Detection of covert Voice over Internet Protocol communications using sliding window-based steganalysis

The authors describe a reliable and accurate steganalysis method for detecting covert voice-over Internet protocol (VoIP) communication channels. The proposed method utilises a unique sliding window mechanism and an improved regular singular (RS) algorithm for VoIP steganalysis, which detects the presence of least significant bit embedded VoIP streams. With this mechanism, the detection window moves forward one packet or several packets each time to screen VoIP streams. The optimum detection threshold for the proposed detection metric is computed by modelling the distributions of the new metric for stego and cover VoIP streams. Experimental analysis reveals that the proposed method improves the detection time significantly, utilising less memory resources for VoIP steganalysis, thereby enabling real-time detection of stego VoIP streams. The proposed method also provides a significant improvement on precision in detecting multiple covert VoIP channels when compared to the conventional RS method

Steganography in inactive frames of VoIP streams encoded by source codec

This paper describes a novel high capacity steganography algorithm for embedding data in the inactive frames of low bit rate audio streams encoded by G.723.1 source codec, which is used extensively in Voice over Internet Protocol (VoIP). This study reveals that, contrary to existing thoughts, the inactive frames of VoIP streams are more suitable for data embedding than the active frames of the streams, that is, steganography in the inactive audio frames attains a larger data embedding capacity than that in the active audio frames under the same imperceptibility. By analysing the concealment of steganography in the inactive frames of low bit rate audio streams encoded by G.723.1 codec with 6.3kbps, the authors propose a new algorithm for steganography in different speech parameters of the inactive frames. Performance evaluation shows embedding data in various speech parameters led to different levels of concealment. An improved voice activity detection algorithm is suggested for detecting inactive audio frames taking into packet loss account. Experimental results show our proposed steganography algorithm not only achieved perfect imperceptibility but also gained a high data embedding rate up to 101 bits/frame, indicating that the data embedding capacity of the proposed algorithm is very much larger than those of previously suggested algorithms

Are synthetic a priori propositions informative?

According to rationalists, synthetic a priori propositions convey new knowledge, whereas analytic propositions are non-informative or vacuous conceptual truths. However, as we argue in this article, each a priori proposition is necessarily true because of its semantic constituents and the way they are combined, and hence can be transformed into its equivalent analytic form. So each synthetic a priori proposition conveys only non-informative conceptual truths like analytic propositions

Are synthetic a priori propositions informative?

According to rationalists, synthetic a priori propositions convey new knowledge, whereas analytic propositions are non-informative or vacuous conceptual truths. However, as we argue in this article, each a priori proposition is necessarily true because of its semantic constituents and the way they are combined, and hence can be transformed into its equivalent analytic form. So each synthetic a priori proposition conveys only non-informative conceptual truths like analytic propositions

Network service registration based on role-goal-process-service meta-model in a P2P network

Service composition-based network software customisation is currently a research hotspot in the field of software engineering. A key problem of the hotspot is how to efficiently discover services distributed over the Internet. In the service oriented architecture, service discovery suffers from the performance bottleneck of centralised universal description discovery and integration (UDDI), and inaccurate matching of service semantics. In this study, the authors describe a novel method for service labelling, registration and discovery, which is based on the role-goal-process-service meta-model. This approach enables ones to achieve accurate matching of service semantics by extending web service description language with RGP demand-information. The authors also suggest a peer-to-peer (P2P)-based architecture of service discovery to address the issues in the UDDI bottleneck and the complexity of semantic computation. By adopting the proposed approach, an experiment prototype system has been designed and implemented in Beijing municipal transportation system. The experimental results show the proposed approach is effective in addressing the aforementioned problems

Pharmacological Basis for Use of Armillaria mellea

Armillaria mellea, an edible fungus, exhibits various pharmacological activities, including antioxidant and antiapoptotic properties. However, the effects of A. mellea on Alzheimer’s disease (AD) have not been systemically reported. The present study aimed to explore the protective effects of mycelium polysaccharides (AMPS) obtained from A. mellea, especially AMPSc via 70% ethanol precipitation in a L-glutamic acid- (L-Glu-) induced HT22 cell apoptosis model and an AlCl3 plus D-galactose- (D-gal-) induced AD mouse model. AMPSc significantly enhanced cell viability, suppressed nuclear apoptosis, inhibited intracellular reactive oxygen species accumulation, prevented caspase-3 activation, and restored mitochondrial membrane potential (MMP). In AD mice, AMPSc enhanced horizontal movements in an autonomic activity test, improved endurance times in a rotarod test, and decreased escape latency time in a water maze test. Furthermore, AMPSc reduced the apoptosis rate, amyloid beta (Aβ) deposition, oxidative damage, and p-Tau aggregations in the AD mouse hippocampus. The central cholinergic system functions in AD mice improved after a 4-week course of AMPSc administration, as indicated by enhanced acetylcholine (Ach) and choline acetyltransferase (ChAT) concentrations, and reduced acetylcholine esterase (AchE) levels in serum and hypothalamus. Our findings provide experimental evidence suggesting A. mellea as a neuroprotective candidate for treating or preventing neurodegenerative diseases

FMMRec: Fairness-aware Multimodal Recommendation

Recently, multimodal recommendations have gained increasing attention for effectively addressing the data sparsity problem by incorporating modality-based representations. Although multimodal recommendations excel in accuracy, the introduction of different modalities (e.g., images, text, and audio) may expose more users' sensitive information (e.g., gender and age) to recommender systems, resulting in potentially more serious unfairness issues. Despite many efforts on fairness, existing fairness-aware methods are either incompatible with multimodal scenarios, or lead to suboptimal fairness performance due to neglecting sensitive information of multimodal content. To achieve counterfactual fairness in multimodal recommendations, we propose a novel fairness-aware multimodal recommendation approach (dubbed as FMMRec) to disentangle the sensitive and non-sensitive information from modal representations and leverage the disentangled modal representations to guide fairer representation learning. Specifically, we first disentangle biased and filtered modal representations by maximizing and minimizing their sensitive attribute prediction ability respectively. With the disentangled modal representations, we mine the modality-based unfair and fair (corresponding to biased and filtered) user-user structures for enhancing explicit user representation with the biased and filtered neighbors from the corresponding structures, followed by adversarially filtering out sensitive information. Experiments on two real-world public datasets demonstrate the superiority of our FMMRec relative to the state-of-the-art baselines. Our source code is available at https://anonymous.4open.science/r/FMMRec