Base station coordination is considered as a promising technique to mitigate inter-cell interference and improve the cell-edge performance in cellular orthogonal frequency division multiple-access (OFDMA) networks. The problem to design an efficient radio resource allocation scheme for coordinated cellular OFDMA networks incorporating base station coordination has been only partially investigated. In this contribution, a novel radio resource allocation algorithm with universal frequency reuse is proposed to support base station coordinated transmission. Firstly, with the assumption of global coordination between all base station sectors in the network, a coordinated subchannel assignment algorithm is proposed. Then, by dividing the entire network into a number of disjoint coordinated clusters of base station sectors, a reduced-feedback algorithm for subchannel assignment is proposed for practical use. The utility function based on the user average throughput is used to balance the efficiency and fairness of wireless resource allocation. System level simulation results demonstrate that the reduced-feedback subchannel assignment algorithm significantly improves the cell-edge average throughput and the fairness index of users in the network, with acceptable degradation of cell-average performance.

A novel fast calculation algorithm (FCA) for calculating the decision metric of the multiple-symbol differential detection (MSDD) considering the autocorrelation of a received sequence is proposed. In correspondence to the star quadrature amplitude modulation (QAM), the M algorithm is adopted to MSDD over Rayleigh fading channels, in order to reduce the number of search paths. The computational complexity of the decision metric can be greatly reduced by the proposed FCA and the M algorithm. Through computer simulations, it is confirmed that the symbol error rate (SER) performance of the MSDD considering autocorrelation is closer to that of the ideal coherent detection as the length of an observed sequence becomes larger over Rayleigh fading channels.

To improve Last-Level Cache (LLC) management, numerous approaches have been proposed requiring additional hardware budget and increased overhead. A number of these approaches even change the organization of the existing cache design. In this letter, we propose Adaptive Insertion and Promotion (AIP) policies based on Least Recently Used (LRU) replacement. AIP dynamically inserts a missed line in the middle of the cache list and promotes a reused line several steps left, realizing the combination of LRU and LFU policies deliberately under a single unified scheme. As a result, it benefits workloads with high locality as well as with many frequently reused lines. Most importantly, AIP requires no additional hardware other than a typical LRU list, thus it can be easily integrated into the existing hardware with minimal changes. Other issues around LLC such as scans, thrashing and dead lines are all explored in our study. Experimental results on the gem5 simulator with SPEC CUP2006 benchmarks indicate that AIP outperforms LRU replacement policy by an average of 5.8% on the misses per thousand instructions metric.

Hovering unmanned aerial vehicles (UAVs) with mutual sense and communication capability form a new-fashioned airborne ad hoc network. Traditional routing protocols assume that there has already existed an end-to-end path before the message forwarding starts which, however, is not always available in the airborne network featuring randomly violent topological changes. Local heuristic information without complex computational cost should be considered to help route in this specific delay tolerant network (DTN). In this letter, we take Crowd Density (CD) and Relative Velocity Direction (RVD) as the fuzzy inputs, and use approximate reasoning to calculate priority of alternative candidates. Finally, the proposed mechanism is compared with some existing protocols.

A whitelisting approach is a promising solution to prevent unwanted processes (e.g., malware) getting executed. However, previous solutions suffer from limitations in that: 1) Most methods place the whitelist information in the kernel space, which could be tempered by attackers; 2) Most methods cannot prevent the execution of kernel processes. In this paper, we present VAW, a novel application whitelisting system by using the virtualization technology. Our system is able to block the execution of unauthorized user and kernel processes. Compared with the previous solutions, our approach can achieve stronger security guarantees. The experiments show that VAW can deny the execution of unwanted processes effectively with a little performance overhead.

Soccer player tracking and labeling suffer from the similar appearance of the players in the same team, especially in long-shot scenes where the faces and the numbers of the players are too blurry to identify. In this paper, we propose an efficient multi-player tracking system. The tracking system takes the detection responses of a human detector as inputs. To realize real-time player detection, we generate a spatial proposal to minimize the scanning scope of the detector. The tracking system utilizes the discriminative appearance models trained using the online Boosting method to reduce data-association ambiguity caused by the appearance similarity of the players. We also propose to build an online learned player recognition model which can be embedded in the tracking system to approach online player recognition and labeling in tracking applications for long-shot scenes by two stages. At the first stage, to build the model, we utilize the fast k-means clustering method instead of classic k-means clustering to build and update a visual word vocabulary in an efficient online manner, using the informative descriptors extracted from the training samples drawn at each time step of multi-player tracking. The first stage finishes when the vocabulary is ready. At the second stage, given the obtained visual word vocabulary, an incremental vector quantization strategy is used to recognize and label each tracked player. We also perform importance recognition validation to avoid mistakenly recognizing an outlier, namely, people we do not need to recognize, as a player. Both quantitative and qualitative experimental results on the long-shot video clips of a real soccer game video demonstrate that, the proposed player recognition model performs much better than some state-of-the-art online learned models, and our tracking system also performs quite effectively even under very complicated situations.

We propose a novel adaptive multiple-symbol differential detection (MSDD) scheme that has excellent performance over frequency selective fading (FSF) channels. The adaptive MSDD scheme consists of an adaptive noncoherent least mean square channel estimator that can accomplish channel estimation without any decision delay and the MSDD. The M-algorithm is introduced into this detection scheme to reduce the complication of computation due to increasing observed sequence length in the MSDD. Because of the application of the adaptive channel estimator and the M-algorithm, this adaptive MSDD make it possible that channel estimation is accomplished for every symbol along M surviving paths without any decision delay. And the SER performance of this adaptive MSDD is not affected by phase fluctuation introduced by a channel because the MSDD and the noncoherent channel estimator are applied. The adaptive MSDD scheme is applied to typical constellation of 16APSK, the (4,12) QAM and the star QAM. The excellent tracking performance of this adaptive MSDD scheme over FSF channels is confirmed by computer simulations.

Relation extraction is one of the key basic tasks in natural language processing in which distant supervision is widely used for obtaining large-scale labeled data without expensive labor cost. However, the automatically generated data contains massive noise because of the wrong labeling problem in distant supervision. To address this problem, the existing research work mainly focuses on removing sentence-level noise with various sentence selection strategies, which however could be incompetent for disposing word-level noise. In this paper, we propose a novel neural framework considering both intra-sentence and inter-sentence relevance to deal with word-level and sentence-level noise from distant supervision, which is denoted as Sentence-Related Gated Piecewise Convolutional Neural Networks (SR-GPCNN). Specifically, 1) a gate mechanism with multi-head self-attention is adopted to reduce word-level noise inside sentences; 2) a soft-label strategy is utilized to alleviate wrong-labeling propagation problem; and 3) a sentence-related selection model is designed to filter sentence-level noise further. The extensive experimental results on NYT dataset demonstrate that our approach filters word-level and sentence-level noise effectively, thus significantly outperforms all the baseline models in terms of both AUC and top-n precision metrics.

Heap buffer overflow has been extensively studied for many years, but it remains a severe threat to software security. Previous solutions suffer from limitations in that: 1) Some methods need to modify the target programs; 2) Most methods could impose considerable performance overhead. In this paper, we present iCruiser, an efficient heap buffer overflow monitoring system that uses the multi-core technology. Our system is compatible with existing programs, and it can detect the heap buffer overflows concurrently. Compared with the latest heap protection systems, our approach can achieves stronger security guarantees. Experiments show that iCruiser can detect heap buffer overflow attacks effectively with a little performance overhead.

As more and more software vulnerabilities are exposed, shellcode has become very popular in recent years. It is widely used by attackers to exploit vulnerabilities and then hijack program's execution. Previous solutions suffer from limitations in that: 1) Some methods based on static analysis may fail to detect the shellcode using obfuscation techniques. 2) Other methods based on dynamic analysis could impose considerable performance overhead. In this paper, we propose Lemo, an efficient shellcode detection system. Our system is compatible with commodity hardware and operating systems, which enables deployment. To improve the performance of our system, we make use of the multi-core technology. The experiments show that our system can detect shellcode efficiently.

With the growing complexity and scale of software, detecting and repairing errant behaviors at an early stage are critical to reduce the cost of software development. In the practice of fault localization, a typical process usually includes three steps: execution of input domain test cases, construction of model domain test vectors and suspiciousness evaluation. The effectiveness of model domain test vectors is significant for locating the faulty code. However, test vectors with failing labels usually account for a small portion, which inevitably degrades the effectiveness of fault localization. In this paper, we propose a data augmentation method PVaug by using fault propagation context and variational autoencoder (VAE). Our empirical results on 14 programs illustrate that PVaug has promoted the effectiveness of fault localization.