Blockchain is a distributed and immutable ledger managed by all participants. the full nodes which store the entire ledger play an essential role in managing it in a transparent and decentralised manner. However, it i...
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ISBN:
(纸本)9798350310191
Blockchain is a distributed and immutable ledger managed by all participants. the full nodes which store the entire ledger play an essential role in managing it in a transparent and decentralised manner. However, it is difficult t o v erify that full nodes store the entire ledger in their dedicated storage due to Sybil, outsourcing, or generation attacks. Existing work on proving storage for cloud computing and remote data storage applications has high latency for decryption, and its impact on decentralisation is unclear, rendering it impractical for use in blockchain. In this paper, we propose a decentralised Practical Proof of Storage (PPoS) solution for blockchain full nodes with asymmetric latencies for encryption and decryption, which introduces a chained encryption and decryption architecture. To generate a unique replica of a block, each full node performs encryption with its own address and a previously encrypted block, storing the unique block in its dedicated storage. In PPoS, encryption is expensive and time consuming, enabling it to detect outsourcing and generation attacks and to deter Sybil attacks. Simultaneously, decryption is about 25 times faster than encryption, resulting in minimal performance overhead. the proof process is also decentralised by randomly selecting provers, verifiers, a nd e ncrypted b locks. Our e xperiments u se u p t o 720 real BitCoin blocks to evaluate the performance and quantify the decentralisation of PPoS. Our results show that PPoS's asymmetric design reduces decryption time 25-fold over existing approaches, while maintaining a high degree of decentralisation, confirming i ts s uitability f or b lockchain f ull nodes.
Recently, user privacy in distributedcomputing has received increasing attention. Matrix multiplication is one of the fundamental high-frequency operations in distributed machine learning (e.g., gradient descent, lin...
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Symmetrical fault is the most severe fault in the power network. this occurs rarely however with equal amount of severe currents in the three phases. Now, in an active distribution network (ADN) shunt faults such as l...
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As one of the most abundant renewable biomaterials, cellulose has been widely used in flexible electronic devices. Here we proposed a flexible and environmentally friendly thin film pressure sensor composed of TEMPO-o...
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this study quantifies the yearly electricity self-sufficiency rate and self-consumption rate of a small energy community for different distributed energy resources. the efficacy of different technologies on grid-indep...
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ISBN:
(数字)9781665405577
ISBN:
(纸本)9781665405577
this study quantifies the yearly electricity self-sufficiency rate and self-consumption rate of a small energy community for different distributed energy resources. the efficacy of different technologies on grid-independency are presented for varying community goals and resident comfort levels. the study simulates demand side management of appliances using a non-linear integer programming algorithm. Results show that the efficacy of distributed energy resources is highly dependent on resident engagement and community goals and highlight the importance of defining goals and values of residents before constructing community energy systems.
Food waste management at the global level requires new technologies for an item-level monitoring of the volatile molecules emitted by food inside the package. the use of conventional chemical sensors requiring complex...
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distributed tracing plays a vital role in microservice infrastructure, and learning-based trace analysis has been utilized to detect anomalies within such systems. However, existing approaches for learning-based trace...
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ISBN:
(纸本)9798350313062;9798350313079
distributed tracing plays a vital role in microservice infrastructure, and learning-based trace analysis has been utilized to detect anomalies within such systems. However, existing approaches for learning-based trace-based anomaly detection face certain limitations. Some assume that trace patterns can be learned solely from normal executions, while others depend on anomaly injection to generate labeled traces categorized as normal or anomalous. However, in practical scenarios, anomalies may also happen during the normal execution. Moreover, a wide variety of anomalies may occur in practice, which cannot be captured solely through anomaly injection. To address these issues, we propose a Trace-Driven Anomaly Detection (TDAD) approach based on a Span Causal Graph (SCG) representation, which trains a model using a Graph Neural Network (GNN) and Positive and Unlabeled (PU) learning. this technique allows the model parameters to be optimized by estimating the underlying data distribution. As a result, TDAD can be effectively trained using a small number of labeled anomalous traces along with a relatively large number of unlabeled traces. Our evaluation reveals that TDAD outperforms not only the existing unsupervised trace-based anomaly detection methods by 11.9% in terms of F-1-score but also a supervised learning-based benchmark by 12x in terms of detection time.
With rapid falling of investment cost of PV and battery storage, and increasing peak-valley difference electricity price on the user-side, the distributed PV plus battery storage system (DPBS) is going to have economi...
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the widespread use of synchronous data networks today is driving the rapid development of precision time synchronization systems such as PTP (ieee 1588). this paper describes implementations that allow to perform sync...
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the proceedings contain 139 papers. the topics discussed include: artificial level language: a library of computing engine for natural languages;fourth order sliding mode control of position for electro-hydraulic actu...
ISBN:
(纸本)9798350334128
the proceedings contain 139 papers. the topics discussed include: artificial level language: a library of computing engine for natural languages;fourth order sliding mode control of position for electro-hydraulic actuator;design of hexapod robot with adaptive gait transition on rough terrain;energy-efficient data aggregation schemes for low-power distributed wireless sensor networks using compressed sensing;adaptive neural attitude and altitude control of a quadrotor UAV with external disturbances via sliding mode control;an indigenous 32 bit pipelined processor for space applications;design of ins aided receiver integrity monitoring algorithm (ARIM) for GNSS integrated navigation systems;algebraic topology as an analytic tool to check lossless nature of geometric algorithms;implementing a land registration system using non-fungible tokens to represent land in the system and side-chain for data storage;a novel general purpose processing ASIC for inertial sensors;semi-autonomous non-singular terminal sliding mode control of a micro aerial robot for outdoor wall climbing operation;investigations on recent advancements in the fabrication of in-plane micro-supercapacitors as portable energy storage devices;Reswave-Net: a wavelet based residual U-Net for brain tumor segmentation and overall survival prediction;and bidirectional voltage level shifter with configurable voltage range.
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