates the over-smoothing problem. We further optimize the decoder to reconstruct both the structure and node attributes of the graph, greatly improving the model’s representation ability. This addresses the issue where most existing graph variational autoencoder methods only focus on reconstructing the structural information during decoding, without fully exploiting the benefits of low-dimensional representation information. Experimental results on real datasets show that the VGAEHA achieves state-of-the-art performance on link prediction, node classification, and visualization.
Privacy-Preserving Direction-Aware Why-Not Spatial Keyword Top-k Queries on Cloud Platform

Direction-aware why-not spatial keyword top-k queries, which aim to retrieve a refined spatial keyword query that includes the missing objects with smallest cost on search direction, have recently received considerable attention from the database community. To offload the data management, when the why-not spatial keyword query processing services are motivated to be outsourced to the cloud for cost savings and flexibility, it may cause serious privacy concerns. To this end, in this paper, we first define and address the problem of privacy-preserving direction-aware why-not spatial keyword top-k queries. To support direction computations in ciphertext, we first propose two novel secure computation protocols, i.e., Secure Modulus Protocol and Secure Angle Computation Protocol. Based on the proposed protocols, to facilitate the secure why-not query processing, we further present a Secure Direction-Aware Why-Not Spatial Keyword Top-k Query (SDAWNkQ) approach for retrieving the refined queries with the smallest penalty by obtaining the best approximate refined directions. Thorough analysis shows the security and computational complexity of our approach, and extensive experimental results on both real and synthetic datasets further demonstrate the query performance of our approach.
Syndepositional precipitation of calcium sulfate in Gale Crater, Mars

Lacustrine sedimentary rocks of the Murray formation, Gale Crater, Mars, contain evidence for early diagenetic mineral precipitation. High‐resolution MAHLI images permit detailed morphological and spatial analysis of these features. Millimetre‐scale lenticular features occur in massive to well‐laminated mudstone and are interpreted as pseudomorphs after gypsum. The distribution and orientation of lenticular features indicate deposition at or near the sediment–water or sediment–air interface, and the lenticular form suggests crystallization in the presence of organic constituents. Original crystals were likely poikilotopic (i.e., incorporating elements of the matrix), and the original mineralogy was lost during later diagenetic fluid flow. Evidence for lenticular gypsum imaged in Gale Crater, along with earlier observations of potential early diagenetic evaporite precipitation made by the Opportunity rover, indicate that deposition of evaporitic sulfate minerals may have been widespread on early Mars.
An Investigation of Using K-d Tree to Improve Image Retrieval Efficiency

Content-based image retrieval problem is in essence to determine distance or similarity between multi-dimensional image feature vectors. Linear comparison will be too slow when vector dimensions and image database are large. We investigate the use of K-d tree in image retrieval based on color histograms and found that K-d trees improve retrieval efficiency significantly. We evaluate the impact of leaf node size and the number of images required to retrieval on the retrieval performance. Finally, we discuss the relationship between vector dimensions and retrieval efficiency.
Automatic Program Repair via Learning Edits on Sequence Code Property Graph

In recent years, deep learning has been widely applied in the research of automatic program repair, namely, learning-based program repair, which treats the program repair task as either a neural machine translation problem or a transformation toward the abstract syntax tree of the program. However, all existing learning-based program repair solutions have not adequately modeled the deep semantic information of the programs. To be specific, those solutions based on neural machine translation do not involve any semantic information of the programs at all, while other solutions based on abstract syntax tree transformations also do not involve information such as control flow and data flow. Additionally, the neural networks they use are also unable to sufficiently learn the semantic information. As a result, they cannot fix some bugs that involve semantic information.In this paper, we propose a new learning-based program repair approach called SGEPR. SGEPR uses a novel intermediate representation named sequence code property graph (SCPG) to model program semantic information, which can cover multiple types of semantic information in the program. SGEPR then utilizes a graph neural network based on the attention mechanism to learn the semantic information within SCPG. Finally, a well-designed prediction network is employed to generate the repair patches for the buggy program. To evaluate the performance of SGEPR, we conducted comparative experiments on a real-world dataset and the results show that SGEPR can achieve a top-3 accuracy of 56.85%, which is 15.69% higher than the state-of-the-art learning-based program repair method.
Dynamic Priority Job Scheduling on a Hadoop YARN Platform

In Hadoop’s big data processing systems, YARN is responsible for resource management and job scheduling. The built-in job scheduling algorithms in YARN are simple to execute, but have some limitations such as job starvation, excessive server load, and load imbalance. In this paper, we propose a new Hybrid Dynamic Priority job Scheduling algorithm (HDPS) to address these limitations. HDPS dynamically adjusts the priority of a job as its waiting time increases to prevent job starvation. It also features a task assignment strategy designed specifically to address data locality by considering the available resources of servers and the distribution of data blocks stored on servers to reduce data transfer time and improve job execution efficiency. We implement and integrate HDPS into YARN and conduct experiments in a real Hadoop system using built-in benchmark test cases of Hadoop. Experimental results show that HDPS exhibits comprehensive superior performance over existing algorithms in terms of execution efficiency and load balance.
Knowledge-aware Multi-view Cross Learning for Edge-based Collaborative Recommendation

In order to effectively overcome the sparsity and cold start challenges of collaborative filtering in actual scenarios, and provide more accurate suggestions, knowledge graphs are commonly utilized in recommender systems. Graph neural networks are increasingly used in knowledge graph-based recommender systems. Nevertheless, conventional recommender systems overlook the relationship information between items and entities as well as the sparse supervision signal, potentially leading to a decline in the actual recommendation performance. In this investigation, we propose KMCL, a Knowledge-aware Multi-view Cross-learning framework for edge-based collaborative recommendation. In contrast to the typical manner of dealing with knowledge graphs, our approach investigates three different graph views to improve users’ and items’ embedding representations, respectively. Firstly, the merged user-item KG graph is utilized to integrate the multi-hop neighbor information of each user, with the Graph Attention Network assigning various weights to different neighbors. Secondly, Graph Neural Network collects the top-x neighbor nodes for learning in the collaborative view of user-item graph and item-entity graph. Finally, a cross module is developed to share high-level information between embeddings with the same features. In addition, we also introduce the idea of edge computing, which preprocesses and calculates the locally obtained user preferences first, and then inputs them into different views to improve the recommendation efficiency. Experimental results demonstrate that KMCL delivers better suggestions than state-of-the-art baseline models in delivering superior recommendations. Furthermore, our approach maintains higher accuracy in predicting outcomes in less familiar interaction scenarios.
Enhancing Cybersecurity in Industrial Control System with Autonomous Defense Using Normalized Proximal Policy Optimization Model

Industrial control networks are frequent targets of cyber attacks, calling for autonomous defense strategies to combat threats effectively and promptly. We propose to leverage Reinforcement Learning (RL) to train defenders how to select the most appropriate actions in response to attackers’ behavior. We model the defender and attacker as agents in RL environments and define action space and reward function. We focus on evaluating value-based and policy gradient-based RL algorithms and propose enhancements to Proximal Policy Optimization (PPO) method through normalization. We then leverage CybORG, a simulation tool to study how the RL algorithms perform to achieve autonomous defense. Our extensive experiments reveal that the proposed normalized PPO outperforms other models in terms of stability, robustness, and average rewards. The results also demonstrate the effectiveness of the defender in protecting the operational server upon changing the attacker’s position. Additionally, increasing the number of attackers had a significant impact on policy-based algorithms, particularly PPO, with decreased reward values highlighting the increased vulnerability of the network.
FlashRL: A Reinforcement Learning Platform for Flash Games

Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential in among others successfully playing computer games. However, there only exists a few game platforms that provide diversity in tasks and state-space needed to advance RL algorithms. The existing platforms offer RL access to Atari- and a few web-based games, but no platform fully expose access to Flash games. This is unfortunate because applying RL to Flash games have potential to push the research of RL algorithms. This paper introduces the Flash Reinforcement Learning platform (FlashRL) which attempts to fill this gap by providing an environment for thousands of Flash games on a novel platform for Flash automation. It opens up easy experimentation with RL algorithms for Flash games, which has previously been challenging. The platform shows excellent performance with as little as 5% CPU utilization on consumer hardware. It shows promising results for novel reinforcement learning algorithms.
Single-Stage, Improved Power Quality Converter for Electric Vehicle Battery Charging Applications

This paper presents a single-stage, improved power quality, on-board charger based on isolated cuk converter topology for battery charging in electric vehicle (EV) applications. To obtain sinusoidal current at the utility side, a voltage follower control strategy is opted for. The converter is designed such that it operates in real inductive discontinuous conduction mode which results into unity power factor operation. Lead acid battery is charged using this converter topology in constant current (CC) and constant voltage (CV) modes respectively. A 0.76 kW, CC-CV charger with a maximum current rating of 11.7A is developed to charge a 100Ah e-bike battery. Performance of such a charger is simulated in MATLAB environment in discrete time frame. Controller is able to overcome any variations in supply at rated load conditions. At 20kHz switching frequency, operation of the converter shows harmonics-free input current for the presented circuit which is in coherence with IEC 61000-3-2.
Towards a Lightweight Stress Prediction Model: a Study on Dimension Reduction and Individual Models in HRV Analysis

Occupational stress has emerged as an undeniable concern. Fortunately, leveraging IoT and AI technologies allows us to gather vital sign data and assess cardiovascular health, individual stress levels, physiological resilience, and emotional states. This study highlights the potential of Heart Rate Variability (HRV) analysis in constructing stress prediction models, with a specific focus on developing an efficient model with minimal data requirements. Convolutional Neural Networks (CNN) have been employed to process raw waveform data for feature extraction. Simultaneously, R-R Interval (RRI) analysis was conducted to derive a set of statistical features. Various dimension reduction algorithms Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Random Projection (RP) were tested, and PCA demonstrated its significance in reducing dataset complexity, enabling swift model training without compromising accuracy excessively. This research aims to explore the feasibility of predicting an individual’s stress levels using minimal and simpler data. The objective is to pave the way for predictive models to be integrated onto lightweight platforms such as millimeter-wave chips. Our approach emphasizes non-contact monitoring of heartbeat variations, particularly beat-to-beat intervals (BBI), offering a novel method for non-invasive stress detection suitable for real-time applications on compact devices.
Feature Engineering for Highly Irregular Network Traffic Prediction

Predicting network traffic with reasonable accuracy in short-term horizons is often a challenging task due to high randomness. Network traffic data can be decomposed into a number of features creating a highly complex multivariate time series. Usually, this randomness is not equally distributed across all related time series, which decreases the predictability of the whole multivariate time series. This paper presents a novel feature engineering solution that can potentially increase prediction performance for models where the prediction horizon becomes especially short (e.g., in 5 seconds range). Specifically, we propose two feature extraction techniques based on a) IP changes and b) flow direction flips. We compare the predictability performance of our proposed approaches with the baseline scheme (i.e., no feature extraction). The comparison was performed on a network traffic dataset collected from commercial live networks. Although the proposed approaches capture the specifics of the provided dataset, the outcomes of our research can be generalized to any encrypted network traffic. Our results show improvements in MAPE metrics and up to a 93% reduction in computation time with our proposed approaches.
Utilizing LoRa for Control Link in Software-defined Aerial Networks: Analysis and Implementation

Software Defined Aerial Networks require longrange and reliable control links to carry out their missions/tasks especially when deployed in the vast areas. Unfortunately, existing control links provide services mainly tailored for controlling/monitoring the UAV flights and fall back to Wi-Fi for data transmission, which, on one hand drastically limits the effective mission areas of aerial networks, and on the other, necessitates multi-hop WiFi links to carry data over long distances. In this paper, we propose LoRA-based control link, on which both OpenFlow and flight control messages are carried in LoRa frames. This proposed link opportunistically tunes required spreading factor to ensure robust connectivity as the mission area grows. To this end, we build proof-of-concept drone using COTS hardware and open-source software, showcasing both network control and flight control data can be seamlessly transmitted over LoRA channels. Furthermore, we validate feasibility of our method for large-scale setup by extensive OMNET ++ simulations where we observe that Software Defined Aerial Networks can operate up to 3000m range and can successfully admit new network flows and update flight paths with only %3 percent packet drop rate.