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Motivated by modern network communication applications which require low latency, we study codes that correct erasures with low decoding delay. We provide a simple explicit construction that yields convolutional codes that can correct both burst and arbitrary erasures under a maximum decoding delay constraint $T$. Our proposed code has efficient encoding/decoding algorithms and requires a field size that is linear in $T$. We study the performance of our code over the Gilbert-Elliot channel; our simulation results show significant performance gains over low-delay codes existing in the literature., Comment: Accepted for publication in IEEE ICASSP 2023

Zero-shot voice conversion (VC) where both source and target speakers are unseen in the training dataset has become a new research direction. Using speaker embeddings instead of one-hot vectors to represent speaker identity is a key point, which makes VC models work on unseen speakers. In our work, a newly designed neural network was used to adjust the speaker embeddings of unseen speakers. This enables speaker embeddings to perform better on zero-shot VC. In addition, disentangled representation of features is the mainstream method to achieve zero-shot VC. In terms of input features of VC model, we use Mel-cepstral and F0 as simple acoustic features (SAF) rather than Mel-spectrograms. This avoids F0 conflicts in decoder that existed in the previous methods. The evaluations demonstrate that our proposed methods improve the quality of converted speech in terms of naturalness and similarity.

The increasing demand for portable electronic devices and hybrid electric vehicles stimulates the development of supercapacitors as an advanced energy storage system. Here, we demonstrate a binder-free nickel hydroxide@nanoporous gold/Ni foam (Ni(OH)2@NPG/Ni foam) electrode for high-performance supercapacitors, which is prepared by a facile three-step fabrication route including electrodeposition of Au-Sn alloy on Ni foam, chemical dealloying of Sn and electrodepostion of Ni(OH)2 on NPG/Ni foam. Such Ni(OH)2@ NPG/Ni foam electrode is composed of a thin layer of conformable Ni(OH)2 nanoflakes supported on three-dimensional (3D) hierarchically porous NPG/Ni foam substrate. The resulting Ni(OH)2@NPG/Ni foam electrode can offer highways for both electron transfer and ion transport and lead to an excellent electrochemical performance with an ultrahigh specific capacitance of 3,380 F g–1 at a current density of 2 A g–1. Even when the current density was increased to 50 A g–1, it still retained a high capacitance of 1,927 F g–1. The promising performance of the Ni(OH)2@NPG/Ni foam electrode is mainly ascribed to the 3D hierarchical porosity and the highly conductive network on the NPG/Ni foam composite current collector, as well as the conformal electrodeposition of Ni(OH)2 active material on the NPG/Ni foam, which induces the formation of interconnected porosity both on the top surface and on the inner surface of the electrode. This inspiring electrochemical performance would make the as-designed electrode material become one of the most promising candidates for future electrochemical energy storage systems.

Malware writers frequently try to hide the activities of their agents within tunnelled traffic. Within the Kill Chain model the infection time is often measured in seconds, and if the infection is not detected and blocked, the malware agent, such as a bot, will often then set up a secret channel to communicate with its controller. In the case of ransomware the communicated payload may include the encryption key used for the infected host to register its infection. As a malware infection can spread across a network in seconds, it is often important to detect its activities on the air, in memory and at-rest. Malware increasingly uses encrypted channels for communicating with their controllers. This paper presents a new approach to discovering the cryptographic artefacts of real malware clients that use cryptographic libraries of the Microsoft Windows operating system. This enables malware secret communications to be discovered without any prior malware knowledge.

In the area of full duplex (FD)-enabled small cell networks, limited works have been done on consideration of cache and mobile edge communication (MEC). In this paper, a virtual FD-enabled small cell network with cache and MEC is investigated for two heterogeneous services, high-data-rate service and computation-sensitive service. In our proposed scheme, content caching and FD communication are closely combined to offer high-data-rate services without the cost of backhaul resource. Computing offloading is conducted to guarantee the delay requirement of users. Then we formulate a virtual resource allocation problem, in which user association, power control, caching and computing offloading policies and resource allocation are jointly considered. Since the original problem is a mixed combinatorial problem, necessary variables relaxation and reformulation are conducted to transfer the original problem to a convex problem. Furthermore, alternating direction method of multipliers (ADMM) algorithm is adopted to obtain the optimal solution. Finally, extensive simulations are conducted with different system configurations to verify the effectiveness of the proposed scheme.

The quality of feature has significant impact on the performance of detection techniques used for Denial-of-Service (DoS) attack. The features that fail to provide accurate characterization for network traffic records make the techniques suffer from low accuracy in detection. Although researches have been conducted and attempted to overcome this problem, there are some constraints in these works. In this paper, we propose a technique based on Euclidean Distance Map (EDM) for optimal feature extraction. The proposed technique runs analysis on original feature space (first-order statistics) and extracts the multivariate correlations between the first-order statistics. The extracted multivariate correlations, namely second-order statistics, preserve significant discriminative information for accurate characterizations of network traffic records, and these multivariate correlations can be the high-quality potential features for DoS attack detection. The effectiveness of the proposed technique is evaluated using KDD CUP 99 dataset and experimental analysis shows encouraging results. © 2011 Springer-Verlag.

The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order statistics from the observed network traffic records. These second-order statistics extracted by the proposed analysis approach can provide important correlative information hiding among the features. By making use of this hidden information, the detection accuracy can be significantly enhanced. The effectiveness of the proposed multivariate correlation analysis approach is evaluated on the KDD CUP 99 dataset. The evaluation shows encouraging results with average 99.96% detection rate and 2.08% false positive rate. Comparisons also show that our multivariate correlation analysis based detection approach outperforms some other current researches in detecting DoS attacks. © 2011 Springer-Verlag.

Endophytic bacteria isolated from Guangdong bamboo (Bambusa blumeana) plants were obtained under aerobic and anaerobic cultural conditions. Using the acetylene reduction assay (ARA), 40 strains were identified as diazotrophs. Diazotrophic isolates were grouped into five clusters (I, II, III, IV and V) by SDS-PAGE whole-cell protein patterns. The analysis of full-length 16S rDNA sequences of the representative strains of each group indicated that the endophytic diazotrophs showed great diversity and belonged to the generaAzospirillum(α-subclass of proteobacteria),EscherichiaandPseudomonas(γ-subclass of proteobacteria), andAquaspirillum(β-subclass of proteobacteria).

Proceedings of the 8th Australian Information Security Mangement Conference, Edith Cowan University, Perth Western Australia, 30th November 2010, Web servers and web-based applications are commonly used as attack targets. The main issues are how to prevent unauthorised access and to protect web servers from the attack. Intrusion Detection Systems (IDSs) are widely used security tools to detect cyber-attacks and malicious activities in computer systems and networks. In this paper, we focus on the detection of various web-based attacks using Geometrical Structure Anomaly Detection (GSAD) model and we also propose a novel algorithm for the selection of most discriminating features to improve the computational complexity of payload-based GSAD model. Linear Discriminant method (LDA) is used for the feature reduction and classification of the incoming network traffic. GSAD model is based on a pattern recognition technique used in image processing. It analyses the correlations between various payload features and uses Mahalanobis Distance Map (MDM) to calculate the difference between normal and abnormal network traffic. We focus on the detection of generic attacks, shell code attacks, polymorphic attacks and polymorphic blending attacks. We evaluate accuracy of GSAD model experimentally on the real-world attacks dataset created at Georgia Institute of Technology. We conducted preliminary experiments on the DARPA 99 dataset to evaluate the accuracy of feature reduction.