The traditional tests are planned and designed at the early stages, but it is possible to execute test cases after implementing source code. Since there is a time difference between design stage and testing stage, by the time a software design error is found it will be too late. To solve this problem, this paper suggests a virtual pre-testing process. While the virtual pre-testing process can find software and testing errors before the developing stage, it can automatically generate and execute test cases with modeling and simulation (M&S) in a virtual environment. The first part of this method is to create test cases with state transition tree based on state diagram, which include state, transition, instruction pair, and all path coverage. The second part is to model and simulate a virtual target, which then pre-test the target with test cases. In other words, these generated test cases are automatically transformed into the event list. This simultaneously executes test cases to the simulated target within a virtual environment. As a result, it is possible to find the design and test error at the early stages of the development cycle and in turn can reduce development time and cost as much as possible.

Automatically recognizing pain and estimating pain intensity is an emerging research area that has promising applications in the medical and healthcare field, and this task possesses a crucial role in the diagnosis and treatment of patients who have limited ability to communicate verbally and remains a challenge in pattern recognition. Recently, deep learning has achieved impressive results in many domains. However, deep architectures require a significant amount of labeled data for training, and they may fail to outperform conventional handcrafted features due to insufficient data, which is also the problem faced by pain detection. Furthermore, the latest studies show that handcrafted features may provide complementary information to deep-learned features; hence, combining these features may result in improved performance. Motived by the above considerations, in this paper, we propose an innovative method based on the combination of deep spatiotemporal and handcrafted features for pain intensity estimation. We use C3D, a deep 3-dimensional convolutional network that takes a continuous sequence of video frames as input, to extract spatiotemporal facial features. C3D models the appearance and motion of videos simultaneously. For handcrafted features, we propose extracting the geometric information by computing the distance between normalized facial landmarks per frame and the ones of the mean face shape, and we extract the appearance information using the histogram of oriented gradients (HOG) features around normalized facial landmarks per frame. Two levels of SVRs are trained using spatiotemporal, geometric and appearance features to obtain estimation results. We tested our proposed method on the UNBC-McMaster shoulder pain expression archive database and obtained experimental results that outperform the current state-of-the-art.

This paper proposes a novel domain adaptation method that can utilize out-of-domain text resources and partially domain matched text resources in language modeling. A major problem in domain adaptation is that it is hard to obtain adequate adaptation effects from out-of-domain text resources. To tackle the problem, our idea is to carry out model merger in a latent variable space created from latent words language models (LWLMs). The latent variables in the LWLMs are represented as specific words selected from the observed word space, so LWLMs can share a common latent variable space. It enables us to perform flexible mixture modeling with consideration of the latent variable space. This paper presents two types of mixture modeling, i.e., LWLM mixture models and LWLM cross-mixture models. The LWLM mixture models can perform a latent word space mixture modeling to mitigate domain mismatch problem. Furthermore, in the LWLM cross-mixture models, LMs which individually constructed from partially matched text resources are split into two element models, each of which can be subjected to mixture modeling. For the approaches, this paper also describes methods to optimize mixture weights using a validation data set. Experiments show that the mixture in latent word space can achieve performance improvements for both target domain and out-of-domain compared with that in observed word space.

Automatic speech recognition (ASR) and keyword search (KWS) have more and more found their way into our everyday lives, and their successes could boil down lots of factors. In these factors, large scale of speech data used for acoustic modeling is the key factor. However, it is difficult and time-consuming to acquire large scale of transcribed speech data for some languages, especially for low-resource languages. Thus, at low-resource condition, it becomes important with which transcribed data for acoustic modeling for improving the performance of ASR and KWS. In view of using acoustic data for acoustic modeling, there are two different ways. One is using the target language data, and another is using large scale of other source languages data for cross-lingual transfer. In this paper, we propose some approaches for efficient selecting acoustic data for acoustic modeling. For target language data, a submodular based unsupervised data selection approach is proposed. The submodular based unsupervised data selection could select more informative and representative utterances for manual transcription for acoustic modeling. For other source languages data, the high misclassified as target language based submodular multilingual data selection approach and knowledge based group multilingual data selection approach are proposed. When using selected multilingual data for multilingual deep neural network training for cross-lingual transfer, it could improve the performance of ASR and KWS of target language. When comparing our proposed multilingual data selection approach with language identification based multilingual data selection approach, our proposed approach also obtains better effect. In this paper, we also analyze and compare the language factor and the acoustic factor influence on the performance of ASR and KWS. The influence of different scale of target language data on the performance of ASR and KWS at mono-lingual condition and cross-lingual condition are also compared and analyzed, and some significant conclusions can be concluded.

Bandwidth reservation is an important way to guarantee deterministic end-to-end service quality. However, with the traditional bandwidth reservation mechanism, the allocated bandwidth at each link is by default the same without considering the available resource of each link, which may lead to unbalanced resource utilization and limit the number of user connections that network can accommodate. In this paper, we propose a non-uniform bandwidth reservation method, which can further balance the resource utilization of network by optimizing the reserved bandwidth at each link according to its link load. Furthermore, to implement the proposed method, we devise a flexible and automatic bandwidth reservation mechanism based on meter table of Openflow. Through simulations, it is showed that our method can achieve better load balancing performance and make network accommodate more user connections comparing with the traditional methods in most application scenarios.

Synthetic aperture radar (SAR) image classification is a popular yet challenging research topic in the field of SAR image interpretation. This paper presents a new classification method based on extreme learning machine (ELM) and the superpixel-guided composite kernels (SGCK). By introducing the generalized likelihood ratio (GLR) similarity, a modified simple linear iterative clustering (SLIC) algorithm is firstly developed to generate superpixel for SAR image. Instead of using a fixed-size region, the shape-adaptive superpixel is used to exploit the spatial information, which is effective to classify the pixels in the detailed and near-edge regions. Following the framework of composite kernels, the SGCK is constructed base on the spatial information and backscatter intensity information. Finally, the SGCK is incorporated an ELM classifier. Experimental results on both simulated SAR image and real SAR image demonstrate that the proposed framework is superior to some traditional classification methods.

Distributed Reflective Denial of Services (DRDoS) attacks have gained huge popularity and become a major factor in a number of massive cyber-attacks. Usually, the attackers launch this kind of attack with small volume of requests to generate a large volume of attack traffic aiming at the victim by using IP spoofing from legitimate hosts. There have been several approaches, such as static threshold based approach and confirmation-based approach, focusing on DRDoS attack detection at victim's side. However, these approaches have significant disadvantages: (1) they are only passive defences after the attack and (2) it is hard to trace back the attackers. To address this problem, considerable attention has been paid to the study of detecting DRDoS attack at source side. Because the existing proposals following this direction are supposed to be ineffective to deal with small volume of attack traffic, there is still a room for improvement. In this paper, we propose a novel method to detect DRDoS attack request traffic on SDN(Software Defined Network)-enabled gateways in the source side of attack traffic. Our method adjusts the sampling rate and provides a traffic-aware adaptive threshold along with the margin based on analysing observed traffic behind gateways. Experimental results show that the proposed method is a promising solution to detect DRDoS attack request in the source side.

Regarding IEEE 802.11 wireless local area networks (WLANs), many researchers are focusing on signal-to-noise ratio (SNR)-based rate adaptation schemes, because these schemes have the advantage of accurately selecting transmission rates that suit the channel. However, even SNR-based rate adaptation schemes work poorly in a rapidly varying channel environment. If a transmitter cannot receive accurate rate information due to fast channel fading, it encounters continuous channel errors, because the cycle of rate adaptation and rate information feedback breaks. A well-designed request-to-send/clear-to-send (RTS/CTS) frame exchange policy that accurately reflects the network situation is an indispensable element for enhancing the performance of SNR-based rate adaptation schemes. In this paper, a novel rate adaptation scheme called adaptive RTS/CTS-exchange and rate prediction (ARRP) is proposed, which adapts the transmission rate efficiently for variable network situations, including rapidly varying channels. ARRP selects a transmission rate by predicting the SNR of the data frame to transmit when the channel condition becomes worse. Accordingly, ARRP prevents continuous channel errors through a pre-emptive transmission rate adjustment. Moreover, ARRP utilizes an efficient RTS/CTS frame exchange algorithm that considers the number of contending stations and the current transmission rate of data frames, which drastically reduces both frame collisions and RTS/CTS-exchange overhead simultaneously. Simulation results show that ARRP achieves better performance than other rate adaptation schemes.

The 1-bit band-pass delta-sigma modulator (BP-DSM) achieves high resolution if it uses an oversampling technique. This method can generate concurrent dual-band RF signals from a digitally modulated signal using a 1-bit digital pulse train. It was previously reported that the adjacent channel leakage ratio (ACLR) deteriorates owing to the asymmetrical waveform created by the pulse transition mismatch error of the rising and falling waveforms in the time domain and that the ACLR can be improved by distortion compensation. However, the reported distortion compensation method can only be performed for single-band transmission, and it fails to support multi-band transmission because the asymmetrical waveform compensated signal extends over a wide frequency range and is itself a harmful distortion outside the target band. Unfortunately, the increase of out-of-band power causes the BP-DSM unstable. We therefore propose a distortion compensator for a concurrent dual-band 1-bit BP-DSM that consists of a noise transfer function with a quasi-elliptic filter that can control the out-of-band gain frequency response against out-of-band oscillation. We demonstrate that dual-band LTE signals, each with 40MHz (2×20MHz) bandwidth, at 1.5 and 3.0GHz, can be compensated concurrently for spurious distortion under various combinations of rising and falling times and ACLR of up to 48dB, each with 120MHz bandwidth, including the double sided adjacent channels and next adjacent channels, is achieved.

A problem of global stabilization of a class of approximately feedback linearized systems is considered. A new system structural feature is the presence of non-trivial diagonal terms along with nonlinearity, which has not been addressed by the previous control results. The stability analysis reveals a new relationship between the time-varying rates of system parameters and system nonlinearity along with our controller. Two examples are given for illustration.

Several new memories are being studied as candidates of future DRAM that seems difficult to be scaled. However, the read signal in these new memories needs to be amplified in a single-end manner with reference signal supplied if they are aimed for being applied to the high-density main memory. This scheme, which is fortunately not necessary in DRAM's 1/2Vdd pre-charge sense amp, can become a serious bottleneck in the new memory development, because the device electrical parameters in these new memory cells are prone to large cell-to-cell variations without exception. Furthermore, the extent to which the parameter fluctuates in data “1” is generally not the same as in data “0”. In these situations, a new sensing scheme is proposed that can minimize the sensing error rate for high-density single-end emerging memories like STT-MRAM, ReRAM and PCRAM. The scheme is based on averaging multiple dummy cell pairs that are written “1” and “0” in a weighted manner according to the fluctuation unbalance between “1” and “0”. A detailed analysis shows that this scheme is effective in designing 128Mb 1T1MTJ STT-MRAM with the results that the required TMR ratio of an MTJ can be relaxed from 130% to 90% for the fluctuation of 6% sigma-to-average ratio of MTJ resistance in a 16 pair-dummy cell averaging case by using this technology when compared with the arithmetic averaging method.

This letter studies secure transmission design with finite alphabet input for cooperative jamming network under individual power constraint. By adopting the zero-force scheme, where the jamming signal is fully laid in the null space of the relay-destination channel, the problem of enhancing the achievable secrecy rate is decomposed into two independent subproblems: relay weights design and power control. We reveal that the problem of relay weights design is identical to the problem of minimizing the maximal equivalent source-eavesdropper channel gain, which can be transformed into a semi-definite programming (SDP) problem and thus is tackled using interior point method. Besides, the problem of power control is solved with the fundamental relation between mutual information and minimum mean square error (MMSE). Numerical results show that the proposed scheme achieves significant performance gains compared to the conventional Gaussian design.

The microfabrication technique based on SR (Synchrotron Radiation) direct etching process has recently been applied to construct PTFE microstructures. This paper attempts to fabricate an integrated PTFE-filled waveguide Butler matrix for short millimeter-wave by SR direct etching. First, a cruciform 3-dB directional coupler and an intersection circuit (0-dB coupler) are designed at 180 GHz. Then, a 4×4 Butler matrix with horn antennas is designed and fabricated. Finally, the measured radiation patterns of the Butler matrix are shown.

The fifth generation (5G) system using millimeter waves is considered for application to high traffic areas with a dense population of pedestrians. In such an environment, the effects of shadowing and scattering of radio waves by human bodies (HBs) on propagation channels cannot be ignored. In this paper, we clarify based on measurement the characteristics of waves scattered by the HB for typical non-line-of-sight scenarios in street canyon environments. In these scenarios, there are street intersections with pedestrians, and the angles that are formed by the transmission point, HB, and reception point are nearly equal to 90 degrees. We use a wide-band channel sounder for the 67-GHz band with a 1-GHz bandwidth and horn antennas in the measurements. The distance parameter between antennas and the HB is changed in the measurements. Moreover, the direction of the HB is changed from 0 to 360 degrees. The evaluation results show that the radar cross section (RCS) of the HB fluctuates randomly over the range of approximately 20dB. Moreover, the distribution of the RCS of the HB is a Gaussian distribution with a mean value of -9.4dBsm and the standard deviation of 4.2dBsm.

In this study, the effect of atomically flat Si(100) surface on Hf-based Metal-Oxide-Nitride-Oxide-Silicon (MONOS) structure was investigated. After the atomically flat Si(100) surface formation by annealing at 1050/60min in Ar/4%H2 ambient, HfO2(O)/HfN1.0(N)/HfO2(O) structure with thickness of 10/3/2nm, respectively, was in-situ deposited by electron cyclotron resonance (ECR) plasma sputtering. The memory window (MW) of Al/HfO2/HfN1.0/HfO2/p-Si(100) diodes was increased from 1.0V to 2.5V by flattening of Si(100) surface. The program and erase (P/E) voltage/time were set as 10V/5s and -8V/5s, respectively. Furthermore, it was found that the gate current density after the 103P/E cycles was decreased one order of magnitude by flattening of Si(100) surface in Ar/4.0%H2 ambient.

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactive applications which call for higher frame rate and lower delay for a better experience. The large amount of processing data and the complex computation in a local feature based matching system, make it difficult to achieve a high process speed and ultra-low delay matching with limited resource. Aiming at a matching system with the process speed of more than 1000 fps and with the delay of less than 1 ms/frame, this paper puts forward a local binary feature based matching system with field-programmable gate array (FPGA). Pixel selection based 4-1-4 parallel matching and intensity directed symmetry are proposed for the implementation of this system. To design a basic framework with the high process speed and ultra-low delay using limited resource, pixel selection based 4-1-4 parallel matching is proposed, which makes it possible to use only one-thread resource consumption to achieve a four-thread processing. Assumes that the orientation of the keypoint will bisect the patch best and will point to the region with high intensity, intensity directed symmetry is proposed to calculate the keypoint orientation in a hardware friendly way, which is an important part for a rotation-robust matching system. Software experiment result shows that the proposed keypoint orientation calculation method achieves almost the same performance with the state-of-art intensity centroid orientation calculation method in a matching system. Hardware experiment result shows that the designed image process core supports to process VGA (640×480) videos at a process speed of 1306 fps and with a delay of 0.8083 ms/frame.

Predicting the service performance of Internet applications is important in service selection, especially for video services. In order to design a predictor for forecasting video service performance in third-party application, two famous service providers in China, Iqiyi and Letv, are monitored and analyzed. The study highlights that the measured performance in the observation period is time-series data, and it has strong autocorrelation, which means it is predictable. In order to combine the temporal information and map the measured data to a proper feature space, the authors propose a predictor based on a Conditional Restricted Boltzmann Machine (CRBM), which can capture the potential temporal relationship of the historical information. Meanwhile, the measured data of different sources are combined to enhance the training process, which can enlarge the training size and avoid the over-fit problem. Experiments show that combining the measured results from different resolutions for a video can raise prediction performance, and the CRBM algorithm shows better prediction ability and more stable performance than the baseline algorithms.

This paper addresses the issue of text matching for plagiarism detection. This task aims at identifying the matching plagiarism segments in a pair of suspicious document and its plagiarism source document. All the time, heuristic-based methods are mainly utilized to resolve this problem. But the heuristics rely on the experts' experiences and fail to integrate more features to detect the high obfuscation plagiarism matches. In this paper, a statistical machine learning approach, named the Ranking-based Text Matching Approach for Plagiarism Detection, is proposed to deal with the issues of high obfuscation plagiarism detection. The plagiarism text matching is formalized as a ranking problem, and a pairwise learning to rank algorithm is exploited to identify the most probable plagiarism matches for a given suspicious segment. Especially, the Meteor evaluation metrics of machine translation are subsumed by the proposed method to capture the lexical and semantic text similarity. The proposed method is evaluated on PAN12 and PAN13 text alignment corpus of plagiarism detection and compared to the methods achieved the best performance in PAN12, PAN13 and PAN14. Experimental results demonstrate that the proposed method achieves statistically significantly better performance than the baseline methods in all twelve document collections belonging to five different plagiarism categories. Especially at the PAN12 Artificial-high Obfuscation sub-corpus and PAN13 Summary Obfuscation plagiarism sub-corpus, the main evaluation metrics PlagDet of the proposed method are even 22% and 43% relative improvements than the baselines. Moreover, the efficiency of the proposed method is also better than that of baseline methods.

We experimentally demonstrate transistor operation in a vertical p+-MoS2/n-HfS2 van der Waals (vdW) heterostructure configuration for the first time. The HfS2/MoS2 heterojunction transistor exhibits an ON/OFF ratio of 104 and a maximum drain current of 20 nA. These values are comparable with the corresponding reported values for vdW heterojunction TFETs. Moreover, we study the effect of atmospheric exposure on the subthreshold slope (SS) of the HfS2/MoS2 transistor. Unpassivated and passivated devices are compared in terms of their SS values and IDS-VGS hysteresis. While the unpassivated HfS2/MoS2 heterojunction transistor exhibits a minimum SS value of 2000 mV/dec, the same device passivated with a 20-nm-thick HfO2 film exhibits a significantly lower SS value of 700 mV/dec. HfO2 passivation protects the device from contamination caused by atmospheric moisture and oxygen and also reduces the effect of surface traps. We believe that our findings will contribute to the practical realization of HfS2-based vdW heterojunction TFETs.

In this paper, we have investigated the PdEr-silicide formation utilizing a developed PdEr-alloy target for sputtering, and evaluated the contact resistivity of PdEr-silicide layer formed on n-Si(100) by dopant segregation process for the first time. Pd2Si and ErSi2 have same hexagonal structure, while the Schottky barrier height for electron (Φbn) is different as 0.75 eV and 0.28 eV, respectively. A 20 nm-thick PdEr-alloy layer was deposited on the n-Si(100) substrates utilizing a developed PdEr-alloy target by the RF magnetron sputtering at room temperature. Then, 10 nm-thick TiN encapsulating layer was in-situ deposited at room temperature. Next, silicidation was carried out by the RTA at 500 for 5 min in N2/4.9%H2 followed by the selective etching. From the J-V characteristics of fabricated Schottky diode, qΦbn was reduced from 0.75 eV of Pd2Si to 0.43 eV of PdEr-silicide. Furthermore, 4.0x10-8Ωcm2 was extracted for the PdEr-silicide to n-Si(100) by the dopant segregation process.