The likelihood-ratio based score level fusion (LR fusion) scheme is known as one of the most promising multibiometric fusion schemes. This scheme verifies a user by computing a log-likelihood ratio (LLR) for each modality, and comparing the total LLR to a threshold. It can happen in practice that genuine LLRs tend to be less than 0 for some modalities (e.g., the user is a “goat”, who is inherently difficult to recognize, for some modalities; the user suffers from temporary physical conditions such as injuries and illness). The LR fusion scheme can handle such cases by allowing the user to select a subset of modalities at the authentication phase and setting LLRs corresponding to missing query samples to 0. A recent study, however, proposed a modality selection attack, in which an impostor inputs only query samples whose LLRs are greater than 0 (i.e., takes an optimal strategy), and proved that this attack degrades the overall accuracy even if the genuine user also takes this optimal strategy. In this paper, we investigate the impact of the modality selection attack in more details. Specifically, we investigate whether the overall accuracy is improved by eliminating “goat” templates, whose LLRs tend to be less than 0 for genuine users, from the database (i.e., restricting modality selection). As an overall performance measure, we use the KL (Kullback-Leibler) divergence between a genuine score distribution and an impostor's one. We first prove the modality restriction hardly increases the KL divergence when a user can select a subset of modalities (i.e., selective LR fusion). We second prove that the modality restriction increases the KL divergence when a user needs to input all biometric samples (i.e., non-selective LR fusion). We conduct experiments using three real datasets (NIST BSSR1 Set1, Biosecure DS2, and CASIA-Iris-Thousand), and discuss directions of multibiometric fusion systems.

Distributed channel selection schemes are proposed in this paper to mitigate the flow-in-the-middle (FIM) starvation in dense wireless local area networks (WLANs). The FIM starvation occurs when the middle transmitter is within the carrier sense range of two exterior transmitters, while the two exterior transmitters are not within the carrier sense range of each other. Since an exterior transmitter sends a frame regardless of the other, the middle transmitter has a high probability of detecting the channel being occupied. Under heavy traffic conditions, the middle transmitter suffers from extremely low transmission opportunities, i.e., throughput starvation. The basic idea of the proposed schemes is to let each access point (AP) select the channel which has less three-node-chain topologies within its two-hop neighborhood. The proposed schemes are formulated in strategic form games. Payoff functions are designed so that they are proved to be potential games. Therefore, the convergence is guaranteed when the proposed schemes are conducted in a distributed manner by using unilateral improvement dynamics. Moreover, we conduct evaluations through graph-based simulations and the ns-3 simulator. Simulations confirm that the FIM starvation has been mitigated since the number of three-node-chain topologies has been significantly reduced. The 5th percentile throughput has been improved.

This letter proposes a relay selection scheme in order to improve a performance in the wireless cooperative communication system. The cooperative communication uses the relays in order to obtain a improved performance. The relay selection scheme has a great influence on the performance of the wireless cooperative communication. Because the diversity gain is affected by the superposition of the channels, a superposition of the channels is important in the wireless cooperative communication. The constructive superposition of the channels can improve the performance of the wireless cooperative communication. Because the conventional schemes do not consider the superposition of the channels, the conventional schemes are not suitable for the cooperative communication using the multiple relays. The new scheme considers the superposition of channels and selects the relays that can achieve the constructive superposition. Therefore, the new scheme can provide the improved performance by using the phase information between channels. The simulation results show that the bit error performance of the proposed scheme is better than the conventional schemes.

Multi-label classification is an appealing and challenging supervised learning problem, where multiple labels, rather than a single label, are associated with an unseen test instance. To remove possible noises in labels and features of high-dimensionality, multi-label dimension reduction has attracted more and more attentions in recent years. The existing methods usually suffer from several problems, such as ignoring label outliers and label correlations. In addition, most of them emphasize on conducting dimension reduction in an unsupervised or supervised way, therefore, unable to utilize the label information or a large amount of unlabeled data to improve the performance. In order to cope with these problems, we propose a novel method termed Robust sEmi-supervised multi-lAbel DimEnsion Reduction, shortly READER. From the viewpoint of empirical risk minimization, READER selects most discriminative features for all the labels in a semi-supervised way. Specifically, the ℓ2,1-norm induced loss function and regularization term make READER robust to the outliers in the data points. READER finds a feature subspace so as to keep originally neighbor instances close and embeds labels into a low-dimensional latent space nonlinearly. To optimize the objective function, an efficient algorithm is developed with convergence property. Extensive empirical studies on real-world datasets demonstrate the superior performance of the proposed method.

The rapid development of information techniques has lead to more and more high-dimensional datasets, making classification more difficult. However, not all of the features are useful for classification, and some of these features may even cause low classification accuracy. Feature selection is a useful technique, which aims to reduce the dimensionality of datasets, for solving classification problems. In this paper, we propose a modified bat algorithm (BA) for feature selection, called MBAFS, using a SVM. Some mechanisms are designed for avoiding the premature convergence. On the one hand, in order to maintain the diversity of bats, they are guided by the combination of a random bat and the global best bat. On the other hand, to enhance the ability of escaping from local optimization, MBAFS employs one mutation mechanism while the algorithm trapped into local optima. Furthermore, the performance of MBAFS was tested on twelve benchmark datasets, and was compared with other BA based algorithms and some well-known BPSO based algorithms. Experimental results indicated that the proposed algorithm outperforms than other methods. Also, the comparison details showed that MBAFS is competitive in terms of computational time.

In this paper, we clarify the issues in a metropolitan environment involving overlying frequency bands with various bandwidths and propose a cell selection scheme that improves the communications quality based on user and network characteristics. Different frequency bands with various signal bandwidths will be overlaid on each other in forthcoming fifth-generation (5G) radio access networks. At the same time, services, applications or features of sets of user equipment (UEs) will become more diversified and the requirements for the quality of communications will become more varied. Moreover, in real environments, roads and buildings have irregular constructions. Especially in an urban or metropolitan environment, the complex architecture present in a metropolis directly affects radio propagation. Under these conditions, the communications quality is degraded because cell radio resources are depleted due to many UE connections and the mismatch between service requirements and cell capabilities. The proposed scheme prevents this degradation in communications quality. The effectiveness of the proposed scheme is evaluated in an ideal regular deployment and in a non-regular metropolitan environment based on computer simulations. Simulation results show that the average of the time for the proposed scheme from the start of transmission to the completion of reception at the UE is improved by approximately 40% compared to an existing cell selection scheme that is based on the Maximum Signal-to-Interference plus Noise power Ratio (SINR).

Anomaly detection is one approach in intrusion detection systems (IDSs) which aims at capturing any deviation from the profiles of normal network activities. However, it suffers from high false alarm rate since it has impediment to distinguish the boundaries between normal and attack profiles. In this paper, we propose an effective anomaly detection approach by hybridizing three techniques, i.e. particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) for feature selection and ensemble of four tree-based classifiers, i.e. random forest (RF), naive bayes tree (NBT), logistic model trees (LMT), and reduces error pruning tree (REPT) for classification. Proposed approach is implemented on NSL-KDD dataset and from the experimental result, it significantly outperforms the existing methods in terms of accuracy and false alarm rate.

This letter proposes a heuristic algorithm to select check variables, which are points of comparison for error detection, for soft-error tolerant datapaths. Our soft-error tolerance scheme is based on check-and-retry computation and an efficient resource management named speculative resource sharing (SRS). Starting with the smallest set of check variables, the proposed algorithm repeats to add new check variable one by one incrementally and find the minimum latency solution among the series of generated solutions. During the process, each new check variable is selected so that the opportunity of SRS is enlarged. Experimental results show that improvements in latency are achieved compared with the choice of the smallest set of check variables.

This paper investigates the performance of an overloaded multiple-input multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) system with and without antenna selection. In the overloaded MIMO-OFDM system, even if only a small amount of feedback is available, performance can be improved by selecting the transmit antennas. Thus, this paper compares the performance of an overloaded MIMO system with and without antenna selection under different code rates. It is shown that the performance of the MIMO-OFDM system for six signal streams with QPSK modulation is about 2.0dB better than that for three signal streams with 16QAM modulation while it is about 5.0dB better than that of the MIMO-OFDM system for two signal streams with 64QAM modulation at a bit error rate (BER) of 10-3. However, it is also shown that the performance of the overloaded MIMO system is worse if the code rate of the repetition code increases.

In this paper, we investigate a user and antenna joint selection problem in multi-user large-scale MIMO downlink networks, where a BS with N transmit antennas serves K users, and N is much larger than K. The BS activates only S(S≤N) antennas for data transmission to reduce hardware cost and computation complexity, and selects the set of users to which data is to be transmitted by maximizing the sum-rate. The optimal user and antenna joint selection scheme based on exhaustive search causes considerable computation complexity. Thus, we propose a new joint selection algorithm with low complexity and analyze the performance of the proposed scheme in terms of sum-rate and complexity. When S=7, N=10, K=5, and SNR=10dB, the sum-rate of the proposed scheme is 5.1% lower than that of the optimal scheme, while the computation complexity of the proposed scheme is reduced by 99.0% compared to that of the optimal scheme.

Microblog, especially twitter, has become an integral part of our daily life for searching latest news and events information. Due to the short length characteristics of tweets and frequent use of unconventional abbreviations, content-relevance based search cannot satisfy user's information need. Recent research has shown that considering temporal and contextual aspects in this regard has improved the retrieval performance significantly. In this paper, we focus on microblog retrieval, emphasizing the alleviation of the vocabulary mismatch, and the leverage of the temporal (e.g., recency and burst nature) and contextual characteristics of tweets. To address the temporal and contextual aspect of tweets, we propose new features based on query-tweet time, word embedding, and query-tweet sentiment correlation. We also introduce some popularity features to estimate the importance of a tweet. A three-stage query expansion technique is applied to improve the relevancy of tweets. Moreover, to determine the temporal and sentiment sensitivity of a query, we introduce query type determination techniques. After supervised feature selection, we apply random forest as a feature ranking method to estimate the importance of selected features. Then, we make use of ensemble of learning to rank (L2R) framework to estimate the relevance of query-tweet pair. We conducted experiments on TREC Microblog 2011 and 2012 test collections over the TREC Tweets2011 corpus. Experimental results demonstrate the effectiveness of our method over the baseline and known related works in terms of precision at 30 (P@30), mean average precision (MAP), normalized discounted cumulative gain at 30 (NDCG@30), and R-precision (R-Prec) metrics.

Feature extractor plays an important role in visual tracking, but most state-of-the-art methods employ the same feature representation in all scenes. Taking into account the diverseness, a tracker should choose different features according to the videos. In this work, we propose a novel feature adaptive correlation tracker, which decomposes the tracking task into translation and scale estimation. According to the luminance of the target, our approach automatically selects either hierarchical convolutional features or histogram of oriented gradient features in translation for varied scenarios. Furthermore, we employ a discriminative correlation filter to handle scale variations. Extensive experiments are performed on a large-scale benchmark challenging dataset. And the results show that the proposed algorithm outperforms state-of-the-art trackers in accuracy and robustness.

In this paper, we introduce a self-constructive Normalized Gaussian Network (NGnet) for online learning tasks. In online tasks, data samples are received sequentially, and domain knowledge is often limited. Then, we need to employ learning methods to the NGnet that possess robust performance and dynamically select an accurate model size. We revise a previously proposed localized forgetting approach for the NGnet and adapt some unit manipulation mechanisms to it for dynamic model selection. The mechanisms are improved for more robustness in negative interference prone environments, and a new merge manipulation is considered to deal with model redundancies. The effectiveness of the proposed method is compared with the previous localized forgetting approach and an established learning method for the NGnet. Several experiments are conducted for a function approximation and chaotic time series forecasting task. The proposed approach possesses robust and favorable performance in different learning situations over all testbeds.

Protection of the licensed user (LU) and utilization of the spectrum are the most important goals in cognitive radio networks. To achieve the first goal, a cognitive user (CU) is required to sense for a longer time period, but this adversely affects the second goal, i.e., throughput or utilization of the network, because of the reduced time left for transmission in a time slot. This tradeoff can be controlled by simultaneous sensing and data transmission for the whole frame duration. However, increasing the sensing time to the frame duration consumes more energy. We propose a new frame structure in this paper, in which transmission is done for the whole frame duration whereas sensing is performed only until the required detection probability is satisfied. This means the CU is not required to perform sensing for the whole frame duration, and thus, conserves some energy by sensing for a smaller duration. With the proposed frame structure, throughput of all the CUs is estimated for the frame and, based on the estimated throughput and consumed energy in sensing and transmission, the energy efficient pair of CUs (transmitter and receiver) that maximizes system throughput by consuming less energy, is selected for a time slot. The selected CUs transmits data for the whole time slot, whereas sensing is performed only for certain duration. The performance improvement of the proposed scheme is demonstrated through simulations by comparing it with existing schemes.

Self-paced e-learning provides much more freedom in time and locale than traditional education as well as diversity of learning contents and learning media and tools. However, its limitations must not be ignored. Lack of information on learners' states is a serious issue that can lead to severe problems, such as low learning efficiency, motivation loss, and even dropping out of e-learning. We have designed a novel e-learning support system that can visually observe learners' non-verbal behaviors and estimate their learning states and that can be easily integrated into practical e-learning environments. Three pairs of internal states closely related to learning performance, concentration-distraction, difficulty-ease, and interest-boredom, were selected as targets of recognition. In addition, we investigated the practical problem of estimating the learning states of a new learner whose characteristics are not known in advance. Experimental results show the potential of our system.

Process variation has become prominent in the advanced CMOS technology, making the timing of fabricated circuits more uncertain. In this paper, we propose a Layout-Aware Path Selection (LAPS) technique to accurately estimate the circuit timing variation from a small set of paths. Three features of paths are considered during the path selection. Experiments conducted on benchmark circuits with process variation simulated with VARIUS show that, by selecting only hundreds of paths, the fitting errors of timing distribution are kept below 5.3% when both spatial correlated and spatial uncorrelated process variations exist.

This paper presents human-centered video feature selection via mRMR-SCMMCCA (minimum Redundancy and Maximum Relevance-Specific Correlation Maximization Multiset Canonical Correlation Analysis) algorithm for preference extraction. The proposed method derives SCMMCCA, which simultaneously maximizes two kinds of correlations, correlation between video features and users' viewing behavior features and correlation between video features and their corresponding rating scores. By monitoring the derived correlations, the selection of the optimal video features that represent users' individual preference becomes feasible.

In ad hoc networks, broadcast forwarding protocols called OR (opportunistic routing) have been proposed to gain path diversity for higher packet delivery rates and shorter end-to-end delays. In general backoff-based OR protocols, each receiver autonomously makes a forwarding decision by using certain metrics to determine if a random backoff time is to be applied. However, each forwarder candidate must wait for the expiration of the backoff timer before forwarding a packet. Moreover, they cannot gain path diversity if the forwarding path includes local sparse areas, and this degrades performance as it strongly depends on the terminal density. In this paper, we propose a novel OR protocol called PRIOR (prioritized forwarding for opportunistic routing). In PRIOR, a terminal, called a prioritized forwarder and which forwards packets without using a backoff time, is selected from among the neighbours. In addition, PRIOR uses lightweight hop-by-hop retransmission control to mitigate the effect of terminal density. Moreover, we introduce an enhancement to PRIOR to reduce unnecessary forwarding by using an explicit acknowledgement. We evaluate PRIOR in comparison with conventional protocols in computer simulations.

Ordinal classification is a class of special tasks in machine learning and pattern recognition. As to ordinal classification, there is an ordinal structure among different decision values. The monotonicity constraint between features and decision should be taken into account as the fundamental assumption. However, in real-world applications, this assumption may be not true. Only some candidate features, instead of all, are monotonic with decision. So the existing feature selection algorithms which are designed for nominal classification or monotonic classification are not suitable for ordinal classification. In this paper, we propose a feature selection algorithm for ordinal classification based on considering the non-monotonic and monotonic features separately. We first introduce an assumption of hybrid monotonic classification consistency and define a feature evaluation function to calculate the relevance between the features and decision for ordinal classification. Then, we combine the reported measure and genetic algorithm (GA) to search the optimal feature subset. A collection of numerical experiments are implemented to show that the proposed approach can effectively reduce the feature size and improve the classification performance.

Networks-on-Chip (or NoCs, for short) play important roles in modern and future multi-core processors as they are highly related to both performance and power consumption of the entire chip. Up to date, many optimization techniques have been developed to improve NoC's bandwidth, latency and power consumption. But a clear answer to how energy efficiency is affected with these optimization techniques is yet to be found since each of these optimization techniques comes with its own benefits and overheads while there are also too many of them. Thus, here comes the problem of when and how such optimization techniques should be applied. In order to solve this problem, we build a runtime framework to throttle these optimization techniques based on concise performance and energy models. With the help of this framework, we can successfully establish adaptive selections over multiple optimization techniques to further improve performance or energy efficiency of the network at runtime.