545 research outputs found
Detecting Routing Misbehavior In Mobile Ad Hoc Network
Routing misbehavior in MANETs (Mobile Ad Hoc Networks) is studied in this thesis. In general, routing protocols for MANETs are designed based on the assumption that all par- ticipating nodes are fully cooperative. However, due to the open structure and scarcely available battery-based energy, node misbehaviors may exist. One such routing misbehavior is that some selfish nodes will participate in the route discovery and maintenance processes but refuse to forward data packets. Therefore, we propose the 2ACK scheme that serves as an add-on technique for routing schemes to detect routing misbehavior and to mitigate their adverse effect. The main idea of the 2ACK scheme is to send two-hop acknowledgment packets in the opposite direction of the routing path. In order to reduce additional routing overhead, only a fraction of the received data packets are acknowledged in the 2ACK scheme. Analytical and simulation results are presented to evaluate the performance of the proposed scheme
Detecting Routing Misbehavior In Mobile Ad Hoc Network
Routing misbehavior in MANETs (Mobile Ad Hoc Networks) is studied in this thesis. In general, routing protocols for MANETs are designed based on the assumption that all par- ticipating nodes are fully cooperative. However, due to the open structure and scarcely available battery-based energy, node misbehaviors may exist. One such routing misbehavior is that some selfish nodes will participate in the route discovery and maintenance processes but refuse to forward data packets. Therefore, we propose the 2ACK scheme that serves as an add-on technique for routing schemes to detect routing misbehavior and to mitigate their adverse effect. The main idea of the 2ACK scheme is to send two-hop acknowledgment packets in the opposite direction of the routing path. In order to reduce additional routing overhead, only a fraction of the received data packets are acknowledged in the 2ACK scheme. Analytical and simulation results are presented to evaluate the performance of the proposed scheme
Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening
Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems
Stochastic Nature of Overbank Flow Turbulence in Straight Compound Channels with Vegetated Floodplains
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
Self-supervised facial representation has recently attracted increasing
attention due to its ability to perform face understanding without relying on
large-scale annotated datasets heavily. However, analytically, current
contrastive-based self-supervised learning still performs unsatisfactorily for
learning facial representation. More specifically, existing contrastive
learning (CL) tends to learn pose-invariant features that cannot depict the
pose details of faces, compromising the learning performance. To conquer the
above limitation of CL, we propose a novel Pose-disentangled Contrastive
Learning (PCL) method for general self-supervised facial representation. Our
PCL first devises a pose-disentangled decoder (PDD) with a delicately designed
orthogonalizing regulation, which disentangles the pose-related features from
the face-aware features; therefore, pose-related and other pose-unrelated
facial information could be performed in individual subnetworks and do not
affect each other's training. Furthermore, we introduce a pose-related
contrastive learning scheme that learns pose-related information based on data
augmentation of the same image, which would deliver more effective face-aware
representation for various downstream tasks. We conducted a comprehensive
linear evaluation on three challenging downstream facial understanding tasks,
i.e., facial expression recognition, face recognition, and AU detection.
Experimental results demonstrate that our method outperforms cutting-edge
contrastive and other self-supervised learning methods with a great margin
Bitstream-Corrupted Video Recovery: A Novel Benchmark Dataset and Method
The past decade has witnessed great strides in video recovery by specialist
technologies, like video inpainting, completion, and error concealment.
However, they typically simulate the missing content by manual-designed error
masks, thus failing to fill in the realistic video loss in video communication
(e.g., telepresence, live streaming, and internet video) and multimedia
forensics. To address this, we introduce the bitstream-corrupted video (BSCV)
benchmark, the first benchmark dataset with more than 28,000 video clips, which
can be used for bitstream-corrupted video recovery in the real world. The BSCV
is a collection of 1) a proposed three-parameter corruption model for video
bitstream, 2) a large-scale dataset containing rich error patterns, multiple
corruption levels, and flexible dataset branches, and 3) a plug-and-play module
in video recovery framework that serves as a benchmark. We evaluate
state-of-the-art video inpainting methods on the BSCV dataset, demonstrating
existing approaches' limitations and our framework's advantages in solving the
bitstream-corrupted video recovery problem. The benchmark and dataset are
released at https://github.com/LIUTIGHE/BSCV-Dataset.Comment: Accepted by NeurIPS Dataset and Benchmark Track 202
Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1
Abstract- RNA-binding proteins (RBPs) play diverse roles in eukaryotic RNA processing. Despite their pervasive functions in coding and non-coding RNA biogenesis and regulation, elucidating the specificities that define protein-RNA interactions remains a major challenge. Here, we describe a novel model-based approach — RNAMotifModeler to identify binding consensus of RBPs by integrating sequence features and RNA secondary structures. Using RNA sequences derived from Cross-linking immunoprecipitation (CLIP) followed by high-throughput sequencing for SRSF1 proteins, we identified a purine-rich octamer ‘AGAAGAAG ’ in a highly singlestranded RNA context, which is consistent with previous knowledge. The successful implementation on SRSF1 CLIPseq data demonstrates great potential to improve our understanding on the binding specificity of RNA binding proteins
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