The application of smart contracts in electric power systems is widespread. However, vulnerabilities in smart contracts can cause significant economic losses and require careful attention. Smart contracts in electric power systems have domain-specific characteristics that differ from traditional public blockchain applications. As a result, existing vulnerability detection tools cannot be directly applied to these systems. To address this challenge, we design a vulnerability detection tool called E-Guard specifically for smart contracts in electric power systems. E-Guard uses a tailored intermediate representation (IR) known as EIR, which provides control flow and data flow information more suited to the business logic of electric power systems than traditional static analysis tools. We identify and summarize three types of vulnerabilities unique to electric power systems based on expert knowledge. Experimental results show that E-Guard significantly outperforms traditional static analysis tools in detecting these three types of vulnerabilities. Additionally, the extra overhead generated by using EIR is minimal and negligible. This demonstrates that E-Guard is an effective and efficient tool for enhancing the security of smart contracts in electric power systems.

Face recognition has witnessed significant progress with the advances of deep convolutional neural networks (CNNs), and the central task of which is how to improve the feature discrimination. To this end, several margin-based (e.g., angular, additive and additive angular margins) softmax loss functions have been proposed to increase the feature margin between different classes. However, despite great achievements have been made, they mainly suffer from four issues: 1) They are based on the assumption of well-cleaned training sets, without considering the consequence of noisy labels inherently existing in most of face recognition datasets; 2) They ignore the importance of informative (e.g., semi-hard) features mining for discriminative learning; 3) They encourage the feature margin only from the perspective of ground truth class, without realizing the discriminability from other non-ground truth classes; and 4) They set the feature margin between different classes to be same and fixed, which may not adapt the situation of unbalanced data in different classes very well. To cope with these issues, this paper develops a novel loss function, which explicitly estimates the noisy labels to drop them and adaptively emphasizes the semi-hard feature vectors from the remaining reliable ones to guide the discriminative feature learning. Thus we can address all the above issues and achieve more discriminative features for face recognition. To the best of our knowledge, this is the first attempt to inherit the advantages of feature-based noisy labels detection, feature mining and feature margin into a unified loss function. Extensive experimental results on a variety of face recognition benchmarks have demonstrated the effectiveness of our method over state-of-the-art alternatives. Our source code is available at http://www.cbsr.ia.ac.cn/users/xiaobowang/.

Datacenters currently deploy shallow-buffered switches to achieve low latency by avoiding long waiting time in the data plane. However, the limited buffer space in the switch causes the frequent overflow and the notorious TCP incast problem. Moreover, the simple scheduling strategy in buffer deprives the switch of the ability to offer deeply differentiated services. Therefore, we present a novel priority-based flow-aware in-network caching scheme, named Poche, which supplies more control capabilities for the network side through introducing some additional cache resource into switches. Poche classifies network traffic into multiple priorities according to the latency requirements of flows. The end server adds priority tags to packets and sets different RTO values for flows with distinct priorities. The switch monitors the buffer utilization of each port and performs the priority-based flow-aware caching and injecting strategies based on the analysis of the scheduling model between the buffer and cache. We conduct comprehensive experiments to compare Poche with the state-of-the-art traffic optimization schemes. The results demonstrate that Poche can reduce the FCTs of latency-sensitive flows by at least 59.1% and improve the network throughput by at least 54.4%, while ensuring the finite cached volume and effectively addressing the incast problem.

Existing Web service eco-systems are typically managed in a centralized manner, which hinders their further development due to inherent disadvantages such as trust issues, interest disputes, value separation and so on. The recently emerged blockchains provide distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states, which provide a natural solution. Based on the INKchain, which is an open-source permissioned blockchain mechanism extending the Hyperledger Fabric, this paper proposes Trusted Decentralized Service Eco-System (T-DSES). T-DSES achieves not only fundamental functionalities of conventional systems, but also offers mechanisms to stimulate participants to bring trustworthiness to the whole system. The trustworthiness of T-DSES is realized by three strategies: reliable information of services and mashups, reliable records of participants’ rights, and reliable measurement of participants’ contributions. A customized token “SToken” is created to act as the media of value circulation. In this paper, the overall framework and detailed design of T-DSES are presented, especially including how to utilize Kubernetes to establish a cloud-based environment. A tailored Web front-end ensures the usability of operations. Over real-world data from ProgrammableWeb.com, analyses and experiments have been conducted to verify the feasibility and effectiveness of the presented approach.

Automatic family labeling for malware is in demand, especially for today's malware scale. While business Anti-Virus engines provide an efficient family labeling method, the raw labels tend to be inconsistent. Prior works mitigate such inconsistency by detecting the aliases and majority voting to obtain the final family label. However, these methods solve the inconsistency in a coarse-grained and vulnerable manner, and the obtained family label is inaccurate sometimes. In this work, we propose NLabel to conduct familial clustering based on AV engines' raw labels. On the one hand, NLabel uses word embedding techniques to capture the similarity among raw labels, transform the inconsistent labels of the same family into similar semantic representations, and mitigate the inconsistency at finer granularity. On the other hand, we propose a hierarchical family clustering method to boost the performance of large-scale data sets. Experimental results show that our method outperforms the SOTA.

A large amount of malware families use the domain generation algorithms (DGA) to randomly generate a large amount of domain names. It is a good way to bypass conventional blacklists of domain names, because we cannot predict which of the randomly generated domain names are selected for command and control (C&C) communications. An effective approach for detecting known DGA families is to investigate the malware with reverse engineering to find the adopted generation algorithms. As reverse engineering cannot handle the variants of DGA families, some researches leverage supervised learning to find new variants. However, the explainability of supervised learning is low and cannot find previously unseen DGA families. In this paper, we propose a graph-based semi-supervised learning scheme to track the evolution of known DGA families and find previously unseen DGA families. With a domain relation graph, we can clearly figure out how new variants relate to known DGA domain names, which induces better explainability. We deployed the proposed scheme on real network scenarios and show that the proposed scheme can not only comprehensively and precisely find known DGA families, but also can find new DGA families which have not seen before.

Promotion infection is an attack where adversaries exploit website weakness to inject illicit content into web pages for promoting illicit products, e.g., gambling and unlicensed drugs, etc. As the development of online underground economy, promotion infection is widely utilized by adversaries. To escape legal supervision, adversaries have exploited ingenious anti-regulatory techniques, making it challenging to access the infected webpage, let alone the collection of a large scale corpus. Limited by the small size of corpus, the effectiveness of the literature in detecting promotion infection is not proven yet. Towards this end, we propose a novel and efficient approach to (semi-)automatically collect infected webpages. Our insight is that the goal of infection is to promote the underground business, mainly through search engines. Exploiting this observation, we propose the search-engine dork technique to crawl the infected webpages by querying underground advertisings (i.e., a list of black keywords) on search engines. Running with an initial 58 seed black keywords, we have collected 22,939 infected pages ranging 2,563 domains, and along the way automatically gathered 8,374 new black keywords. This large scale dataset thereby enables us to build a machine-learning model to distinguish infected webpages from normal ones. The experimental results show that the model, thanks to the large-scale and high-quality dataset, can provide high detection rate and low false alarm rate.

Higher bandwidth and spatial reuse through densification of wireless cells are necessary to meet the increase of wireless traffic demand in the next decade. Millimeter wave network can fulfill both of these criteria with the availability of abundant bandwidth and higher spatial reuse through directional beamforming. However, millimeter wave network suffers from coverage hole in urban environment due to blockage because of high path loss for millimeter wave signals. Wireless backhaul allows densification of mobile networks without incurring additional fiber deployment cost. Millimeter wave band based Integrated access and backhaul (IAB) is expected to provide a low cost coverage enhancement and capacity improvement solution. This paper focuses on the system performance of IAB node deployment in terms of coverage and user throughput by field experiment using millimeter wave band. The trial results prove that coverage ratio is significant improved with deploying IAB node. Meanwhile, dynamic resource allocation, including dynamic time division multiplexing (TDM) and dynamic TDM with flexible space division multiplexing (SDM) between access and backhaul link, brings obvious user throughput gain due to better resource utilization and providing more access transmission opportunities for User Equipment (UE) in backhaul slot.

Recently, the security of Internet of Things (IoT) has been an issue of great concern. Physical layer security methods can help IoT networks achieve information-theoretical secrecy. Nevertheless, utilizing physical security methods, such as artificial noise (AN) may cost extra power, which leads to low secure energy efficiency. In this paper, the hybrid precoding technique is employed to improve the secure energy efficiency of the IoT network. A secure energy efficiency optimization problem is formulated for the IoT network. Due to the nonconvexity of the problem and the feasible domain, the problem is first transformed into a tractable suboptimal form. Then, a secure hybrid precoding energy efficient (SEEHP) algorithm is proposed to tackle the problem. The numerical results indicate that the proposed SEEHP algorithm achieves higher secure energy efficiency compared with three existing physical layer security algorithms, especially when the number of transmit antennas is large.

Service ecosystems, consisting of various kinds of services and mashups, usually keep evolving over time.Existing works on the evolution of service ecosystems focus on either evaluating the impacts of single services' changes on the usage of services and the stability of the whole ecosystem, or discovering co-occurrence relationship between services, but fail to disclose any knowledge from the aspect of the evolution of service composition patterns.Based on our previous work, this paper moves one step further, revealing the latent service composition trends in a service ecosystem and providing more distinct explanation of different topic evolution patterns.A novel methodology, named Extended Dependency-Compensated Service Co-occurrence LDA (EDC-SeCo-LDA), is developed to calculate the directed dependencies between different topics and build topic evolution graph.The evolution trend

Link state measurement is the fundamental part of routing control and has received wide attention in research community. However, due to the massive number of network nodes, it is considerably expensive to measure the network timely. In this work, we propose LTSM, a lightweight and time sliced measurement approach for link state to reduce the measurement cost. In LTSM, the measurement tasks are assigned to all nodes evenly and launched at proper time. Theoretically, the measurement cost can be reduced by 50%. We design a prototype of the approach and implement it on a real-world network consisting of 37 public cloud nodes and 1, 332 links. Experimental results show that our approach can acquire the link state of the network efficiently.

Computational Sprinting has proven to be an effective way to boost the computing performance for bursty workloads, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with certain phase-changing materials. However, extra power available for sprinting is constrained by existing power distribution infrastructures. Using batteries alone to provide the additional power to achieve performance target not only limits the effectiveness of sprinting, but also negatively impacts the lifetime of the batteries. Leveraging renewable power supply in a green data center provides an opportunity to exploit the maximal potential of Computational Sprinting. However, the intermittent nature of renewable energy makes it very challenging. In this paper, we propose GreenSprint, a renewable energy driven approach that enables a data center to boost its computing performance efficiently by conducting computational sprinting. We present four sprinting strategies to address the challenge imposed by the intermittent and time-varying nature of renewable energy supply. We build an experimental prototype to evaluate GreenSprint on a cluster of 10 servers with a simulated solar power generator. The results show that renewable energy by itself can sustain different duration lengths of sprinting when its supply is sufficient and can improve performance by up to 4.8x for representative interactive applications. We also show the effectiveness of core-count and frequency scaling in the presence of varied renewable power and limited battery energy.

The commercialization of 5G is planned by multiple operators worldwide in the coming few years around 2020. 5G is expected to support new services and higher frequency bands. In particular, the use of higher frequency bands above 6 GHz for mobile communications is one new and important technical challenge tackled by 5G. However, in the initial deployments of 5G, higher frequency bands would only be used for limited areas, e.g., small cell deployments, or fixed wireless access. To further extend the benefits of higher frequency bands to wider coverage and usage, it is important to enable a wide use of higher frequency bands to a similar level as those of existing low frequency bands in terms of coverage, power consumption, etc. In this paper, we present our outdoor experimental trials of long range mobile communications using 39 GHz band. The trial results demonstrate the potential of higher frequency bands to support Gbps class data rates while still providing coverage and supporting mobility over a coverage area of above 1 km.

The fifth generation mobile communications (5G) system will need to support ultra-reliable and low-latency communications (URLLC) to enable future mission-critical applications, e.g., self-driving cars and remote control. In this paper, we clarify coverage and mobility performance of URLLC by field experimental trials. To achieve URLLC requirements in 5G, we use a new frame structure, which adopts wider subcarrier spacing and acknowledgement/negative acknowledgement-less (ACK/NACK-less) retransmission to reduce the user-plane latency and improve the packet success probability, in this trial. We evaluate the URLLC coverage for several packet sizes using our prototype test-bed. The results show that URLLC requirement in 3GPP can be achieved in UL even when SNR is approximately -3.1 dB. Furthermore, we clarify that the requirement can be satisfied with 25 km/h mobility.

Three 5G enabling technologies, i.e, sparse code multiple access (SCMA), Polar codes and filtered OFDM (f-OFDM), are implemented into a massive MIMO system in the field trial conducted by HUAWEI and NTT DOCOMO to investigate their impacts on the system spectral efficiency. Taking massive MIMO with OFDMA and Turbo codes as the baseline, we observe about 30% downlink spectral efficiency improvement in the trial. Results from test field are summarized and analyzed in this paper, which verified that SCMA, Polar codes and f- OFDM are also feasible for massive MIMO systems in terms of spectral efficiency improvement.

Drug Drug Interactions (DDIs) can cause harmful effect. Two shared tasks, DDIExtraction 2011 and DDIExtraction 2013, have been held to promote the implementation and comparative assessment of natural language processing techniques in the field of the pharmacovigilance domain. However, few model can meanwhile achieve state-of-the-art performance on both tasks. A major reason is the lack of representation of DDI instance structure in common. Therefore, in this paper, we propose a novel method to make full use of the DDI structure based on deep learning, in which we grasp the skeleton structure of DDI instances by a skeleton long short term memory (skeleton-LSTM) network. The experimental results show that our method can achieve an F-score of 0.677 on DDIExtraction 2011 and an F-score of 0.714 on DDIExtraction 2013, both of which are state-of-the-art.

Massive multiple-input multiple-output (MIMO) has been considered as one of the key technologies in 5G communication, owing to its promising potential to increase the spectrum efficiency significantly. The capacity grows rapidly with the increasing number of antennas. However, in real-time environment, the more antennas are employed, the more computing resources are required, and this increase is even in a nonlinear progression. In this work, radio computing architecture (RCA) with multiple parallel general purpose processors (GPPs) and automatic distribution system (ADS) are utilized to overcome the strict real-time constraints and establish a highly flexible prototype platform for massive MIMO research. The feasibility and performance of the GPP-based prototype has been investigated through field trial. A collocated 64-RF-channel base station (BS) with each RF channel driving 3 antennas can simultaneously serve twelve 8-antenna user equipments (UEs). The maximum overall bandwidth is 200 MHz, and the maximum flow number is pre-defined as 24. In this field trial, a peak total user throughput of 11.29 Gbps and a cell spectrum efficiency of 79.82 bps/Hz are achieved. As far as we know, this is the world's highest throughput ever achieved in the sub 6 GHz band. Furthermore, apart from multi-user MIMO, single-user MIMO is also tested in this field trial.

A large scale field trial is conducted to investigate the integrated performance of massive multiple-input multiple-output (MIMO) and polar code, which are two key technologies for 5th generation (5G) mobile system. A more simple and effective algorithm named polarization weight (PW) is used in polar construction. Based on uplink and downlink channel reciprocity in the time-division duplex (TDD) mode, the practical performance of single user (SU) massive MIMO prototype with 64 independent RF channels and 200 MHz bandwidth is investigated under different user equipment (UE) deployments, different layer numbers and moving speeds. Compared to massive MIMO with turbo code, it is shown that significant performance gains can be obtained in TDD massive MIMO system with polar code. For 3 layers scheduled in massive MIMO system with polar code, the maximum user throughput of 1.64 Gbps and spectrum efficiency of 11.64 bit/s/Hz can be achieved. Based on these observations, the feasibility and performance of TDD massive MIMO system with polar code are verified.

This paper presents field trial results of innovative air interface technologies: sparse code multiple access (SCMA), filtered orthogonal frequency division multiplexing (f-OFDM), and massive multiple-input multiple-output (MIMO). To confirm the improvement of the promising technologies, the counterparts in long term evolution (LTE) are selected as the comparison baseline. For each technology, detailed parameter configurations and deployment scenarios are addressed at first, then precise test procedures are described, finally, extensive analysis are provided to testify the high spectral efficiency, flexibility and system capacity introduced by the new technologies. According to the trial results, SCMA can achieve 3 times connection number and throughput than SC-FDMA in uplink, and 90% throughput gains over OFDMA in downlink. Waveform f-OFDM is validated to support flexible resources allocation thus avoiding asynchronous transmission in both uplink and downlink. The large number of antennas in Massive MIMO accommodates more user and offers around 10 times throughput than single user scenario in downlink.

This paper presents a field experimental trial on frequency-domain multiplexing of a mixture of multiple numerologies. The goal is to support diverse applications and services in the fifth generation mobile communications (5G) system. Frequency-domain multiplexing of mixed numerology has been discussed for 5G in order to flexibly support the variety of requirements introduced by future applications. However, frequency-domain multiplexing of mixed numerology causes inter sub-band interference. In this paper, we apply filtered-orthogonal frequency division multiplexing (f-OFDM), which is a new waveform, in order to reduce the inter sub-band interference. Through the field trial, we show that f-OFDM realizes mixed numerology while reducing the impact from inter sub-band interference compared to conventional cyclic prefix OFDM. The field trial results show that f-OFDM has no throughput degradation when using more than one guard subcarrier.

Radio frequency (RF) channel reciprocity calibration is one of the most important technologies in massive multiple-input multiple-output (MIMO) system. With the increasing of the antenna size, the calibration procedure becomes much more challenging. In this paper, wired- and wireless-calibration schemes have been proposed and investigated through field-trial test based on a real-time massive MIMO prototype with 64 independent RF channels. The prototype is capable of supporting 12 200-MHz user equipments (UEs) simultaneously. The field trial test results validate both schemes can significantly improve the overall system throughput. For single-user (SU) MIMO scenario, the throughput can be enhanced from 0.71 Gbps to 1.46 Gbps and 1.54 Gbps with wireless- and wired-calibration schemes, respectively. For multi-user (MU) MIMO scenario, the corresponding enhancement can be from 0.26 Gbps to 9.18 Gbps and 11.29 Gbps, respectively. In terms of calibration accuracy, the wired-calibration scheme has better performance, but the wireless one has lower system complexity and cost. To the best of our knowledge, this is the first field trial investigation of wireless-calibration scheme and comparison with wired calibration in a real-time high-throughput massive MIMO prototype.

The 5th generation of mobile communications system (5G) is nowadays expected to support very diverse applications, devices, and services such as enhanced mobile broadband (eMBB) and the Internet of things (IoTs). In order to meet the future requirements, new 5G candidate radio access technologies have been proposed and studied. For example, Massive multiple-input multiple-output (MIMO) is an attractive technology to further improve the spectrum efficiency and to boost system capacity. This paper presents a large scale field experimental trial of downlink time division duplex (TDD) Massive MIMO using the developed 5G test bed at the 4.5 GHz band. Specifically, the system performance of multi-user (MU) Massive MIMO with different numbers of sets of user equipment (UEs) and spatial layers is investigated, including the impact of different inter-UE separations and UE deployments, which have not been well investigated compared to the previous studies. The experimental results show that the throughput performance for a wide UE separation linearly increases as the number of layers and UEs increases, while the performance improvement is diminished when inter-UE separation narrows. In addition, the maximum total user throughput of 11.29 Gbps, which corresponds to a spectrum efficiency of 79.82 bps/Hz/cell, was achieved with 24 layers. The results indicate that TDD Massive MIMO using the developed 5G test bed greatly improves the spectrum efficiency for large scale MU-MIMO.

We explore the feasibility of 5G for enhancing cooperative automated driving. A V2X solution for enhancing road safety through connected cars based on 5G radio technology is shown. Based on a flexible, re-configurable software defined radio test-bed, we examine the benefit of ultra low latency and high reliability (URLLC) profile for enhanced emergency brake maneuver. This use case reveals the advantages and additional requirements of using 5G for automated emergency braking based on vehicle-to-vehicle communication. We analyze the impact of communication latency and reliability on the maneuver performance and associated safety aspect. The results provide insights into the joint-design of a V2X communication system for enhancing road safety through cooperative automated driving.

Ultrahigh data rate, massive connectivity, ultralow latency, and high reliability, as well as ultraflexible air interface design to support diversified usage scenarios, are the major targets of 5G radio access network design. To meet these targets, advanced new radio transmission technologies, including new waveform, new channel coding, non-orthogonal multiple access, as well as massive antenna techniques, have been proposed and studied worldwide in both academic and industries. Most of the gains claimed in the literature are from simulations or by small scale lab testing. Instead, this paper elaborates on the first hand results and analysis of the large-scale field trials carried out in China for a selection of key 5G technology components as well as some of their combinations, including filtered-orthogonal frequency division multiplexing (OFDM) (f-OFDM), polar code, sparse code multiple access, and massive multi-input multi-output (MIMO) for both uplink and downlink cellular networks. Testing results verify the feasibility and benefit of each technology contributing toward the diversified 5G targets, as well as the feasibility for these technologies to work jointly for higher spectrum efficiency, larger connectivity, and lower latency.

Service diversity is expected in the upcoming fifth-generation (5G) cellular networks, which poses great challenges to the underlying waveforms to accommodate heterogeneous service requirements in a flexible way. By dividing the bandwidth into several subbands, each having a different numerology, this paper reports a field trial in time division duplex downlink conducted on a configurable test bed in a real-world environment for the performance evaluations of orthogonal frequency-division multiplexing (OFDM)-based 5G waveform candidates, i.e., cyclically prefixed OFDM (CP-OFDM), windowing OFDM (W-OFDM), and filtered OFDM (f-OFDM), in the presence of mixed numerologies. Field trial results confirm the feasibility of mixed numerologies and reveal the impact of several important system parameters, e.g., guard bandwidth, data bandwidth, signal-to-noise ratio (SNR), and transmit power. The results also suggest that f-OFDM outperforms CP-OFDM and W-OFDM in terms of both the spectrum efficiency and robustness in a high SNR regime, and the gain increases with a higher inter-numerology out-of-band interference. In some specific scenarios, ideal spectrum utilization can be realized by f-OFDM which completely removes the guard band.

Spectral efficiency is always a key factor to be improved and optimized along mobile communication networks evolving generation by generation. 5G enabling technologies must take spectral efficiency into consideration. In this paper, we show the performance of three key 5G technologies in sense of spectral efficiency improvement. Sparse code multiple access, polar codes, and filtered orthogonal frequency-division multiplexing are novel multiple access technology, channel coding scheme, and waveform, respectively. The combination of them is implemented in a 5G field trial testbed by NTT DOCOMO and Huawei for the first time. According to the field test results, we achieve over 100% spectral efficiency improvement comparison with baseline, where orthogonal frequency-division multiple access and turbo coding as LTE are used.

Data compression is widely used in storage systems to reduce redundant data and thus save storage space. One challenge facing the traditional compression approaches is the limitation of compression windows size, which fails to reduce redundancy globally. In this paper, we present DEC, a Deduplication-Enhanced Compression approach that effectively combines deduplication and traditional compressors to increase compression ratio and efficiency. Specifically, we make full use of deduplication to (1) accelerate data reduction by fast but global deduplication and (2) exploit data locality to compress similar chunks by clustering the data chunks which are adjacent to the same duplicate chunks. Our experimental results of a DEC prototype based on real-world datasets show that DEC increases the compression ratio by 20% to 71% and speeds up the compression throughput by 17%~183% compared to traditional compressors, without sacrificing the decompression throughput by leveraging deduplication in traditional compression approaches.

The K Nearest Neighbor (KNN) technique is very simple, highly efficient and effective in the field of text categorization, pattern recognition, object recognition etc. However, its efficiency is challenged by Big Data, which is characterized with volume, variety and velocity. All these characters require a new KNN algorithm with better time complexity, which is lower than O(n) at least Additionally, the characters witness that centralized variants executed in a single thread are no longer reasonable. In this paper, we propose an efficient and scalable strategy, which we called as BALLKNN, for KNN classifier. In BALLKNN, since computation of some samples are reduced by replacing complicated quadratic sum with simple subduction and comparison, time efficiency improvement is obtained; the disadvantage of dependency on global data structure is overcome with the help of temporary variable(s), BALLKNN can be implemented with multiple threads easily, and it scales well with the size of the dataset. Meanwhile, as all samples are handled, the classification accuracy is guaranteed different from traditional sample reduction methods. Furthermore, detailed theoretical analysis on relationship between dimensionality of input and time complexity is provided. Finally, extensive experiments in large real datasets, with tens or hundreds of thousands of records and up to 618 dimensions, have demonstrated the efficiency and scalability of our methods.

With the rapid adoption of Service Oriented Architecture (SOA), increasingly more application-level services are developed through composing service components offered by different service providers. While such application development mode offers advantages in terms of cost-effectiveness and flexibility, application developers cannot understand or deal with risks potentially resulting from vulnerabilities within composed services due to non-transparency of the service providers. Furthermore, some of the vulnerabilities in practice are deeply hidden in dependency structures underlying composed services, thus making even the service providers fail to know the vulnerabilities. This paper proposes a risk-evaluation assisted service selection system, called Risk Evaluation-as-a-Service(or REaaS), which aims to assist application developers to understand vulnerability risks hidden within alternative services when the developers at first attempt to adopt their applications. In particular, for a given application developer's service selection requirement, REaaS produces a ranking list based upon vulnerability risks of alternative services to serve as a guideline regarding which service has the lowest potential risks (e.g., Bugs) for this application deployment. REaaS achieves this goal through the following three steps: 1) generating a package dependency graph for each alternative service, 2) assigning threat-degrees to packages in each dependency graph, and 3) analyzing each dependency graph and evaluating service-risk of each service. We have built a REaaS prototype and used real case study to demonstrate the practicality of REaaS.

Recommendation systems, as efficient measures to handle the information overload and personalized service problems, have attracted considerable attention in research community. Collaborative filtering is one of the most successful techniques based on the user-item matrix in recommendation systems. Usually the matrix is extremely sparse due to the massive number of users and items. And the sparsity of users and items tends to differ significantly in degree. The feature of the matrix changes with the variation of users/items data and hence, leads to poor scalability of the recommendation method. This paper proposes a dynamic-weighted collaborative filtering approach (DWCF) to address sparsity and adaptivity issues. In this approach, the relationship between the distributions of similar users and items is considered to get better recommendation, i.e., the contributions of the user part and the item part to recommendation results depend on their similarity ratios. Moreover, the effect strength of different parts is controlled by an averaging parameter. Experiments on MovieLens dataset illustrate that the DWCF approach proposed in this paper can obtain good recommendation result given different conditions of data sparsity and perform better than a user-based predictor, an item-based predictor and a conventional hybrid approach.

ABSTRACT The Web of Things (WoT), inherited from the Internet of Things (IoT), encapsulates functionalities into publishable services on the Web to enable the IoT a seamless integration with the Web. The openness of the Web, in turn, directly exposes WoT to existing attacks from the Web. In addition, WoT possesses characteristics of high security and privacy concerns, mobility, and limited capabilities, which require specific and additional security and privacy protection beyond existing mechanisms. More importantly, WoT is inherently connected to its context, so context information must be taken into account in its security and privacy measures. To address these challenges, we propose a context‐aware usage control model (ConUCON), which leverages the context information to enhance data, resource, and service protection for WoT. On the basis of ConUCON, we also design and implement a context‐aware usage control framework on the middleware layer in our ongoing SmartHome project, to provide security and privacy protection. ConUCON is designed specifically to express the context‐aware usage policy specification, such that security and privacy requirements can be easily specified and enforced with the proposed model and framework. Finally, we apply ConUCON to a remote appliance management prototype, as a case study, to demonstrates its feasibility in a real environment. Copyright © 2012 John Wiley & Sons, Ltd.

The optimization of Fast Fourier Transfer (FFT) problems that can fit into GPU memory has been studied extensively. Such on-card FFT libraries like CUFFT can generally achieve much better performance than their counterparts on a CPU, as the data transfer between CPU and GPU is usually not counted in their performance. This high performance, however, is limited by the GPU memory size. When the FFT problem size increases, the data transfer between system and GPU memory can comprise a substantial part of the overall execution time. Therefore, optimizations for FFT problems that outgrow the GPU memory can not bypass the tuning of data transfer between CPU and GPU. However, no prior study has attacked this problem. This paper is the first effort of using GPUs to efficiently compute large FFTs in the CPU memory of a single compute node.

Most existing attestation schemes deal with binaries and typically require an exhaustive list of known-good measurements beforehand in order to perform verification. However, many programs nowadays are custom-built: the end user is allowed to tailor, compile and build the source code into various versions, or even build everything from scratch. As a result, it is very difficult, if not impossible, for existing schemes to attest the custom-built software with theoretically unlimited number of valid binaries available. This paper introduce SCOBA, a new Source COde Based Attestation framework, to specifically deal with the attestation on custom software. Instead of trying to obtain a know-good measurement list, SCOBA focuses on the source code and provides a trusted building process to attest the resulting binaries based on the source files and building configuration. SCOBA introduces a trusted verifier to certify the binary code of custom-build program according to its source code and building configuration. For custom-built software based on open-source distributions, we implemented a fully automatic trusted building system prototype for SCOBA based on GCC and TPM. As a case study, we also applied SCOBA to Gentoo and its Portage, which is a source code based package management system. Experimental results show that remote attestation, one of the key TCG features, can be made practically available to the free software community.

In this paper, a Cooley-Tukey algorithm based multidimensional FFT computation framework on GPU is proposed. This framework generalizes the decomposition of multi-dimensional FFT on GPUs using an I/O tensor representation, and therefore provides a systematic description of possible FFT implementations on GPUs. The framework is geared to the efficiency of multi-dimensional FFT on GPU architectures. In particular, no global transposition among dimensions is performed and some previously unnoticed grouping and commutability of multiple dimensions are highlighted in order to reduce the number of computational kernels and minimize the number of global memory accesses. Important architectural factors and constraints of CUDA, such as coalesced access, bank conflicts and register pressure are also considered in this framework. Moreover, we adapt codelets, a straight-line style FFT implementation originally developed in FFTW, into our framework and prove that they are highly efficient on GPUs.

The work presented in this paper is subsequent to the paper ldquoProbability Based Prosody Model for Unit Selectionrdquo which was published in ICASSP'2004. In the improved probability prosody model for corpus based concatenative Text-to-Speech (TTS), likelihood is replaced with posterior probability in the cost functions which conduct the following step, unit selection. Objective and subjective experiments show that posterior probability has obvious advantages over likelihood on robustness, flexibility and overall quality.

Fast Fourier Transform in the West(FFTW) is one of the most successful adaptive Discrete Fourier Transform(DFT) libraries. The high-performance of FFTW mostly derives from its empirical search engine that includes all major DFT algorithms. We propose an adaptive model-driven FT performance prediction technique to replace the empirical search engine in FFTW. Our model achieves over 94% of the DFT performance and uses less than 5% of the search time compared with FFTW Exhaustive search on four test platforms.

This paper presents our recent development of the real-time speech recognition component in the IBM English/Iraqi Arabic speech-to-speech translation system for the DARPA Transtac project. We describe the details of the acoustic and language modeling that lead to high recognition accuracy and noise robustness and give the performance of the system on the evaluation sets of spontaneous conversational speech. We also introduce the streaming decoding structure and several speedup techniques that achieves best recognition accuracy at about 0.3×RT recognition speed.

Speech signals are semi-stationary and speech features in neighboring frames are likely to share similar Gaussian distributions. A fast Gaussian computation algorithm is hence proposed to speed up the computation of the N-best posterior probabilities based on a large set of Gaussian distributions for the task of large vocabulary continuous speech recognition. The maximum probability increase between the current speech frame and a previous reference frame is estimated for all Gaussian distributions in order to reduce explicit computations of posteriors for a large number of Gaussians. The method was applied to the fMPE front-end of IBM’s state-of-the-art speech recognizer resulting a decoding speed-up of 40% in probability computation for a loss-less mode and more than 55% in an approximated implementation, respectively.

In this work we explore a technique for increasing recognition accuracy on speech affected by corrupting noise of an undetermined nature, by the addition of a known and well-behaved noise (masking noise). The same type of noise used for masking is added to the training data, thus reducing the gap between training and test conditions, independent of the type of corrupting noise, or whether it is stationary or not. While still in an early development stage, the new approach shows consistent improvements in accuracy and robustness for a variety of conditions, where no use is made of a-priori knowledge of the corrupting noise. The approach is shown to be of particular interest to the case of cross-talk corrupting noise, a complicated situation in speech recognition for which the relative gain with the proposed approach is over 24%.

In this paper, we describe the IBM MASTOR systems which handle spontaneous free-form speech-to-speech translation on both laptop and hand-held PDAs. Challenges include speech recognition and machine translation in adverse environments, lack of data and linguistic resources for under-studied languages, and the need to rapidly develop capabilities for new languages. Importantly, the code and models must fit within the limited memory and computational resources of hand-held devices. We describe our approaches, experience, and success in building working free-form S2S systems that can handle two language pairs (including a low-resource language).

This paper focuses on the use of both text and phonetic information in a speech translation system in order to make translation results more robust to speech recognition errors. Conventional statistical speech translation formulas are extended to exploit both text-form and phonetic speech recognition results. A novel data-driven word/text tying algorithm is then proposed to group words based on both pronunciation similarity and meaning equivalency. In our speech-to-text translation experiments, significant improvement was achieved by using phonetic information and the proposed word tying algorithm.

The IBM Multilingual Automatic Speech-To-Speech TranslatOR (MASTOR) system is a research prototype developed for the Defense Advanced Research Projects Agency (DARPA) Babylon/CAST speech-to-speech machine translation program. The system consists of cascaded components of large-vocabulary conversational spontaneous speech recognition, statistical machine translation, and concatenative text-to-speech synthesis. To achieve highly accurate and robust conversational spoken language translation, a unique concept-based speech-to-speech translation approach is proposed that performs the translation by first understanding the meaning of the automatically recognized text. A decision-tree based statistical natural language understanding algorithm extracts the semantic information from the input sentences, while a natural language generation (NLG) algorithm predicts the translated text via maximum-entropy-based statistical models. One critical component in our statistical NLG approach is natural concept generation (NCG). The goal of NCG is not only to generate the correct set of concepts in the target language, but also to produce them in an appropriate order. To improve maximum-entropy-based concept generation, a set of new approaches is proposed. One approach improves concept sequence generation in the target language via forward–backward modeling, which selects the hypothesis with the highest combined conditional probability based on both the forward and backward generation models. This paradigm allows the exploration of both the left and right context information in the source and target languages during concept generation. Another approach selects bilingual features that enable maximum-entropy-based model training on the preannotated parallel corpora. This feature is augmented with word-level information in order to achieve higher NCG accuracy while minimizing the total number of distinct concepts and, hence, greatly reducing the concept annotation and natural language understanding effort. These features are further expanded to multiple sets to enhance model robustness. Finally, a confidence threshold is introduced to alleviate data sparseness problems in our training corpora. Experiments show a dramatic concept generation error rate reduction of more than 40% in our speech translation corpus within limited domains. Significant improvements of both word error rate and BiLingual Evaluation Understudy (BLEU) score are also achieved in our experiments on speech-to-speech translation.

Statistical methods commonly used in developing interactive dialogue systems require large amounts of training data to achieve high accuracy and robustness. This becomes a major bottleneck in building free-style dialogue systems in a new domain or for a new language. Portability challenges hence arise regarding how to build statistical models rapidly and with low cost in terms of data collection, transcription and annotation. In this paper, we discuss challenges as well as potential solutions in several critical issues of efficient language modeling, utilization of untranscribed speech data, automatic annotation, and cross-lingual modeling. We believe that current approaches in these areas are far from mature and call for serious efforts from the research community.

As speech recognition systems are used in ever more applications, it is crucial for the systems to be able to deal with accented speakers. Various techniques, such as acoustic model adaptation and pronunciation adaptation, have been reported to improve the recognition of non-native or accented speech. In this paper, we propose a new approach that combines accent detection, accent discriminative acoustic features, acoustic adaptation and model selection for accented Chinese speech recognition. Experimental results show that this approach can improve the recognition of accented speech. 1.

Various language modeling issues in a speech-to-speech translation system are described in this paper. First, the language models for the speech recognizer need to be adapted to the specific domain to improve the recognition performance for in-domain utterances, while keeping the domain coverage as broad as possible. Second, when a maximum entropy based statistical natural language generation model is used to generate target language sentence as the translation output, serious inflection and synonym issues arise, because the compromised solution is used in semantic representation to avoid the data sparseness problem. We use N-gram models as a postprocessing step to enhance the generation performance. When an interpolated language model is applied to a Chinese-to-English translation task, the translation performance, measured by an objective metric of BLEU, improves substantially to 0.514 from 0.318 when we use the correct transcription as input. Similarly, the BLEU score is improved to 0.300 from 0.194 for the same task when the input is speech data.

This paper describes various noise robustness issues in a speech-to-speech translation system. We present quantitative measures for noise robustness in the context of speech recognition accuracy and speech-to-speech translation performance. To enhance noise immunity, we explore two approaches to improve the overall speech-to-speech translation performance. First, a multi-style training technique is used to tackle the issue of environmental degradation at the acoustic model level. Second, a pre-processing technique, CDCN, is exploited to compensate for the acoustic distortion at the signal level. Further improvement can be obtained by combining both schemes. In addition to recognition accuracy for speech recognition, this paper studies and examines how closely speech recognition accuracy is related the overall speech-to-speech recognition. When we apply the proposed schemes to an English-to-Chinese translation task, the word error rate for our speech recognition subsystem is substantially reduced by 28% relative, to 13.2% from 18.9% for test data of 15dB SNR. The corresponding BLEU score improves to 0.478 from 0.43 for the overall speech-to-speech translation. Similar improvements are also observed for a lower SNR condition. Full Paper

Two approaches are proposed for the design of tied-mixture hidden Markov models (TMHMM). One approach improves parameter sharing via partial tying of TMHMM states. To facilitate tying at the substate level, the state emission probabilities are constructed in two stages or, equivalently, are viewed as a mixture of mixtures of Gaussians. This paradigm allows, and is complemented with, an optimization technique to seek the best complexity-accuracy tradeoff solution, which jointly exploits Gaussian density sharing and substate tying. Another approach to enhance model training is combined training and reduction of model parameters. The procedure starts by training a system with a large universal codebook of Gaussian densities. It then iteratively reduces the size of both the codebook and the mixing coefficient matrix, followed by parameter re-training. The additional cost in design complexity is modest. Experimental results on the ISOLET database and its E-set subset show that substate tying reduces the classification error rate by over 15%, compared to standard Gaussian sharing and whole-state tying. TMHMM design with combined training and reduction of parameters reduces the classification error rate by over 20% compared to conventional TMHMM design. When the two proposed approaches were integrated, 25% error rate reduction over TMHMM with whole-state tying was achieved.

In this paper, an approach for robust singing signal detection in speech/music discrimination is proposed and applied to applications of audio indexing. Conventional approaches in speech/music discrimination can provide reasonable performance with regular music signals but often perform poorly with singing segments. This is due mainly to the fact that speech and singing signals are extremely close and traditional features used in speech recognition do not provide a reliable cue for speech and singing signal discrimination. In order to improve the robustness of speech/music discrimination, a new set of features derived from the harmonic coefficient and its 4 Hz modulation values are developed in this paper, and these new features provide additional and reliable cues to separate speech from singing. In addition, a rule-based post-filtering scheme is also described which leads to further improvements in speech/music discrimination. Source-independent audio indexing experiments on the PBS Skills database indicate that the proposed approach can greatly reduce the classification error rate on singing segments in the audio stream. Comparing with existing approaches, the overall segmentation error rate is reduced by more than 30%, averaged over all shows in the database.

Perceptual harmonic cepstral coefficients (PHCC) are proposed as features to extract from speech for recognition in noisy environments. A weighting function, which depends on the prominence of the harmonic structure, is applied to the power spectrum to ensure accurate representation of the voiced speech spectral envelope. The harmonics' weighted power spectrum undergoes mel-scaled band-pass filtering, and the log-energy of the filters' output is discrete cosine transformed to produce cepstral coefficients. Lower spectral clipping is applied to the power spectrum, followed by within-filter root-power amplitude compression to reduce amplitude variation without compromise of the gain invariance properties. Experiments show significant recognition gains of PHCC over MFCC, with 23% and 36% error rate reduction for the Mandarin digit database in white and babble noise environments.

This paper presents a significant modification of our previously proposed speech recognizer's front-end based on perceptual harmonic cepstral coefficients. The spectrum is split into two frequency bands, which correspond to the harmonic and non-harmonic components. A weighting function, which depends both on the voiced/unvoiced/ transitional classification and on the prominence of harmonic structures, is applied to the harmonic band, and ensures accurate representation of the voiced and transitional speech spectral envelope. Conventional smoothed spectrum is used in the non-harmonic band. The mixed spectrum undergoes melscaled band-pass filtering, and the log-energy of the filters' output is discrete cosine transformed to produce cepstral coefficients. Experiments with Mandarin digit and E-set databases show significant recognition gains over plain perceptual harmonic cepstral coefficients and considerable gains over standard techniques.

An approach is proposed for partial tying of states of tied-mixture hidden Markov models. To facilitate tying at the sub-state level, the state emission probabilities are constructed in two stages, or equivalently, are viewed as a "mixture of mixtures of Gaussians." This paradigm allows, and is complemented with, an optimization technique to seek the best complexity-accuracy tradeoff solution, which jointly exploits Gaussian density sharing and sub-state tying. Experimental results on the E-set show that the classification error rate is reduced by over 20% compared to standard Gaussian sharing and whole-state tying. The approach is then embedded within the recently developed procedure of combined parameter training and reduction technique. Experiments with the overall technique show that the error rate is further reduced by 8%.

A deterministic annealing algorithm for the design of tiedmixture HMM recognizers is proposed, which reduces the training sensitivity to parameter initialization, automatically smoothes the classification error cost function to allow gradientbased optimization, and seeks better solutions than known techniques. The new approach introduces randomness into the classification rule during the training process, and minimizes the expected error rate while controlling the level of randomness via a constraint on the Shannon entropy. As the entropy constraint is gradually relaxed, the effective cost function converges to the classification error rate and the system becomes a hard (nonrandom) recognizer. Experiments show that the proposed method outperforms design by maximum likelihood reestimation and by generalized probabilistic descent.

Perceptual harmonic cepstral coefficients (PHCC) are proposed as features to extract for speech recognition. Pitch estimation and classification into voiced, unvoiced, and transitional speech are performed by a spectro-temporal auto-correlation technique. A peak picking algorithm is then employed to precisely locate pitch harmonics. A weighting function, which depends on the classification and the pitch harmonics, is applied to the power spectrum and ensures accurate representation of the voiced speech spectral envelope. The harmonics weighted power spectrum undergoes mel-scaled band-pass filtering, and the logenergy of the filters' output is discrete cosine transformed to produce cepstral coefficients. For perceptual considerations, within-filter cubic-root amplitude compression is applied to reduce amplitude variation without compromise of the gain invariance properties. Experiments show substantial recognition gains of PHCC over MFCC, with 48% and 15% error rate reduction for the Mandarin digit database and E-set, respectively.