Recording source code changes comes to be well recognized as an effective means for understanding the evolution of existing software and making its future changes efficient. Therefore, modern integrated development environments (IDEs) tend to employ tools that record fine-grained textual changes of source code. However, there is still no satisfactory tool that accurately records textual changes. We propose ChangeMacroRecorder that automatically and silently records all textual changes of source code and in real time correlates those textual changes with actions causing them while a programmer is writing and modifying it on the Eclipse's Java editor. The improvement with respect to the accuracy of recorded textual changes enables both programmers and researchers to exactly understand how the source code was evolved. This paper presents detailed information on how ChangeMacroRecorder achieves the accurate recording of textual changes and demonstrates how accurate textual changes were recorded in our experiment consisting of nine programming tasks.

The recent development of semiconductor technology has led to downsized, large-scaled and low-power VLSI systems. However, the incidence of soft errors has increased. Soft errors are temporary events caused by striking of α-rays and high energy neutron radiation. Since the scale of VLSI has become smaller in recent development, it is necessary to consider the occurrence of not only single node upset (SNU) but also double node upset (DNU). The existing High-performance, Low-cost, and DNU Tolerant Latch design (HLDTL) does not completely tolerate DNU. This paper presents a new design of a DNU tolerant latch to resolve this issue by adding some transistors to the HLDTL latch.

For the blind estimation of short-code direct sequence spread spectrum (DSSS) signal pseudo-noise (PN) sequences, the eigenvalue decomposition (EVD) algorithm, the singular value decomposition (SVD) algorithm and the double-periodic projection approximation subspace tracking with deflation (DPASTd) algorithm are often used to estimate the PN sequence. However, when the asynchronous time delay is unknown, the largest eigenvalue and the second largest eigenvalue may be very close, resulting in the estimated largest eigenvector being any non-zero linear combination of the really required largest eigenvector and the really required second largest eigenvector. In other words, the estimated largest eigenvector exhibits unitary ambiguity. This degrades the performance of any algorithm estimating the PN sequence from the estimated largest eigenvector. To tackle this problem, this paper proposes a spreading sequence blind estimation algorithm based on the rotation matrix. First of all, the received signal is divided into two-information-period-length temporal vectors overlapped by one-information-period. The SVD or DPASTd algorithm can then be applied to obtain the largest eigenvector and the second largest eigenvector. The matrix composed of the largest eigenvector and the second largest eigenvector can be rotated by the rotation matrix to eliminate any unitary ambiguity. In this way, the best estimation of the PN sequence can be obtained. Simulation results show that the proposed algorithm not only solves the problem of estimating the PN sequence when the largest eigenvalue and the second largest eigenvalue are close, but also performs well at low signal-to-noise ratio (SNR) values.

A planar electromagnetic field stirrer with periodically arranged metal patterns and diode switches is proposed for improving uneven heating of a heated object placed in a microwave oven. The reflection phase of the proposed stirrer changes by switching the states of diodes mounted on the stirrer and the electromagnetic field in the microwave oven is stirred. The temperature distribution of a heated object located in a microwave oven was simulated and measured using the stirrer in order to evaluate the improving effect of the uneven heating. As the result, the heated parts of the objects were changed with the diode states and the improving effect of the uneven heating was experimentally indicated.

A limited number of types of sound event occur in an acoustic scene and some sound events tend to co-occur in the scene; for example, the sound events “dishes” and “glass jingling” are likely to co-occur in the acoustic scene “cooking.” In this paper, we propose a method of sound event detection using graph Laplacian regularization with sound event co-occurrence taken into account. In the proposed method, the occurrences of sound events are expressed as a graph whose nodes indicate the frequencies of event occurrence and whose edges indicate the sound event co-occurrences. This graph representation is then utilized for the model training of sound event detection, which is optimized under an objective function with a regularization term considering the graph structure of sound event occurrence and co-occurrence. Evaluation experiments using the TUT Sound Events 2016 and 2017 detasets, and the TUT Acoustic Scenes 2016 dataset show that the proposed method improves the performance of sound event detection by 7.9 percentage points compared with the conventional CNN-BiGRU-based detection method in terms of the segment-based F1 score. In particular, the experimental results indicate that the proposed method enables the detection of co-occurring sound events more accurately than the conventional method.

The choreography realization problem is a design challenge for systems based on service-oriented architecture. In our previous studies, we studied the problem on a case where choreography was given by one or two scenarios and was expressed by an acyclic relation of events; we introduced the notion of re-constructibility as a property of acyclic relations to be satisfied. However, when choreography is defined by multiple scenarios, the resulting behavior cannot be expressed by an acyclic relation. An event structure is composed of an acyclic relation and a conflict relation. Because event structures are a generalization of acyclic relations, a wider class of systems can be expressed by event structures. In this paper, we propose the use of event structures to express choreography, introduce the re-constructibility of event structures, and show a necessary condition for an event structure to be re-constructible.

In this paper, we explore the security of single-key Even-Mansour ciphers against key-recovery attacks. First, we introduce a simple key-recovery attack using key relations on an n-bit r-round single-key Even-Mansour cipher (r-SEM). This attack is feasible with queries of DTr=O(2rn) and $2^{rac{2r}{r + 1}n}$ memory accesses, which is $2^{rac{1}{r + 1}n}$ times smaller than the previous generic attacks on r-SEM, where D and T are the number of queries to the encryption function EK and the internal permutation P, respectively. Next, we further reduce the time complexity of the key recovery attack on 2-SEM by a start-in-the-middle approach. This is the first attack that is more efficient than an exhaustive key search while keeping the query bound of DT2=O(22n). Finally, we leverage the start-in-the-middle approach to directly improve the previous attacks on 2-SEM by Dinur et al., which exploit t-way collisions of the underlying function. Our improved attacks do not keep the bound of DT2=O(22n), but are the most time-efficient attacks among the existing ones. For n=64, 128 and 256, our attack is feasible with the time complexity of about $2^{n} cdot rac{1}{2 n}$ in the chosen-plaintext model, while Dinur et al.'s attack requires $2^{n} cdot rac{{ m log}(n)}{ n} $ in the known-plaintext model.

Cybersecurity has become a serious concern in our daily lives. The malicious functions inserted into hardware devices have been well known as hardware Trojans. In this letter, we propose a hardware-Trojan classification method at gate-level netlists utilizing boundary net structures. We first use a machine-learning-based hardware-Trojan detection method and classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. Based on the classification results, we investigate the net structures around the boundary between normal nets and Trojan nets, and extract the features of the nets mistakenly identified to be normal nets or Trojan nets. Finally, based on the extracted features of the boundary nets, we again classify the nets in a given netlist into a set of normal nets and a set of Trojan nets. The experimental results demonstrate that our proposed method outperforms an existing machine-learning-based hardware-Trojan detection method in terms of its true positive rate.

In this letter, we propose a more secure modeling and simulation approach that can systematically detect state variable corruptions caused by buffer overflows in simulation models. Using our approach, developers may not consider secure coding practices related to the corruptions. We have implemented a prototype of the approach based on a modeling and simulation formalism and an open source simulator. Through optimization, the prototype could show better performance, compared to the original simulator, and detect state variable corruptions.

Biometric template protection techniques have been proposed to address security and privacy issues inherent to biometric-based authentication systems. However, it has been shown that the robustness of most of such techniques against reversibility and linkability attacks are overestimated. Thus, a thorough security analysis of recently proposed template protection schemes has to be carried out. Negative iris recognition is an interesting iris template protection scheme based on the concept of negative databases. In this paper, we present a comprehensive security analysis of this scheme in order to validate its practical usefulness. Although the authors of negative iris recognition claim that their scheme possesses both irreversibility and unlinkability, we demonstrate that more than 75% of the original iris-code bits can be recovered using a single protected template. Moreover, we show that the negative iris recognition scheme is vulnerable to attacks via record multiplicity where an adversary can combine several transformed templates to recover more proportion of the original iris-code. Finally, we demonstrate that the scheme does not possess unlinkability. The experimental results, on the CASIA-IrisV3 Interval public database, support our theory and confirm that the negative iris recognition scheme is susceptible to reversibility, linkability, and record multiplicity attacks.

For high definition (HD) videos, the 3D-High Efficiency Video Coding (3D-HEVC) reference algorithm incurs dramatically highly computation loads. Therefore, with the demands for the real-time processing of HD video, a hardware implementation is necessary. In this paper, a reconfigurable architecture is proposed that can support both median filtering preprocessing and mean filtering preprocessing to satisfy different scene depth maps. The architecture sends different instructions to the corresponding processing elements according to different scenarios. Mean filter is used to process near-range images, and median filter is used to process long-range images. The simulation results show that the designed architecture achieves an averaged PSNR of 34.55dB for the tested images. The hardware design for the proposed virtual view synthesis system operates at a maximum clock frequency of 160MHz on the BEE4 platform which is equipped with four Virtex-6 FF1759 LX550T Field-Programmable Gate Array (FPGA) for outputting 720p (1024×768) video at 124fps.

As smartphones and IoT devices become widespread, probabilistic event streams, which are continuous analysis results of sensing data, have received a lot of attention. One of the applications of probabilistic event streams is monitoring of time series events based on regular expressions. That is, we describe a monitoring query such as “Has the tracked object moved from RoomA to RoomB in the past 30 minutes?” by using a regular expression, and then check whether corresponding events occur in a probabilistic event stream with a sliding window. Although we proposed the fundamental monitoring method of time series events in our previous work, three problems remain: 1) it is based on an unusual assumption about slide size of a sliding window, 2) the grammar of pattern queries did not include “negation”, and 3) it was not optimized for multiple monitoring queries. In this paper, we propose several techniques to solve the above problems. First, we remove the assumption about slide size, and propose adaptive slicing of sliding windows for efficient probability calculation. Second, we calculate the occurrence probability of a negation pattern by using an inverted DFA. Finally, we propose the merge of multiple DFAs based on disjunction to process multiple queries efficiently. Experimental results using real and synthetic datasets demonstrate effectiveness of our approach.

We present a retrieval method for 3D CAD assemblies consisted of multiple components. The proposed method distinguishes not only shapes of 3D CAD assemblies but also layouts of their components. Similarity between two assemblies is computed from feature quantities of the components constituting the assemblies. In order to make the similarity robust to translation and rotation of an assembly in 3D space, we use the 3D Radon transform and the spherical harmonic transform. We show that this method has better retrieval precision and efficiency than targets for comparison by experimental evaluation.

Due to the superior performance, deep learning has been widely applied to various applications, including image classification, bioinformatics, and cybersecurity. Nevertheless, the research investigations on deep learning in the adversarial environment are still on their preliminary stage. The emerging adversarial learning methods, e.g., generative adversarial networks, have introduced two vital questions: to what degree the security of deep learning with the presence of adversarial examples is; how to evaluate the performance of deep learning models in adversarial environment, thus, to raise security advice such that the selected application system based on deep learning is resistant to adversarial examples. To see the answers, we leverage image classification as an example application scenario to propose a framework of Evaluating Deep Learning for Image Classification (EDLIC) to conduct comprehensively quantitative analysis. Moreover, we introduce a set of evaluating metrics to measure the performance of different attacking and defensive techniques. After that, we conduct extensive experiments towards the performance of deep learning for image classification under different adversarial environments to validate the scalability of EDLIC. Finally, we give some advice about the selection of deep learning models for image classification based on these comparative results.

Cross sections that cause single event upsets by heavy ions are sensitive to doping concentration in the source and drain regions, and the structure of the raised source and drain regions especially in FDSOI. Due to the parasitic bipolar effect (PBE), radiation-hardened flip flops with stacked transistors in FDSOI tend to have soft errors, which is consistent with measurement results by heavy-ion irradiation. Device-simulation results in this study show that the cross section is proportional to the silicon thickness of the raised layer and inversely proportional to the doping concentration in the drain. Increasing the doping concentration in the source and drain region enhance the Auger recombination of carriers there and suppresses the parasitic bipolar effect. PBE is also suppressed by decreasing the silicon thickness of the raised layer. Cgg-Vgs and Ids-Vgs characteristics change smaller than soft error tolerance change. Soft error tolerance can be effectively optimized by using these two determinants with only a small impact on transistor characteristics.

The outcome of document clustering depends on the scheme used to assign a weight to each term in a document. While recent works have tried to use distributions related to class to enhance the discrimination ability. It is worth exploring whether a deviation approach or an entropy approach is more effective. This paper presents a comparison between deviation-based distribution and entropy-based distribution as constraints in term weighting. In addition, their potential combinations are investigated to find optimal solutions in guiding the clustering process. In the experiments, the seeded k-means method is used for clustering, and the performances of deviation-based, entropy-based, and hybrid approaches, are analyzed using two English and one Thai text datasets. The result showed that the deviation-based distribution outperformed the entropy-based distribution, and a suitable combination of these distributions increases the clustering accuracy by 10%.

The degradation of a SiC-MOSFET-based DC-AC converter-circuit efficiency due to aging of the electrically active devices is investigated. The newly developed compact aging model HiSIM_HSiC for high-voltage SiC-MOSFETs is used in the investigation. The model considers explicitly the carrier-trap-density increase in the solution of the Poisson equation. Measured converter characteristics during a 3-phase line-to-ground (3LG) fault is correctly reproduced by the model. It is verified that the MOSFETs experience additional stress due to the high biases occurring during the fault event, which translates to severe MOSFET aging. Simulation results predict a 0.5% reduction of converter efficiency due to a single 70ms-3LG, which is equivalent to a year of operation under normal conditions, where no additional stress is applied. With the developed compact model, prediction of the efficiency degradation of the converter circuit under prolonged stress, for which measurements are difficult to obtain and typically not available, is also feasible.

Recently, criminals frequently utilize logical attacks to Automated Teller Machines (ATMs) and financial institutes' (FIs') networks to steal cash. We proposed a security measure utilizing peripheral devices in an ATM for smart card transactions to prevent “unauthorized cash withdrawals” of logical attacks, and the fundamental framework as a generalized model of the measure in other paper. As the measure can prevent those logical attacks with tamper-proof hardware, it is quite difficult for criminals to compromise the measure. However, criminals can still carry out different types of logical attacks to ATMs, such as “unauthorized deposit”, to steal cash. In this paper, we propose a security measure utilizing peripheral devices to prevent unauthorized deposits with a smart card. The measure needs to protect multiple transaction sub-processes in a deposit transaction from multiple types of logical attacks and to be harmonized with existing ATM system/operations. A suitable implementation of the fundamental framework is required for the measure and such implementation design is confusing due to many items to be considered. Thus, the measure also provides an implementation model analysis of the fundamental framework to derive suitable implementation for each defense point in a deposit transaction. Two types of measure implementation are derived as the result of the analysis.

Attention mechanism, which selectively focuses on source-side information to learn a context vector for generating target words, has been shown to be an effective method for neural machine translation (NMT). In fact, generating target words depends on not only the source-side information but also the target-side information. Although the vanilla NMT can acquire target-side information implicitly by recurrent neural networks (RNN), RNN cannot adequately capture the global relationship between target-side words. To solve this problem, this paper proposes a novel target-attention approach to capture this information, thus enhancing target word predictions in NMT. Specifically, we propose three variants of target-attention model to directly obtain the global relationship among target words: 1) a forward target-attention model that uses a target attention mechanism to incorporate previous historical target words into the prediction of the current target word; 2) a reverse target-attention model that adopts a reverse RNN model to obtain the entire reverse target words information, and then to combine with source context information to generate target sequence; 3) a bidirectional target-attention model that combines the forward target-attention model and reverse target-attention model together, which can make full use of target words to further improve the performance of NMT. Our methods can be integrated into both RNN based NMT and self-attention based NMT, and help NMT get global target-side information to improve translation performance. Experiments on the NIST Chinese-to-English and the WMT English-to-German translation tasks show that the proposed models achieve significant improvements over state-of-the-art baselines.

In this paper, we describe a novel low-delay 4K 120-fps real-time HEVC decoder with a parallel processing architecture that conforms to the HEVC main 4:2:2 10 profile. It supports the hierarchical temporal scalable streams required for Ultra High Definition high-frame-rate broadcasting and also supports low-delay and high-bitrate decoding for video transmission uses. To achieve this support, the decoding processes are parallelized and pipelined at the frame level, slice level, and coding tree unit row level. The proposed decoder was implemented on three FPGAs operated at 133 and 150 MHz, and it achieved 300-Mbps stream decoding and 37-msec end-to-end delay with our concurrently developed 4K 120-fps encoder.