As one of the popular topics in the field of human-computer interaction, the Speech Emotion Recognition (SER) aims to classify the emotional tendency from the speakers' utterances. Using the existing deep learning methods, and with a large amount of training data, we can achieve a highly accurate performance result. Unfortunately, it's time consuming and difficult job to build such a huge emotional speech database that can be applicable universally. However, the Siamese Neural Network (SNN), which we discuss in this paper, can yield extremely precise results with just a limited amount of training data through pairwise training which mitigates the impacts of sample deficiency and provides enough iterations. To obtain enough SER training, this study proposes a novel method which uses Siamese Attention-based Long Short-Term Memory Networks. In this framework, we designed two Attention-based Long Short-Term Memory Networks which shares the same weights, and we input frame level acoustic emotional features to the Siamese network rather than utterance level emotional features. The proposed solution has been evaluated on EMODB, ABC and UYGSEDB corpora, and showed significant improvement on SER results, compared to conventional deep learning methods.

A triple-band CP rectenna for ambient RF energy harvesting is presented in this paper. A simple broadband CP slot antenna has been proposed with the bandwidth of 51.1% operating from 1.53 to 2.58GHz, which can cover GSM-1800, UMTS-2100 and 2.45GHz WLAN bands. Accordingly, a triple-band rectifying circuit is designed to convert RF energy in the above bands, with the maximum RF-DC conversion efficiency of 42.5% at a relatively low input power of -5dBm. Additionally, the rectenna achieves the maximum conversion efficiency of 12.7% in the laboratory measurements. The measured results show a good performance in the laboratory measurements.

Logging is a practical and useful way of diagnosing failures in software systems. The logged events are crucially important to learning what happened during a failure. If key events are not logged, it is almost impossible to track error propagations in the diagnosis. Tracking an error propagation becomes utterly complicated if inter-thread data dependency is involved. An inter-thread data dependency arises when one thread accesses to share data corrupted by another thread. Since the erroneous state propagates from a buggy thread to a failing thread through the corrupt shared data, the root cause cannot be tracked back solely by investigating the failing thread. This paper presents the design and implementation of K9, a tool that inserts logging code automatically to trace inter-thread data dependencies. K9 is designed to be “practical”; it scales to one million lines of code in C, causes negligible runtime overheads, and provides clues to tracking inter-thread dependencies in real-world bugs. To scale to one million lines of code, K9 ditches rigorous static analysis of pointers to detect code locations where inter-thread data dependency can occur. Instead, K9 takes the best-effort approach and finds out “most” of those code locations by making use of coding conventions. This paper demonstrates that K9 is applicable to Linux and captures relevant code locations, in spite of the best-effort approach, enough to provide useful clues to root causes in real-world bugs, including a previously unknown bug in Linux. The paper also shows K9 runtime overhead is negligible. K9 incurs 1.25% throughput degradation and 0.18% CPU usage increase, on average, in our evaluation.

This work aims to determine the propensity of password creation through the lens of language spheres. To this end, we consider four different countries, each with a different culture/language: China/Chinese, United Kingdom (UK) and India/English, and Japan/Japanese. We first employ a user study to verify whether language and culture are reflected in password creation. We found that users in India, Japan, and the UK prefer to create their passwords from base words, and the kinds of words they are incorporated into passwords vary between countries. We then test whether the findings obtained through the user study are reflected in a corpus of leaked passwords. We found that users in China and Japan prefer dates, while users in India, Japan, and the UK prefer names. We also found that cultural words (e.g., “sakura” in Japan and “football” in the UK) are frequently used to create passwords. Finally, we demonstrate that the knowledge on the linguistic background of targeted users can be exploited to increase the speed of the password guessing process.

This paper demonstrates how to achieve simulation-based strong attribute hiding against adaptive adversaries for predicate encryption (PE) schemes supporting expressive predicate families under standard computational assumptions in bilinear groups. Our main result is a simulation-based adaptively strongly partially-hiding PE (PHPE) scheme for predicates computing arithmetic branching programs (ABP) on public attributes, followed by an inner-product predicate on private attributes. This simultaneously generalizes attribute-based encryption (ABE) for boolean formulas and ABP's as well as strongly attribute-hiding PE schemes for inner products. The proposed scheme is proven secure for any a priori bounded number of ciphertexts and an unbounded (polynomial) number of decryption keys, which is the best possible in the simulation-based adaptive security framework. This directly implies that our construction also achieves indistinguishability-based strongly partially-hiding security against adversaries requesting an unbounded (polynomial) number of ciphertexts and decryption keys. The security of the proposed scheme is derived under (asymmetric version of) the well-studied decisional linear (DLIN) assumption. Our work resolves an open problem posed by Wee in TCC 2017, where his result was limited to the semi-adaptive setting. Moreover, our result advances the current state of the art in both the fields of simulation-based and indistinguishability-based strongly attribute-hiding PE schemes. Our main technical contribution lies in extending the strong attribute hiding methodology of Okamoto and Takashima [EUROCRYPT 2012, ASIACRYPT 2012] to the framework of simulation-based security and beyond inner products.

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.

Aiming at the problem of low accuracy of target selection in indirect touch input, an indirect multi-touch input device was designed and built. We explored here four indirect touch input techniques which were TarConstant, TarEnlarge, TarAttract, TarEnlargeAttract, and investigated their performance when subjects completing the target selection tasks through comparative experiments. Results showed that TarEnlargeAttract enabled the shortest movement time along with the lowest error rate, 2349.9ms and 10.9% respectively. In terms of learning effect, both TarAttract and TarEnlargeAttract had learning effect on movement time, which indicated that the speed of these two techniques can be improved with training. Finally, the strategy of improving the accuracy of indirect touch input was given, which has reference significance for the interface design of indirect touch input.

The eyes are continuously fluctuating during fixation. These fluctuations are called fixational eye movements. Fixational eye movements consist of tremors, microsaccades, and ocular drifts. Fixational eye movements aid our vision by shaping spatial-temporal characteristics. Here, it is known that photoreceptors, the first input layer of the retinal network, have a spatially non-uniform cell alignment called the cone mosaic. The roles of fixational eye movements are being gradually uncovered; however, the effects of the cone mosaic are not considered. Here we constructed a large-scale visual system model to explore the effect of the cone mosaic on the visual signal processing associated with fixational eye movements. The visual system model consisted of a brainstem, eye optics, and photoreceptors. In the simulation, we focused on the roles of fixational eye movements on signal processing with sparse sampling by photoreceptors given their spatially non-uniform mosaic. To analyze quantitatively the effect of fixational eye movements, the capacity of information processing in the simulated photoreceptor responses was evaluated by information rate. We confirmed that the information rate by sparse sampling due to the cone mosaic was increased with fixational eye movements. We also confirmed that the increase of the information rate was derived from the increase of the responses for the edges of objects. These results suggest that visual information is already enhanced at the level of the photoreceptors by fixational eye movements.

The concrete quality of supporting layer in ballastless track is important for the safe operation of a high-speed railway (HSR). However, the supporting layer is covered by the upper track slab and the functional layer, and it is difficult to detect concealed defects inside the supporting layer. To solve this problem, a method of elastic wave velocity imaging is proposed to analyze the concrete quality. First, the propagation path of the elastic wave in the supporting layer is analyzed, and a head-wave arrival-time (HWAT) extraction method based on the wavelet spectrum correlation analysis (WSCA) is proposed. Then, a grid model is established to analyze the relationships among the grid wave velocity, travel route, and travel time. A loss function based on the total variation is constructed, and an inverse method is applied to evaluate the elastic wave velocity in the supporting layer. Finally, simulation and field experiments are conducted to verify the suppression of noise signals and the accuracy of an inverse imaging for the elastic wave velocity estimation. The results show that the WSCA analysis could extract the HWAT efficiently, and the inverse imaging method could accurately estimate wave velocity in the supporting layer.

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.

The static deployment of Virtualized Network Functions (VNFs) introduces 1) significant degradation of Quality of Service (QoS), 2) inefficiency in the network and computing resource utilization, and 3) Network Function Virtualization (NFV)-based services with insufficient scalability, optimality, and flexibility. Caching VNFs is a promising solution to satisfy the dynamic demand to deploy a variety of VNFs and to maximize the performance as well as cost effectiveness. Although the concept of Content Delivery Network (CDN) is popular for efficiently caching and distributing contents, VNF deployment does not realize the benefit of CDN-based caching approaches. The challenges to caching VNFs are 1) to cover the large variety of VNFs and their properties, including the necessity of service chaining, and 2) to achieve high acceptance ratio given the limited availability of resources. This paper proposes Function Delivery Network (FDN), which is a cluster of distributed edge hypervisors for caching VNFs over a Software-Defined Network (SDN). The deployment and quality of the network function can be significantly improved by serving them closer to the end-users from the cached VNFs. FDN introduces a new strategy called Value-based caching that considers 1) the locality of reference and performance parameters of network and edge hypervisors together and 2) a partial deployment of service chains across multiple edge hypervisors for further efficient utilization of hypervisors resources. Evaluations on different patterns of input requests confirm that Value-based caching introduces significant improvement on both QoS and resource utilization in NFV.

Android occupies a very large market share in the field of mobile devices, and quantities of applications are created everyday allowing users to easily use them. However, privacy leaks on Android terminals may result in serious losses to businesses and individuals. Current permission model cannot effectively prevent privacy data leakage. In this paper, we find a way to protect privacy data on Android terminals from the perspective of privacy information propagation by porting the concept of contextual integrity to the realm of privacy protection. We propose a computational model of contextual integrity suiting for Android platform and design a privacy protection system based on the model. The system consists of an online phase and offline phase; the main function of online phase is to computing the value of distribution norm and making privacy decisions, while the main function of offline phase is to create a classification model that can calculate the value of the appropriateness norm. Based on the 6 million permission requests records along with 2.3 million runtime contextual records collected by dynamic analysis, we build the system and verify its feasibility. Experiment shows that the accuracy of offline classifier reaches up to 0.94. The experiment of the overall system feasibility illustrates that 70% location data requests, 84% phone data requests and 46% storage requests etc., violate the contextual integrity.

We have designed gate arrays using low-temperature poly-Si thin-film transistors and confirmed the correct operations. Various kinds of logic gates are beforehand prepared, contact holes are later bored, and mutual wiring is formed between the logic gates on demand. A half adder, two-bit decoder, and flip flop are composed as examples. The static behaviors are evaluated, and it is confirmed that the correct waveforms are output. The dynamic behaviors are also evaluated, and it is concluded that the dynamic behaviors of the gate array are less deteriorated than that of the independent circuit.

De-identification[1]-[5], [30]-[71] is the process that organizations can use to remove personal information from data that they collect, use, archive, and share with other organizations. It is recognized as an important tool for organizations to balance requirements between the use of data and privacy protection of personal information. Its objective is to remove the association between a set of identifying attributes and the data principal where identifying attribute is attribute in a dataset that is able to contribute to uniquely identifying a data principal within a specific operational context and data principal is entity to which data relates. This paper provides an overview of de-identification techniques including the data release models. It also describes the current standardization activities by the standardization development organizations in terms of de-identification. It suggests future standardization directions including potential future work items.

Full duplex (FD) communication can potentially double the throughput of a point-to-point link in wireless communication. Additionally, FD communication can mitigate the hidden node collision problem. The MAC protocols for FD communications are classified into two types; synchronous FD MAC and asynchronous one. Though the synchronous FD MAC mitigates hidden node collisions by using control frame, overhead duration for each data frame transmission may be a bottleneck for the networks. On the other hand, the asynchronous FD MAC mitigates the hidden node collisions by FD communication. However, it wastes more time due to transmission failure than synchronous FD MAC. Clarifying the effect of two major FD MAC types on networks requires a quantitative evaluation of the effectiveness of these protocols in networks with hidden node collisions. This paper proposes performance analysis of FD MAC protocols for wireless local area networks with hidden node collisions. Through the proposed analytical model, the saturated throughputs in FD WLANs with both asynchronous and synchronous FD MAC for any number of STAs and any payload size can be obtained.

We propose two algorithms for the gathering of k mobile agents in asynchronous Byzantine environments. For both algorithms, we assume that graph topology is arbitrary, each node is equipped with an authenticated whiteboard, agents have unique IDs, and at most f weakly Byzantine agents exist. Here, a weakly Byzantine agent can make arbitrary behavior except falsifying its ID. Under these assumptions, the first algorithm achieves a gathering without termination detection in O(m+fn) moves per agent (m is the number of edges and n is the number of nodes). The second algorithm achieves a gathering with termination detection in O(m+fn) moves per agent by additionally assuming that agents on the same node are synchronized, $f

Due to most of the existing graph repartitioning methods are known for poor efficiency in distributed environments. In this paper, we introduce a new graph repartitioning method with two phases in distributed environments. In the first phase, a local method is designed to identify all the potential candidate vertices that should be moved to the other partitions at once in each partition locally. In the second phase, a streaming graph processing model is adopted to reassign the candidate vertices to achieve lightweight graph repartitioning. During the reassignment of the vertex, we propose an objective function to balance both the load balance and the number of crossing edges among the distributed partitions. The experimental results with a large set of real word and synthetic graph datasets show that the communication cost can be reduced by nearly 1 to 2 orders of magnitude compared with the existing methods.

Locally repairable codes (LRCs) with locality r and availability t are a class of codes which can recover data from erasures by accessing other t disjoint repair groups, that every group contain at most r other code symbols. This letter will investigate constructions of LRCs derived from cyclic codes and generalized quadrangle. On the one hand, two classes of cyclic LRC with given locality m-1 and availability em are proposed via trace function. Our LRCs have the same locality, availability, minimum distance and code rate, but have short length and low dimension. On the other hand, an LRC with $(2,(p+1)lfloorrac{s}{2} floor)$ is presented based on sets of points in PG(k, q) which form generalized quadrangles with order (s, p). For k=3, 4, 5, LRCs with r=2 and different t are determined.

The OGY method is one of control methods for a chaotic system. In the method, we have to calculate a target periodic orbit embedded in its chaotic attractor. Thus, we cannot use this method in the case where a precise mathematical model of the chaotic system cannot be identified. In this case, the delayed feedback control proposed by Pyragas is useful. However, even in the delayed feedback control, we need the mathematical model to determine a feedback gain that stabilizes the periodic orbit. Thus, we propose a reinforcement learning algorithm to the design of a controller for the chaotic system. Recently, reinforcement learning algorithms with deep neural networks have been paid much attention to. Those algorithms make it possible to control complex systems. We propose a controller design method consisting of two steps, where we determine a region including a target periodic point first, and make the controller learn an optimal control policy for its stabilization. The controller efficiently explores its control policy only in the region.

With our ever increasing dependence on computers, many governments around the world have started to investigate strengthening the regulations on vulnerabilities and their lifecycle management. Although many previous works have studied this problem space for mainstream software packages and web applications, relatively few have studied this for consumer IoT devices. As our first step towards filling this void, this paper presents a pilot study on the vulnerability disclosures and patch releases of three prominent consumer IoT vendors in Japan and three in the United States. Our goals include (i) characterizing the trends and risks in the vulnerability lifecycle management of consumer IoT devices using accurate long-term data, and (ii) identifying problems, challenges, and potential approaches for future studies of this problem space. To this end, we collected all published vulnerabilities and patches related to the consumer IoT products by the included vendors between 2006 and 2017; then, we analyzed our dataset from multiple perspectives, such as the severity of the included vulnerabilities and the timing of the included patch releases with respect to the corresponding disclosures and exploits. Our work has uncovered several important findings that may inform future studies. These findings include (i) a stark contrast between how the vulnerabilities in our dataset were disclosed in the two markets, (ii) three alarming practices by the included vendors that may significantly increase the risk of 1-day exploits for customers, and (iii) challenges in data collection including crawling automation and long-term data availability. For each finding, we also provide discussions on its consequences and/or potential migrations or suggestions.