Voice-over-IP systems are quite frequently attacked with the intent of service theft. While VoIP security has been intensively researched in the past, devised solutions often demand significant changes to the VoIP sys...
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ISBN:
(纸本)9781467321723
Voice-over-IP systems are quite frequently attacked with the intent of service theft. While VoIP security has been intensively researched in the past, devised solutions often demand significant changes to the VoIP systems. In addition, several solutions propose the filtering of telephone calls, but these solutions only have a limited focus on the privacy rights of the call participants. We propose a method for analyzing communication records with the primary purpose to prevent VoIP attacks. Moreover, our approach integrates with little effort into common VoIP usage scenarios. As an example we use the prevention of toll-fraud attacks as a running example. The analysis of the communication records, however, requires investigating personal information in the communication records, e.g., call habits and phone numbers. Consequently we give an overview of major US and EU laws and regulations to elicit privacy requirements. We also demonstrate how these requirements can be implemented using Comercial-Off-The-Shelf VoIP systems.
Certificateless public key cryptography (CLPKC) eliminates certificate management in traditional public key infrastructure and solves the problem of the key escrow in identity-based cryptography. Certificateless signa...
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The Super Sketch-Pad (SSP) is a dynamic geometry software of China, which is a Chinese version designed for the teaching and learning of mathematics. This paper mainly introduces its motion driving mechanism, some app...
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The Super Sketch-Pad (SSP) is a dynamic geometry software of China, which is a Chinese version designed for the teaching and learning of mathematics. This paper mainly introduces its motion driving mechanism, some application in dynamic geometry, locus, and trace, and so forth. SSP lead students to understand the geometrical characters of graphs visually, to see the beauty of geometry and to enhance their interests in mathematics. Dynamic geometry softwares and SSP will develop rapidly in mathematics education.
作者：
Baker, CKrull, RSnyder, GLincoln, WMalone, TBClifford C. Baker
CIE CHFEP is a senior staff scientist at Carlow International Incorporated. He has applied most of his 24 years of experience in the application of human engineering technology to maritime systems. Mr. Baker has directed much of Carlow's efforts to reduce ship workload and to improve human performance and maritime safety through application of human factors methods and data. He is a Certified Industrial Ergonomist (CIE) as well as a Certified Human Factors Engineering Professional (CHFEP). Both certifications were granted by Oxford Research where Mr. Baker also serves as an Advisory Board member. Russell D. Krull
P.E. is a senior engineer with A&T/Proteus Engineering with more than 18 years of experi-ence in marine engineering naval architecture and program management including 16 years of active duty in the U.S. Coast Guard. Recent experience includes advanced ship design studies engineering software development technical support for the USMC Advanced Amphibious Assault Vehicle propulsion systems analyses ship structural engineering and cargo handling systems engineering. Mr. Krull has an M.S.E. in naval architecture and marine engineering and an M.S.E. in industrial and operations engineering from University of Michigan and a B.S. in ocean engineering from the U.S. Coast Guard Academy. Capt. Glenn L. Snyder
USCG. Regrettably since this paper was originally written Capt. Snyder has passed away. At the time of his death he was an operations specialist assigned to the Coast Guard's Deepwater Capabilities Replacement Project as Chief of Human Systems Integration. He served as commanding officer of the patrol boat Cape George (WPB-95306) the icebreaking tug Biscayne Bay (WTGB-104) and the cutter Legare (WMEC-911). A 1975 graduate of the U.S. Coast Guard Academy Capt. Snyder held an M.A. in national security and strategic studies from the U.S. Naval War College and an M.A. in international relations from Salve Regina College. In addition he was a 1998 fellow of the Foreign Service
The U.S. Coast Guard is in the concept exploration phase of its Integrated Deepwater System (IDS) acquisition project. This project will define the next generation of surface, air and command, control, communications,...
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The U.S. Coast Guard is in the concept exploration phase of its Integrated Deepwater System (IDS) acquisition project. This project will define the next generation of surface, air and command, control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR) assets used to perform the Coast Guard's missions in the IDS environment (>50 NM off the U. S. coastline). As part of early technology investigations, the needs exist to: (1) analyze the workload requirements of the IDS, (2) identify alternative means to perform ship's work, and (3) optimize ship manning consistent with ship workload, performance criteria, and the available tools and equipment aboard. To reduce shipboard work requires an understanding of the mission and support requirements placed on the vessel and crew, how these requirements are currently met, and how requirements might otherwise be met to reduce workload and crew size. This study examined currently implemented workload and manpower reducing approaches of commercial maritime fleets, U.S. and foreign navies, and foreign coastguards. These approaches were analyzed according to evaluation criteria approved by the IDS acquisition project team. From this, strategies for shipboard work reduction that may be considered for adoption by the IDS were identified and analyzed according to performance and costs factors. Ten workload-reducing strategies were identified: damage control, bridge, multiple crewing, engineering, risk acceptance, modularity, deck, enabling technologies, ship/personnel readiness, and operability and maintainability.
For Wireless Body Area Networks (WBANs), the security of sensitive data of patients is of the utmost importance, particularly in healthcare environments. This study presents a novel methodology for improving the effic...
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For Wireless Body Area Networks (WBANs), the security of sensitive data of patients is of the utmost importance, particularly in healthcare environments. This study presents a novel methodology for improving the efficacy of signature aggregation in a scenario involving doctors and patients while mitigating concerns about location privacy. Though there have been prior proposals for signature aggregation schemes, the proposed approach seeks to optimize the aggregation process within the considered scenario, thereby improving performance and reducing computational and communication burden. In addition, the proposed scheme integrates a resilient mechanism that safeguards the doctor’s location privacy by utilizing the Chinese Remainder Theorem (CRT). Advanced cryptographic algorithms and location-anonymization techniques are employed in the proposed method to safeguard the confidentiality of the doctors’ location. The security of the proposed scheme is formally analyzed using the Burrows-Abadi-Needham (BAN) logic and formally verified using the automated software validation tool, known as the Scyther tool, and an informal analysis of various security attributes confirms the security robustness of the proposed scheme. The efficacy is evaluated in comparison to analogous works utilizing the Cygwin software. The performance evaluation shows that the proposed scheme has lower communication costs as compared to existing competing schemes. Moreover, the serving ratio in the proposed scheme is high even if the number of patients is low for doctors.
Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of information retrieval (IR) progressively tend to be diversified and personalized. Currently, User-...
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Empowered by the continuous integration of social multimedia and artificial intelligence, the application scenarios of information retrieval (IR) progressively tend to be diversified and personalized. Currently, User-Generated Content (UGC) systems have great potential to handle the interactions between large-scale users and massive media contents. As an emerging multimedia IR, Fashion Compatibility Modeling (FCM) aims to predict the matching degree of each given outfit and provide complementary item recommendation for user queries. Although existing studies attempt to explore the FCM task from a multimodal perspective with promising progress, they still fail to fully leverage the interactions between multimodal information or ignore the item-item contextual connectivities of intra-outfit. In this paper, a novel fashion compatibility modeling scheme is proposed based on Correlation-aware Cross-modal Attention Network. To better tackle these issues, our work mainly focuses on enhancing comprehensive multimodal representations of fashion items by integrating the cross-modal collaborative contents and uncovering the contextual correlations. Since the multimodal information of fashion items can deliver various semantic clues from multiple aspects, a modality-driven collaborative learning module is presented to explicitly model the interactions of modal consistency and complementarity via a co-attention mechanism. Considering the rich connections among numerous items in each outfit as contextual cues, a correlation-aware information aggregation module is further designed to adaptively capture significant intra-correlations of item-item for characterizing the content-aware outfit representations. Experiments conducted on two real-world fashion datasets demonstrate the superiority of our approach over state-of-the-art methods.
Artificial intelligence (AI) empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significa...
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Artificial intelligence (AI) empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significant services provided by multimedia applications, which aims to offer intelligent interactive experiences between humans and machines. However, malicious attackers may exploit spoofed speeches to deceive speech assistants, posing great challenges to the security of multimedia applications. The limited resources of multimedia terminal devices hinder their ability to effectively load speech spoofing detection models. Furthermore, processing and analyzing speech in the cloud can result in poor real-time performance and potential privacy risks. Existing speech spoofing detection methods rely heavily on annotated data and exhibit poor generalization capabilities for unseen spoofed speeches. To address these challenges, this paper first proposes the Coordinate Attention Network (CA2Net) that consists of coordinate attention blocks and Res2Net blocks. CA2Net can simultaneously extract temporal and spectral speech feature information and represent multi-scale speech features at a granularity level. Besides, a contrastive learning-based speech spoofing detection framework named GEMINI is proposed. GEMINI can be effectively deployed on edge nodes and autonomously learn speech features with strong generalization capabilities. GEMINI first performs data augmentation on speech signals and extracts conventional acoustic features to enhance the feature robustness. Subsequently, GEMINI utilizes the proposed CA2Net to further explore the discriminative speech features. Then, a tensor-based multi-attention comparison model is employed to maximize the consistency between speech contexts. GEMINI continuously updates CA2Net with contrastive learning, which enables CA2Net to effectively represent speech signals and accurately detect spoofed speeches. Extensive experiments on
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